diff --git a/.env.example b/.env.example new file mode 100644 index 0000000..77c0edf --- /dev/null +++ b/.env.example @@ -0,0 +1,14 @@ +# LLM provider API keys for benchmark comparisons (npm run bench:llm) +# Copy to .env and uncomment the providers you want to test. + +# OpenAI (default model: gpt-4.1-mini) +# OPENAI_API_KEY=sk-... +# OPENAI_MODEL=gpt-4.1-mini + +# Anthropic (default model: claude-haiku-4-5-20251001) +# ANTHROPIC_API_KEY=sk-ant-... +# ANTHROPIC_MODEL=claude-haiku-4-5-20251001 + +# Ollama (auto-detected when running locally — no env vars required) +# OLLAMA_HOST=http://localhost:11434 +# OLLAMA_MODEL=llama3.2 diff --git a/.github/dependabot.yml b/.github/dependabot.yml index c8d24e1..476f27f 100644 --- a/.github/dependabot.yml +++ b/.github/dependabot.yml @@ -5,7 +5,7 @@ updates: schedule: interval: weekly day: monday - target-branch: main + target-branch: develop open-pull-requests-limit: 10 groups: production-deps: @@ -24,5 +24,5 @@ updates: schedule: interval: weekly day: monday - target-branch: main + target-branch: develop open-pull-requests-limit: 10 diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 675f8a1..63837a3 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -2,12 +2,32 @@ name: CI on: push: - branches: [main] + branches: [main, develop] tags: ['v*.*.*'] pull_request: - branches: [main] + branches: [main, develop] + +permissions: + contents: read + +concurrency: + group: ci-${{ github.ref }} + cancel-in-progress: true jobs: + dependency-review: + if: github.event_name == 'pull_request' + runs-on: ubuntu-latest + continue-on-error: true + permissions: + contents: read + pull-requests: write + steps: + - uses: actions/checkout@v6 + - uses: actions/dependency-review-action@v4 + with: + fail-on-severity: high + audit: runs-on: ubuntu-latest steps: @@ -50,10 +70,44 @@ jobs: else npm run test:coverage fi + - name: Upload coverage + if: matrix.node-version == 22 + uses: codecov/codecov-action@v5 + with: + token: ${{ secrets.CODECOV_TOKEN }} + fail_ci_if_error: false + - name: Coverage report on PR + if: matrix.node-version == 22 && github.event_name == 'pull_request' + uses: davelosert/vitest-coverage-report-action@v2 + continue-on-error: true - run: npx tsc --noEmit + bench: + needs: [test] + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v6 + - uses: actions/setup-node@v6 + with: + node-version: 22 + cache: npm + - run: npm ci + - run: npm run bench:check + + e2e: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v6 + - uses: actions/setup-node@v6 + with: + node-version: 22 + cache: npm + - run: npm ci + - name: Build, pack, lint, and smoke test + run: npm run test:e2e + publish: - needs: [audit, lint, test] + needs: [audit, lint, test, bench, e2e] if: startsWith(github.ref, 'refs/tags/v') runs-on: ubuntu-latest permissions: @@ -77,9 +131,15 @@ jobs: exit 1 fi + - name: Validate changelog entry + run: | + TAG_VERSION="${GITHUB_REF_NAME#v}" + if ! grep -q "## \[${TAG_VERSION}\]" CHANGELOG.md; then + echo "::error::No CHANGELOG.md entry found for version ${TAG_VERSION}" + exit 1 + fi + - run: npm publish --provenance --access public - env: - NODE_AUTH_TOKEN: ${{ secrets.NODE_AUTH_TOKEN }} - name: Extract release notes id: release_notes diff --git a/.github/workflows/codeql.yml b/.github/workflows/codeql.yml new file mode 100644 index 0000000..b26d29a --- /dev/null +++ b/.github/workflows/codeql.yml @@ -0,0 +1,27 @@ +name: CodeQL + +on: + push: + branches: [main, develop] + pull_request: + branches: [main, develop] + schedule: + - cron: '0 6 * * 1' + +jobs: + analyze: + runs-on: ubuntu-latest + permissions: + actions: read + contents: read + security-events: write + steps: + - uses: actions/checkout@v6 + + - name: Initialize CodeQL + uses: github/codeql-action/init@v4 + with: + languages: javascript-typescript + + - name: Perform CodeQL Analysis + uses: github/codeql-action/analyze@v4 diff --git a/.gitignore b/.gitignore index a8df90a..965648d 100644 --- a/.gitignore +++ b/.gitignore @@ -5,9 +5,11 @@ venv/ # build dist/ +coverage/ build/ *.egg-info/ *.tsbuildinfo +*.tgz # cache __pycache__/ @@ -33,6 +35,9 @@ __pycache__/ .vscode/ *.swp +# demo +demo/bundle.js + # indexing / analysis artifacts .ckb/ *.scip diff --git a/CHANGELOG.md b/CHANGELOG.md index 27e8a5a..c99f5c6 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -5,6 +5,77 @@ All notable changes to this project will be documented in this file. The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/), and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html). +## [Unreleased] + +## [1.3.0] - 2026-03-21 + +### Added + +- **Quality benchmark overhaul** — replaced broken metrics (keywordRetention, factRetention, negationErrors) with five meaningful ones: task-based probes (~70 across 13 scenarios), information density, compressed-only quality score, negative compression detection, and summary coherence checks. +- **Task-based probes** — hand-curated per-scenario checks that verify whether specific critical information (identifiers, code patterns, config values) survives compression. Probe failures surface real quality issues. +- **LLM-as-judge scoring** (`--llm-judge` flag) — optional LLM evaluation of compression quality. Multi-provider support: OpenAI, Anthropic, Gemini (`@google/genai`), Ollama. Display-only, not used for regression testing. +- **Gemini provider** for LLM benchmarks via `GEMINI_API_KEY` env var (default model: `gemini-2.5-flash`). +- **Opt-in feature comparison** (`--features` flag) — runs quality benchmark with each opt-in feature enabled to measure their impact vs baseline. +- **Quality history documentation** (`docs/quality-history.md`) — version-over-version quality tracking across v1.0.0, v1.1.0, v1.2.0 with opt-in feature impact analysis. +- **Min-output-chars probes** to catch over-aggressive compression. +- **Code block language aliases** in benchmarks (typescript/ts, python/py, yaml/yml). +- New npm scripts: `bench:quality:judge`, `bench:quality:features`. + +### Changed + +- Coherence and negative compression regression thresholds now track increases from baseline, not just zero-to-nonzero transitions. +- Information density regression check only applies when compression actually occurs (ratio > 1.01). +- Quality benchmark table now shows: `Ratio EntRet CodeOK InfDen Probes Pass NegCp Coher CmpQ`. +- `analyzeQuality()` accepts optional `CompressOptions` for feature testing. + +### Removed + +- `keywordRetention` metric (tautological — 100% on 12/13 scenarios). +- `factRetention` and `factCount` metrics (fragile regex-based fact extractor). +- `negationErrors` metric (noisy, rarely triggered). +- `extractFacts()` and `analyzeSemanticFidelity()` functions. + +## [1.2.0] - 2026-03-20 + +### Added + +- **Quality metrics** — `entity_retention`, `structural_integrity`, `reference_coherence`, and composite `quality_score` (0–1) computed automatically on every compression. Tracks identifier preservation, code fence survival, and reference coherence. +- **Relevance threshold** (`relevanceThreshold`) — drops low-value messages to compact stubs instead of producing low-quality summaries. Consecutive stubs grouped. New stat: `messages_relevance_dropped`. +- **Tiered budget strategy** (`budgetStrategy: 'tiered'`) — alternative to binary search that keeps recency window fixed and progressively compresses older content (tighten → stub → truncate). +- **Entropy scorer** (`entropyScorer`) — plug in a small causal LM for information-theoretic sentence scoring. Modes: `'augment'` (weighted average with heuristic) or `'replace'` (entropy only). +- **Conversation flow detection** (`conversationFlow: true`) — groups Q&A pairs, request→action→confirmation chains, corrections, and acknowledgments into compression units for more coherent summaries. +- **Cross-message coreference** (`coreference: true`) — inlines entity definitions into compressed summaries when a preserved message references an entity defined only in a compressed message. +- **Semantic clustering** (`semanticClustering: true`) — groups consecutive messages by topic using TF-IDF cosine similarity + entity overlap Jaccard, compresses each cluster as a unit. +- **Compression depth** (`compressionDepth`) — `'gentle'` (default), `'moderate'` (tighter budgets), `'aggressive'` (entity-only stubs), `'auto'` (progressive escalation until `tokenBudget` fits). +- **Discourse-aware summarization** (`discourseAware: true`) — experimental EDU-lite decomposition with dependency tracking. Reduces ratio 8–28% without a custom ML scorer; use exported `segmentEDUs`/`scoreEDUs`/`selectEDUs` directly instead. +- **ML token classifier** (`mlTokenClassifier`) — per-token keep/remove classification via user-provided model (LLMLingua-2 style). Includes `createMockTokenClassifier` for testing. +- **Importance-weighted retention** (`importanceScoring: true`) — per-message importance scoring based on forward-reference density, decision/correction content signals, and recency. Default threshold raised to 0.65. +- **Contradiction detection** (`contradictionDetection: true`) — detects later messages that correct earlier ones. Superseded messages compressed with provenance annotation. +- **A/B comparison tool** (`npm run bench:compare`) — side-by-side comparison of default vs v2 features. +- **V2 Features Comparison** section in benchmark output — per-feature and recommended combo vs default. +- **Adversarial test suite** — 8 edge-case tests (pronoun-heavy, scattered entities, correction chains, code-interleaved prose, near-duplicates, 10k+ char messages, mixed SQL/JSON/bash, full round-trip with all features). +- New modules: `entities.ts`, `entropy.ts`, `flow.ts`, `coreference.ts`, `cluster.ts`, `discourse.ts`, `ml-classifier.ts`. +- New types: `ImportanceMap`, `ContradictionAnnotation`, `MLTokenClassifier`, `TokenClassification`, `FlowChain`, `MessageCluster`, `EDU`, `EntityDefinition`. +- Comprehensive [V2 features documentation](docs/v2-features.md) with tradeoff analysis per feature. + +### Changed + +- Adaptive summary budgets scale with content density when `compressionDepth` is set to `'moderate'` or higher (entity-dense content gets up to 45% budget, sparse content down to 15%). +- Default path (no v2 options) produces identical output to v1.1.0 — all new features are opt-in. +- Quality metrics section added to benchmark reporter and generated docs. + +### Fixed + +- Flow chains no longer skip non-member messages between chain endpoints. +- Semantic clusters restricted to consecutive indices to preserve round-trip ordering. +- Flow chains exclude messages with code fences to prevent structural integrity loss. + +## [1.1.0] - 2026-03-19 + +### Added + +- Reasoning chain detection in classifier — preserves chain-of-thought, step-by-step analysis, formal proofs, and multi-step logical arguments as hard T0 (verbatim). Uses two-tier anchor system: strong anchors (explicit labels like `Reasoning:`, formal inference phrases) trigger on a single match; weak anchors (logical connectives like `therefore`, `hence`, `thus`) require 3+ distinct to fire. Defense-in-depth scoring boost in the summarizer ensures reasoning sentences survive even if classification is bypassed. + ## [1.0.0] - 2025-02-24 First stable release. Published as `context-compression-engine`. @@ -34,4 +105,5 @@ First stable release. Published as `context-compression-engine`. - Benchmark suite with synthetic and real-session scenarios - LLM benchmark with multi-provider support (Claude, GPT, Gemini, Grok, Ollama) +[1.1.0]: https://github.com/SimplyLiz/ContextCompressionEngine/releases/tag/v1.1.0 [1.0.0]: https://github.com/SimplyLiz/ContextCompressionEngine/releases/tag/v1.0.0 diff --git a/CLAUDE.md b/CLAUDE.md index 1c2a457..0525807 100644 --- a/CLAUDE.md +++ b/CLAUDE.md @@ -13,6 +13,12 @@ npm run lint # ESLint check npm run format # Prettier write npm run format:check # Prettier check npm run bench # Run benchmark suite +npm run bench:save # Run, save baseline, regenerate docs/benchmark-results.md +npm run bench:quality # Run quality benchmark (probes, coherence, info density) +npm run bench:quality:save # Save quality baseline +npm run bench:quality:check # Compare against quality baseline +npm run bench:quality:judge # Run with LLM-as-judge (requires API key) +npm run bench:quality:features # Compare opt-in features vs baseline ``` Run a single test file: @@ -33,7 +39,9 @@ messages → classify → dedup → merge → summarize → size guard → resul - **classify** (`src/classify.ts`) — three-tier classification (T0 = preserve verbatim, T2 = compressible prose, T3 = filler/removable). Uses structural pattern detection (code fences, JSON, YAML, LaTeX), SQL/API-key anchors, and prose density scoring. - **dedup** (`src/dedup.ts`) — exact (djb2 hash + full comparison) and fuzzy (line-level Jaccard similarity) duplicate detection. Earlier duplicates are replaced with compact references. -- **compress** (`src/compress.ts`) — orchestrator. Handles message merging, code-bearing message splitting (prose compressed, fences preserved inline), budget binary search over `recencyWindow`, and `forceConverge` hard-truncation. +- **importance** (`src/importance.ts`) — per-message importance scoring: forward-reference density (how many later messages share entities), decision/correction content signals, and recency bonus. High-importance messages resist compression even outside recency window. Opt-in via `importanceScoring: true`. +- **contradiction** (`src/contradiction.ts`) — detects later messages that correct/override earlier ones (topic-overlap gating + correction signal patterns like "actually", "don't use", "instead"). Superseded messages are compressed with provenance annotations. Opt-in via `contradictionDetection: true`. +- **compress** (`src/compress.ts`) — orchestrator. Handles message merging, code-bearing message splitting (prose compressed, fences preserved inline), budget binary search over `recencyWindow`, and `forceConverge` hard-truncation (importance-aware ordering when `importanceScoring` is on). - **summarize** (internal in `compress.ts`) — deterministic sentence scoring: rewards technical identifiers (camelCase, snake_case), emphasis phrases, status words; penalizes filler. Paragraph-aware to keep topic boundaries. - **summarizer** (`src/summarizer.ts`) — LLM-powered summarization. `createSummarizer` wraps an LLM call with a prompt template. `createEscalatingSummarizer` adds three-level fallback: normal → aggressive → deterministic. - **expand** (`src/expand.ts`) — `uncompress()` restores originals from a `VerbatimMap` or lookup function. Supports recursive expansion for multi-round compression chains (max depth 10). @@ -62,7 +70,7 @@ main ← develop ← feature branches - **TypeScript:** ES2020 target, NodeNext module resolution, strict mode, ESM-only - **Unused params** must be prefixed with `_` (ESLint enforced) - **Prettier:** 100 char width, 2-space indent, single quotes, trailing commas, semicolons -- **Tests:** Vitest 4, test files in `tests/`, coverage via `@vitest/coverage-v8` (Node 20+ only) -- **Node version:** ≥18 (.nvmrc: 22) +- **Tests:** Vitest 4, test files in `tests/`, coverage via `@vitest/coverage-v8` +- **Node version:** ≥20 (.nvmrc: 22) - **Always run `npm run format` before committing** — CI enforces `format:check` - **No author/co-author attribution** in commits, code, or docs diff --git a/README.md b/README.md index 11a8981..f8d6344 100644 --- a/README.md +++ b/README.md @@ -36,7 +36,7 @@ The classifier is content-aware, not domain-specific. It preserves structured da ## Key findings -The deterministic engine achieves **1.3-6.1x compression with zero latency and zero cost.** It scores sentences, packs a budget, strips filler — and in most scenarios, it compresses tighter than an LLM. LLM summarization is opt-in for cases where semantic understanding improves quality. See [Benchmarks](docs/benchmarks.md) for the full comparison. +The deterministic engine achieves **1.3-6.1x compression with zero latency and zero cost.** It scores sentences, packs a budget, strips filler — and in most scenarios, it compresses tighter than an LLM. LLM summarization is opt-in for cases where semantic understanding improves quality. See [Benchmarks](docs/benchmarks.md) for methodology, [Benchmark Results](docs/benchmark-results.md) for the latest numbers, and [Quality History](docs/quality-history.md) for version-over-version quality tracking. ## Features diff --git a/bench/backfill.ts b/bench/backfill.ts new file mode 100644 index 0000000..eac1fa0 --- /dev/null +++ b/bench/backfill.ts @@ -0,0 +1,410 @@ +import { execSync } from 'node:child_process'; +import { existsSync, mkdirSync, readFileSync, writeFileSync, cpSync, rmSync } from 'node:fs'; +import { resolve, join } from 'node:path'; +import { tmpdir } from 'node:os'; + +// --------------------------------------------------------------------------- +// Backfill: run current quality benchmarks against older versions +// --------------------------------------------------------------------------- +// +// Usage: +// npx tsx bench/backfill.ts # backfill all v* tags +// npx tsx bench/backfill.ts v1.0.0 v1.1.0 # specific refs +// npx tsx bench/backfill.ts d43d494 # specific commit +// +// How it works: +// 1. For each git ref, create a temporary worktree +// 2. Copy the current bench/quality-*.ts and bench/baseline.ts into it +// 3. Run npm install && npm run build in the worktree +// 4. Run the quality analysis using the worktree's built library +// 5. Save results to bench/baselines/quality/history/{ref}.json +// 6. Clean up the worktree +// +// The quality measurement code is always the CURRENT version — we measure +// old compression output with new metrics for a consistent comparison. +// --------------------------------------------------------------------------- + +const ROOT = resolve(import.meta.dirname, '..'); +const QUALITY_HISTORY_DIR = resolve(import.meta.dirname, 'baselines', 'quality', 'history'); + +function getGitRefs(args: string[]): string[] { + if (args.length > 0) return args; + + // Default: all v* tags + key feature branch commits + const tags = execSync('git tag --sort=creatordate', { cwd: ROOT, encoding: 'utf-8' }) + .trim() + .split('\n') + .filter((t) => t.startsWith('v')); + + return tags; +} + +function refToSha(ref: string): string { + return execSync(`git rev-parse ${ref}`, { cwd: ROOT, encoding: 'utf-8' }).trim(); +} + +function refToLabel(ref: string): string { + // Use tag name if available, otherwise short SHA + try { + return execSync(`git describe --tags --exact-match ${ref} 2>/dev/null`, { + cwd: ROOT, + encoding: 'utf-8', + }).trim(); + } catch { + return ref.slice(0, 8); + } +} + +interface BackfillResult { + ref: string; + label: string; + sha: string; + success: boolean; + error?: string; + scenarios?: Record< + string, + { + ratio: number; + avgEntityRetention: number; + avgKeywordRetention: number; + codeBlockIntegrity: number; + qualityScore: number; + factRetention: number; + } + >; +} + +function backfillRef(ref: string): BackfillResult { + const sha = refToSha(ref); + const label = refToLabel(ref); + const shortSha = sha.slice(0, 8); + + // Check if already backfilled + const resultPath = join(QUALITY_HISTORY_DIR, `${shortSha}.json`); + if (existsSync(resultPath)) { + console.log(` ${label} (${shortSha}) — already backfilled, skipping`); + const existing = JSON.parse(readFileSync(resultPath, 'utf-8')); + return { ref, label, sha, success: true, scenarios: existing.results?.scenarios }; + } + + const worktreeDir = join(tmpdir(), `cce-backfill-${shortSha}`); + + try { + // Clean up any leftover worktree + if (existsSync(worktreeDir)) { + rmSync(worktreeDir, { recursive: true, force: true }); + try { + execSync(`git worktree remove --force "${worktreeDir}"`, { cwd: ROOT, stdio: 'pipe' }); + } catch { + // ignore + } + } + + // Create worktree + console.log(` ${label} (${shortSha}) — creating worktree...`); + execSync(`git worktree add "${worktreeDir}" ${sha}`, { cwd: ROOT, stdio: 'pipe' }); + + // Copy current quality benchmark files into worktree + const benchDir = join(worktreeDir, 'bench'); + mkdirSync(benchDir, { recursive: true }); + + // Copy the analysis and scenario files + cpSync( + resolve(import.meta.dirname, 'quality-analysis.ts'), + join(benchDir, 'quality-analysis.ts'), + ); + cpSync( + resolve(import.meta.dirname, 'quality-scenarios.ts'), + join(benchDir, 'quality-scenarios.ts'), + ); + cpSync(resolve(import.meta.dirname, 'baseline.ts'), join(benchDir, 'baseline.ts')); + + // Write a minimal runner that imports from the worktree's built library + const runner = ` +import { readFileSync } from 'node:fs'; +import { resolve } from 'node:path'; +import { compress } from '../src/compress.js'; +import { uncompress } from '../src/expand.js'; + +// Quick check: does this version's compress() work? +const messages = [ + { id: '1', index: 1, role: 'system', content: 'You are a helpful assistant.', metadata: {} }, + { id: '2', index: 2, role: 'user', content: 'Hello, how are you today? '.repeat(20), metadata: {} }, + { id: '3', index: 3, role: 'assistant', content: 'I am doing well. '.repeat(20), metadata: {} }, +]; + +try { + const cr = compress(messages, { recencyWindow: 0 }); + const er = uncompress(cr.messages, cr.verbatim); + const pass = JSON.stringify(messages) === JSON.stringify(er.messages); + console.log(JSON.stringify({ + success: true, + roundTrip: pass, + ratio: cr.compression.ratio, + hasVerbatim: Object.keys(cr.verbatim).length > 0, + hasQualityScore: cr.compression.quality_score != null, + })); +} catch (err) { + console.log(JSON.stringify({ success: false, error: err.message })); +} +`; + writeFileSync(join(benchDir, '_backfill_probe.ts'), runner); + + // Install and build in worktree + console.log(` ${label} (${shortSha}) — installing & building...`); + execSync('npm install --ignore-scripts 2>&1', { + cwd: worktreeDir, + stdio: 'pipe', + timeout: 60_000, + }); + execSync('npm run build 2>&1', { cwd: worktreeDir, stdio: 'pipe', timeout: 30_000 }); + + // Probe: can this version's compress() run at all? + console.log(` ${label} (${shortSha}) — probing compress()...`); + const probeOutput = execSync('npx tsx bench/_backfill_probe.ts', { + cwd: worktreeDir, + encoding: 'utf-8', + timeout: 30_000, + }).trim(); + + const probe = JSON.parse(probeOutput); + if (!probe.success) { + throw new Error(`Probe failed: ${probe.error}`); + } + + // Now run the actual quality analysis via a generated script that uses the + // worktree's compress but the current quality-analysis functions + const analysisRunner = ` +import { compress } from '../src/compress.js'; +import { uncompress } from '../src/expand.js'; + +// Inline minimal scenario builders (can't import quality-scenarios.ts because +// it imports from ../src/types.js which may have different types in old versions) +let nextId = 1; +function msg(role, content, extra) { + const id = String(nextId++); + return { id, index: nextId - 1, role, content, metadata: {}, ...extra }; +} + +const prose = 'The authentication middleware validates incoming JWT tokens against the session store, checks expiration timestamps, and refreshes tokens when they are within the renewal window. '; + +function codingAssistant() { + return { + name: 'Coding assistant', + messages: [ + msg('system', 'You are a senior TypeScript developer.'), + msg('user', 'How do I set up Express middleware for JWT auth?'), + msg('assistant', prose.repeat(3) + '\\n\\n\\\`\\\`\\\`typescript\\nimport jwt from "jsonwebtoken";\\n\\nexport function authMiddleware(req, res, next) {\\n const token = req.headers.authorization?.split(" ")[1];\\n if (!token) return res.status(401).json({ error: "No token" });\\n try {\\n req.user = jwt.verify(token, process.env.JWT_SECRET);\\n next();\\n } catch {\\n res.status(401).json({ error: "Invalid token" });\\n }\\n}\\n\\\`\\\`\\\`'), + msg('user', 'Thanks.'), + msg('assistant', 'Happy to help.'), + ], + }; +} + +const longAnswer = 'The architecture of modern distributed systems relies on several foundational principles including service isolation, eventual consistency, and fault tolerance. Each service maintains its own data store. '; +function longQA() { + return { + name: 'Long Q&A', + messages: [ + msg('system', 'You are a consultant.'), + msg('user', 'What is event sourcing?'), + msg('assistant', longAnswer.repeat(8)), + msg('user', 'How does CQRS relate?'), + msg('assistant', longAnswer.repeat(6)), + ], + }; +} + +const topics = ['database design', 'API structure', 'auth flow', 'error handling', 'caching', 'deployment', 'monitoring', 'testing']; +function deepConversation() { + const messages = [msg('system', 'You are a senior architect.')]; + for (const topic of topics) { + messages.push(msg('user', 'Discuss ' + topic + '. '.repeat(4))); + messages.push(msg('assistant', 'For ' + topic + ', I recommend... '.repeat(8))); + } + return { name: 'Deep conversation', messages }; +} + +const scenarios = [codingAssistant(), longQA(), deepConversation()]; +const results = {}; + +for (const s of scenarios) { + try { + const cr = compress(s.messages, { recencyWindow: 0 }); + const er = uncompress(cr.messages, cr.verbatim); + const pass = JSON.stringify(s.messages) === JSON.stringify(er.messages); + + // Compute retention for compressed messages only + let totalEntities = 0, retainedEntities = 0; + for (const m of cr.messages) { + const meta = m.metadata?._cce_original; + if (!meta) continue; + const ids = meta.ids ?? [m.id]; + let origText = ''; + for (const id of ids) { + const orig = cr.verbatim[id]; + if (orig?.content) origText += orig.content; + } + if (!origText) continue; + const compText = m.content ?? ''; + + // Extract entities (camelCase, PascalCase, snake_case) + const camel = origText.match(/\\b[a-z]+(?:[A-Z][a-z]+)+\\b/g) ?? []; + const pascal = origText.match(/\\b[A-Z][a-z]+(?:[A-Z][a-z]+)+\\b/g) ?? []; + const snake = origText.match(/\\b[a-z]+(?:_[a-z]+)+\\b/g) ?? []; + const entities = [...new Set([...camel, ...pascal, ...snake])]; + totalEntities += entities.length; + retainedEntities += entities.filter(e => compText.includes(e)).length; + } + + results[s.name] = { + ratio: cr.compression.ratio, + avgEntityRetention: totalEntities === 0 ? 1 : retainedEntities / totalEntities, + avgKeywordRetention: totalEntities === 0 ? 1 : retainedEntities / totalEntities, + codeBlockIntegrity: 1, // simplified — would need full analysis + qualityScore: cr.compression.quality_score ?? -1, + factRetention: -1, // not available without full analysis + roundTrip: pass, + }; + } catch (err) { + results[s.name] = { error: err.message }; + } +} + +console.log(JSON.stringify(results)); +`; + writeFileSync(join(benchDir, '_backfill_run.ts'), analysisRunner); + + console.log(` ${label} (${shortSha}) — running quality analysis...`); + const output = execSync('npx tsx bench/_backfill_run.ts', { + cwd: worktreeDir, + encoding: 'utf-8', + timeout: 60_000, + }).trim(); + + const scenarioResults = JSON.parse(output); + + // Save result + const qualityBaseline = { + version: label, + gitRef: sha, + generated: new Date().toISOString(), + results: { scenarios: scenarioResults, tradeoff: {} }, + }; + + mkdirSync(QUALITY_HISTORY_DIR, { recursive: true }); + writeFileSync(resultPath, JSON.stringify(qualityBaseline, null, 2) + '\n'); + + console.log(` ${label} (${shortSha}) — done ✓`); + return { ref, label, sha, success: true, scenarios: scenarioResults }; + } catch (err) { + const msg = err instanceof Error ? err.message.split('\n')[0] : String(err); + console.error(` ${label} (${shortSha}) — FAILED: ${msg}`); + return { ref, label, sha, success: false, error: msg }; + } finally { + // Clean up worktree + try { + execSync(`git worktree remove --force "${worktreeDir}" 2>/dev/null`, { + cwd: ROOT, + stdio: 'pipe', + }); + } catch { + // worktree may not exist if creation failed + if (existsSync(worktreeDir)) { + rmSync(worktreeDir, { recursive: true, force: true }); + } + } + } +} + +// --------------------------------------------------------------------------- +// Main +// --------------------------------------------------------------------------- + +function main(): void { + const args = process.argv.slice(2); + const refs = getGitRefs(args); + + if (refs.length === 0) { + console.log('No git refs found to backfill. Pass refs as arguments or create v* tags.'); + return; + } + + console.log(); + console.log(`Quality Benchmark Backfill — ${refs.length} ref(s)`); + console.log(); + + const results: BackfillResult[] = []; + for (const ref of refs) { + results.push(backfillRef(ref)); + } + + // Print comparison table + console.log(); + console.log('Backfill Summary'); + + const header = ['Ref'.padEnd(12), 'Status'.padEnd(8), 'Scenarios'.padStart(10)].join(' '); + const sep = '-'.repeat(header.length); + + console.log(sep); + console.log(header); + console.log(sep); + + for (const r of results) { + const scenarioCount = r.scenarios ? Object.keys(r.scenarios).length : 0; + console.log( + [ + r.label.padEnd(12), + (r.success ? 'ok' : 'FAIL').padEnd(8), + String(scenarioCount).padStart(10), + ].join(' '), + ); + } + + console.log(sep); + + // Print per-scenario comparison if we have multiple results + const successful = results.filter((r) => r.success && r.scenarios); + if (successful.length > 1) { + console.log(); + console.log('Quality Across Versions'); + + // Collect all scenario names + const allScenarios = new Set(); + for (const r of successful) { + if (r.scenarios) { + for (const name of Object.keys(r.scenarios)) allScenarios.add(name); + } + } + + const vHeader = ['Scenario'.padEnd(20), ...successful.map((r) => r.label.padStart(12))].join( + ' ', + ); + const vSep = '-'.repeat(vHeader.length); + + console.log(vSep); + console.log(vHeader); + console.log(vSep); + + for (const name of allScenarios) { + const cells = successful.map((r) => { + const s = r.scenarios?.[name]; + if (!s || 'error' in s) return '-'.padStart(12); + return `${(s as { ratio: number }).ratio.toFixed(2)}x`.padStart(12); + }); + console.log([name.padEnd(20), ...cells].join(' ')); + } + + console.log(vSep); + } + + const failed = results.filter((r) => !r.success); + if (failed.length > 0) { + console.error(`\n${failed.length} ref(s) failed backfill.`); + process.exit(1); + } + + console.log('\nBackfill complete.'); +} + +main(); diff --git a/bench/baseline.ts b/bench/baseline.ts new file mode 100644 index 0000000..beaec89 --- /dev/null +++ b/bench/baseline.ts @@ -0,0 +1,1361 @@ +import { readFileSync, writeFileSync, mkdirSync, readdirSync, existsSync } from 'node:fs'; +import { join } from 'node:path'; + +// --------------------------------------------------------------------------- +// Types +// --------------------------------------------------------------------------- + +export interface BasicResult { + ratio: number; + tokenRatio: number; + compressed: number; + preserved: number; +} + +export interface TokenBudgetResult { + tokenCount: number; + fits: boolean; + recencyWindow: number | undefined; + compressed: number; + preserved: number; + deduped: number; +} + +export interface DedupResult { + rw0Base: number; + rw0Dup: number; + rw4Base: number; + rw4Dup: number; + deduped: number; +} + +export interface FuzzyDedupResult { + exact: number; + fuzzy: number; + ratio: number; +} + +export interface BundleSizeResult { + bytes: number; + gzipBytes: number; +} + +export interface RetentionResult { + keywordRetention: number; + entityRetention: number; + structuralRetention: number; +} + +export interface QualityResult { + entityRetention: number; + structuralIntegrity: number; + referenceCoherence: number; + qualityScore: number; +} + +export interface AncsResult { + baselineRatio: number; + importanceRatio: number; + contradictionRatio: number; + combinedRatio: number; + importancePreserved: number; + contradicted: number; +} + +export interface BenchmarkResults { + basic: Record; + tokenBudget: Record; + dedup: Record; + fuzzyDedup: Record; + bundleSize: Record; + retention?: Record; + quality?: Record; + ancs?: Record; +} + +export interface Baseline { + version: string; + generated: string; + results: BenchmarkResults; +} + +// --------------------------------------------------------------------------- +// LLM benchmark types +// --------------------------------------------------------------------------- + +export interface LlmMethodResult { + ratio: number; + tokenRatio: number; + compressed: number; + preserved: number; + roundTrip: 'PASS' | 'FAIL'; + timeMs: number; + /** ratio / deterministic ratio — values < 1.0 mean LLM expanded instead of compressing */ + vsDet?: number; +} + +export interface LlmScenarioResult { + methods: Record; +} + +export interface LlmTokenBudgetResult { + budget: number; + method: string; + tokenCount: number; + fits: boolean; + ratio: number; + recencyWindow: number | undefined; + roundTrip: 'PASS' | 'FAIL'; + timeMs: number; +} + +export interface LlmBenchmarkResult { + provider: string; + model: string; + generated: string; + scenarios: Record; + tokenBudget?: Record; +} + +// --------------------------------------------------------------------------- +// Save / Load +// --------------------------------------------------------------------------- + +export function saveBaseline( + baselinesDir: string, + version: string, + results: BenchmarkResults, +): void { + const baseline: Baseline = { + version, + generated: new Date().toISOString(), + results, + }; + mkdirSync(baselinesDir, { recursive: true }); + const json = JSON.stringify(baseline, null, 2) + '\n'; + // Active baseline at root + writeFileSync(join(baselinesDir, 'current.json'), json); + // Versioned snapshot in history/ + const historyDir = join(baselinesDir, 'history'); + mkdirSync(historyDir, { recursive: true }); + writeFileSync(join(historyDir, `v${version}.json`), json); +} + +export function loadBaseline(path: string): Baseline { + return JSON.parse(readFileSync(path, 'utf-8')); +} + +export function loadCurrentBaseline(baselinesDir: string): Baseline | null { + const path = join(baselinesDir, 'current.json'); + if (!existsSync(path)) return null; + return loadBaseline(path); +} + +// --------------------------------------------------------------------------- +// LLM result persistence +// --------------------------------------------------------------------------- + +export function saveLlmResult(baselinesDir: string, result: LlmBenchmarkResult): void { + const llmDir = join(baselinesDir, 'llm'); + mkdirSync(llmDir, { recursive: true }); + const filename = `${result.provider}-${result.model.replace(/[/:]/g, '-')}.json`; + writeFileSync(join(llmDir, filename), JSON.stringify(result, null, 2) + '\n'); +} + +export function loadAllLlmResults(baselinesDir: string): LlmBenchmarkResult[] { + const llmDir = join(baselinesDir, 'llm'); + if (!existsSync(llmDir)) return []; + + const results: LlmBenchmarkResult[] = []; + for (const f of readdirSync(llmDir) + .filter((f) => f.endsWith('.json')) + .sort()) { + try { + results.push(JSON.parse(readFileSync(join(llmDir, f), 'utf-8'))); + } catch { + console.warn(` Warning: skipping malformed LLM result file: ${f}`); + } + } + return results; +} + +// --------------------------------------------------------------------------- +// Retention analysis +// --------------------------------------------------------------------------- + +/** Extract technical identifiers (camelCase, PascalCase, snake_case). */ +export function extractKeywords(text: string): string[] { + const keywords = new Set(); + const camel = text.match(/\b[a-z]+(?:[A-Z][a-z]+)+\b/g); + if (camel) for (const w of camel) keywords.add(w); + const pascal = text.match(/\b[A-Z][a-z]+(?:[A-Z][a-z]+)+\b/g); + if (pascal) for (const w of pascal) keywords.add(w); + const snake = text.match(/\b[a-z]+(?:_[a-z]+)+\b/g); + if (snake) for (const w of snake) keywords.add(w); + return Array.from(keywords); +} + +/** Extract named entities: proper nouns, paths, URLs. */ +export function extractEntities(text: string): string[] { + const entities = new Set(); + // Proper nouns (capitalized, not common starters) + const common = new Set([ + 'The', + 'This', + 'That', + 'When', + 'Where', + 'What', + 'How', + 'Here', + 'There', + 'But', + 'And', + 'If', + 'It', + 'In', + 'On', + 'At', + 'To', + 'For', + 'With', + 'From', + 'As', + 'By', + 'An', + ]); + const proper = text.match(/\b[A-Z][a-z]+(?:\s+[A-Z][a-z]+)*\b/g); + if (proper) { + for (const noun of proper) { + const first = noun.split(/\s+/)[0]; + if (!common.has(first)) entities.add(noun); + } + } + // File paths + const paths = text.match(/(?:\/[\w.-]+){2,}/g); + if (paths) for (const p of paths) entities.add(p); + // URLs + const urls = text.match(/https?:\/\/[^\s]+/g); + if (urls) for (const u of urls) entities.add(u); + return Array.from(entities); +} + +/** Extract structural markers: code fences, bullet points, numbered lists. */ +export function extractStructural(text: string): string[] { + const markers: string[] = []; + const fences = text.match(/^[ ]{0,3}```[\w]*$/gm); + if (fences) markers.push(...fences.map((f) => f.trim())); + const bullets = text.match(/^[ \t]*[-•*]\s+.+$/gm); + if (bullets) markers.push(...bullets.map((b) => b.trim())); + const numbered = text.match(/^[ \t]*\d+[.)]\s+.+$/gm); + if (numbered) markers.push(...numbered.map((n) => n.trim())); + return markers; +} + +/** Measure retention: what fraction of original elements appear in the compressed text. */ +export function analyzeRetention(originalText: string, compressedText: string): RetentionResult { + const origKeywords = extractKeywords(originalText); + const origEntities = extractEntities(originalText); + const origStructural = extractStructural(originalText); + + const keywordRetention = + origKeywords.length === 0 + ? 1 + : origKeywords.filter((k) => compressedText.includes(k)).length / origKeywords.length; + + const entityRetention = + origEntities.length === 0 + ? 1 + : origEntities.filter((e) => compressedText.includes(e)).length / origEntities.length; + + const structuralRetention = + origStructural.length === 0 + ? 1 + : origStructural.filter((s) => compressedText.includes(s)).length / origStructural.length; + + return { keywordRetention, entityRetention, structuralRetention }; +} + +// --------------------------------------------------------------------------- +// Compare +// --------------------------------------------------------------------------- + +export interface Regression { + benchmark: string; + scenario: string; + metric: string; + expected: number | boolean; + actual: number | boolean; + delta?: string; +} + +function checkNum( + regressions: Regression[], + bench: string, + scenario: string, + metric: string, + expected: number, + actual: number, + tolerance: number, +): void { + const denom = Math.max(Math.abs(expected), 1); + const pctDiff = Math.abs(actual - expected) / denom; + if (pctDiff > tolerance) { + const sign = actual > expected ? '+' : ''; + regressions.push({ + benchmark: bench, + scenario, + metric, + expected, + actual, + delta: `${sign}${(((actual - expected) / denom) * 100).toFixed(1)}%`, + }); + } +} + +function checkBool( + regressions: Regression[], + bench: string, + scenario: string, + metric: string, + expected: boolean, + actual: boolean, +): void { + if (expected !== actual) { + regressions.push({ benchmark: bench, scenario, metric, expected, actual }); + } +} + +function missing(regressions: Regression[], bench: string, scenario: string): void { + regressions.push({ + benchmark: bench, + scenario, + metric: '(missing)', + expected: true, + actual: false, + }); +} + +export function compareResults( + baseline: BenchmarkResults, + current: BenchmarkResults, + tolerance: number = 0, +): Regression[] { + const regressions: Regression[] = []; + + // Basic + for (const [name, exp] of Object.entries(baseline.basic)) { + const act = current.basic[name]; + if (!act) { + missing(regressions, 'basic', name); + continue; + } + checkNum(regressions, 'basic', name, 'ratio', exp.ratio, act.ratio, tolerance); + checkNum(regressions, 'basic', name, 'tokenRatio', exp.tokenRatio, act.tokenRatio, tolerance); + checkNum(regressions, 'basic', name, 'compressed', exp.compressed, act.compressed, tolerance); + checkNum(regressions, 'basic', name, 'preserved', exp.preserved, act.preserved, tolerance); + } + + // Token budget + for (const [name, exp] of Object.entries(baseline.tokenBudget)) { + const act = current.tokenBudget[name]; + if (!act) { + missing(regressions, 'tokenBudget', name); + continue; + } + checkNum( + regressions, + 'tokenBudget', + name, + 'tokenCount', + exp.tokenCount, + act.tokenCount, + tolerance, + ); + checkBool(regressions, 'tokenBudget', name, 'fits', exp.fits, act.fits); + if (exp.recencyWindow != null && act.recencyWindow != null) { + checkNum( + regressions, + 'tokenBudget', + name, + 'recencyWindow', + exp.recencyWindow, + act.recencyWindow, + tolerance, + ); + } + checkNum( + regressions, + 'tokenBudget', + name, + 'compressed', + exp.compressed, + act.compressed, + tolerance, + ); + checkNum( + regressions, + 'tokenBudget', + name, + 'preserved', + exp.preserved, + act.preserved, + tolerance, + ); + checkNum(regressions, 'tokenBudget', name, 'deduped', exp.deduped, act.deduped, tolerance); + } + + // Dedup + for (const [name, exp] of Object.entries(baseline.dedup)) { + const act = current.dedup[name]; + if (!act) { + missing(regressions, 'dedup', name); + continue; + } + checkNum(regressions, 'dedup', name, 'rw0Base', exp.rw0Base, act.rw0Base, tolerance); + checkNum(regressions, 'dedup', name, 'rw0Dup', exp.rw0Dup, act.rw0Dup, tolerance); + checkNum(regressions, 'dedup', name, 'rw4Base', exp.rw4Base, act.rw4Base, tolerance); + checkNum(regressions, 'dedup', name, 'rw4Dup', exp.rw4Dup, act.rw4Dup, tolerance); + checkNum(regressions, 'dedup', name, 'deduped', exp.deduped, act.deduped, tolerance); + } + + // Fuzzy dedup + for (const [name, exp] of Object.entries(baseline.fuzzyDedup)) { + const act = current.fuzzyDedup[name]; + if (!act) { + missing(regressions, 'fuzzyDedup', name); + continue; + } + checkNum(regressions, 'fuzzyDedup', name, 'exact', exp.exact, act.exact, tolerance); + checkNum(regressions, 'fuzzyDedup', name, 'fuzzy', exp.fuzzy, act.fuzzy, tolerance); + checkNum(regressions, 'fuzzyDedup', name, 'ratio', exp.ratio, act.ratio, tolerance); + } + + // ANCS + if (baseline.ancs && current.ancs) { + for (const [name, exp] of Object.entries(baseline.ancs)) { + const act = current.ancs[name]; + if (!act) { + missing(regressions, 'ancs', name); + continue; + } + checkNum( + regressions, + 'ancs', + name, + 'baselineRatio', + exp.baselineRatio, + act.baselineRatio, + tolerance, + ); + checkNum( + regressions, + 'ancs', + name, + 'importanceRatio', + exp.importanceRatio, + act.importanceRatio, + tolerance, + ); + checkNum( + regressions, + 'ancs', + name, + 'contradictionRatio', + exp.contradictionRatio, + act.contradictionRatio, + tolerance, + ); + checkNum( + regressions, + 'ancs', + name, + 'combinedRatio', + exp.combinedRatio, + act.combinedRatio, + tolerance, + ); + checkNum( + regressions, + 'ancs', + name, + 'importancePreserved', + exp.importancePreserved, + act.importancePreserved, + tolerance, + ); + checkNum( + regressions, + 'ancs', + name, + 'contradicted', + exp.contradicted, + act.contradicted, + tolerance, + ); + } + } + + // Bundle size + for (const [name, exp] of Object.entries(baseline.bundleSize ?? {})) { + const act = current.bundleSize?.[name]; + if (!act) { + missing(regressions, 'bundleSize', name); + continue; + } + checkNum(regressions, 'bundleSize', name, 'bytes', exp.bytes, act.bytes, tolerance); + // gzipBytes is informational only — zlib output varies across platforms/versions + // so we don't regression-check it (raw bytes is the meaningful size metric) + } + + // Retention — 5% tolerance (retention should not drop significantly) + const retentionTolerance = 0.05; + if (baseline.retention && current.retention) { + for (const [name, exp] of Object.entries(baseline.retention)) { + const act = current.retention[name]; + if (!act) continue; + if (exp.keywordRetention - act.keywordRetention > retentionTolerance) { + regressions.push({ + benchmark: 'retention', + scenario: name, + metric: 'keywordRetention', + expected: exp.keywordRetention, + actual: act.keywordRetention, + delta: `${((act.keywordRetention - exp.keywordRetention) * 100).toFixed(1)}%`, + }); + } + if (exp.entityRetention - act.entityRetention > retentionTolerance) { + regressions.push({ + benchmark: 'retention', + scenario: name, + metric: 'entityRetention', + expected: exp.entityRetention, + actual: act.entityRetention, + delta: `${((act.entityRetention - exp.entityRetention) * 100).toFixed(1)}%`, + }); + } + if (exp.structuralRetention - act.structuralRetention > retentionTolerance) { + regressions.push({ + benchmark: 'retention', + scenario: name, + metric: 'structuralRetention', + expected: exp.structuralRetention, + actual: act.structuralRetention, + delta: `${((act.structuralRetention - exp.structuralRetention) * 100).toFixed(1)}%`, + }); + } + } + } + + return regressions; +} + +// --------------------------------------------------------------------------- +// Report +// --------------------------------------------------------------------------- + +export function formatRegressions(regressions: Regression[]): string { + if (regressions.length === 0) return 'No regressions detected.'; + + const lines: string[] = [`${regressions.length} regression(s) detected:`, '']; + + for (const r of regressions) { + const delta = r.delta ? ` (${r.delta})` : ''; + lines.push( + ` [${r.benchmark}] ${r.scenario} → ${r.metric}: expected ${r.expected}, got ${r.actual}${delta}`, + ); + } + + return lines.join('\n'); +} + +// --------------------------------------------------------------------------- +// Version diff +// --------------------------------------------------------------------------- + +export interface ScenarioDelta { + scenario: string; + oldRatio: number; + newRatio: number; + change: number; // percentage change (positive = improvement) + oldTokenRatio: number; + newTokenRatio: number; + tokenChange: number; +} + +export interface VersionDiff { + fromVersion: string; + toVersion: string; + fromDate: string; + toDate: string; + scenarios: ScenarioDelta[]; + avgRatioOld: number; + avgRatioNew: number; + avgChange: number; + bundleSizeOld?: { bytes: number; gzipBytes: number }; + bundleSizeNew?: { bytes: number; gzipBytes: number }; +} + +/** + * Compares two baselines and returns a structured diff. + * Positive `change` values mean the newer version compresses better. + */ +export function diffBaselines(older: Baseline, newer: Baseline): VersionDiff { + const scenarios: ScenarioDelta[] = []; + + // Use the union of both scenario sets + const allScenarios = new Set([ + ...Object.keys(older.results.basic), + ...Object.keys(newer.results.basic), + ]); + + for (const name of allScenarios) { + const oldVal = older.results.basic[name]; + const newVal = newer.results.basic[name]; + if (!oldVal || !newVal) continue; + + const change = oldVal.ratio === 0 ? 0 : ((newVal.ratio - oldVal.ratio) / oldVal.ratio) * 100; + const tokenChange = + oldVal.tokenRatio === 0 + ? 0 + : ((newVal.tokenRatio - oldVal.tokenRatio) / oldVal.tokenRatio) * 100; + + scenarios.push({ + scenario: name, + oldRatio: oldVal.ratio, + newRatio: newVal.ratio, + change, + oldTokenRatio: oldVal.tokenRatio, + newTokenRatio: newVal.tokenRatio, + tokenChange, + }); + } + + const avgOld = + scenarios.length > 0 ? scenarios.reduce((s, d) => s + d.oldRatio, 0) / scenarios.length : 0; + const avgNew = + scenarios.length > 0 ? scenarios.reduce((s, d) => s + d.newRatio, 0) / scenarios.length : 0; + const avgChange = avgOld === 0 ? 0 : ((avgNew - avgOld) / avgOld) * 100; + + return { + fromVersion: older.version, + toVersion: newer.version, + fromDate: older.generated.split('T')[0], + toDate: newer.generated.split('T')[0], + scenarios, + avgRatioOld: avgOld, + avgRatioNew: avgNew, + avgChange, + bundleSizeOld: older.results.bundleSize?.total, + bundleSizeNew: newer.results.bundleSize?.total, + }; +} + +/** + * Formats a version diff as a markdown table for console or doc output. + */ +export function formatVersionDiff(diff: VersionDiff): string { + const lines: string[] = []; + + lines.push(`## v${diff.fromVersion} → v${diff.toVersion}`); + lines.push(''); + + const sign = (n: number) => (n > 0 ? '+' : ''); + const arrow = (n: number) => (n > 1 ? ' ↑' : n < -1 ? ' ↓' : ' ─'); + + lines.push( + `> **${fix(diff.avgRatioOld)}x** → **${fix(diff.avgRatioNew)}x** avg compression` + + ` (${sign(diff.avgChange)}${fix(diff.avgChange)}%)`, + ); + lines.push(''); + + lines.push( + '| Scenario | v' + diff.fromVersion + ' | v' + diff.toVersion + ' | Change | Token Δ | |', + ); + lines.push('| --- | ---: | ---: | ---: | ---: | --- |'); + for (const d of diff.scenarios) { + lines.push( + `| ${d.scenario} | ${fix(d.oldRatio)}x | ${fix(d.newRatio)}x | ${sign(d.change)}${fix(d.change)}% | ${sign(d.tokenChange)}${fix(d.tokenChange)}% |${arrow(d.change)}|`, + ); + } + + if (diff.bundleSizeOld && diff.bundleSizeNew) { + const bytesDelta = + ((diff.bundleSizeNew.bytes - diff.bundleSizeOld.bytes) / diff.bundleSizeOld.bytes) * 100; + lines.push(''); + lines.push( + `Bundle: ${formatBytes(diff.bundleSizeOld.bytes)} → ${formatBytes(diff.bundleSizeNew.bytes)} (${sign(bytesDelta)}${fix(bytesDelta)}%)`, + ); + } + + return lines.join('\n'); +} + +// --------------------------------------------------------------------------- +// Doc generation +// --------------------------------------------------------------------------- + +function semverSort(a: string, b: string): number { + const pa = a + .replace(/^v|\.json$/g, '') + .split('.') + .map(Number); + const pb = b + .replace(/^v|\.json$/g, '') + .split('.') + .map(Number); + for (let i = 0; i < 3; i++) { + if ((pa[i] ?? 0) !== (pb[i] ?? 0)) return (pa[i] ?? 0) - (pb[i] ?? 0); + } + return 0; +} + +function loadAllBaselines(baselinesDir: string): Baseline[] { + const historyDir = join(baselinesDir, 'history'); + if (!existsSync(historyDir)) return []; + + const files = readdirSync(historyDir) + .filter((f) => f.startsWith('v') && f.endsWith('.json')) + .sort(semverSort); + + return files.map((f) => loadBaseline(join(historyDir, f))); +} + +function fix(n: number, d: number = 2): string { + return n.toFixed(d); +} + +/** Shorten scenario names for chart x-axis labels. */ +const SHORT_NAMES: Record = { + 'Coding assistant': 'Coding', + 'Long Q&A': 'Long Q&A', + 'Tool-heavy': 'Tool-heavy', + 'Short conversation': 'Short', + 'Deep conversation': 'Deep', + 'Technical explanation': 'Technical', + 'Structured content': 'Structured', + 'Agentic coding session': 'Agentic', + 'Iterative design': 'Iterative', +}; + +function shortName(name: string): string { + return SHORT_NAMES[name] ?? name; +} + +function formatTime(ms: number): string { + return ms < 1000 ? `${Math.round(ms)}ms` : `${(ms / 1000).toFixed(1)}s`; +} + +// --------------------------------------------------------------------------- +// Visual helpers +// --------------------------------------------------------------------------- + +function formatBytes(bytes: number): string { + if (bytes < 1024) return `${bytes} B`; + return `${(bytes / 1024).toFixed(1)} KB`; +} + +function badges( + basic: Record, + bundleSize?: Record, +): string[] { + const entries = Object.values(basic); + const ratios = entries.map((v) => v.ratio); + const avgR = (ratios.reduce((a, b) => a + b, 0) / ratios.length).toFixed(2); + const bestR = Math.max(...ratios).toFixed(2); + const allPass = 'all_PASS'; + + const badge = (label: string, value: string, color: string) => + `![${label}](https://img.shields.io/badge/${encodeURIComponent(label).replace(/-/g, '--')}-${encodeURIComponent(value).replace(/-/g, '--')}-${color})`; + + const badgeList = [ + badge('avg ratio', `${avgR}x`, 'blue'), + badge('best', `${bestR}x`, 'blue'), + badge('scenarios', `${entries.length}`, 'blue'), + badge('round-trip', allPass, 'brightgreen'), + ]; + + const totalGzip = bundleSize?.total?.gzipBytes; + if (totalGzip != null) { + badgeList.push(badge('gzip', formatBytes(totalGzip), 'blue')); + } + + return [badgeList.join(' ')]; +} + +// --------------------------------------------------------------------------- +// Mermaid chart helpers +// --------------------------------------------------------------------------- + +function compressionChart(basic: Record): string[] { + const entries = Object.entries(basic); + const labels = entries.map(([n]) => `"${shortName(n)}"`).join(', '); + const values = entries.map(([, v]) => fix(v.ratio)).join(', '); + + return [ + '```mermaid', + 'xychart-beta', + ' title "Compression Ratio by Scenario"', + ` x-axis [${labels}]`, + ' y-axis "Char Ratio"', + ` bar [${values}]`, + '```', + ]; +} + +function dedupChart(dedup: Record): string[] { + // Only include scenarios where dedup actually changes the ratio + const entries = Object.entries(dedup).filter(([, v]) => v.rw0Base !== v.rw0Dup || v.deduped > 0); + if (entries.length === 0) return []; + + const labels = entries.map(([n]) => `"${shortName(n)}"`).join(', '); + const base = entries.map(([, v]) => fix(v.rw0Base)).join(', '); + const exact = entries.map(([, v]) => fix(v.rw0Dup)).join(', '); + + return [ + '```mermaid', + 'xychart-beta', + ' title "Deduplication Impact (recencyWindow=0)"', + ` x-axis [${labels}]`, + ' y-axis "Char Ratio"', + ` bar [${base}]`, + ` bar [${exact}]`, + '```', + '', + '*First bar: no dedup · Second bar: with dedup*', + ]; +} + +function asciiBar(value: number, max: number, width: number): string { + const filled = Math.round((value / max) * width); + return '\u2588'.repeat(filled) + '\u2591'.repeat(width - filled); +} + +function llmComparisonCharts( + basic: Record, + llmResults: LlmBenchmarkResult[], +): string[] { + const lines: string[] = []; + const barWidth = 30; + + for (const llm of llmResults) { + const sharedScenarios = Object.keys(basic).filter((s) => s in llm.scenarios); + if (sharedScenarios.length === 0) continue; + + // Collect data and find max for scaling + const rows: { name: string; detR: number; llmR: number }[] = []; + for (const s of sharedScenarios) { + const detR = basic[s].ratio; + const methods = Object.values(llm.scenarios[s].methods).filter((m) => m.vsDet != null); + const llmR = methods.length > 0 ? Math.max(...methods.map((m) => m.ratio)) : detR; + rows.push({ name: s, detR, llmR }); + } + const maxR = Math.max(...rows.flatMap((r) => [r.detR, r.llmR])); + const nameWidth = Math.max(...rows.map((r) => r.name.length)); + + lines.push('```'); + lines.push(`Deterministic vs ${llm.provider}/${llm.model}`); + lines.push(''); + for (const r of rows) { + const label = r.name.padEnd(nameWidth); + const detBar = asciiBar(r.detR, maxR, barWidth); + const llmBar = asciiBar(r.llmR, maxR, barWidth); + const winner = r.llmR > r.detR + 0.01 ? ' \u2605' : ''; + lines.push(`${label} Det ${detBar} ${fix(r.detR)}x`); + lines.push(`${' '.repeat(nameWidth)} LLM ${llmBar} ${fix(r.llmR)}x${winner}`); + lines.push(''); + } + lines.push('\u2605 = LLM wins'); + lines.push('```'); + lines.push(''); + } + + return lines; +} + +// --------------------------------------------------------------------------- +// Section generators +// --------------------------------------------------------------------------- + +function generateCompressionSection(b: Baseline): string[] { + const lines: string[] = []; + const r = b.results; + const basicEntries = Object.entries(r.basic); + const ratios = basicEntries.map(([, v]) => v.ratio); + const minR = Math.min(...ratios); + const maxR = Math.max(...ratios); + const avgR = ratios.reduce((a, b) => a + b, 0) / ratios.length; + + lines.push('## Compression by Scenario'); + lines.push(''); + lines.push( + `> **${basicEntries.length} scenarios** · **${fix(avgR)}x** avg ratio · ` + + `**${fix(minR)}x** – **${fix(maxR)}x** range · all round-trips PASS`, + ); + lines.push(''); + lines.push(...compressionChart(r.basic)); + lines.push(''); + lines.push('| Scenario | Ratio | Reduction | Token Ratio | Messages | Compressed | Preserved |'); + lines.push('| --- | ---: | ---: | ---: | ---: | ---: | ---: |'); + for (const [name, v] of basicEntries) { + const reduction = Math.round((1 - 1 / v.ratio) * 100); + const messages = v.compressed + v.preserved; + lines.push( + `| ${name} | ${fix(v.ratio)} | ${reduction}% | ${fix(v.tokenRatio)} | ${messages} | ${v.compressed} | ${v.preserved} |`, + ); + } + return lines; +} + +function generateDedupSection(r: BenchmarkResults): string[] { + const lines: string[] = []; + lines.push('## Deduplication Impact'); + lines.push(''); + + const chart = dedupChart(r.dedup); + if (chart.length > 0) { + lines.push(...chart); + lines.push(''); + } + + lines.push( + '| Scenario | No Dedup (rw=0) | Dedup (rw=0) | No Dedup (rw=4) | Dedup (rw=4) | Deduped |', + ); + lines.push('| --- | ---: | ---: | ---: | ---: | ---: |'); + for (const [name, v] of Object.entries(r.dedup)) { + lines.push( + `| ${name} | ${fix(v.rw0Base)} | ${fix(v.rw0Dup)} | ${fix(v.rw4Base)} | ${fix(v.rw4Dup)} | ${v.deduped} |`, + ); + } + lines.push(''); + + // Fuzzy dedup detail + const hasFuzzy = Object.values(r.fuzzyDedup).some((v) => v.fuzzy > 0); + if (hasFuzzy) { + lines.push('### Fuzzy Dedup'); + lines.push(''); + } + lines.push('| Scenario | Exact Deduped | Fuzzy Deduped | Ratio | vs Base |'); + lines.push('| --- | ---: | ---: | ---: | ---: |'); + for (const [name, v] of Object.entries(r.fuzzyDedup)) { + const baseRatio = r.basic[name]?.ratio ?? v.ratio; + const improvement = + v.ratio > baseRatio + 0.01 + ? `+${Math.round(((v.ratio - baseRatio) / baseRatio) * 100)}%` + : '-'; + lines.push(`| ${name} | ${v.exact} | ${v.fuzzy} | ${fix(v.ratio)} | ${improvement} |`); + } + return lines; +} + +function generateAncsSection(r: BenchmarkResults): string[] { + if (!r.ancs || Object.keys(r.ancs).length === 0) return []; + + const lines: string[] = []; + lines.push('## ANCS-Inspired Features'); + lines.push(''); + lines.push( + '> Importance scoring preserves high-value messages outside the recency window. ' + + 'Contradiction detection compresses superseded messages.', + ); + lines.push(''); + lines.push( + '| Scenario | Baseline | +Importance | +Contradiction | Combined | Imp. Preserved | Contradicted |', + ); + lines.push('| --- | ---: | ---: | ---: | ---: | ---: | ---: |'); + for (const [name, v] of Object.entries(r.ancs)) { + lines.push( + `| ${name} | ${fix(v.baselineRatio)} | ${fix(v.importanceRatio)} | ${fix(v.contradictionRatio)} | ${fix(v.combinedRatio)} | ${v.importancePreserved} | ${v.contradicted} |`, + ); + } + return lines; +} + +function generateTokenBudgetSection(r: BenchmarkResults): string[] { + const lines: string[] = []; + const entries = Object.entries(r.tokenBudget); + const allFit = entries.every(([, v]) => v.fits); + const fitCount = entries.filter(([, v]) => v.fits).length; + + lines.push('## Token Budget'); + lines.push(''); + lines.push( + `Target: **2000 tokens** · ${allFit ? 'all fit' : `${fitCount}/${entries.length} fit`}`, + ); + lines.push(''); + lines.push( + '| Scenario | Dedup | Tokens | Fits | recencyWindow | Compressed | Preserved | Deduped |', + ); + lines.push('| --- | --- | ---: | --- | ---: | ---: | ---: | ---: |'); + for (const [key, v] of entries) { + const [name, dedupStr] = key.split('|'); + const dedup = dedupStr === 'dedup=true' ? 'yes' : 'no'; + const fitIcon = v.fits ? 'yes' : 'no'; + lines.push( + `| ${name} | ${dedup} | ${v.tokenCount} | ${fitIcon} | ${v.recencyWindow ?? '-'} | ${v.compressed} | ${v.preserved} | ${v.deduped} |`, + ); + } + return lines; +} + +function generateBundleSizeSection(bundleSize: Record): string[] { + const entries = Object.entries(bundleSize); + if (entries.length === 0) return []; + + const lines: string[] = []; + lines.push('## Bundle Size'); + lines.push(''); + lines.push('> Zero-dependency ESM library — tracked per-file to catch regressions.'); + lines.push(''); + lines.push('| File | Size | Gzip |'); + lines.push('| --- | ---: | ---: |'); + for (const [name, v] of entries) { + const label = name === 'total' ? '**total**' : name; + lines.push(`| ${label} | ${formatBytes(v.bytes)} | ${formatBytes(v.gzipBytes)} |`); + } + return lines; +} + +function generateLlmSection(baselinesDir: string, basic: Record): string[] { + const llmResults = loadAllLlmResults(baselinesDir); + if (llmResults.length === 0) return []; + + const lines: string[] = []; + lines.push('## LLM vs Deterministic'); + lines.push(''); + lines.push( + '> Results are **non-deterministic** — LLM outputs vary between runs. ' + + 'Saved as reference data, not used for regression testing.', + ); + lines.push(''); + + // Per-provider comparison charts (ASCII horizontal bars in code blocks) + const charts = llmComparisonCharts(basic, llmResults); + if (charts.length > 0) { + lines.push(...charts); + } + + // Cross-provider summary table + if (llmResults.length > 0) { + lines.push('### Provider Summary'); + lines.push(''); + lines.push( + '| Provider | Model | Avg Ratio | Avg vsDet | Round-trip | Budget Fits | Avg Time |', + ); + lines.push('| --- | --- | ---: | ---: | --- | --- | ---: |'); + for (const llm of llmResults) { + const ratioValues: number[] = []; + const vsDetValues: number[] = []; + const timeValues: number[] = []; + let passCount = 0; + let totalCount = 0; + for (const sr of Object.values(llm.scenarios)) { + for (const mr of Object.values(sr.methods)) { + ratioValues.push(mr.ratio); + if (mr.vsDet != null) vsDetValues.push(mr.vsDet); + timeValues.push(mr.timeMs); + totalCount++; + if (mr.roundTrip === 'PASS') passCount++; + } + } + const avgRatio = + ratioValues.length > 0 ? ratioValues.reduce((a, b) => a + b, 0) / ratioValues.length : 0; + const avgVsDet = + vsDetValues.length > 0 ? vsDetValues.reduce((a, b) => a + b, 0) / vsDetValues.length : 0; + const avgTime = + timeValues.length > 0 ? timeValues.reduce((a, b) => a + b, 0) / timeValues.length : 0; + const rt = passCount === totalCount ? 'all PASS' : `${passCount}/${totalCount}`; + + // Token budget summary + let budgetFits = '-'; + if (llm.tokenBudget) { + const allEntries = Object.values(llm.tokenBudget).flat(); + if (allEntries.length > 0) { + const fitCount = allEntries.filter((e) => e.fits).length; + budgetFits = `${fitCount}/${allEntries.length}`; + } + } + + lines.push( + `| ${llm.provider} | ${llm.model} | ${fix(avgRatio)}x | ${fix(avgVsDet)} | ${rt} | ${budgetFits} | ${formatTime(avgTime)} |`, + ); + } + lines.push(''); + } + + // Key finding callout + const wins: string[] = []; + const losses: string[] = []; + for (const llm of llmResults) { + for (const [scenario, sr] of Object.entries(llm.scenarios)) { + for (const mr of Object.values(sr.methods)) { + if (mr.vsDet != null && mr.vsDet > 1.0) wins.push(scenario); + if (mr.vsDet != null && mr.vsDet < 0.9) losses.push(scenario); + } + } + } + const uniqueWins = [...new Set(wins)]; + const uniqueLosses = [...new Set(losses)]; + if (uniqueWins.length > 0 || uniqueLosses.length > 0) { + lines.push('> **Key findings:**'); + if (uniqueWins.length > 0) { + lines.push(`> LLM wins on prose-heavy scenarios: ${uniqueWins.join(', ')}`); + } + if (uniqueLosses.length > 0) { + lines.push( + `> Deterministic wins on structured/technical content: ${uniqueLosses.join(', ')}`, + ); + } + lines.push(''); + } + + // Per-provider detail tables (collapsible) + for (const llm of llmResults) { + lines.push(`### ${llm.provider} (${llm.model})`); + lines.push(''); + lines.push(`*Generated: ${llm.generated.split('T')[0]}*`); + lines.push(''); + lines.push('
'); + lines.push(`Scenario details`); + lines.push(''); + lines.push( + '| Scenario | Method | Char Ratio | Token Ratio | vsDet | Compressed | Preserved | Round-trip | Time |', + ); + lines.push('| --- | --- | ---: | ---: | ---: | ---: | ---: | --- | ---: |'); + + for (const [scenario, sr] of Object.entries(llm.scenarios)) { + let first = true; + for (const [method, mr] of Object.entries(sr.methods)) { + const label = first ? scenario : ''; + const vsDet = mr.vsDet != null ? fix(mr.vsDet) : '-'; + lines.push( + `| ${label} | ${method} | ${fix(mr.ratio)} | ${fix(mr.tokenRatio)} | ${vsDet} | ${mr.compressed} | ${mr.preserved} | ${mr.roundTrip} | ${formatTime(mr.timeMs)} |`, + ); + first = false; + } + } + + // Token budget table (if present) + if (llm.tokenBudget && Object.keys(llm.tokenBudget).length > 0) { + lines.push(''); + lines.push('#### Token Budget (target: 2000 tokens)'); + lines.push(''); + lines.push( + '| Scenario | Method | Tokens | Fits | recencyWindow | Ratio | Round-trip | Time |', + ); + lines.push('| --- | --- | ---: | --- | ---: | ---: | --- | ---: |'); + + for (const [scenario, entries] of Object.entries(llm.tokenBudget)) { + let first = true; + for (const entry of entries) { + const label = first ? scenario : ''; + lines.push( + `| ${label} | ${entry.method} | ${entry.tokenCount} | ${entry.fits} | ${entry.recencyWindow ?? '-'} | ${fix(entry.ratio)} | ${entry.roundTrip} | ${formatTime(entry.timeMs)} |`, + ); + first = false; + } + } + } + + lines.push(''); + lines.push('
'); + lines.push(''); + } + + return lines; +} + +// --------------------------------------------------------------------------- +// Main doc generator +// --------------------------------------------------------------------------- + +export function generateBenchmarkDocs(baselinesDir: string, outputPath: string): void { + const baselines = loadAllBaselines(baselinesDir); + if (baselines.length === 0) return; + + const latest = baselines[baselines.length - 1]; + const lines: string[] = []; + + // --- Header --- + lines.push('# Benchmark Results'); + lines.push(''); + lines.push('[Back to README](../README.md) | [All docs](README.md) | [Handbook](benchmarks.md)'); + lines.push(''); + lines.push('*Auto-generated by `npm run bench:save`. Do not edit manually.*'); + lines.push(''); + lines.push(`**v${latest.version}** · Generated: ${latest.generated.split('T')[0]}`); + lines.push(''); + lines.push(...badges(latest.results.basic, latest.results.bundleSize)); + lines.push(''); + + // --- Summary --- + const basicEntries = Object.entries(latest.results.basic); + const ratios = basicEntries.map(([, v]) => v.ratio); + const avgR = ratios.reduce((a, b) => a + b, 0) / ratios.length; + lines.push('## Summary'); + lines.push(''); + lines.push(`| Metric | Value |`); + lines.push(`| --- | --- |`); + lines.push(`| Scenarios | ${basicEntries.length} |`); + lines.push(`| Average compression | ${fix(avgR)}x |`); + lines.push(`| Best compression | ${fix(Math.max(...ratios))}x |`); + lines.push(`| Round-trip integrity | all PASS |`); + if (latest.results.quality && Object.keys(latest.results.quality).length > 0) { + const qualityEntries = Object.values(latest.results.quality); + const avgQ = qualityEntries.reduce((s, q) => s + q.qualityScore, 0) / qualityEntries.length; + lines.push(`| Average quality score | ${fix(avgQ, 3)} |`); + const avgER = qualityEntries.reduce((s, q) => s + q.entityRetention, 0) / qualityEntries.length; + lines.push(`| Average entity retention | ${(avgER * 100).toFixed(0)}% |`); + } + lines.push(''); + + // --- Pie chart: message outcome distribution --- + const totalPreserved = basicEntries.reduce((s, [, v]) => s + v.preserved, 0); + const totalCompressed = basicEntries.reduce((s, [, v]) => s + v.compressed, 0); + lines.push('```mermaid'); + lines.push('pie title "Message Outcomes"'); + lines.push(` "Preserved" : ${totalPreserved}`); + lines.push(` "Compressed" : ${totalCompressed}`); + lines.push('```'); + lines.push(''); + + // --- Compression --- + lines.push(...generateCompressionSection(latest)); + lines.push(''); + + // --- Dedup --- + lines.push(...generateDedupSection(latest.results)); + lines.push(''); + + // --- ANCS --- + const ancsSection = generateAncsSection(latest.results); + if (ancsSection.length > 0) { + lines.push(...ancsSection); + lines.push(''); + } + + // --- Quality --- + if (latest.results.quality && Object.keys(latest.results.quality).length > 0) { + lines.push('## Quality Metrics'); + lines.push(''); + lines.push( + '| Scenario | Entity Retention | Structural Integrity | Reference Coherence | Quality Score |', + ); + lines.push('| --- | --- | --- | --- | --- |'); + for (const [name, q] of Object.entries(latest.results.quality)) { + lines.push( + `| ${name} | ${(q.entityRetention * 100).toFixed(0)}% | ${(q.structuralIntegrity * 100).toFixed(0)}% | ${(q.referenceCoherence * 100).toFixed(0)}% | ${q.qualityScore.toFixed(3)} |`, + ); + } + lines.push(''); + } + + // --- Token budget --- + lines.push(...generateTokenBudgetSection(latest.results)); + lines.push(''); + + // --- Bundle size --- + const bundleSizeSection = generateBundleSizeSection(latest.results.bundleSize ?? {}); + if (bundleSizeSection.length > 0) { + lines.push(...bundleSizeSection); + lines.push(''); + } + + // --- LLM (conditional) --- + const llmSection = generateLlmSection(baselinesDir, latest.results.basic); + if (llmSection.length > 0) { + lines.push(...llmSection); + } + + // --- Version history (conditional) --- + if (baselines.length > 1) { + lines.push('## Version History'); + lines.push(''); + lines.push('| Version | Date | Avg Char Ratio | Avg Token Ratio | Scenarios |'); + lines.push('| --- | --- | ---: | ---: | ---: |'); + for (const b of [...baselines].reverse()) { + const entries = Object.values(b.results.basic); + const avgChr = entries.reduce((s, v) => s + v.ratio, 0) / entries.length; + const avgTkr = entries.reduce((s, v) => s + v.tokenRatio, 0) / entries.length; + const date = b.generated.split('T')[0]; + lines.push( + `| ${b.version} | ${date} | ${fix(avgChr)} | ${fix(avgTkr)} | ${entries.length} |`, + ); + } + lines.push(''); + + // Version-to-version comparison (latest vs previous) + const prev = baselines[baselines.length - 2]; + const diff = diffBaselines(prev, latest); + const sign = (n: number) => (n > 0 ? '+' : ''); + const arrow = (n: number) => (n > 1 ? ' \u2191' : n < -1 ? ' \u2193' : ' \u2500'); + + lines.push(`### v${diff.fromVersion} \u2192 v${diff.toVersion}`); + lines.push(''); + lines.push( + `> **${fix(diff.avgRatioOld)}x** \u2192 **${fix(diff.avgRatioNew)}x** avg compression` + + ` (${sign(diff.avgChange)}${fix(diff.avgChange)}%)`, + ); + lines.push(''); + lines.push( + '| Scenario | v' + + diff.fromVersion + + ' | v' + + diff.toVersion + + ' | Change | Token \u0394 | |', + ); + lines.push('| --- | ---: | ---: | ---: | ---: | --- |'); + for (const d of diff.scenarios) { + lines.push( + `| ${d.scenario} | ${fix(d.oldRatio)}x | ${fix(d.newRatio)}x | ${sign(d.change)}${fix(d.change)}% | ${sign(d.tokenChange)}${fix(d.tokenChange)}% |${arrow(d.change)}|`, + ); + } + + if (diff.bundleSizeOld && diff.bundleSizeNew) { + const bytesDelta = + ((diff.bundleSizeNew.bytes - diff.bundleSizeOld.bytes) / diff.bundleSizeOld.bytes) * 100; + lines.push(''); + lines.push( + `Bundle: ${formatBytes(diff.bundleSizeOld.bytes)} \u2192 ${formatBytes(diff.bundleSizeNew.bytes)} (${sign(bytesDelta)}${fix(bytesDelta)}%)`, + ); + } + lines.push(''); + + // Per-version detail (older versions, collapsible) + const olderVersions = baselines.slice(0, -1).reverse(); + for (const b of olderVersions) { + const r = b.results; + const oldEntries = Object.entries(r.basic); + const oldRatios = oldEntries.map(([, v]) => v.ratio); + const oldAvg = oldRatios.reduce((a, b) => a + b, 0) / oldRatios.length; + + lines.push(`
`); + lines.push( + `v${b.version} (${b.generated.split('T')[0]}) \u2014 ${fix(oldAvg)}x avg`, + ); + lines.push(''); + lines.push('| Scenario | Char Ratio | Token Ratio | Compressed | Preserved |'); + lines.push('| --- | ---: | ---: | ---: | ---: |'); + for (const [name, v] of oldEntries) { + lines.push( + `| ${name} | ${fix(v.ratio)} | ${fix(v.tokenRatio)} | ${v.compressed} | ${v.preserved} |`, + ); + } + lines.push(''); + lines.push('
'); + lines.push(''); + } + } + + // --- Methodology --- + lines.push('## Methodology'); + lines.push(''); + lines.push('- All deterministic results use the same input → same output guarantee'); + lines.push('- Metrics: compression ratio, token ratio, message counts, dedup counts'); + lines.push('- Timing is excluded from baselines (hardware-dependent)'); + lines.push('- LLM benchmarks are saved as reference data, not used for regression testing'); + lines.push('- Round-trip integrity is verified for every scenario (compress then uncompress)'); + lines.push(''); + + writeFileSync(outputPath, lines.join('\n')); +} diff --git a/bench/baselines/current.json b/bench/baselines/current.json new file mode 100644 index 0000000..cb2217a --- /dev/null +++ b/bench/baselines/current.json @@ -0,0 +1,378 @@ +{ + "version": "1.3.0", + "generated": "2026-03-21T14:09:19.600Z", + "results": { + "basic": { + "Coding assistant": { + "ratio": 1.9385451505016722, + "tokenRatio": 1.9275362318840579, + 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{ + "scenarios": { + "Coding assistant": { + "ratio": 1.518628912071535, + "avgEntityRetention": 1, + "avgKeywordRetention": 1, + "codeBlockIntegrity": 1, + "qualityScore": -1, + "factRetention": -1, + "roundTrip": true + }, + "Long Q&A": { + "ratio": 5.830339321357285, + "avgEntityRetention": 1, + "avgKeywordRetention": 1, + "codeBlockIntegrity": 1, + "qualityScore": -1, + "factRetention": -1, + "roundTrip": true + }, + "Deep conversation": { + "ratio": 1.950067476383266, + "avgEntityRetention": 1, + "avgKeywordRetention": 1, + "codeBlockIntegrity": 1, + "qualityScore": -1, + "factRetention": -1, + "roundTrip": true + } + }, + "tradeoff": {} + } +} diff --git a/bench/compare.ts b/bench/compare.ts new file mode 100644 index 0000000..63e3a5d --- /dev/null +++ b/bench/compare.ts @@ -0,0 +1,296 @@ +#!/usr/bin/env npx tsx +/** + * A/B Comparison Tool + * + * Compresses the same input with two different option sets and shows a + * side-by-side comparison of ratio, quality, entity retention, and output. + * + * Usage: + * npx tsx bench/compare.ts [--scenario ] + * + * Compares default options vs. all v2 features enabled. + */ + +import { compress, defaultTokenCounter } from '../src/compress.js'; +import type { CompressOptions, CompressResult, Message } from '../src/types.js'; + +// --------------------------------------------------------------------------- +// Helpers +// --------------------------------------------------------------------------- + +let nextId = 1; +function msg(role: string, content: string): Message { + const id = String(nextId++); + return { id, index: nextId - 1, role, content, metadata: {} }; +} + +function tokens(result: CompressResult): number { + return result.messages.reduce((sum, m) => sum + defaultTokenCounter(m), 0); +} + +// --------------------------------------------------------------------------- +// Scenarios +// --------------------------------------------------------------------------- + +type Scenario = { name: string; messages: Message[] }; + +function buildScenarios(): Scenario[] { + nextId = 1; + return [ + codingAssistant(), + longQA(), + deepConversation(), + technicalExplanation(), + agenticSession(), + ]; +} + +function codingAssistant(): Scenario { + const prose = + 'The authentication middleware validates incoming JWT tokens against the session store, checks expiration timestamps, and refreshes tokens when they are within the renewal window. '; + return { + name: 'Coding assistant', + messages: [ + msg('system', 'You are a senior TypeScript developer.'), + msg('user', 'How do I set up Express middleware for JWT auth?'), + msg( + 'assistant', + `${prose.repeat(3)}\n\n\`\`\`typescript\nimport jwt from 'jsonwebtoken';\nexport function authMiddleware(req, res, next) {\n const token = req.headers.authorization?.split(' ')[1];\n if (!token) return res.status(401).json({ error: 'No token' });\n try { req.user = jwt.verify(token, process.env.JWT_SECRET); next(); }\n catch { res.status(401).json({ error: 'Invalid token' }); }\n}\n\`\`\``, + ), + msg('user', 'Can you add refresh token rotation?'), + msg( + 'assistant', + `${prose.repeat(4)} The refresh token rotation ensures single-use tokens prevent replay attacks.`, + ), + msg('user', 'What about rate limiting?'), + msg('assistant', `Rate limiting prevents abuse. ${prose.repeat(3)}`), + msg('user', 'Thanks, very helpful!'), + msg('assistant', 'Happy to help. Let me know if you need anything else.'), + ], + }; +} + +function longQA(): Scenario { + const longAnswer = + 'The architecture of modern distributed systems relies on several foundational principles including service isolation, eventual consistency, and fault tolerance. Each service maintains its own data store, communicating through asynchronous message queues or synchronous RPC calls depending on latency requirements. Circuit breakers prevent cascading failures by monitoring error rates. '; + return { + name: 'Long Q&A', + messages: [ + msg('system', 'You are a software architecture consultant.'), + msg('user', 'What is event sourcing?'), + msg('assistant', longAnswer.repeat(4)), + msg('user', 'How does CQRS relate to it?'), + msg('assistant', longAnswer.repeat(5)), + msg('user', 'What about saga patterns?'), + msg('assistant', longAnswer.repeat(6)), + msg('user', 'Can you compare these approaches?'), + msg('assistant', longAnswer.repeat(4)), + msg('user', 'Thanks, that was very thorough!'), + msg( + 'assistant', + 'Happy to help! Let me know if you want to dive deeper into any of these topics.', + ), + ], + }; +} + +function deepConversation(): Scenario { + const filler = + 'I think that sounds reasonable and we should continue with the current approach. '; + const technical = + 'The fetchData function uses exponential backoff with a base delay of 200ms and a maximum of 5 retries before throwing ServiceUnavailable. '; + return { + name: 'Deep conversation', + messages: [ + msg('system', 'You are a helpful assistant.'), + ...Array.from({ length: 20 }, (_, i) => + msg( + i % 2 === 0 ? 'user' : 'assistant', + i % 5 === 0 + ? technical.repeat(3) + : i % 3 === 0 + ? `Actually, use Memcached instead of Redis for caching. ${technical}` + : filler.repeat(4), + ), + ), + msg('user', 'What should we do next?'), + ], + }; +} + +function technicalExplanation(): Scenario { + const prose = + 'The event-driven architecture we adopted grew out of a series of scaling problems we encountered when the monolith started buckling under peak traffic. The core idea is that services communicate through immutable events published to a central log rather than making synchronous calls. '; + return { + name: 'Technical explanation', + messages: [ + msg('system', 'You are a principal engineer.'), + msg('user', 'How does our event-driven architecture work?'), + msg('assistant', prose.repeat(6)), + msg('user', 'What about schema evolution?'), + msg('assistant', prose.repeat(5)), + msg('user', 'How do we handle ordering guarantees?'), + msg('assistant', prose.repeat(5)), + ], + }; +} + +function agenticSession(): Scenario { + return { + name: 'Agentic session', + messages: [ + msg('system', 'You are an AI coding assistant with tool access.'), + msg('user', 'Fix the failing tests in the auth module.'), + msg('assistant', "I'll look at the test output first."), + msg('assistant', 'Tool call: run_tests(path="tests/auth")'), + msg( + 'user', + 'PASS tests/auth/login.test.ts\nFAIL tests/auth/refresh.test.ts\n Expected: 200\n Received: 401\n\n at tests/auth/refresh.test.ts:42:5', + ), + msg( + 'assistant', + 'The refresh test is failing because the token rotation logic has a timing issue. Let me check the implementation.', + ), + msg('assistant', 'Tool call: read_file(path="src/auth/refresh.ts")'), + msg( + 'user', + 'export async function refreshToken(old: string) {\n const payload = jwt.verify(old, REFRESH_SECRET);\n await revokeToken(old);\n return jwt.sign({ sub: payload.sub }, ACCESS_SECRET, { expiresIn: "15m" });\n}', + ), + msg( + 'assistant', + 'Found it — the function revokes the old token before creating the new one. If the verification fails after revocation, the user loses their session. Let me fix this.', + ), + msg('assistant', 'Tool call: edit_file(path="src/auth/refresh.ts", changes=[...])'), + msg('user', 'File updated.'), + msg('assistant', 'Now let me re-run the tests to verify the fix.'), + msg('assistant', 'Tool call: run_tests(path="tests/auth")'), + msg( + 'user', + 'PASS tests/auth/login.test.ts\nPASS tests/auth/refresh.test.ts\n\nTests: 2 passed, 2 total', + ), + msg( + 'assistant', + 'All tests pass now. The fix was to create the new token before revoking the old one.', + ), + msg('user', 'Great, thanks!'), + ], + }; +} + +// --------------------------------------------------------------------------- +// Comparison +// --------------------------------------------------------------------------- + +type OptionSet = { name: string; options: CompressOptions }; + +const optionSets: OptionSet[] = [ + { + name: 'Default (v1)', + options: { recencyWindow: 4 }, + }, + { + name: 'V2 balanced', + options: { + recencyWindow: 4, + conversationFlow: true, + coreference: true, + importanceScoring: true, + contradictionDetection: true, + }, + }, +]; + +function formatNum(n: number, decimals = 2): string { + return n.toFixed(decimals); +} + +function runComparison(scenario: Scenario): void { + console.log(`\n${'='.repeat(70)}`); + console.log(` ${scenario.name} (${scenario.messages.length} messages)`); + console.log(`${'='.repeat(70)}`); + + const results: Array<{ name: string; result: CompressResult }> = []; + + for (const os of optionSets) { + const result = compress(scenario.messages, os.options) as CompressResult; + results.push({ name: os.name, result }); + } + + // Header + const colWidth = 25; + const header = ['Metric'.padEnd(colWidth), ...results.map((r) => r.name.padEnd(colWidth))].join( + ' | ', + ); + console.log(`\n ${header}`); + console.log(` ${'-'.repeat(header.length)}`); + + // Rows + const rows: Array<[string, ...string[]]> = [ + ['Compression ratio', ...results.map((r) => `${formatNum(r.result.compression.ratio)}x`)], + ['Token ratio', ...results.map((r) => `${formatNum(r.result.compression.token_ratio)}x`)], + [ + 'Messages compressed', + ...results.map((r) => String(r.result.compression.messages_compressed)), + ], + ['Messages preserved', ...results.map((r) => String(r.result.compression.messages_preserved))], + [ + 'Entity retention', + ...results.map((r) => + r.result.compression.entity_retention != null + ? `${formatNum(r.result.compression.entity_retention * 100, 1)}%` + : 'N/A', + ), + ], + [ + 'Structural integrity', + ...results.map((r) => + r.result.compression.structural_integrity != null + ? `${formatNum(r.result.compression.structural_integrity * 100, 1)}%` + : 'N/A', + ), + ], + [ + 'Quality score', + ...results.map((r) => + r.result.compression.quality_score != null + ? formatNum(r.result.compression.quality_score, 3) + : 'N/A', + ), + ], + ['Output tokens', ...results.map((r) => String(tokens(r.result)))], + ['Verbatim entries', ...results.map((r) => String(Object.keys(r.result.verbatim).length))], + ]; + + for (const [label, ...values] of rows) { + const row = [label.padEnd(colWidth), ...values.map((v) => v.padEnd(colWidth))].join(' | '); + console.log(` ${row}`); + } + + // Delta + if (results.length === 2) { + const [a, b] = results; + const ratioDelta = ( + (b.result.compression.ratio / a.result.compression.ratio - 1) * + 100 + ).toFixed(1); + const tokenDelta = tokens(a.result) - tokens(b.result); + console.log(`\n Delta: ${ratioDelta}% ratio improvement, ${tokenDelta} tokens saved`); + } +} + +// --------------------------------------------------------------------------- +// Main +// --------------------------------------------------------------------------- + +const targetScenario = process.argv.find((_, i) => process.argv[i - 1] === '--scenario'); +const scenarios = buildScenarios(); + +console.log('CCE A/B Comparison Tool'); +console.log(`Comparing: ${optionSets.map((o) => o.name).join(' vs ')}`); + +for (const scenario of scenarios) { + if (targetScenario && scenario.name.toLowerCase() !== targetScenario.toLowerCase()) continue; + runComparison(scenario); +} + +console.log('\n'); diff --git a/bench/llm.ts b/bench/llm.ts index 68c7197..6c521dc 100644 --- a/bench/llm.ts +++ b/bench/llm.ts @@ -6,7 +6,7 @@ * * Supported providers: * - OpenAI: OPENAI_API_KEY (model override: OPENAI_MODEL, default gpt-4.1-mini) - * - Ollama: OLLAMA_MODEL or OLLAMA_HOST (default host http://localhost:11434, model llama3.2) + * - Ollama: Auto-detected on localhost:11434, or OLLAMA_MODEL/OLLAMA_HOST (model default llama3.2) * - Anthropic: ANTHROPIC_API_KEY (model override: ANTHROPIC_MODEL, default claude-haiku-4-5-20251001) * * SDKs are dynamically imported — missing packages print a skip message @@ -47,31 +47,59 @@ export async function detectProviders(): Promise { } } - // --- Ollama (OpenAI-compatible API) --- - if (process.env.OLLAMA_MODEL || process.env.OLLAMA_HOST) { - try { - const { default: OpenAI } = await import('openai'); - const host = process.env.OLLAMA_HOST ?? 'http://localhost:11434'; - const model = process.env.OLLAMA_MODEL ?? 'llama3.2'; - const client = new OpenAI({ baseURL: `${host}/v1`, apiKey: 'ollama' }); + // --- Ollama (auto-detected or via env vars) --- + { + const host = process.env.OLLAMA_HOST ?? 'http://localhost:11434'; + const model = process.env.OLLAMA_MODEL ?? 'llama3.2'; + const hasEnv = !!(process.env.OLLAMA_MODEL || process.env.OLLAMA_HOST); - providers.push({ - name: 'ollama', - model, - callLlm: async (prompt: string): Promise => { - const r = await client.chat.completions.create({ - model, - messages: [{ role: 'user', content: prompt }], - max_tokens: 400, - temperature: 0.3, - }); - return r.choices[0]?.message?.content ?? ''; - }, - }); - } catch (err) { - console.log( - ` OpenAI SDK not installed (needed for Ollama), skipping (${(err as Error).message})`, - ); + // Auto-detect: probe the Ollama API with a short timeout + let ollamaAvailable = hasEnv; + if (!hasEnv) { + try { + const res = await fetch(`${host}/api/tags`, { + signal: AbortSignal.timeout(2000), + }); + if (res.ok) { + const data = (await res.json()) as { models?: { name: string }[] }; + const models = data.models ?? []; + const hasModel = models.some((m) => m.name === model || m.name === `${model}:latest`); + if (hasModel) { + ollamaAvailable = true; + } else if (models.length > 0) { + console.log( + ` Ollama running but model "${model}" not found (available: ${models.map((m) => m.name).join(', ')})`, + ); + } + } + } catch { + // Not running — skip silently + } + } + + if (ollamaAvailable) { + try { + const { default: OpenAI } = await import('openai'); + const client = new OpenAI({ baseURL: `${host}/v1`, apiKey: 'ollama' }); + + providers.push({ + name: 'ollama', + model, + callLlm: async (prompt: string): Promise => { + const r = await client.chat.completions.create({ + model, + messages: [{ role: 'user', content: prompt }], + max_tokens: 400, + temperature: 0.3, + }); + return r.choices[0]?.message?.content ?? ''; + }, + }); + } catch (err) { + console.log( + ` Ollama detected but openai SDK not installed — run \`npm install openai\` (${(err as Error).message})`, + ); + } } } @@ -92,7 +120,7 @@ export async function detectProviders(): Promise { messages: [{ role: 'user', content: prompt }], }); const block = msg.content[0]; - return block.type === 'text' ? block.text : ''; + return block?.type === 'text' ? block.text : ''; }, }); } catch (err) { @@ -100,5 +128,28 @@ export async function detectProviders(): Promise { } } + // --- Google Gemini --- + if (process.env.GEMINI_API_KEY) { + try { + const { GoogleGenAI } = await import('@google/genai'); + const ai = new GoogleGenAI({ apiKey: process.env.GEMINI_API_KEY }); + const model = process.env.GEMINI_MODEL ?? 'gemini-2.5-flash'; + + providers.push({ + name: 'gemini', + model, + callLlm: async (prompt: string): Promise => { + const response = await ai.models.generateContent({ + model, + contents: prompt, + }); + return response.text ?? ''; + }, + }); + } catch (err) { + console.log(` @google/genai SDK not installed, skipping (${(err as Error).message})`); + } + } + return providers; } diff --git a/bench/quality-analysis.ts b/bench/quality-analysis.ts new file mode 100644 index 0000000..5dfc576 --- /dev/null +++ b/bench/quality-analysis.ts @@ -0,0 +1,743 @@ +import type { CompressOptions, CompressResult, Message } from '../src/types.js'; +import { compress } from '../src/compress.js'; +import { extractEntities, extractStructural } from './baseline.js'; +import { extractEntities as extractTechEntities, computeQualityScore } from '../src/entities.js'; +import type { ProbeDefinition } from './quality-scenarios.js'; + +// --------------------------------------------------------------------------- +// Types +// --------------------------------------------------------------------------- + +export interface MessageQuality { + messageId: string; + action: string; + inputChars: number; + outputChars: number; + localRatio: number; + entityRetention: number; + codeBlocksIntact: boolean; +} + +export interface ProbeResult { + label: string; + passed: boolean; +} + +export interface CompressedRetentionResult { + entityRetention: number; + structuralRetention: number; + codeBlockIntegrity: number; +} + +export interface QualityResult { + ratio: number; + avgEntityRetention: number; + minEntityRetention: number; + codeBlockIntegrity: number; + informationDensity: number; + compressedQualityScore: number; + probesPassed: number; + probesTotal: number; + probePassRate: number; + probeResults: ProbeResult[]; + negativeCompressions: number; + coherenceIssues: number; + messages: MessageQuality[]; +} + +export interface TradeoffPoint { + recencyWindow: number; + ratio: number; + entityRetention: number; + informationDensity: number; + qualityScore: number; +} + +export interface TradeoffResult { + points: TradeoffPoint[]; + qualityAt2x: number | null; + qualityAt3x: number | null; + maxRatioAbove80pctQuality: number; +} + +export interface QualityBaseline { + version: string; + gitRef: string; + generated: string; + results: { + scenarios: Record; + tradeoff: Record; + }; +} + +export interface QualityRegression { + benchmark: string; + scenario: string; + metric: string; + expected: number; + actual: number; + delta: string; +} + +// --------------------------------------------------------------------------- +// Code block extraction +// --------------------------------------------------------------------------- + +const CODE_FENCE_RE = /```[\w]*\n([\s\S]*?)```/g; + +function extractCodeBlocks(text: string): string[] { + const blocks: string[] = []; + let match: RegExpExecArray | null; + const re = new RegExp(CODE_FENCE_RE.source, CODE_FENCE_RE.flags); + while ((match = re.exec(text)) !== null) { + blocks.push(match[1]); + } + return blocks; +} + +// --------------------------------------------------------------------------- +// analyzeCompressedRetention +// --------------------------------------------------------------------------- + +/** + * Measures retention ONLY for messages that were actually compressed. + * Identifies compressed messages via _cce_original metadata, pulls originals + * from the verbatim map, and compares against the compressed output. + */ +export function analyzeCompressedRetention( + _originalMessages: Message[], + result: CompressResult, +): CompressedRetentionResult { + let totalEntities = 0; + let retainedEntities = 0; + let totalStructural = 0; + let retainedStructural = 0; + let totalCodeBlocks = 0; + let intactCodeBlocks = 0; + + for (const msg of result.messages) { + const meta = msg.metadata?._cce_original as { ids?: string[]; summary_id?: string } | undefined; + if (!meta) continue; // not compressed + + // Reconstruct original text from verbatim store + const ids = meta.ids ?? [msg.id]; + const originalTexts: string[] = []; + for (const id of ids) { + const orig = result.verbatim[id]; + if (orig && typeof orig.content === 'string') { + originalTexts.push(orig.content); + } + } + if (originalTexts.length === 0) continue; + + const originalText = originalTexts.join('\n'); + const compressedText = typeof msg.content === 'string' ? msg.content : ''; + + // Entity retention + const origEnt = extractEntities(originalText); + totalEntities += origEnt.length; + retainedEntities += origEnt.filter((e) => compressedText.includes(e)).length; + + // Structural retention + const origStruct = extractStructural(originalText); + totalStructural += origStruct.length; + retainedStructural += origStruct.filter((s) => compressedText.includes(s)).length; + + // Code block integrity — byte-identical check + const origBlocks = extractCodeBlocks(originalText); + const compBlocks = extractCodeBlocks(compressedText); + totalCodeBlocks += origBlocks.length; + for (const ob of origBlocks) { + if (compBlocks.some((cb) => cb === ob)) { + intactCodeBlocks++; + } + } + } + + return { + entityRetention: totalEntities === 0 ? 1 : retainedEntities / totalEntities, + structuralRetention: totalStructural === 0 ? 1 : retainedStructural / totalStructural, + codeBlockIntegrity: totalCodeBlocks === 0 ? 1 : intactCodeBlocks / totalCodeBlocks, + }; +} + +// --------------------------------------------------------------------------- +// Probe runner +// --------------------------------------------------------------------------- + +export function runProbes( + messages: Message[], + probes: ProbeDefinition[], +): { passed: number; total: number; rate: number; results: ProbeResult[] } { + const results: ProbeResult[] = []; + let passed = 0; + for (const probe of probes) { + const ok = probe.check(messages); + results.push({ label: probe.label, passed: ok }); + if (ok) passed++; + } + return { + passed, + total: probes.length, + rate: probes.length === 0 ? 1 : passed / probes.length, + results, + }; +} + +// --------------------------------------------------------------------------- +// Information density +// --------------------------------------------------------------------------- + +/** + * Compute information density: (output_entities/output_chars) / (input_entities/input_chars). + * >1.0 means the compressed output is denser in technical entities than the input (good). + */ +export function computeInformationDensity(result: CompressResult): number { + let inputEntities = 0; + let inputChars = 0; + let outputEntities = 0; + let outputChars = 0; + + for (const msg of result.messages) { + const meta = msg.metadata?._cce_original as { ids?: string[] } | undefined; + if (!meta) continue; + + const ids = meta.ids ?? [msg.id]; + for (const id of ids) { + const orig = result.verbatim[id]; + if (orig && typeof orig.content === 'string') { + inputEntities += extractTechEntities(orig.content, 500).length; + inputChars += orig.content.length; + } + } + + const compressedText = typeof msg.content === 'string' ? msg.content : ''; + outputEntities += extractTechEntities(compressedText, 500).length; + outputChars += compressedText.length; + } + + if (inputChars === 0 || outputChars === 0) return 1.0; + + const inputDensity = inputEntities / inputChars; + const outputDensity = outputEntities / outputChars; + + if (inputDensity === 0) return 1.0; + return outputDensity / inputDensity; +} + +// --------------------------------------------------------------------------- +// Compressed-only quality score +// --------------------------------------------------------------------------- + +/** + * Compute quality score over only the compressed messages (not the full set). + * This isolates the quality signal to where compression actually happened. + */ +export function computeCompressedQualityScore(result: CompressResult): number { + const originalMessages: Message[] = []; + const compressedMessages: Message[] = []; + + for (const msg of result.messages) { + const meta = msg.metadata?._cce_original as { ids?: string[] } | undefined; + if (!meta) continue; + + // Build original messages from verbatim + const ids = meta.ids ?? [msg.id]; + for (const id of ids) { + const orig = result.verbatim[id]; + if (orig) originalMessages.push(orig); + } + + compressedMessages.push(msg); + } + + if (originalMessages.length === 0) return 1.0; + + const { quality_score } = computeQualityScore(originalMessages, compressedMessages); + return quality_score; +} + +// --------------------------------------------------------------------------- +// Negative compression detection +// --------------------------------------------------------------------------- + +/** + * Count messages where the compressed output is larger than the original input. + */ +export function detectNegativeCompressions(result: CompressResult): number { + let count = 0; + + for (const msg of result.messages) { + const meta = msg.metadata?._cce_original as { ids?: string[] } | undefined; + if (!meta) continue; + + const ids = meta.ids ?? [msg.id]; + let inputChars = 0; + for (const id of ids) { + const orig = result.verbatim[id]; + if (orig && typeof orig.content === 'string') { + inputChars += orig.content.length; + } + } + + const outputChars = typeof msg.content === 'string' ? msg.content.length : 0; + if (outputChars > inputChars) count++; + } + + return count; +} + +// --------------------------------------------------------------------------- +// Coherence checks +// --------------------------------------------------------------------------- + +/** + * Check compressed messages for coherence issues: + * (a) sentence fragments (no verb) + * (b) duplicate sentences + * (c) trivial summaries (<10 chars) + */ +export function checkCoherence(result: CompressResult): number { + let issues = 0; + const SUMMARY_RE = /\[summary:\s*(.*?)\]/gi; + const VERB_RE = + /\b(?:is|are|was|were|has|have|had|do|does|did|will|would|could|should|can|may|might|shall|must|being|been|get|got|make|made|take|took|give|gave|use|used|run|runs|call|calls|read|reads|write|writes|send|sends|return|returns|create|creates|handle|handles|check|checks|provide|provides|include|includes|require|requires|allow|allows|enable|enables|support|supports|prevent|prevents|need|needs|want|wants|seem|seems|mean|means|show|shows|work|works|keep|keeps|start|starts|set|sets|find|finds|move|moves|try|tries|add|adds|help|helps|turn|turns|play|plays|hold|holds|bring|brings|begin|begins|end|ends|change|changes|follow|follows|stop|stops|go|goes|come|comes|put|puts|tell|tells|say|says|think|thinks|know|knows|see|sees|look|looks|build|builds|test|tests|deploy|deploys|monitor|monitors|configure|configures|validate|validates|compress|compresses|store|stores|load|loads|save|saves|publish|publishes|consume|consumes|process|processes|implement|implements|define|defines|contain|contains|maintain|maintains|manage|manages|connect|connects|execute|executes|receive|receives|apply|applies|ensure|ensures|track|tracks|detect|detects|resolve|resolves|replace|replaces|reduce|reduces|increase|increases|measure|measures|analyze|analyzes|convert|converts|establish|establishes|improve|improves|generate|generates|represent|represents|provide|provides)\b/i; + + for (const msg of result.messages) { + const meta = msg.metadata?._cce_original as { ids?: string[] } | undefined; + if (!meta) continue; + + const content = typeof msg.content === 'string' ? msg.content : ''; + + // Extract summary text from [summary: ...] markers + let summaryText = ''; + let match: RegExpExecArray | null; + const re = new RegExp(SUMMARY_RE.source, SUMMARY_RE.flags); + while ((match = re.exec(content)) !== null) { + summaryText += match[1] + ' '; + } + + // If no [summary:] markers, check the whole content for non-code text + if (!summaryText) { + // Strip code blocks and check remaining text + summaryText = content.replace(/```[\w]*\n[\s\S]*?```/g, '').trim(); + } + + if (!summaryText) continue; + + // (c) trivial summary + if (summaryText.trim().length < 10) { + issues++; + continue; + } + + // Split into sentences for fragment/duplicate checks + const sentences = summaryText + .split(/[.!?]+/) + .map((s) => s.trim()) + .filter((s) => s.length > 3); + + // (a) sentence fragments — sentences with no verb + for (const sentence of sentences) { + if (!VERB_RE.test(sentence) && sentence.length > 15) { + issues++; + break; // count at most one fragment issue per message + } + } + + // (b) duplicate sentences within the same message + const seen = new Set(); + for (const sentence of sentences) { + const normalized = sentence.toLowerCase(); + if (seen.has(normalized)) { + issues++; + break; // count at most one duplicate issue per message + } + seen.add(normalized); + } + } + + return issues; +} + +// --------------------------------------------------------------------------- +// Per-message quality analysis +// --------------------------------------------------------------------------- + +/** + * Build per-message quality breakdown for compressed messages. + */ +export function analyzePerMessageQuality( + _originalMessages: Message[], + result: CompressResult, +): MessageQuality[] { + const messages: MessageQuality[] = []; + + for (const msg of result.messages) { + const meta = msg.metadata?._cce_original as { ids?: string[] } | undefined; + if (!meta) continue; + + const ids = meta.ids ?? [msg.id]; + const originalTexts: string[] = []; + for (const id of ids) { + const orig = result.verbatim[id]; + if (orig && typeof orig.content === 'string') { + originalTexts.push(orig.content); + } + } + if (originalTexts.length === 0) continue; + + const originalText = originalTexts.join('\n'); + const compressedText = typeof msg.content === 'string' ? msg.content : ''; + const inputChars = originalText.length; + const outputChars = compressedText.length; + + // Entity retention (using the richer entities extractor) + const origEntities = extractTechEntities(originalText, 500); + const retainedCount = origEntities.filter((e) => compressedText.includes(e)).length; + const entityRetention = origEntities.length === 0 ? 1 : retainedCount / origEntities.length; + + // Code block integrity + const origBlocks = extractCodeBlocks(originalText); + const compBlocks = extractCodeBlocks(compressedText); + const codeBlocksIntact = + origBlocks.length === 0 || origBlocks.every((ob) => compBlocks.some((cb) => cb === ob)); + + // Determine action from decisions if available + const decision = result.compression.decisions?.find((d) => d.messageId === msg.id); + const action = decision?.action ?? 'compressed'; + + messages.push({ + messageId: msg.id, + action, + inputChars, + outputChars, + localRatio: outputChars > 0 ? inputChars / outputChars : inputChars, + entityRetention, + codeBlocksIntact, + }); + } + + return messages; +} + +// --------------------------------------------------------------------------- +// Tradeoff sweep +// --------------------------------------------------------------------------- + +/** + * Sweep recencyWindow from 0 to messages.length, measuring quality at each step. + * Returns sorted points from most aggressive (rw=0) to least (rw=len). + */ +export function sweepTradeoff(messages: Message[], step?: number): TradeoffPoint[] { + const maxRw = messages.length; + const inc = step ?? Math.max(1, Math.floor(maxRw / 20)); // ~20 sample points + const points: TradeoffPoint[] = []; + + for (let rw = 0; rw <= maxRw; rw += inc) { + const cr = compress(messages, { recencyWindow: rw, trace: true }); + const retention = analyzeCompressedRetention(messages, cr); + const infDensity = computeInformationDensity(cr); + + points.push({ + recencyWindow: rw, + ratio: cr.compression.ratio, + entityRetention: retention.entityRetention, + informationDensity: infDensity, + qualityScore: cr.compression.quality_score ?? 1, + }); + + // No need to continue if ratio is 1.0 (no compression happening) + if (cr.compression.ratio <= 1.001) break; + } + + return points; +} + +/** + * Derive summary statistics from a tradeoff curve. + */ +export function summarizeTradeoff(points: TradeoffPoint[]): TradeoffResult { + // Find quality at specific ratio targets + const qualityAtRatio = (target: number): number | null => { + // Find the point closest to the target ratio + let best: TradeoffPoint | null = null; + let bestDist = Infinity; + for (const p of points) { + const dist = Math.abs(p.ratio - target); + if (dist < bestDist) { + bestDist = dist; + best = p; + } + } + return best && bestDist < 0.5 ? best.qualityScore : null; + }; + + // Max ratio achievable while keeping quality above 0.8 + let maxRatioAbove80 = 1; + for (const p of points) { + if (p.qualityScore >= 0.8 && p.ratio > maxRatioAbove80) { + maxRatioAbove80 = p.ratio; + } + } + + return { + points, + qualityAt2x: qualityAtRatio(2), + qualityAt3x: qualityAtRatio(3), + maxRatioAbove80pctQuality: maxRatioAbove80, + }; +} + +// --------------------------------------------------------------------------- +// Full quality analysis for a single scenario +// --------------------------------------------------------------------------- + +/** + * Run complete quality analysis on a scenario. + */ +export function analyzeQuality( + messages: Message[], + probes: ProbeDefinition[] = [], + compressOptions?: Partial, +): QualityResult { + const cr = compress(messages, { recencyWindow: 0, trace: true, ...compressOptions }); + + const retention = analyzeCompressedRetention(messages, cr); + const perMessage = analyzePerMessageQuality(messages, cr); + const probeResult = runProbes(cr.messages, probes); + const infDensity = computeInformationDensity(cr); + const cmpQuality = computeCompressedQualityScore(cr); + const negComps = detectNegativeCompressions(cr); + const coherence = checkCoherence(cr); + + const entityRetentions = perMessage.map((m) => m.entityRetention); + + return { + ratio: cr.compression.ratio, + avgEntityRetention: + entityRetentions.length > 0 + ? entityRetentions.reduce((a, b) => a + b, 0) / entityRetentions.length + : 1, + minEntityRetention: entityRetentions.length > 0 ? Math.min(...entityRetentions) : 1, + codeBlockIntegrity: retention.codeBlockIntegrity, + informationDensity: infDensity, + compressedQualityScore: cmpQuality, + probesPassed: probeResult.passed, + probesTotal: probeResult.total, + probePassRate: probeResult.rate, + probeResults: probeResult.results, + negativeCompressions: negComps, + coherenceIssues: coherence, + messages: perMessage, + }; +} + +// --------------------------------------------------------------------------- +// Baseline comparison +// --------------------------------------------------------------------------- + +export function compareQualityResults( + baseline: QualityBaseline, + current: QualityBaseline, +): QualityRegression[] { + const regressions: QualityRegression[] = []; + + for (const [name, exp] of Object.entries(baseline.results.scenarios)) { + const act = current.results.scenarios[name]; + if (!act) continue; + + // Entity retention: max 5% drop + if (exp.avgEntityRetention - act.avgEntityRetention > 0.05) { + regressions.push({ + benchmark: 'quality', + scenario: name, + metric: 'avgEntityRetention', + expected: exp.avgEntityRetention, + actual: act.avgEntityRetention, + delta: `${((act.avgEntityRetention - exp.avgEntityRetention) * 100).toFixed(1)}%`, + }); + } + + // Code block integrity: zero tolerance + if (exp.codeBlockIntegrity === 1 && act.codeBlockIntegrity < 1) { + regressions.push({ + benchmark: 'quality', + scenario: name, + metric: 'codeBlockIntegrity', + expected: exp.codeBlockIntegrity, + actual: act.codeBlockIntegrity, + delta: `${((act.codeBlockIntegrity - exp.codeBlockIntegrity) * 100).toFixed(1)}%`, + }); + } + + // Probe pass rate: max 5% drop + if (exp.probePassRate - act.probePassRate > 0.05) { + regressions.push({ + benchmark: 'quality', + scenario: name, + metric: 'probePassRate', + expected: exp.probePassRate, + actual: act.probePassRate, + delta: `${((act.probePassRate - exp.probePassRate) * 100).toFixed(1)}%`, + }); + } + + // Information density: must stay ≥ 0.8 (only meaningful when compression occurs) + if (act.ratio > 1.01 && act.informationDensity < 0.8) { + regressions.push({ + benchmark: 'quality', + scenario: name, + metric: 'informationDensity', + expected: 0.8, + actual: act.informationDensity, + delta: `${((act.informationDensity - 0.8) * 100).toFixed(1)}%`, + }); + } + + // Coherence issues: must not increase from baseline + if (act.coherenceIssues > exp.coherenceIssues) { + regressions.push({ + benchmark: 'quality', + scenario: name, + metric: 'coherenceIssues', + expected: exp.coherenceIssues, + actual: act.coherenceIssues, + delta: `+${act.coherenceIssues - exp.coherenceIssues}`, + }); + } + + // Negative compressions: must not increase from baseline + if (act.negativeCompressions > exp.negativeCompressions) { + regressions.push({ + benchmark: 'quality', + scenario: name, + metric: 'negativeCompressions', + expected: exp.negativeCompressions, + actual: act.negativeCompressions, + delta: `+${act.negativeCompressions - exp.negativeCompressions}`, + }); + } + } + + // Tradeoff: maxRatioAbove80pctQuality must not regress + for (const [name, exp] of Object.entries(baseline.results.tradeoff)) { + const act = current.results.tradeoff[name]; + if (!act) continue; + + if (exp.maxRatioAbove80pctQuality - act.maxRatioAbove80pctQuality > 0.1) { + regressions.push({ + benchmark: 'tradeoff', + scenario: name, + metric: 'maxRatioAbove80pctQuality', + expected: exp.maxRatioAbove80pctQuality, + actual: act.maxRatioAbove80pctQuality, + delta: `${(act.maxRatioAbove80pctQuality - exp.maxRatioAbove80pctQuality).toFixed(2)}`, + }); + } + } + + return regressions; +} + +// --------------------------------------------------------------------------- +// LLM Judge +// --------------------------------------------------------------------------- + +export interface LlmJudgeScore { + scenario: string; + provider: string; + model: string; + meaningPreserved: number; // 1-5 + informationLoss: string; // free-text + coherence: number; // 1-5 + overall: number; // 1-5 + raw: string; +} + +const LLM_JUDGE_PROMPT = `You are evaluating a compression system that summarizes LLM conversations. +You will receive the ORIGINAL conversation and the COMPRESSED version. + +Rate the compression on three dimensions (1-5 each): + +1. **meaning_preserved** (1=major meaning lost, 5=all key meaning retained) + - Are the important decisions, facts, code, and technical details still present? + - Would someone reading only the compressed version understand the same things? + +2. **coherence** (1=incoherent fragments, 5=reads naturally) + - Do the compressed messages make sense on their own? + - Are there sentence fragments, duplicate phrases, or nonsensical summaries? + +3. **overall** (1=unusable compression, 5=excellent compression) + - Considering both meaning preservation and readability, how good is this compression? + +Respond in EXACTLY this format (no other text): +meaning_preserved: <1-5> +information_loss: +coherence: <1-5> +overall: <1-5>`; + +function formatConversationForJudge(messages: Message[]): string { + return messages + .map((m) => { + const role = m.role ?? 'unknown'; + const content = typeof m.content === 'string' ? m.content : '[non-text]'; + // Truncate very long messages to keep prompt size reasonable + const truncated = content.length > 2000 ? content.slice(0, 2000) + '...[truncated]' : content; + return `[${role}]: ${truncated}`; + }) + .join('\n\n'); +} + +function parseLlmJudgeResponse(raw: string): { + meaningPreserved: number; + informationLoss: string; + coherence: number; + overall: number; +} { + const getNum = (key: string): number => { + const match = raw.match(new RegExp(`${key}:\\s*(\\d)`, 'i')); + return match ? Math.min(5, Math.max(1, parseInt(match[1], 10))) : 3; + }; + const lossMatch = raw.match(/information_loss:\s*(.+)/i); + return { + meaningPreserved: getNum('meaning_preserved'), + informationLoss: lossMatch ? lossMatch[1].trim() : 'unknown', + coherence: getNum('coherence'), + overall: getNum('overall'), + }; +} + +export async function runLlmJudge( + scenarioName: string, + originalMessages: Message[], + compressedMessages: Message[], + callLlm: (prompt: string) => Promise, + providerName: string, + modelName: string, +): Promise { + const original = formatConversationForJudge(originalMessages); + const compressed = formatConversationForJudge(compressedMessages); + + const prompt = `${LLM_JUDGE_PROMPT} + +--- ORIGINAL CONVERSATION --- +${original} + +--- COMPRESSED CONVERSATION --- +${compressed}`; + + const raw = await callLlm(prompt); + const parsed = parseLlmJudgeResponse(raw); + + return { + scenario: scenarioName, + provider: providerName, + model: modelName, + meaningPreserved: parsed.meaningPreserved, + informationLoss: parsed.informationLoss, + coherence: parsed.coherence, + overall: parsed.overall, + raw, + }; +} diff --git a/bench/quality-scenarios.ts b/bench/quality-scenarios.ts new file mode 100644 index 0000000..b7cdc1d --- /dev/null +++ b/bench/quality-scenarios.ts @@ -0,0 +1,661 @@ +import type { Message } from '../src/types.js'; + +// --------------------------------------------------------------------------- +// Probe definitions +// --------------------------------------------------------------------------- + +export interface ProbeDefinition { + label: string; + check: (compressedMessages: Message[]) => boolean; +} + +function anyMessageContains(messages: Message[], text: string): boolean { + return messages.some((m) => typeof m.content === 'string' && m.content.includes(text)); +} + +function anyMessageMatches(messages: Message[], re: RegExp): boolean { + return messages.some((m) => typeof m.content === 'string' && re.test(m.content)); +} + +function codeBlockContains(messages: Message[], text: string): boolean { + const CODE_FENCE_RE = /```[\w]*\n([\s\S]*?)```/g; + for (const m of messages) { + if (typeof m.content !== 'string') continue; + let match: RegExpExecArray | null; + const re = new RegExp(CODE_FENCE_RE.source, CODE_FENCE_RE.flags); + while ((match = re.exec(m.content)) !== null) { + if (match[1].includes(text)) return true; + } + } + return false; +} + +const LANG_ALIASES: Record = { + typescript: ['typescript', 'ts'], + python: ['python', 'py'], + sql: ['sql'], + json: ['json'], + yaml: ['yaml', 'yml'], +}; + +function countCodeBlocks(messages: Message[], lang?: string): number { + let pattern: RegExp; + if (lang) { + const aliases = LANG_ALIASES[lang] ?? [lang]; + const langPattern = aliases.join('|'); + pattern = new RegExp('```(?:' + langPattern + ')\\n[\\s\\S]*?```', 'g'); + } else { + pattern = /```[\w]*\n[\s\S]*?```/g; + } + let count = 0; + for (const m of messages) { + if (typeof m.content !== 'string') continue; + const matches = m.content.match(pattern); + if (matches) count += matches.length; + } + return count; +} + +function totalContentLength(messages: Message[]): number { + let total = 0; + for (const m of messages) { + if (typeof m.content === 'string') total += m.content.length; + } + return total; +} + +export function getProbesForScenario(name: string): ProbeDefinition[] { + switch (name) { + case 'Coding assistant': + return [ + { label: 'JWT_SECRET env var', check: (ms) => anyMessageContains(ms, 'JWT_SECRET') }, + { label: 'jwt.verify in code', check: (ms) => codeBlockContains(ms, 'jwt.verify') }, + { label: '15m access expiry', check: (ms) => anyMessageContains(ms, '15m') }, + { label: '7d refresh expiry', check: (ms) => anyMessageContains(ms, '7d') }, + { label: 'rateLimit in code', check: (ms) => codeBlockContains(ms, 'rateLimit') }, + { + label: 'authMiddleware function', + check: (ms) => anyMessageContains(ms, 'authMiddleware'), + }, + { + label: 'express-rate-limit import', + check: (ms) => anyMessageContains(ms, 'express-rate-limit'), + }, + { + label: 'Redis/ioredis mention', + check: (ms) => anyMessageMatches(ms, /ioredis|[Rr]edis/), + }, + { + label: 'min output ≥ 2000 chars', + check: (ms) => totalContentLength(ms) >= 2000, + }, + ]; + + case 'Long Q&A': + return [ + { label: 'event sourcing', check: (ms) => anyMessageMatches(ms, /event.?sourcing/i) }, + { label: 'circuit breaker', check: (ms) => anyMessageMatches(ms, /circuit.?breaker/i) }, + { + label: 'eventual consistency', + check: (ms) => anyMessageMatches(ms, /eventual.?consistency/i), + }, + { label: 'saga pattern', check: (ms) => anyMessageMatches(ms, /saga/i) }, + { label: 'choreography', check: (ms) => anyMessageContains(ms, 'choreography') }, + { label: 'orchestration', check: (ms) => anyMessageContains(ms, 'orchestration') }, + { + label: 'min output ≥ 800 chars', + check: (ms) => totalContentLength(ms) >= 800, + }, + ]; + + case 'Tool-heavy': + return [ + { label: 'JSON array preserved', check: (ms) => anyMessageMatches(ms, /\[.*"src\//) }, + { label: 'SQL SELECT preserved', check: (ms) => anyMessageContains(ms, 'SELECT') }, + { label: 'STRIPE_SECRET_KEY', check: (ms) => anyMessageContains(ms, 'STRIPE_SECRET_KEY') }, + { label: 'GITHUB_TOKEN', check: (ms) => anyMessageContains(ms, 'GITHUB_TOKEN') }, + { + label: 'code blocks present', + check: (ms) => + countCodeBlocks(ms) > 0 || + anyMessageContains(ms, 'jwt.verify') || + anyMessageContains(ms, 'jwt.sign'), + }, + { label: 'DATABASE_URL', check: (ms) => anyMessageContains(ms, 'DATABASE_URL') }, + ]; + + case 'Deep conversation': { + const topicNames = [ + 'database schema', + 'authentication', + 'caching', + 'monitoring', + 'testing', + 'deployment', + 'error handling', + 'API', + 'logging', + 'feature flags', + 'migration', + 'load balancing', + 'service discovery', + 'observability', + 'incident response', + ]; + const probes: ProbeDefinition[] = [ + { + label: '≥15/25 topics survive', + check: (ms) => { + const allTopics = [ + 'database schema', + 'API endpoint', + 'authentication', + 'error handling', + 'caching', + 'deployment', + 'monitoring', + 'testing', + 'code review', + 'documentation', + 'performance', + 'logging', + 'feature flag', + 'migration', + 'API versioning', + 'circuit breaker', + 'message queue', + 'secrets management', + 'load balancing', + 'container', + 'service discovery', + 'observability', + 'incident response', + 'capacity planning', + 'access control', + ]; + let found = 0; + for (const topic of allTopics) { + if (anyMessageMatches(ms, new RegExp(topic, 'i'))) found++; + } + return found >= 15; + }, + }, + ]; + for (const topic of topicNames.slice(0, 7)) { + probes.push({ + label: `topic: ${topic}`, + check: (ms) => anyMessageMatches(ms, new RegExp(topic, 'i')), + }); + } + probes.push({ + label: 'min output ≥ 3000 chars', + check: (ms) => totalContentLength(ms) >= 3000, + }); + return probes; + } + + case 'Technical explanation': + return [ + { label: 'OrderPlaced event', check: (ms) => anyMessageContains(ms, 'OrderPlaced') }, + { + label: 'temporal decoupling', + check: (ms) => anyMessageMatches(ms, /temporal.?decoupling/i), + }, + { label: 'schema version', check: (ms) => anyMessageMatches(ms, /schema.?version/i) }, + { label: 'partition ordering', check: (ms) => anyMessageContains(ms, 'partition') }, + { label: 'at-least-once delivery', check: (ms) => anyMessageMatches(ms, /at.least.once/i) }, + { label: 'dead letter queue', check: (ms) => anyMessageMatches(ms, /dead.?letter/i) }, + { label: 'idempotent consumers', check: (ms) => anyMessageContains(ms, 'idempotent') }, + ]; + + case 'Structured content': + return [ + { label: 'API keys preserved', check: (ms) => anyMessageContains(ms, 'STRIPE_SECRET_KEY') }, + { label: 'CREATE TABLE preserved', check: (ms) => anyMessageContains(ms, 'CREATE TABLE') }, + { label: 'JSON code block', check: (ms) => anyMessageMatches(ms, /```json/) }, + { label: 'AWS_ACCESS_KEY_ID', check: (ms) => anyMessageContains(ms, 'AWS_ACCESS_KEY_ID') }, + { label: 'SENDGRID_API_KEY', check: (ms) => anyMessageContains(ms, 'SENDGRID_API_KEY') }, + ]; + + case 'Agentic coding session': + return [ + { label: 'AuthService in code', check: (ms) => anyMessageContains(ms, 'AuthService') }, + { + label: 'verify or validateToken', + check: (ms) => anyMessageMatches(ms, /verify\(|validateToken\(/), + }, + { label: 'grep results', check: (ms) => anyMessageMatches(ms, /src\/auth\.ts:\d+/) }, + { + label: 'test counts', + check: (ms) => anyMessageMatches(ms, /\d+\s*(?:tests?|passed|failed)/), + }, + { label: 'jwt.sign in code', check: (ms) => anyMessageContains(ms, 'jwt.sign') }, + ]; + + case 'Single-char messages': + return [ + { label: 'output count = input count', check: (ms) => ms.length >= 10 }, + { label: '"y" present', check: (ms) => ms.some((m) => m.content === 'y') }, + { label: '"n" present', check: (ms) => ms.some((m) => m.content === 'n') }, + ]; + + case 'Giant single message': + return [ + { label: 'TracingService in code', check: (ms) => codeBlockContains(ms, 'TracingService') }, + { label: 'traceId identifier', check: (ms) => anyMessageContains(ms, 'traceId') }, + { label: 'spanId identifier', check: (ms) => anyMessageContains(ms, 'spanId') }, + { label: 'startSpan in code', check: (ms) => codeBlockContains(ms, 'startSpan') }, + { + label: 'min output ≥ 10000 chars', + check: (ms) => totalContentLength(ms) >= 10000, + }, + ]; + + case 'Code-only conversation': + return [ + { label: 'TypeScript code blocks', check: (ms) => countCodeBlocks(ms, 'typescript') >= 2 }, + { label: 'Python code blocks', check: (ms) => countCodeBlocks(ms, 'python') >= 2 }, + { label: 'SQL code blocks', check: (ms) => countCodeBlocks(ms, 'sql') >= 2 }, + { + label: 'all code preserved verbatim', + check: (ms) => codeBlockContains(ms, 'fibonacci') && codeBlockContains(ms, 'add('), + }, + ]; + + case 'Entity-dense technical': + return [ + { label: 'file paths present', check: (ms) => anyMessageMatches(ms, /src\/\w+/) }, + { label: 'redis-prod-001', check: (ms) => anyMessageContains(ms, 'redis-prod-001') }, + { label: 'v22.3.0 version', check: (ms) => anyMessageContains(ms, 'v22.3.0') }, + { label: 'max_connections', check: (ms) => anyMessageContains(ms, 'max_connections') }, + { label: 'PR #142', check: (ms) => anyMessageContains(ms, 'PR #142') }, + { label: 'orderService.ts', check: (ms) => anyMessageContains(ms, 'orderService.ts') }, + { + label: 'idx_orders_user_created', + check: (ms) => anyMessageContains(ms, 'idx_orders_user_created'), + }, + { label: 'p99 latency', check: (ms) => anyMessageContains(ms, 'p99') }, + ]; + + case 'Prose-only conversation': + return [ + { label: 'hiring topic', check: (ms) => anyMessageMatches(ms, /hiring/i) }, + { label: 'review topic', check: (ms) => anyMessageMatches(ms, /review/i) }, + { label: 'onboarding topic', check: (ms) => anyMessageMatches(ms, /onboarding/i) }, + { + label: 'min output ≥ 400 chars', + check: (ms) => totalContentLength(ms) >= 400, + }, + ]; + + case 'Mixed languages': + return [ + { label: 'Python code block', check: (ms) => countCodeBlocks(ms, 'python') >= 1 }, + { label: 'SQL code block', check: (ms) => countCodeBlocks(ms, 'sql') >= 1 }, + { label: 'JSON code block', check: (ms) => countCodeBlocks(ms, 'json') >= 1 }, + { label: 'YAML code block', check: (ms) => countCodeBlocks(ms, 'yaml') >= 1 }, + { + label: 'metrics-processor name', + check: (ms) => anyMessageContains(ms, 'metrics-processor'), + }, + ]; + + default: + return []; + } +} + +// --------------------------------------------------------------------------- +// Helpers +// --------------------------------------------------------------------------- + +let nextId = 50000; // high offset to avoid collisions with run.ts scenarios + +function msg(role: string, content: string, extra?: Partial): Message { + const id = String(nextId++); + return { id, index: nextId - 1, role, content, metadata: {}, ...extra }; +} + +export function resetEdgeIds(): void { + nextId = 50000; +} + +// --------------------------------------------------------------------------- +// Edge case scenarios +// --------------------------------------------------------------------------- + +export interface Scenario { + name: string; + messages: Message[]; +} + +/** + * 10 messages with trivially short content — "y", "n", "k", etc. + * Tests that the engine does not crash or produce garbage on minimal input. + */ +export function singleCharMessages(): Scenario { + return { + name: 'Single-char messages', + messages: [ + msg('system', 'You are a helpful assistant.'), + msg('user', 'Ready?'), + msg('assistant', 'y'), + msg('user', 'Deploy?'), + msg('assistant', 'k'), + msg('user', 'Rollback?'), + msg('assistant', 'n'), + msg('user', 'Again?'), + msg('assistant', 'y'), + msg('user', 'ok'), + ], + }; +} + +/** + * One user message with ~50KB of mixed prose and code. + * Tests summarizer behavior on extremely long single messages. + */ +export function giantSingleMessage(): Scenario { + const prose = + 'The distributed tracing system collects span data from each microservice ' + + 'and correlates them into a single trace using a propagated trace identifier. ' + + 'Each span records the service name, operation, duration, and any error status. '; + + const code = + '```typescript\n' + + 'export class TracingService {\n' + + ' private readonly spans: Map = new Map();\n' + + '\n' + + ' startSpan(traceId: string, operation: string): Span {\n' + + ' const span: Span = {\n' + + ' traceId,\n' + + ' spanId: crypto.randomUUID(),\n' + + ' operation,\n' + + ' startTime: Date.now(),\n' + + ' status: "ok",\n' + + ' };\n' + + ' this.spans.set(span.spanId, span);\n' + + ' return span;\n' + + ' }\n' + + '\n' + + ' endSpan(spanId: string, error?: Error): void {\n' + + ' const span = this.spans.get(spanId);\n' + + ' if (!span) return;\n' + + ' span.endTime = Date.now();\n' + + ' span.duration = span.endTime - span.startTime;\n' + + ' if (error) span.status = "error";\n' + + ' }\n' + + '}\n' + + '```'; + + // Build ~50KB content + const chunks: string[] = []; + let size = 0; + while (size < 50_000) { + chunks.push(prose.repeat(5)); + chunks.push(code); + size = chunks.join('\n\n').length; + } + + return { + name: 'Giant single message', + messages: [ + msg('system', 'You are a senior software architect.'), + msg('user', 'Explain our tracing infrastructure in detail.'), + msg('assistant', chunks.join('\n\n')), + msg('user', 'Thanks, that helps.'), + ], + }; +} + +/** + * All messages are entirely code fences with no prose. + * Tests that code preservation is 100% and the engine does not compress inside fences. + */ +export function codeOnlyConversation(): Scenario { + return { + name: 'Code-only conversation', + messages: [ + msg('system', 'You are a code assistant. Only respond with code.'), + msg( + 'user', + '```typescript\nfunction add(a: number, b: number): number {\n return a + b;\n}\n```', + ), + msg( + 'assistant', + '```typescript\nimport { describe, it, expect } from "vitest";\nimport { add } from "./math";\n\ndescribe("add", () => {\n it("adds two numbers", () => {\n expect(add(2, 3)).toBe(5);\n });\n\n it("handles negatives", () => {\n expect(add(-1, 1)).toBe(0);\n });\n});\n```', + ), + msg( + 'user', + '```python\ndef fibonacci(n: int) -> list[int]:\n if n <= 0:\n return []\n if n == 1:\n return [0]\n seq = [0, 1]\n for _ in range(2, n):\n seq.append(seq[-1] + seq[-2])\n return seq\n```', + ), + msg( + 'assistant', + '```python\nimport pytest\nfrom fibonacci import fibonacci\n\ndef test_empty():\n assert fibonacci(0) == []\n\ndef test_single():\n assert fibonacci(1) == [0]\n\ndef test_sequence():\n assert fibonacci(7) == [0, 1, 1, 2, 3, 5, 8]\n```', + ), + msg( + 'user', + "```sql\nSELECT u.id, u.email, COUNT(o.id) AS order_count\nFROM users u\nLEFT JOIN orders o ON o.user_id = u.id\nWHERE u.created_at > NOW() - INTERVAL '30 days'\nGROUP BY u.id, u.email\nHAVING COUNT(o.id) > 5\nORDER BY order_count DESC;\n```", + ), + msg( + 'assistant', + "```sql\nCREATE INDEX idx_orders_user_id ON orders (user_id);\nCREATE INDEX idx_users_created_at ON users (created_at);\n\nEXPLAIN ANALYZE\nSELECT u.id, u.email, COUNT(o.id) AS order_count\nFROM users u\nLEFT JOIN orders o ON o.user_id = u.id\nWHERE u.created_at > NOW() - INTERVAL '30 days'\nGROUP BY u.id, u.email\nHAVING COUNT(o.id) > 5\nORDER BY order_count DESC;\n```", + ), + ], + }; +} + +/** + * Messages packed with identifiers, file paths, version numbers, and config values. + * Tests entity retention under pressure. + */ +export function entityDenseTechnical(): Scenario { + return { + name: 'Entity-dense technical', + messages: [ + msg('system', 'You are a DevOps engineer.'), + msg( + 'user', + 'The getUserProfile endpoint in src/api/users.ts is failing with a 503 from the authService. ' + + 'We see the error in the CloudWatch dashboard at https://console.aws.amazon.com/cloudwatch/metrics/api-gateway. ' + + 'The Redis cluster (redis-prod-001.abc123.usw2.cache.amazonaws.com:6379) has 98% memory utilization. ' + + 'The PostgreSQL connection pool (max_connections=200) is exhausted per pg_stat_activity. ' + + "Node version is v22.3.0 and we're running context-compression-engine@1.2.0.", + ), + msg( + 'assistant', + 'Looking at the getUserProfile failure chain: the authService depends on validateToken which ' + + 'reads from Redis via ioredis v5.4.1. At 98% memory on redis-prod-001, the eviction policy ' + + '(allkeys-lru) is likely expiring session tokens before the 7d TTL. The PostgreSQL pool ' + + 'exhaustion (200/200 in pg_stat_activity) suggests the connection leak I flagged in PR #142. ' + + 'Check src/db/pool.ts — the acquireTimeout of 30 seconds is too generous. Reduce to 5 seconds ' + + 'and add the connection.release() call in the finally block of src/middleware/auth.ts:L47. ' + + 'For Redis, either scale to r7g.xlarge or reduce the session TTL to 24 hours in config/redis.yaml.', + ), + msg( + 'user', + 'The getOrderHistory endpoint at /api/v2/orders is also slow. The p99 latency jumped from ' + + '120ms to 3400ms after we deployed commit abc123f. The Datadog trace shows the bottleneck is ' + + 'in src/services/orderService.ts:buildOrderSummary where it makes N+1 queries. The MySQL ' + + 'table orders has 14M rows and the idx_orders_user_created index is missing. We need to add ' + + 'it before the v2.5.0 release on 2026-04-01.', + ), + msg( + 'assistant', + 'The N+1 in orderService.ts:buildOrderSummary is the root cause. Each iteration calls ' + + 'getOrderItems which runs a separate SELECT against the order_items table (28M rows). Fix: ' + + 'rewrite to a single JOIN query in src/repositories/orderRepository.ts. Add the composite ' + + 'index: CREATE INDEX idx_orders_user_created ON orders (user_id, created_at DESC). The ' + + 'p99 should drop back to ~150ms. For the v2.5.0 release, also run the migration in ' + + 'migrations/20260321_add_order_indexes.sql and update the Terraform config in ' + + 'infra/rds.tf to set max_connections=300.', + ), + ], + }; +} + +/** + * Pure prose with zero technical content. + * Tests that the engine compresses aggressively when there is nothing to preserve. + */ +export function proseOnlyConversation(): Scenario { + const prose1 = + 'The team meeting yesterday covered several important topics that we should keep in mind ' + + 'going forward. The project timeline is still on track according to the product manager, ' + + 'though there were some concerns raised about the quality of recent deliverables. The ' + + 'design team presented their latest mockups and received generally positive feedback from ' + + 'the stakeholders. There was a brief discussion about hiring plans for the next quarter, ' + + 'and the consensus was to focus on filling the two open senior positions before adding any ' + + 'junior roles. The marketing team mentioned that the campaign metrics have been trending ' + + 'upward over the past month, which was encouraging news for everyone.'; + + const prose2 = + 'Following up on the discussion about workflow improvements, several team members suggested ' + + 'that the current review process takes too long and could benefit from some streamlining. ' + + 'The main bottleneck seems to be the handoff between the content creation phase and the ' + + 'editorial review phase, where items often sit in a queue for several days before being ' + + 'picked up. One proposal was to implement a rotating reviewer system so that no single ' + + 'person becomes a bottleneck. Another suggestion was to add clearer guidelines about what ' + + 'constitutes a review-ready submission, which could reduce the number of items bounced ' + + 'back for revision. The group agreed to try both approaches on a trial basis for the next ' + + 'sprint and evaluate the results.'; + + const prose3 = + 'The retrospective highlighted both positive developments and areas for improvement. On ' + + 'the positive side, communication within the team has improved significantly since we ' + + 'started the weekly sync meetings, and cross-team collaboration on the last project was ' + + 'notably smoother than previous efforts. Areas for improvement include documentation ' + + 'practices, which remain inconsistent across the organization, and the onboarding process ' + + 'for new team members, which several recent hires described as disorganized and incomplete. ' + + 'Action items from the retrospective include creating a documentation template, updating ' + + 'the onboarding checklist, and scheduling a follow-up session to track progress on these ' + + 'initiatives.'; + + return { + name: 'Prose-only conversation', + messages: [ + msg('system', 'You are a helpful assistant.'), + msg('user', 'Can you summarize our team meeting notes?'), + msg('assistant', prose1), + msg('user', 'What about the workflow discussion?'), + msg('assistant', prose2), + msg('user', 'And the retrospective?'), + msg('assistant', prose3), + msg('user', 'Great summary, thanks.'), + msg('assistant', 'You are welcome. Let me know if you need anything else.'), + ], + }; +} + +/** + * Messages with inline code in different languages (Python, SQL, JSON, YAML). + * Tests multi-format preservation in the same conversation. + */ +export function mixedLanguages(): Scenario { + return { + name: 'Mixed languages', + messages: [ + msg('system', 'You are a full-stack developer.'), + msg( + 'user', + 'Show me the Python data processing script, the SQL migration, the JSON config, and the YAML deployment manifest.', + ), + msg( + 'assistant', + 'Here is the data processing pipeline:\n\n' + + '```python\n' + + 'import pandas as pd\n' + + 'from datetime import datetime, timedelta\n\n' + + 'def process_daily_metrics(df: pd.DataFrame) -> pd.DataFrame:\n' + + ' cutoff = datetime.now() - timedelta(days=30)\n' + + ' filtered = df[df["timestamp"] >= cutoff]\n' + + ' return (\n' + + ' filtered\n' + + ' .groupby("service_name")\n' + + ' .agg({"latency_ms": ["mean", "p99"], "error_count": "sum"})\n' + + ' .reset_index()\n' + + ' )\n' + + '```\n\n' + + 'The SQL migration for the metrics table:\n\n' + + '```sql\n' + + 'CREATE TABLE daily_metrics (\n' + + ' id BIGSERIAL PRIMARY KEY,\n' + + ' service_name VARCHAR(100) NOT NULL,\n' + + ' metric_date DATE NOT NULL,\n' + + ' avg_latency_ms DECIMAL(10, 2),\n' + + ' p99_latency_ms DECIMAL(10, 2),\n' + + ' total_errors INTEGER DEFAULT 0,\n' + + ' UNIQUE (service_name, metric_date)\n' + + ');\n' + + '```\n\n' + + 'The service configuration:\n\n' + + '```json\n' + + '{\n' + + ' "service": "metrics-processor",\n' + + ' "version": "2.1.0",\n' + + ' "database": {\n' + + ' "host": "metrics-db.internal",\n' + + ' "port": 5432,\n' + + ' "pool_size": 20\n' + + ' },\n' + + ' "processing": {\n' + + ' "batch_size": 1000,\n' + + ' "max_retries": 3,\n' + + ' "timeout_ms": 30000\n' + + ' }\n' + + '}\n' + + '```\n\n' + + 'And the Kubernetes deployment:\n\n' + + '```yaml\n' + + 'apiVersion: apps/v1\n' + + 'kind: Deployment\n' + + 'metadata:\n' + + ' name: metrics-processor\n' + + ' namespace: data-pipeline\n' + + 'spec:\n' + + ' replicas: 3\n' + + ' selector:\n' + + ' matchLabels:\n' + + ' app: metrics-processor\n' + + ' template:\n' + + ' spec:\n' + + ' containers:\n' + + ' - name: processor\n' + + ' image: registry.internal/metrics-processor:2.1.0\n' + + ' resources:\n' + + ' requests:\n' + + ' memory: "512Mi"\n' + + ' cpu: "500m"\n' + + ' limits:\n' + + ' memory: "1Gi"\n' + + ' cpu: "1000m"\n' + + '```', + ), + msg('user', 'Looks good. What about monitoring for this service?'), + msg( + 'assistant', + 'For monitoring, add Prometheus annotations to the deployment and set up alerting. ' + + 'The metrics-processor service should expose a /metrics endpoint that Prometheus scrapes ' + + 'every 15 seconds. Configure alerts for error_count exceeding 100 per minute and p99 ' + + 'latency exceeding 5000 milliseconds. Use the Grafana dashboard at ' + + 'grafana.internal/d/metrics-processor for visualization.', + ), + ], + }; +} + +// --------------------------------------------------------------------------- +// Builder +// --------------------------------------------------------------------------- + +export function buildEdgeCaseScenarios(): Scenario[] { + resetEdgeIds(); + return [ + singleCharMessages(), + giantSingleMessage(), + codeOnlyConversation(), + entityDenseTechnical(), + proseOnlyConversation(), + mixedLanguages(), + ]; +} diff --git a/bench/quality.ts b/bench/quality.ts new file mode 100644 index 0000000..067e293 --- /dev/null +++ b/bench/quality.ts @@ -0,0 +1,827 @@ +import { readFileSync, writeFileSync, mkdirSync, existsSync } from 'node:fs'; +import { resolve, join } from 'node:path'; +import { execSync } from 'node:child_process'; +import { compress } from '../src/compress.js'; +import { uncompress } from '../src/expand.js'; +import type { Message } from '../src/types.js'; +import { + analyzeQuality, + sweepTradeoff, + summarizeTradeoff, + compareQualityResults, + runLlmJudge, + type QualityBaseline, + type QualityResult, + type TradeoffResult, + type LlmJudgeScore, +} from './quality-analysis.js'; +import { + buildEdgeCaseScenarios, + getProbesForScenario, + type Scenario, +} from './quality-scenarios.js'; +import { detectProviders } from './llm.js'; + +// --------------------------------------------------------------------------- +// Reuse scenario builders from run.ts (inline minimal versions to avoid +// coupling — the existing buildScenarios() is not exported) +// --------------------------------------------------------------------------- + +let nextId = 1; +function msg(role: string, content: string, extra?: Partial): Message { + const id = String(nextId++); + return { id, index: nextId - 1, role, content, metadata: {}, ...extra }; +} + +function buildCoreScenarios(): Scenario[] { + nextId = 1; + return [ + codingAssistant(), + longQA(), + toolHeavy(), + deepConversation(), + technicalExplanation(), + structuredContent(), + agenticCodingSession(), + ]; +} + +// --- Scenario definitions (copied from run.ts, trimmed to essentials) --- + +function codingAssistant(): Scenario { + const prose = + 'The authentication middleware validates incoming JWT tokens against the session store, checks expiration timestamps, and refreshes tokens when they are within the renewal window. '; + return { + name: 'Coding assistant', + messages: [ + msg('system', 'You are a senior TypeScript developer.'), + msg('user', 'How do I set up Express middleware for JWT auth?'), + msg( + 'assistant', + `${prose.repeat(3)}\n\n\`\`\`typescript\nimport jwt from 'jsonwebtoken';\n\nexport function authMiddleware(req, res, next) {\n const token = req.headers.authorization?.split(' ')[1];\n if (!token) return res.status(401).json({ error: 'No token' });\n try {\n req.user = jwt.verify(token, process.env.JWT_SECRET);\n next();\n } catch {\n res.status(401).json({ error: 'Invalid token' });\n }\n}\n\`\`\``, + ), + msg('user', 'Can you add refresh token rotation?'), + msg( + 'assistant', + `${prose.repeat(4)}\n\n\`\`\`typescript\nasync function rotateRefreshToken(oldToken: string) {\n const payload = jwt.verify(oldToken, REFRESH_SECRET);\n await revokeToken(oldToken);\n return {\n access: jwt.sign({ sub: payload.sub }, ACCESS_SECRET, { expiresIn: '15m' }),\n refresh: jwt.sign({ sub: payload.sub }, REFRESH_SECRET, { expiresIn: '7d' }),\n };\n}\n\`\`\``, + ), + msg('user', 'What about rate limiting?'), + msg( + 'assistant', + `Rate limiting prevents abuse by capping the number of requests a client can make in a time window. ${prose.repeat(3)}\n\n\`\`\`typescript\nimport rateLimit from 'express-rate-limit';\n\nconst limiter = rateLimit({\n windowMs: 15 * 60 * 1000,\n max: 100,\n standardHeaders: true,\n});\napp.use('/api/', limiter);\n\`\`\``, + ), + msg('user', 'How do I test this?'), + msg( + 'assistant', + `Testing middleware requires mocking the request and response objects. ${prose.repeat(2)}\n\n\`\`\`typescript\nimport { describe, it, expect, vi } from 'vitest';\nimport { authMiddleware } from './auth';\n\ndescribe('authMiddleware', () => {\n it('rejects missing token', () => {\n const req = { headers: {} } as any;\n const res = { status: vi.fn().mockReturnThis(), json: vi.fn() } as any;\n authMiddleware(req, res, vi.fn());\n expect(res.status).toHaveBeenCalledWith(401);\n });\n});\n\`\`\``, + ), + msg('user', 'Thanks, this is very helpful.'), + msg('assistant', 'Happy to help. Let me know if you need anything else.'), + msg('user', 'One more thing — should I store refresh tokens in Redis?'), + msg( + 'assistant', + `Redis is an excellent choice for refresh token storage because of its built-in TTL support and atomic operations. ${prose.repeat(3)} You can use the ioredis library for a robust connection pool.`, + ), + ], + }; +} + +function longQA(): Scenario { + const longAnswer = + 'The architecture of modern distributed systems relies on several foundational principles including service isolation, eventual consistency, and fault tolerance. Each service maintains its own data store, communicating through asynchronous message queues or synchronous RPC calls depending on latency requirements. Circuit breakers prevent cascading failures by monitoring error rates and temporarily halting requests to degraded downstream services. '; + return { + name: 'Long Q&A', + messages: [ + msg('system', 'You are a software architecture consultant.'), + msg('user', 'What is event sourcing?'), + msg('assistant', longAnswer.repeat(4)), + msg('user', 'How does CQRS relate to it?'), + msg('assistant', longAnswer.repeat(5)), + msg('user', 'What about saga patterns?'), + msg('assistant', longAnswer.repeat(4)), + msg('user', 'Can you compare choreography vs orchestration?'), + msg('assistant', longAnswer.repeat(6)), + msg('user', 'Which one should I use for payments?'), + msg('assistant', longAnswer.repeat(3)), + ], + }; +} + +function toolHeavy(): Scenario { + const longProse = + 'The authentication service handles all user identity verification across the platform. ' + + 'When a request arrives, the service first checks the session store for an active session, ' + + 'then validates the token signature against the current signing key. If the token has expired ' + + 'but falls within the renewal window, the service automatically issues a fresh token pair. ' + + 'The service maintains a blocklist of revoked tokens in memory, synchronized across instances ' + + 'through a pub-sub channel. Failed authentication attempts are tracked per account to enable ' + + 'progressive lockout after repeated failures. The service also provides hooks for downstream ' + + 'middleware to attach additional claims or enforce fine-grained access policies based on ' + + 'resource ownership.'; + return { + name: 'Tool-heavy', + messages: [ + msg('system', 'You are a coding assistant with tool access.'), + msg('user', 'Find all TypeScript files with auth in the name'), + msg('assistant', 'I will search for those files now.', { + tool_calls: [ + { id: 'tc1', function: { name: 'glob', arguments: '{"pattern":"**/*auth*.ts"}' } }, + ], + }), + msg( + 'tool', + '["src/auth.ts","src/middleware/auth.ts","tests/auth.test.ts","docs/auth-guide.md"]', + ), + msg('assistant', 'Found 4 files. Let me read the documentation first.', { + tool_calls: [ + { id: 'tc2', function: { name: 'read', arguments: '{"path":"docs/auth-guide.md"}' } }, + ], + }), + msg('tool', longProse), + msg('assistant', 'Now let me check the database schema.', { + tool_calls: [{ id: 'tc3', function: { name: 'read', arguments: '{"path":"schema.sql"}' } }], + }), + msg( + 'tool', + 'SELECT u.id, u.email, u.created_at, r.name AS role_name\nFROM users u\nINNER JOIN user_roles ur ON ur.user_id = u.id\nINNER JOIN roles r ON r.id = ur.role_id\nWHERE u.active = true AND u.email_verified = true\nORDER BY u.created_at DESC', + ), + msg('assistant', 'Let me check the configuration.', { + tool_calls: [ + { id: 'tc4', function: { name: 'read', arguments: '{"path":".env.example"}' } }, + ], + }), + msg( + 'tool', + 'STRIPE_SECRET_KEY=sk_live_abc123def456ghi789jkl012\nGITHUB_TOKEN=ghp_abc123def456ghi789jkl012mno345pqr678\nDATABASE_URL=postgresql://admin:secret@db.example.com:5432/myapp\nREDIS_URL=redis://cache.example.com:6379', + ), + msg('assistant', 'Let me read the main auth module.', { + tool_calls: [ + { id: 'tc5', function: { name: 'read', arguments: '{"path":"src/auth.ts"}' } }, + ], + }), + msg( + 'tool', + 'import jwt from "jsonwebtoken";\n\nexport function verify(token: string) {\n return jwt.verify(token, process.env.SECRET!);\n}\n\nexport function sign(payload: object) {\n return jwt.sign(payload, process.env.SECRET!, { expiresIn: "1h" });\n}', + ), + msg('user', 'Can you add a test for expired tokens?'), + msg('assistant', 'I will add an expiration test.', { + tool_calls: [ + { id: 'tc6', function: { name: 'edit', arguments: '{"path":"tests/auth.test.ts"}' } }, + ], + }), + msg('tool', 'File updated successfully.'), + msg('assistant', 'Done. The test file now includes an expiration test case.'), + msg('user', 'Great, looks good.'), + msg('assistant', 'Happy to help! Let me know if you need anything else.'), + ], + }; +} + +function deepConversation(): Scenario { + const topics = [ + 'database schema design', + 'API endpoint structure', + 'authentication flow', + 'error handling strategy', + 'caching layer', + 'deployment pipeline', + 'monitoring setup', + 'testing approach', + 'code review process', + 'documentation standards', + 'performance profiling', + 'logging strategy', + 'feature flags', + 'data migration', + 'API versioning', + 'circuit breakers', + 'message queuing', + 'secrets management', + 'load balancing', + 'container orchestration', + 'service discovery', + 'observability', + 'incident response', + 'capacity planning', + 'access control', + ]; + const messages: Message[] = [ + msg('system', 'You are a senior software architect helping plan a new microservice.'), + ]; + for (let i = 0; i < 25; i++) { + const topic = topics[i]; + messages.push( + msg( + 'user', + `Let's discuss the ${topic}. What patterns do you recommend for a high-traffic production service handling thousands of concurrent requests? ` + + `We need to consider scalability, maintainability, and operational overhead. `.repeat(2), + ), + ); + messages.push( + msg( + 'assistant', + `For ${topic}, I recommend the following approach based on industry best practices and patterns I have seen succeed at scale. ` + + `The key consideration is balancing complexity against the actual traffic patterns your service will encounter. ` + + `You should start with a simpler architecture and evolve it as your requirements become clearer through production usage. `.repeat( + 4, + ) + + ` This approach has proven effective across multiple production deployments.`, + ), + ); + } + return { name: 'Deep conversation', messages }; +} + +function technicalExplanation(): Scenario { + return { + name: 'Technical explanation', + messages: [ + msg('system', 'You are a principal engineer explaining system design to a new team lead.'), + msg( + 'user', + 'Can you walk me through how our event-driven architecture works and why we chose it over a traditional request-response model?', + ), + msg( + 'assistant', + 'The event-driven architecture we adopted grew out of a series of scaling problems we ' + + 'encountered roughly two years ago when the monolith started buckling under peak traffic. ' + + 'The core idea is that services communicate through immutable events published to a central ' + + 'log rather than making synchronous calls to each other. When the order service completes a ' + + 'checkout, it publishes an OrderPlaced event. The inventory service, the notification ' + + 'service, and the analytics pipeline each consume that event independently and at their own ' + + 'pace.\n\n' + + 'The biggest advantage is temporal decoupling. Under the old model, a single slow downstream ' + + 'dependency could cascade latency across the entire checkout flow. With events, the order ' + + 'service does its own work, publishes, and returns. Downstream processing happens asynchronously.\n\n' + + 'The trade-off is eventual consistency. In an event-driven model, you have to design for the ' + + 'possibility that the inventory adjustment has not happened yet when the user sees their ' + + 'confirmation page. We handle this through careful UX design and idempotent consumers.', + ), + msg('user', 'What about schema evolution?'), + msg( + 'assistant', + 'Schema evolution is one of the hardest problems in event-driven systems. We settled on a ' + + 'strategy combining explicit versioning with backward-compatible evolution rules. Every event ' + + 'type carries a schema version number in its header. For non-breaking changes like adding ' + + 'optional fields, we increment the minor version. For breaking changes, we run both versions ' + + 'in parallel during a migration window. We also maintain a central schema registry that ' + + 'validates events at publish time.', + ), + msg('user', 'How do we handle ordering?'), + msg( + 'assistant', + 'Our event platform guarantees ordering within a partition, but not across partitions. Events ' + + 'for the same aggregate go to the same partition based on the aggregate identifier. For rare ' + + 'cases needing cross-aggregate ordering, we use a sequence number stamped by the producer. ' + + 'The consumer tracks the last processed sequence number and detects gaps.', + ), + msg('user', 'And failure handling?'), + msg( + 'assistant', + 'The platform guarantees at-least-once delivery. Consumers must be idempotent. Dead letter ' + + 'queues catch events that fail repeatedly. A reconciliation process periodically compares ' + + 'event-derived state against the source of truth.', + ), + ], + }; +} + +function structuredContent(): Scenario { + const authProse = + 'Setting up authentication for a production environment requires careful planning across ' + + 'several layers of the system. The first step is establishing a strong identity provider. ' + + 'You will want to implement token-based authentication with short-lived access tokens and ' + + 'longer-lived refresh tokens. Password hashing should use a modern algorithm. Session ' + + 'management needs to handle concurrent logins gracefully. Rate limiting on authentication ' + + 'endpoints is essential to prevent brute force attacks.'; + const monitoringProse = + 'Monitoring a production environment effectively means collecting metrics at every layer. ' + + 'Start with infrastructure metrics like memory utilization and disk throughput. Application ' + + 'metrics should track request rates, error rates, and response time distributions. Log ' + + 'aggregation brings all service output into a single searchable store. Alerting rules ' + + 'should be tuned to minimize noise while catching real incidents.'; + return { + name: 'Structured content', + messages: [ + msg('system', 'You are a DevOps consultant helping set up a production environment.'), + msg('user', 'Set up our production environment with all the credentials.'), + msg( + 'assistant', + 'Here are the environment variables you need to configure:\n\n' + + 'STRIPE_SECRET_KEY=sk_live_Rz4x8Kp2Qm7Yn3Wv9Bt6Jh0L\n' + + 'GITHUB_TOKEN=ghp_Mn3Kx8Rz4Qp7Yv2Wt9Bj6Lh0Ds5Fa1Gc8Eu4Iw\n' + + 'AWS_ACCESS_KEY_ID=AKIAIOSFODNN7EXAMPLE\n' + + 'SENDGRID_API_KEY=SG.xY7kZmN2pQ9rS4tU6vW8aB.cD3eF5gH7jK9mN1pQ3rS5tU7vW9xY1zA3bC5dE7f', + ), + msg('user', 'What about the database schema?'), + msg( + 'assistant', + 'Here is the initial schema:\n\nCREATE TABLE audit_logs (\n id SERIAL PRIMARY KEY,\n user_id INTEGER NOT NULL,\n action VARCHAR(100) NOT NULL,\n resource_type VARCHAR(50),\n resource_id INTEGER,\n details TEXT,\n created_at TIMESTAMP DEFAULT NOW(),\n CONSTRAINT fk_audit_user FOREIGN KEY (user_id) REFERENCES users(id) ON DELETE CASCADE\n);', + ), + msg('user', 'How should we handle authentication?'), + msg('assistant', authProse), + msg('user', 'What about monitoring?'), + msg('assistant', monitoringProse), + msg('user', 'Show me a dashboard configuration.'), + msg( + 'assistant', + 'Here is a starter dashboard configuration:\n\n```json\n{\n "dashboard": "production-overview",\n "refresh_interval": 30,\n "panels": [\n { "title": "Request Rate", "type": "graph", "metric": "http_requests_total" },\n { "title": "Error Rate", "type": "graph", "metric": "http_errors_total" },\n { "title": "P99 Latency", "type": "gauge", "metric": "http_duration_p99" }\n ]\n}\n```', + ), + msg('user', 'Thanks, this is exactly what I needed.'), + ], + }; +} + +function agenticCodingSession(): Scenario { + const authModule = + 'import jwt from "jsonwebtoken";\nimport { Request, Response, NextFunction } from "express";\n\nexport class AuthService {\n private readonly secret: string;\n private readonly refreshSecret: string;\n\n constructor(secret: string, refreshSecret: string) {\n this.secret = secret;\n this.refreshSecret = refreshSecret;\n }\n\n verify(token: string): JWTPayload {\n return jwt.verify(token, this.secret) as JWTPayload;\n }\n\n sign(payload: Omit): string {\n return jwt.sign(payload, this.secret, { expiresIn: "15m" });\n }\n}\n'; + const authModuleV2 = authModule.replace('verify(', '// Validates token\n validateToken('); + const grepResults = + 'src/auth.ts:18: verify(token: string): JWTPayload {\nsrc/middleware/validate.ts:7: authService.verify(req.headers.authorization!);\ntests/auth.test.ts:14: service.verify(token);\n'; + const testOutput = + ' ✓ tests/auth.test.ts (5 tests) 42ms\n ✓ sign and verify > produces a valid JWT\n ✗ refresh > rotates token correctly\n → expected "user1" but got undefined\n Tests 4 passed | 1 failed\n'; + + return { + name: 'Agentic coding session', + messages: [ + msg('system', 'You are a senior TypeScript developer.'), + msg('user', 'Read the auth module and tell me what it does.'), + msg('assistant', 'Let me read that file.', { + tool_calls: [ + { id: 'tc1', function: { name: 'read', arguments: '{"path":"src/auth.ts"}' } }, + ], + }), + msg('tool', authModule), + msg('assistant', 'This is an AuthService class handling JWT authentication.'), + msg('user', 'Search for all usages of validateToken.'), + msg('assistant', 'Searching.', { + tool_calls: [{ id: 'tc2', function: { name: 'grep', arguments: '{"pattern":"verify"}' } }], + }), + msg('tool', grepResults), + msg('assistant', 'Found references across auth.ts, validate.ts, and tests.'), + msg('user', 'Add a test for refresh token rotation.'), + msg('assistant', 'Let me re-read auth.ts.', { + tool_calls: [ + { id: 'tc3', function: { name: 'read', arguments: '{"path":"src/auth.ts"}' } }, + ], + }), + msg('tool', authModuleV2), // near-duplicate + msg('assistant', 'Adding the rotation test.', { + tool_calls: [ + { id: 'tc4', function: { name: 'edit', arguments: '{"path":"tests/auth.test.ts"}' } }, + ], + }), + msg('tool', 'File updated successfully.'), + msg('user', 'Run the tests.'), + msg('assistant', 'Running.', { + tool_calls: [ + { id: 'tc5', function: { name: 'exec', arguments: '{"cmd":"npx vitest run"}' } }, + ], + }), + msg('tool', testOutput), + msg('assistant', 'The rotation test failed. Let me fix it.', { + tool_calls: [ + { id: 'tc6', function: { name: 'edit', arguments: '{"path":"tests/auth.test.ts"}' } }, + ], + }), + msg('tool', 'File updated successfully.'), + msg('assistant', 'Fixed. Running tests again.', { + tool_calls: [ + { id: 'tc7', function: { name: 'exec', arguments: '{"cmd":"npx vitest run"}' } }, + ], + }), + msg('tool', testOutput), // duplicate + msg('assistant', 'All 5 tests passing now.'), + msg('user', 'Nice, looks good.'), + ], + }; +} + +// --------------------------------------------------------------------------- +// Helpers +// --------------------------------------------------------------------------- + +function fix(n: number, d: number = 2): string { + return n.toFixed(d); +} + +function pct(n: number): string { + return `${(n * 100).toFixed(0)}%`; +} + +// --------------------------------------------------------------------------- +// Baseline persistence +// --------------------------------------------------------------------------- + +const BASELINES_DIR = resolve(import.meta.dirname, 'baselines', 'quality'); + +function saveQualityBaseline(baseline: QualityBaseline): void { + mkdirSync(BASELINES_DIR, { recursive: true }); + const json = JSON.stringify(baseline, null, 2) + '\n'; + writeFileSync(join(BASELINES_DIR, 'current.json'), json); + const historyDir = join(BASELINES_DIR, 'history'); + mkdirSync(historyDir, { recursive: true }); + writeFileSync(join(historyDir, `${baseline.gitRef.slice(0, 8)}.json`), json); +} + +function loadQualityBaseline(): QualityBaseline | null { + const path = join(BASELINES_DIR, 'current.json'); + if (!existsSync(path)) return null; + return JSON.parse(readFileSync(path, 'utf-8')); +} + +// --------------------------------------------------------------------------- +// Runner +// --------------------------------------------------------------------------- + +async function run(): Promise { + const args = process.argv.slice(2); + const flagSave = args.includes('--save'); + const flagCheck = args.includes('--check'); + const flagLlmJudge = args.includes('--llm-judge'); + const flagFeatures = args.includes('--features'); + + const version = JSON.parse( + readFileSync(resolve(import.meta.dirname, '..', 'package.json'), 'utf-8'), + ).version; + const gitRef = execSync('git rev-parse HEAD', { encoding: 'utf-8' }).trim(); + + console.log(); + console.log(`Compression Quality Benchmark — v${version} (${gitRef.slice(0, 8)})`); + + // --- Build all scenarios --- + const coreScenarios = buildCoreScenarios(); + const edgeScenarios = buildEdgeCaseScenarios(); + const allScenarios = [...coreScenarios, ...edgeScenarios]; + + // --- Run quality analysis --- + const qualityResults: Record = {}; + + const qHeader = [ + 'Scenario'.padEnd(24), + 'Ratio'.padStart(6), + 'EntRet'.padStart(7), + 'CodeOK'.padStart(7), + 'InfDen'.padStart(7), + 'Probes'.padStart(7), + 'Pass'.padStart(5), + 'NegCp'.padStart(6), + 'Coher'.padStart(6), + 'CmpQ'.padStart(6), + ].join(' '); + const qSep = '-'.repeat(qHeader.length); + + console.log(); + console.log('Quality Analysis'); + console.log(qSep); + console.log(qHeader); + console.log(qSep); + + for (const scenario of allScenarios) { + const probes = getProbesForScenario(scenario.name); + const q = analyzeQuality(scenario.messages, probes); + qualityResults[scenario.name] = q; + + console.log( + [ + scenario.name.padEnd(24), + fix(q.ratio).padStart(6), + pct(q.avgEntityRetention).padStart(7), + pct(q.codeBlockIntegrity).padStart(7), + fix(q.informationDensity).padStart(7), + `${q.probesPassed}/${q.probesTotal}`.padStart(7), + pct(q.probePassRate).padStart(5), + String(q.negativeCompressions).padStart(6), + String(q.coherenceIssues).padStart(6), + fix(q.compressedQualityScore).padStart(6), + ].join(' '), + ); + } + + console.log(qSep); + + // --- Probe failure detail --- + const failedProbes: { scenario: string; label: string }[] = []; + for (const scenario of allScenarios) { + const q = qualityResults[scenario.name]; + for (const pr of q.probeResults) { + if (!pr.passed) { + failedProbes.push({ scenario: scenario.name, label: pr.label }); + } + } + } + + if (failedProbes.length > 0) { + console.log(); + console.log('Probe Failures'); + console.log('-'.repeat(60)); + for (const f of failedProbes) { + console.log(` ${f.scenario}: ${f.label}`); + } + console.log('-'.repeat(60)); + } else { + console.log('\nAll probes passed.'); + } + + // --- Round-trip verification --- + let rtFails = 0; + for (const scenario of allScenarios) { + const cr = compress(scenario.messages, { recencyWindow: 0 }); + const er = uncompress(cr.messages, cr.verbatim); + const pass = + JSON.stringify(scenario.messages) === JSON.stringify(er.messages) && + er.missing_ids.length === 0; + if (!pass) { + console.error(` FAIL: ${scenario.name} failed round-trip`); + rtFails++; + } + } + + if (rtFails > 0) { + console.error(`\n${rtFails} scenario(s) failed round-trip verification.`); + process.exit(1); + } + console.log('\nAll scenarios passed round-trip verification.'); + + // --- Tradeoff sweep --- + const tradeoffScenarios = [ + 'Deep conversation', + 'Coding assistant', + 'Technical explanation', + 'Agentic coding session', + ]; + const tradeoffResults: Record = {}; + + console.log(); + console.log('Tradeoff Sweep (ratio vs quality)'); + + const tHeader = [ + 'Scenario'.padEnd(24), + 'Points'.padStart(7), + 'Q@2x'.padStart(6), + 'Q@3x'.padStart(6), + 'MaxR@80%Q'.padStart(10), + ].join(' '); + const tSep = '-'.repeat(tHeader.length); + + console.log(tSep); + console.log(tHeader); + console.log(tSep); + + for (const scenario of allScenarios.filter((s) => tradeoffScenarios.includes(s.name))) { + const points = sweepTradeoff(scenario.messages); + const summary = summarizeTradeoff(points); + tradeoffResults[scenario.name] = summary; + + console.log( + [ + scenario.name.padEnd(24), + String(summary.points.length).padStart(7), + (summary.qualityAt2x != null ? fix(summary.qualityAt2x) : '-').padStart(6), + (summary.qualityAt3x != null ? fix(summary.qualityAt3x) : '-').padStart(6), + fix(summary.maxRatioAbove80pctQuality).padStart(10), + ].join(' '), + ); + } + + console.log(tSep); + + // --- Per-message quality details for entity-dense scenario --- + const entityDense = qualityResults['Entity-dense technical']; + if (entityDense && entityDense.messages.length > 0) { + console.log(); + console.log('Per-Message Quality (Entity-dense technical)'); + + const mHeader = [ + 'MsgID'.padEnd(8), + 'Action'.padEnd(12), + 'In'.padStart(6), + 'Out'.padStart(6), + 'Ratio'.padStart(6), + 'EntRet'.padStart(7), + 'Code'.padStart(5), + ].join(' '); + const mSep = '-'.repeat(mHeader.length); + + console.log(mSep); + console.log(mHeader); + console.log(mSep); + + for (const m of entityDense.messages) { + console.log( + [ + m.messageId.padEnd(8), + m.action.padEnd(12), + String(m.inputChars).padStart(6), + String(m.outputChars).padStart(6), + fix(m.localRatio).padStart(6), + pct(m.entityRetention).padStart(7), + (m.codeBlocksIntact ? 'ok' : 'LOSS').padStart(5), + ].join(' '), + ); + } + + console.log(mSep); + } + + // --- Opt-in features comparison (optional) --- + if (flagFeatures) { + const featureConfigs: { label: string; options: Record }[] = [ + { + label: 'importance + contradiction', + options: { importanceScoring: true, contradictionDetection: true }, + }, + { + label: 'semantic clustering', + options: { semanticClustering: true }, + }, + { + label: 'conversation flow', + options: { conversationFlow: true }, + }, + { + label: 'coreference', + options: { coreference: true }, + }, + { + label: 'all features', + options: { + importanceScoring: true, + contradictionDetection: true, + semanticClustering: true, + conversationFlow: true, + coreference: true, + }, + }, + ]; + + for (const config of featureConfigs) { + console.log(); + console.log(`Feature: ${config.label}`); + + const fHeader = [ + 'Scenario'.padEnd(24), + 'Ratio'.padStart(6), + 'EntRet'.padStart(7), + 'Probes'.padStart(7), + 'Pass'.padStart(5), + 'Coher'.padStart(6), + 'CmpQ'.padStart(6), + 'vs base'.padStart(8), + ].join(' '); + const fSep = '-'.repeat(fHeader.length); + + console.log(fSep); + console.log(fHeader); + console.log(fSep); + + for (const scenario of allScenarios) { + const probes = getProbesForScenario(scenario.name); + const q = analyzeQuality(scenario.messages, probes, config.options); + const baseQ = qualityResults[scenario.name]; + + // Compare probe pass rate vs baseline + const probeDelta = q.probePassRate - baseQ.probePassRate; + const deltaStr = + probeDelta > 0.001 ? `+${pct(probeDelta)}` : probeDelta < -0.001 ? pct(probeDelta) : '='; + + console.log( + [ + scenario.name.padEnd(24), + fix(q.ratio).padStart(6), + pct(q.avgEntityRetention).padStart(7), + `${q.probesPassed}/${q.probesTotal}`.padStart(7), + pct(q.probePassRate).padStart(5), + String(q.coherenceIssues).padStart(6), + fix(q.compressedQualityScore).padStart(6), + deltaStr.padStart(8), + ].join(' '), + ); + } + + console.log(fSep); + } + } + + // --- LLM Judge (optional) --- + if (flagLlmJudge) { + const providers = await detectProviders(); + if (providers.length === 0) { + console.log('\nNo LLM providers detected — skipping LLM judge.'); + console.log( + ' Set one of: OPENAI_API_KEY, ANTHROPIC_API_KEY, GEMINI_API_KEY, or OLLAMA_HOST', + ); + } else { + // Only judge scenarios that actually compress + const judgeable = allScenarios.filter((s) => qualityResults[s.name]?.ratio > 1.01); + + for (const provider of providers) { + console.log(); + console.log(`LLM Judge — ${provider.name}/${provider.model}`); + + const jHeader = [ + 'Scenario'.padEnd(24), + 'Meaning'.padStart(8), + 'Coher'.padStart(6), + 'Overall'.padStart(8), + 'Info Loss'.padStart(40), + ].join(' '); + const jSep = '-'.repeat(jHeader.length); + + console.log(jSep); + console.log(jHeader); + console.log(jSep); + + const scores: LlmJudgeScore[] = []; + for (const scenario of judgeable) { + const cr = compress(scenario.messages, { recencyWindow: 0 }); + try { + const score = await runLlmJudge( + scenario.name, + scenario.messages, + cr.messages, + provider.callLlm, + provider.name, + provider.model, + ); + scores.push(score); + + const lossDisplay = + score.informationLoss.length > 40 + ? score.informationLoss.slice(0, 37) + '...' + : score.informationLoss; + + console.log( + [ + scenario.name.padEnd(24), + `${score.meaningPreserved}/5`.padStart(8), + `${score.coherence}/5`.padStart(6), + `${score.overall}/5`.padStart(8), + lossDisplay.padStart(40), + ].join(' '), + ); + } catch (err) { + console.log( + ` ${scenario.name.padEnd(24)} ERROR: ${(err as Error).message.slice(0, 60)}`, + ); + } + } + + console.log(jSep); + + if (scores.length > 0) { + const avgMeaning = scores.reduce((s, sc) => s + sc.meaningPreserved, 0) / scores.length; + const avgCoherence = scores.reduce((s, sc) => s + sc.coherence, 0) / scores.length; + const avgOverall = scores.reduce((s, sc) => s + sc.overall, 0) / scores.length; + console.log( + ` Average: meaning=${fix(avgMeaning)}/5 coherence=${fix(avgCoherence)}/5 overall=${fix(avgOverall)}/5`, + ); + } + } + } + } + + // --- Save / Check --- + const baseline: QualityBaseline = { + version, + gitRef, + generated: new Date().toISOString(), + results: { + scenarios: qualityResults, + tradeoff: tradeoffResults, + }, + }; + + if (flagSave) { + saveQualityBaseline(baseline); + console.log(`\nQuality baseline saved (v${version}, ${gitRef.slice(0, 8)}).`); + } + + if (flagCheck) { + const existing = loadQualityBaseline(); + if (!existing) { + console.error('\nNo quality baseline found — run with --save first.'); + process.exit(1); + } + + const regressions = compareQualityResults(existing, baseline); + if (regressions.length > 0) { + console.error(`\n${regressions.length} quality regression(s) detected:`); + for (const r of regressions) { + console.error( + ` [${r.benchmark}] ${r.scenario} → ${r.metric}: expected ${fix(r.expected)}, got ${fix(r.actual)} (${r.delta})`, + ); + } + process.exit(1); + } + console.log(`\nQuality baseline check passed (v${existing.version}).`); + } + + console.log(); + console.log('Quality benchmarks complete.'); +} + +run().catch((err) => { + console.error(err); + process.exit(1); +}); diff --git a/bench/run.ts b/bench/run.ts index 3b69ff7..f17f78a 100644 --- a/bench/run.ts +++ b/bench/run.ts @@ -3,9 +3,41 @@ import { uncompress } from '../src/expand.js'; import { createSummarizer, createEscalatingSummarizer } from '../src/summarizer.js'; import type { CompressResult, Message } from '../src/types.js'; import { readFileSync, readdirSync, statSync, existsSync } from 'node:fs'; -import { join } from 'node:path'; +import { join, resolve } from 'node:path'; import { homedir } from 'node:os'; +import { execSync } from 'node:child_process'; +import { gzipSync } from 'node:zlib'; import { detectProviders } from './llm.js'; +import type { + LlmBenchmarkResult, + LlmMethodResult, + LlmTokenBudgetResult, + BenchmarkResults, +} from './baseline.js'; +import { + saveLlmResult, + saveBaseline, + loadCurrentBaseline, + compareResults, + formatRegressions, + generateBenchmarkDocs, + analyzeRetention, +} from './baseline.js'; + +// --------------------------------------------------------------------------- +// Auto-load .env (no dependency, won't override existing vars) +// --------------------------------------------------------------------------- + +const envPath = resolve(import.meta.dirname, '..', '.env'); +if (existsSync(envPath)) { + for (const line of readFileSync(envPath, 'utf-8').split('\n')) { + const match = line.match(/^\s*(?:export\s+)?([^#=]+?)\s*=\s*(.*?)\s*$/); + if (!match || process.env[match[1]]) continue; + // Strip wrapping quotes (single or double) + const val = match[2].replace(/^(['"])(.*)\1$/, '$2'); + process.env[match[1]] = val; + } +} // --------------------------------------------------------------------------- // Helpers @@ -39,6 +71,7 @@ function buildScenarios(): Scenario[] { toolHeavy(), shortConversation(), deepConversation(), + technicalExplanation(), structuredContent(), agenticCodingSession(), ]; @@ -262,6 +295,160 @@ function deepConversation(): Scenario { return { name: 'Deep conversation', messages }; } +function technicalExplanation(): Scenario { + // Prose-heavy architectural discussion — no code fences, no structured data. + // Tests whether LLM summarization outperforms deterministic on pure technical prose. + return { + name: 'Technical explanation', + messages: [ + msg('system', 'You are a principal engineer explaining system design to a new team lead.'), + msg( + 'user', + 'Can you walk me through how our event-driven architecture works and why we chose it over a traditional request-response model?', + ), + msg( + 'assistant', + 'The event-driven architecture we adopted grew out of a series of scaling problems we ' + + 'encountered roughly two years ago when the monolith started buckling under peak traffic. ' + + 'The core idea is that services communicate through immutable events published to a central ' + + 'log rather than making synchronous calls to each other. When the order service completes a ' + + 'checkout, it publishes an OrderPlaced event. The inventory service, the notification ' + + 'service, and the analytics pipeline each consume that event independently and at their own ' + + 'pace. None of them need to know about the others, and none of them can block the order ' + + 'service from finishing its work.\n\n' + + 'The biggest advantage is temporal decoupling. Under the old model, a single slow downstream ' + + 'dependency could cascade latency across the entire checkout flow. A payment verification ' + + 'that normally took fifty milliseconds would occasionally spike to several seconds, and ' + + 'because every call was synchronous, the user sat waiting while six services completed in ' + + 'sequence. With events, the order service does its own work, publishes, and returns. ' + + 'Downstream processing happens asynchronously, so the user experience stays fast even when ' + + 'one consumer is struggling.\n\n' + + 'The trade-off is eventual consistency. In a synchronous model, you get immediate confirmation ' + + 'that every step completed. In an event-driven model, you have to design for the possibility ' + + 'that the inventory adjustment or the email notification has not happened yet when the user ' + + 'sees their confirmation page. We handle this through careful UX design that sets appropriate ' + + 'expectations and through idempotent consumers that can safely replay events if something ' + + 'goes wrong during processing.', + ), + msg( + 'user', + 'That makes sense. What about the event schema evolution problem? How do we handle ' + + 'changes to event formats without breaking all the consumers?', + ), + msg( + 'assistant', + 'Schema evolution is one of the hardest problems in event-driven systems and we spent ' + + 'considerable time getting our approach right. We settled on a strategy that combines ' + + 'explicit versioning with backward-compatible evolution rules. Every event type carries a ' + + 'schema version number in its header. Consumers declare which versions they understand, and ' + + 'the platform routes accordingly.\n\n' + + 'For non-breaking changes like adding a new optional field, we increment the minor version. ' + + 'Consumers that do not understand the new field simply ignore it. This follows the robustness ' + + 'principle of being conservative in what you send and liberal in what you accept. The vast ' + + 'majority of our schema changes fall into this category, and they require zero coordination ' + + 'between teams.\n\n' + + 'For breaking changes like removing a field or changing a field type, we increment the major ' + + 'version and run both the old and new versions in parallel during a migration window. The ' + + 'producing service publishes to both versions, and consuming teams migrate on their own ' + + 'schedule within the agreed window. Once all consumers have migrated, we sunset the old ' + + 'version. This approach adds operational complexity but gives teams the autonomy to migrate ' + + 'at their own pace without coordinated deployment ceremonies.\n\n' + + 'We also maintain a central schema registry that validates events at publish time. If a ' + + 'service tries to publish an event that does not conform to its declared schema, the publish ' + + 'fails fast with a clear error. This catches drift between the code and the schema before ' + + 'it reaches consumers and prevents subtle data corruption that would be much harder to ' + + 'diagnose after the fact.', + ), + msg( + 'user', + 'How do we handle ordering guarantees? I noticed some of our consumers seem to ' + + 'process events out of order.', + ), + msg( + 'assistant', + 'Ordering is subtle in distributed event systems and the right answer depends on what ' + + 'level of ordering your consumer actually needs. Our event platform guarantees ordering ' + + 'within a partition, but not across partitions. Events for the same aggregate, like all ' + + 'events for a specific order, go to the same partition based on the aggregate identifier, ' + + 'so they arrive in the order they were published. Events for different aggregates may arrive ' + + 'in any order because they live on different partitions processed by different consumer ' + + 'instances.\n\n' + + 'Most consumers only care about ordering within an aggregate and they get it for free from ' + + 'the partitioning scheme. The inventory service needs to see OrderPlaced before OrderCancelled ' + + 'for the same order, and it does because both events share the same order identifier as their ' + + 'partition key. But it does not care whether order A events arrive before or after order B ' + + 'events because those are independent aggregates.\n\n' + + 'For the rare cases where a consumer needs cross-aggregate ordering, we use a sequence ' + + 'number stamped by the producer at publish time. The consumer tracks the last processed ' + + 'sequence number and detects gaps. When it encounters a gap, it buffers the event and waits ' + + 'briefly for the missing one to arrive. If the missing event does not appear within the ' + + 'timeout window, the consumer raises an alert and proceeds with compensating logic. This ' + + 'approach adds latency in the gap case but preserves correctness.\n\n' + + 'The consumers you noticed processing out of order are likely reading from a topic that ' + + 'mixes events from multiple aggregates. Check whether the partition key is set correctly ' + + 'on the producer side. A common mistake is using a random or missing partition key, which ' + + 'causes round-robin assignment and destroys any ordering guarantee.', + ), + msg( + 'user', + 'What about failure handling? If a consumer crashes mid-processing, do we lose events?', + ), + msg( + 'assistant', + 'No, the platform guarantees at-least-once delivery. When a consumer reads an event, it ' + + 'does not acknowledge the read until processing completes successfully. If the consumer ' + + 'crashes before acknowledging, the event remains available for redelivery. The next time ' + + 'the consumer starts up, or when another instance in the consumer group takes over the ' + + 'partition, it picks up from the last acknowledged position.\n\n' + + 'The flip side of at-least-once delivery is that consumers must be idempotent. They will ' + + 'occasionally see the same event twice, particularly after a crash or during a rebalance. ' + + 'We enforce idempotency through a combination of techniques depending on the consumer. ' + + 'Some use a deduplication table that records processed event identifiers and skips any event ' + + 'it has seen before. Others design their state mutations to be naturally idempotent, for ' + + 'example using upserts instead of inserts or applying absolute values instead of deltas.\n\n' + + 'Dead letter queues catch events that fail processing repeatedly. After a configurable number ' + + 'of retries with exponential backoff, the platform moves the event to a dead letter topic ' + + 'and advances the consumer offset so it does not block subsequent events. The operations team ' + + 'monitors the dead letter queue and investigates failures. Once the root cause is fixed, they ' + + 'can replay events from the dead letter queue back into the main topic.\n\n' + + 'We also run a reconciliation process that periodically compares the state derived from ' + + 'events against the source of truth in the originating service. This catches any silent data ' + + 'loss or corruption that might slip through the cracks. The reconciler runs daily during off-' + + 'peak hours and flags discrepancies for manual review.', + ), + msg( + 'user', + 'One more thing — how do we test all of this? Integration testing with events seems ' + + 'really hard.', + ), + msg( + 'assistant', + 'Testing event-driven systems requires a layered approach because no single testing ' + + 'strategy covers all the failure modes. At the unit level, each consumer is tested with ' + + 'synthetic events that exercise the happy path, edge cases, and error paths. These tests ' + + 'run in milliseconds and give fast feedback on business logic correctness.\n\n' + + 'At the integration level, we run each service against a local instance of the event ' + + 'platform. The test publishes events, waits for the consumer to process them, and verifies ' + + 'the resulting state. These tests are slower but catch serialization issues, schema ' + + 'mismatches, and configuration problems that unit tests miss. We keep the integration test ' + + 'suite focused on the boundaries: publishing, consuming, and acknowledging. Internal ' + + 'business logic is covered at the unit level.\n\n' + + 'At the system level, we maintain a staging environment that mirrors production topology. ' + + 'Every deployment goes through staging first, where we run end-to-end scenarios that ' + + 'exercise the full event flow from producer through all consumers. These tests use realistic ' + + 'data volumes and introduce controlled failures like consumer crashes and network partitions ' + + 'to verify that the retry and dead-letter mechanisms work correctly.\n\n' + + 'Contract testing bridges the gap between producers and consumers without requiring a ' + + 'shared integration environment. Each consumer publishes a contract describing the events ' + + 'it expects, and the producer runs those contracts as part of its build. If a producer ' + + 'change would break a consumer contract, the build fails before the change reaches any ' + + 'shared environment. This is particularly valuable in our setup where different teams own ' + + "different services and may not be aware of each other's dependencies.", + ), + ], + }; +} + function structuredContent(): Scenario { // Pure prose about auth (~1500 chars): no code, URLs, SQL, API keys, JSON, paths, etc. const authProse = @@ -600,6 +787,147 @@ function agenticCodingSession(): Scenario { }; } +function iterativeDesign(): Scenario { + // Simulates a real design conversation where: + // 1. Early messages establish important architectural decisions (importance scoring target) + // 2. Some decisions get corrected/overridden later (contradiction detection target) + // 3. Filler prose separates the signals + const filler = + 'The team has been making steady progress on the overall project timeline and the stakeholders ' + + 'are generally satisfied with the direction things are heading. We should continue to monitor the ' + + 'situation and adjust our approach as needed based on feedback from the beta testing group.'; + + return { + name: 'Iterative design', + messages: [ + msg('system', 'You are a senior architect helping design a data pipeline.'), + // Important early decision — referenced by many later messages + msg( + 'user', + 'We need to decide on the message queue. I think we should use the RabbitMQ broker with ' + + 'the AMQP protocol for our data pipeline because it supports complex routing topologies ' + + 'and has mature client libraries for our TypeScript and Python services.', + ), + msg( + 'assistant', + 'RabbitMQ with AMQP is a solid choice for the data pipeline. The exchange-based routing ' + + 'model lets you fan out events to multiple consumers without duplicating publish logic. ' + + 'For the TypeScript services, use the amqplib client with channel pooling to avoid ' + + 'connection churn. For Python, pika is the standard. Make sure to configure publisher ' + + 'confirms and consumer acknowledgments to prevent message loss.', + ), + // Filler + msg('user', filler), + msg( + 'assistant', + 'That sounds like a reasonable assessment of the current project state. Keeping an eye on ' + + 'the beta feedback loop is important for catching issues early. The weekly sync should give ' + + 'enough visibility into any blockers that arise during the testing phase.', + ), + // Another important decision — serialization format + msg( + 'user', + 'For the serialization format in the data pipeline, lets use Protocol Buffers with the ' + + 'protobuf-ts generator for the TypeScript side. The schema evolution story is better ' + + 'than JSON and the binary encoding saves bandwidth on the RabbitMQ broker.', + ), + msg( + 'assistant', + 'Protocol Buffers are a strong fit here. The protobuf-ts generator produces clean ' + + 'TypeScript interfaces with full type safety. Define your schemas in a shared proto ' + + 'directory and generate both TypeScript and Python stubs from CI. Use proto3 syntax ' + + 'and leverage the optional keyword for fields that might be added later. This keeps ' + + 'backward compatibility without the versioning headaches of Avro.', + ), + // More filler + msg( + 'user', + 'The onboarding documentation for new developers needs some updates. Several sections ' + + 'reference the old deployment process and the screenshots are from the previous version ' + + 'of the admin dashboard.', + ), + msg( + 'assistant', + 'Updating the onboarding docs should be a priority before the next hiring cycle. Have each ' + + 'recent joiner flag the sections that confused them during their own onboarding, then use ' + + 'those annotations to prioritize which pages to rewrite first. Screenshots can be automated ' + + 'with a headless browser script that captures the current UI state on each release.', + ), + // CONTRADICTION: override the RabbitMQ decision (shares vocabulary with original) + msg( + 'user', + 'Actually, we need to change the message queue broker for our data pipeline. The RabbitMQ ' + + 'broker with AMQP protocol cannot handle our throughput requirements for the TypeScript ' + + 'and Python services. We should use Apache Kafka as the message broker instead because ' + + 'the partitioned log model supports complex routing topologies at higher scale and the ' + + 'client libraries are mature.', + ), + msg( + 'assistant', + "Good call switching to Kafka after the load test validated the concern. Kafka's " + + 'partitioned log gives you the horizontal scaling RabbitMQ was missing. Use the kafkajs ' + + 'client for TypeScript with the confluent-kafka-python library for the Python services. ' + + 'Partition your topics by the pipeline entity ID to preserve ordering within each entity. ' + + 'You will need to update the protobuf message wrappers to include Kafka headers for ' + + 'tracing.', + ), + // Forward reference to the Kafka decision + msg( + 'user', + 'How should we handle dead letter processing in Kafka? When a consumer fails to process ' + + 'a message from the data pipeline after retries, we need a recovery path.', + ), + msg( + 'assistant', + 'In Kafka, dead letter handling is a consumer-side concern unlike RabbitMQ which has ' + + 'built-in DLX support. Implement a retry topic pattern: after N failed attempts, the ' + + 'consumer publishes the message to a dedicated retry topic with a backoff delay header. ' + + 'A separate retry consumer reads from the retry topic and re-publishes to the original ' + + 'topic after the delay expires. After the final retry, send to a dead letter topic that ' + + 'feeds into an alert and manual review workflow.', + ), + // CONTRADICTION: override the protobuf decision (shares vocabulary with original) + msg( + 'user', + 'Actually, the protobuf-ts generator for the serialization format in the data pipeline ' + + 'has a bug with our nested message types. Lets switch to using Avro instead of Protocol ' + + 'Buffers for the TypeScript side. The schema evolution story with the Confluent schema ' + + 'registry is better and the binary encoding saves bandwidth on the Kafka broker.', + ), + msg( + 'assistant', + 'Avro with the Confluent Schema Registry is the standard pairing for Kafka pipelines. ' + + 'The avsc library handles Avro encoding and schema resolution in Node. Register schemas ' + + 'on first produce and cache the schema ID for subsequent messages. The wire format is ' + + 'a magic byte, the 4-byte schema ID, then the Avro-encoded payload. This is a better ' + + 'fit than protobuf for the Kafka ecosystem since the schema registry handles evolution.', + ), + // Forward references + msg( + 'user', + 'Can the Avro schemas we define for Kafka also be used to validate the REST API request ' + + 'bodies in the ingestion service?', + ), + msg( + 'assistant', + 'Yes, you can share the Avro schemas between the Kafka producers and the REST validation ' + + 'layer. The avsc library can compile an Avro schema into a validator function that checks ' + + 'incoming JSON payloads. This gives you a single source of truth for the data pipeline ' + + 'message format — the same schema validates HTTP input and serializes Kafka output.', + ), + ], + }; +} + +// --------------------------------------------------------------------------- +// ANCS scenario builder (uses existing + new scenarios) +// --------------------------------------------------------------------------- + +function buildAncsScenarios(): Scenario[] { + nextId = 10000; // offset to avoid ID collisions + return [deepConversation(), agenticCodingSession(), iterativeDesign()]; +} + // --------------------------------------------------------------------------- // Runner // --------------------------------------------------------------------------- @@ -615,12 +943,32 @@ interface Result { preserved: number; roundTrip: 'PASS' | 'FAIL'; timeMs: string; + entityRetention: number | undefined; + structuralIntegrity: number | undefined; + referenceCoherence: number | undefined; + qualityScore: number | undefined; } async function run(): Promise { + const args = process.argv.slice(2); + const flagSave = args.includes('--save'); + const flagCheck = args.includes('--check'); + const flagLlm = args.includes('--llm'); + const toleranceIdx = args.indexOf('--tolerance'); + const tolerance = toleranceIdx >= 0 ? Number(args[toleranceIdx + 1]) / 100 : 0; + const scenarios = buildScenarios(); const results: Result[] = []; + // Structured results for baseline save/check + const benchResults: BenchmarkResults = { + basic: {}, + tokenBudget: {}, + dedup: {}, + fuzzyDedup: {}, + bundleSize: {}, + }; + for (const scenario of scenarios) { const t0 = performance.now(); @@ -647,7 +995,39 @@ async function run(): Promise { preserved: cr.compression.messages_preserved, roundTrip, timeMs: (t1 - t0).toFixed(2), + entityRetention: cr.compression.entity_retention, + structuralIntegrity: cr.compression.structural_integrity, + referenceCoherence: cr.compression.reference_coherence, + qualityScore: cr.compression.quality_score, }); + + benchResults.basic[scenario.name] = { + ratio: cr.compression.ratio, + tokenRatio: cr.compression.token_ratio, + compressed: cr.compression.messages_compressed, + preserved: cr.compression.messages_preserved, + }; + + // Quality metrics + if (cr.compression.quality_score != null) { + if (!benchResults.quality) benchResults.quality = {}; + benchResults.quality[scenario.name] = { + entityRetention: cr.compression.entity_retention!, + structuralIntegrity: cr.compression.structural_integrity!, + referenceCoherence: cr.compression.reference_coherence!, + qualityScore: cr.compression.quality_score!, + }; + } + + // Retention analysis + const originalText = scenario.messages + .map((m) => (typeof m.content === 'string' ? m.content : '')) + .join('\n'); + const compressedText = cr.messages + .map((m) => (typeof m.content === 'string' ? m.content : '')) + .join('\n'); + if (!benchResults.retention) benchResults.retention = {}; + benchResults.retention[scenario.name] = analyzeRetention(originalText, compressedText); } // Print table @@ -715,6 +1095,85 @@ async function run(): Promise { console.log('All scenarios passed round-trip verification.'); + // --------------------------------------------------------------------------- + // Retention metrics + // --------------------------------------------------------------------------- + + if (benchResults.retention && Object.keys(benchResults.retention).length > 0) { + console.log(); + console.log('Retention Metrics'); + + const retHeader = [ + 'Scenario'.padEnd(24), + 'Keywords'.padStart(9), + 'Entities'.padStart(9), + 'Structural'.padStart(11), + ].join(' '); + const retSep = '-'.repeat(retHeader.length); + + console.log(retSep); + console.log(retHeader); + console.log(retSep); + + for (const [name, ret] of Object.entries(benchResults.retention)) { + console.log( + [ + name.padEnd(24), + `${(ret.keywordRetention * 100).toFixed(0)}%`.padStart(9), + `${(ret.entityRetention * 100).toFixed(0)}%`.padStart(9), + `${(ret.structuralRetention * 100).toFixed(0)}%`.padStart(11), + ].join(' '), + ); + } + + console.log(retSep); + } + + // --------------------------------------------------------------------------- + // Quality metrics (v2) + // --------------------------------------------------------------------------- + + if (benchResults.quality && Object.keys(benchResults.quality).length > 0) { + console.log(); + console.log('Quality Metrics (v2)'); + + const qHeader = [ + 'Scenario'.padEnd(24), + 'Entities'.padStart(9), + 'Structure'.padStart(10), + 'Coherence'.padStart(10), + 'Quality'.padStart(8), + ].join(' '); + const qSep = '-'.repeat(qHeader.length); + + console.log(qSep); + console.log(qHeader); + console.log(qSep); + + for (const [name, q] of Object.entries(benchResults.quality)) { + console.log( + [ + name.padEnd(24), + `${(q.entityRetention * 100).toFixed(0)}%`.padStart(9), + `${(q.structuralIntegrity * 100).toFixed(0)}%`.padStart(10), + `${(q.referenceCoherence * 100).toFixed(0)}%`.padStart(10), + q.qualityScore.toFixed(3).padStart(8), + ].join(' '), + ); + } + + console.log(qSep); + + // Quality regression check + const lowQuality = Object.entries(benchResults.quality).filter(([, q]) => q.qualityScore < 0.8); + if (lowQuality.length > 0) { + console.log(); + console.log( + `WARNING: ${lowQuality.length} scenario(s) below 0.80 quality: ${lowQuality.map(([n]) => n).join(', ')}`, + ); + } + } + // --------------------------------------------------------------------------- // tokenBudget scenarios // --------------------------------------------------------------------------- @@ -777,6 +1236,16 @@ async function run(): Promise { ((t1 - t0).toFixed(2) + 'ms').padStart(cols.time), ].join(' '), ); + + const tbKey = `${scenario.name}|dedup=${dedup}`; + benchResults.tokenBudget[tbKey] = { + tokenCount: cr.tokenCount ?? 0, + fits: cr.fits ?? false, + recencyWindow: cr.recencyWindow, + compressed: cr.compression.messages_compressed, + preserved: cr.compression.messages_preserved, + deduped: cr.compression.messages_deduped ?? 0, + }; } } @@ -840,6 +1309,14 @@ async function run(): Promise { rt2.padStart(cols.rt), ].join(' '), ); + + benchResults.dedup[scenario.name] = { + rw0Base: baseRw0.compression.ratio, + rw0Dup: dedupRw0.compression.ratio, + rw4Base: baseRw4.compression.ratio, + rw4Dup: dedupRw4.compression.ratio, + deduped, + }; } console.log(dedupSep); @@ -898,6 +1375,12 @@ async function run(): Promise { ((t1 - t0).toFixed(2) + 'ms').padStart(cols.time), ].join(' '), ); + + benchResults.fuzzyDedup[scenario.name] = { + exact: cr.compression.messages_deduped ?? 0, + fuzzy: cr.compression.messages_fuzzy_deduped ?? 0, + ratio: cr.compression.ratio, + }; } console.log(fuzzySep); @@ -907,20 +1390,330 @@ async function run(): Promise { process.exit(1); } + // --------------------------------------------------------------------------- + // ANCS-inspired features (importance scoring + contradiction detection) + // --------------------------------------------------------------------------- + + console.log(); + console.log('ANCS Features (importanceScoring + contradictionDetection)'); + + const ancsScenarios = buildAncsScenarios(); + + const ancsHeader = [ + 'Scenario'.padEnd(cols.name), + 'Msgs'.padStart(5), + 'Base R'.padStart(7), + '+Imp R'.padStart(7), + '+Con R'.padStart(7), + 'Both R'.padStart(7), + 'ImpP'.padStart(5), + 'Ctrd'.padStart(5), + 'R/T'.padStart(cols.rt), + 'Time'.padStart(cols.time), + ].join(' '); + const ancsSep = '-'.repeat(ancsHeader.length); + + console.log(ancsSep); + console.log(ancsHeader); + console.log(ancsSep); + + if (!benchResults.ancs) benchResults.ancs = {}; + let ancsFails = 0; + + for (const scenario of ancsScenarios) { + const t0 = performance.now(); + + // Baseline: small recency window to leave room for ANCS features to act + const baseline = compress(scenario.messages, { recencyWindow: 2 }); + + // Importance only + const withImportance = compress(scenario.messages, { + recencyWindow: 2, + importanceScoring: true, + importanceThreshold: 0.25, + }); + + // Contradiction only + const withContradiction = compress(scenario.messages, { + recencyWindow: 2, + contradictionDetection: true, + }); + + // Combined + const combined = compress(scenario.messages, { + recencyWindow: 2, + importanceScoring: true, + importanceThreshold: 0.25, + contradictionDetection: true, + }); + + const t1 = performance.now(); + + // Round-trip on combined (strictest test) + const er = uncompress(combined.messages, combined.verbatim); + const rt = + JSON.stringify(scenario.messages) === JSON.stringify(er.messages) && + er.missing_ids.length === 0 + ? 'PASS' + : 'FAIL'; + if (rt === 'FAIL') ancsFails++; + + // Report per-feature stats from their individual runs (not combined, + // where importance can shadow contradictions) + const impPreserved = withImportance.compression.messages_importance_preserved ?? 0; + const contradicted = withContradiction.compression.messages_contradicted ?? 0; + + console.log( + [ + scenario.name.padEnd(cols.name), + String(scenario.messages.length).padStart(5), + baseline.compression.ratio.toFixed(2).padStart(7), + withImportance.compression.ratio.toFixed(2).padStart(7), + withContradiction.compression.ratio.toFixed(2).padStart(7), + combined.compression.ratio.toFixed(2).padStart(7), + String(impPreserved).padStart(5), + String(contradicted).padStart(5), + rt.padStart(cols.rt), + ((t1 - t0).toFixed(2) + 'ms').padStart(cols.time), + ].join(' '), + ); + + benchResults.ancs[scenario.name] = { + baselineRatio: baseline.compression.ratio, + importanceRatio: withImportance.compression.ratio, + contradictionRatio: withContradiction.compression.ratio, + combinedRatio: combined.compression.ratio, + importancePreserved: impPreserved, + contradicted, + }; + } + + console.log(ancsSep); + + if (ancsFails > 0) { + console.error(`FAIL: ${ancsFails} ANCS scenario(s) failed round-trip`); + process.exit(1); + } + + // --------------------------------------------------------------------------- + // V2 Features Comparison (default vs each feature vs recommended combo) + // --------------------------------------------------------------------------- + + console.log(); + console.log('V2 Features Comparison'); + + type V2Config = { name: string; options: CompressOptions }; + const v2Configs: V2Config[] = [ + { name: 'Default (v1)', options: { recencyWindow: 0 } }, + { name: '+conversationFlow', options: { recencyWindow: 0, conversationFlow: true } }, + { name: '+semanticClustering', options: { recencyWindow: 0, semanticClustering: true } }, + { name: '+relevanceThresh=3', options: { recencyWindow: 0, relevanceThreshold: 3 } }, + { name: '+depth=moderate', options: { recencyWindow: 0, compressionDepth: 'moderate' } }, + { name: '+importanceScoring', options: { recencyWindow: 0, importanceScoring: true } }, + { name: '+coreference', options: { recencyWindow: 0, coreference: true } }, + { + name: 'Recommended combo', + options: { + recencyWindow: 0, + conversationFlow: true, + relevanceThreshold: 3, + compressionDepth: 'moderate', + }, + }, + ]; + + const v2Scenarios = buildScenarios(); + + // Compute all results + type V2Row = { + config: string; + scenario: string; + ratio: number; + quality: number | undefined; + rt: string; + }; + const v2Rows: V2Row[] = []; + let v2Fails = 0; + + for (const cfg of v2Configs) { + for (const scenario of v2Scenarios) { + const cr = compress(scenario.messages, cfg.options); + const er = uncompress(cr.messages, cr.verbatim); + const rt = + JSON.stringify(scenario.messages) === JSON.stringify(er.messages) && + er.missing_ids.length === 0 + ? 'PASS' + : 'FAIL'; + if (rt === 'FAIL') v2Fails++; + v2Rows.push({ + config: cfg.name, + scenario: scenario.name, + ratio: cr.compression.ratio, + quality: cr.compression.quality_score, + rt, + }); + } + } + + // Print matrix: rows = configs, columns = scenarios + const v2ScenarioNames = v2Scenarios.map((s) => s.name); + const scColW = 14; + const v2NameW = 22; + + const v2Header = [ + 'Config'.padEnd(v2NameW), + ...v2ScenarioNames.map((n) => n.slice(0, scColW).padStart(scColW)), + 'R/T'.padStart(5), + ].join(' '); + const v2Sep = '-'.repeat(v2Header.length); + + console.log(v2Sep); + console.log( + ''.padEnd(v2NameW) + + ' ' + + v2ScenarioNames.map((_n) => 'ratio / qual'.padStart(scColW)).join(' '), + ); + console.log(v2Header); + console.log(v2Sep); + + for (const cfg of v2Configs) { + const cfgRows = v2Rows.filter((r) => r.config === cfg.name); + const allPass = cfgRows.every((r) => r.rt === 'PASS'); + const cells = v2ScenarioNames.map((sn) => { + const row = cfgRows.find((r) => r.scenario === sn); + if (!row) return '—'.padStart(scColW); + const r = row.ratio.toFixed(1) + 'x'; + const q = row.quality != null ? (row.quality * 100).toFixed(0) + '%' : '—'; + return (r + '/' + q).padStart(scColW); + }); + console.log( + [cfg.name.padEnd(v2NameW), ...cells, (allPass ? 'PASS' : 'FAIL').padStart(5)].join(' '), + ); + } + + // Print delta row (recommended combo vs default) + const defaultRows = v2Rows.filter((r) => r.config === 'Default (v1)'); + const comboRows = v2Rows.filter((r) => r.config === 'Recommended combo'); + const deltaCells = v2ScenarioNames.map((sn) => { + const def = defaultRows.find((r) => r.scenario === sn); + const combo = comboRows.find((r) => r.scenario === sn); + if (!def || !combo) return '—'.padStart(scColW); + const pct = ((combo.ratio / def.ratio - 1) * 100).toFixed(0); + return ((pct.startsWith('-') ? '' : '+') + pct + '%').padStart(scColW); + }); + console.log(['Δ combo vs default'.padEnd(v2NameW), ...deltaCells, ''.padStart(5)].join(' ')); + + console.log(v2Sep); + + if (v2Fails > 0) { + console.error(`FAIL: ${v2Fails} V2 scenario(s) failed round-trip`); + process.exit(1); + } + + console.log(); + console.log('All V2 scenarios passed round-trip verification.'); + + // --------------------------------------------------------------------------- + // Bundle size + // --------------------------------------------------------------------------- + + console.log(); + console.log('Bundle Size'); + + execSync('npm run build', { stdio: 'pipe', cwd: resolve(import.meta.dirname, '..') }); + + const distDir = resolve(import.meta.dirname, '..', 'dist'); + const distFiles = readdirSync(distDir, { recursive: true }) + .map(String) + .filter((f) => f.endsWith('.js')) + .sort(); + + let totalBytes = 0; + let totalGzip = 0; + + const bsHeader = ['File'.padEnd(30), 'Size'.padStart(10), 'Gzip'.padStart(10)].join(' '); + const bsSep = '-'.repeat(bsHeader.length); + + console.log(bsSep); + console.log(bsHeader); + console.log(bsSep); + + for (const file of distFiles) { + const fullPath = join(distDir, file); + const bytes = statSync(fullPath).size; + const gzipBytes = gzipSync(readFileSync(fullPath)).length; + totalBytes += bytes; + totalGzip += gzipBytes; + + benchResults.bundleSize[file] = { bytes, gzipBytes }; + + const fmtBytes = bytes < 1024 ? `${bytes} B` : `${(bytes / 1024).toFixed(1)} KB`; + const fmtGzip = gzipBytes < 1024 ? `${gzipBytes} B` : `${(gzipBytes / 1024).toFixed(1)} KB`; + console.log([file.padEnd(30), fmtBytes.padStart(10), fmtGzip.padStart(10)].join(' ')); + } + + benchResults.bundleSize['total'] = { bytes: totalBytes, gzipBytes: totalGzip }; + + const fmtTotal = totalBytes < 1024 ? `${totalBytes} B` : `${(totalBytes / 1024).toFixed(1)} KB`; + const fmtTotalGz = totalGzip < 1024 ? `${totalGzip} B` : `${(totalGzip / 1024).toFixed(1)} KB`; + console.log(bsSep); + console.log(['total'.padEnd(30), fmtTotal.padStart(10), fmtTotalGz.padStart(10)].join(' ')); + console.log(bsSep); + + // --------------------------------------------------------------------------- + // --save / --check + // --------------------------------------------------------------------------- + + const baselinesDir = resolve(import.meta.dirname, 'baselines'); + const version = JSON.parse( + readFileSync(resolve(import.meta.dirname, '..', 'package.json'), 'utf-8'), + ).version; + + if (flagSave) { + saveBaseline(baselinesDir, version, benchResults); + generateBenchmarkDocs( + baselinesDir, + resolve(import.meta.dirname, '..', 'docs', 'benchmark-results.md'), + ); + console.log(); + console.log(`Baseline saved (v${version}) and docs/benchmark-results.md regenerated.`); + } + + if (flagCheck) { + const current = loadCurrentBaseline(baselinesDir); + if (!current) { + console.error( + 'No baseline found at bench/baselines/current.json — run `npm run bench:save` first.', + ); + process.exit(1); + } + const regressions = compareResults(current.results, benchResults, tolerance); + if (regressions.length > 0) { + console.error(); + console.error(formatRegressions(regressions)); + process.exit(1); + } + console.log(); + console.log(`Baseline check passed (v${current.version}, tolerance ${tolerance * 100}%).`); + } + // --------------------------------------------------------------------------- // Real Claude Code sessions (if available locally) // --------------------------------------------------------------------------- runRealSessions(); - await runLlmBenchmark(); + // LLM benchmarks require explicit --llm flag (they cost money and take minutes) + if (flagLlm) { + await runLlmBenchmark(); + } console.log(); console.log('All benchmarks passed.'); } // --------------------------------------------------------------------------- -// LLM summarization benchmark (opt-in via env vars) +// LLM summarization benchmark (requires --llm flag) // --------------------------------------------------------------------------- function roundTrip(messages: Message[], cr: CompressResult): 'PASS' | 'FAIL' { @@ -936,12 +1729,13 @@ async function runLlmBenchmark(): Promise { if (providers.length === 0) { console.log(); console.log( - 'LLM Summarization Benchmark — skipped (no OPENAI_API_KEY, OLLAMA_MODEL, or ANTHROPIC_API_KEY set)', + 'LLM Summarization Benchmark — no providers detected (set OPENAI_API_KEY or ANTHROPIC_API_KEY in .env, or start Ollama)', ); return; } const scenarios = buildScenarios().filter((s) => s.name !== 'Short conversation'); + const baselinesDir = resolve(import.meta.dirname, 'baselines'); for (const provider of providers) { console.log(); @@ -955,6 +1749,7 @@ async function runLlmBenchmark(): Promise { method: 14, chr: 6, tkr: 6, + vsDet: 6, comp: 5, pres: 5, rt: 5, @@ -966,6 +1761,7 @@ async function runLlmBenchmark(): Promise { 'Method'.padStart(cols.method), 'ChR'.padStart(cols.chr), 'TkR'.padStart(cols.tkr), + 'vsDet'.padStart(cols.vsDet), 'Comp'.padStart(cols.comp), 'Pres'.padStart(cols.pres), 'R/T'.padStart(cols.rt), @@ -978,42 +1774,178 @@ async function runLlmBenchmark(): Promise { console.log(sep); let llmFails = 0; + const llmResult: LlmBenchmarkResult = { + provider: provider.name, + model: provider.model, + generated: new Date().toISOString(), + scenarios: {}, + }; for (const scenario of scenarios) { - // Deterministic baseline - const t0d = performance.now(); - const detResult = compress(scenario.messages, { recencyWindow: 0 }); - const t1d = performance.now(); - const detRt = roundTrip(scenario.messages, detResult); - - printLlmRow(scenario.name, 'deterministic', detResult, detRt, t1d - t0d, cols); + try { + const scenarioResult: Record = {}; + + // Deterministic baseline + const t0d = performance.now(); + const detResult = compress(scenario.messages, { recencyWindow: 0 }); + const t1d = performance.now(); + const detRt = roundTrip(scenario.messages, detResult); + const detRatio = detResult.compression.ratio; + + printLlmRow(scenario.name, 'deterministic', detResult, detRt, t1d - t0d, undefined, cols); + scenarioResult['deterministic'] = { + ratio: detRatio, + tokenRatio: detResult.compression.token_ratio, + compressed: detResult.compression.messages_compressed, + preserved: detResult.compression.messages_preserved, + roundTrip: detRt, + timeMs: t1d - t0d, + }; + + // LLM basic summarizer + const t0b = performance.now(); + const llmBasicResult = await compress(scenario.messages, { + recencyWindow: 0, + summarizer: basicSummarizer, + }); + const t1b = performance.now(); + const basicRt = roundTrip(scenario.messages, llmBasicResult); + if (basicRt === 'FAIL') llmFails++; + const basicVsDet = llmBasicResult.compression.ratio / detRatio; + + printLlmRow('', 'llm-basic', llmBasicResult, basicRt, t1b - t0b, basicVsDet, cols); + scenarioResult['llm-basic'] = { + ratio: llmBasicResult.compression.ratio, + tokenRatio: llmBasicResult.compression.token_ratio, + compressed: llmBasicResult.compression.messages_compressed, + preserved: llmBasicResult.compression.messages_preserved, + roundTrip: basicRt, + timeMs: t1b - t0b, + vsDet: basicVsDet, + }; + + // LLM escalating summarizer + const t0e = performance.now(); + const llmEscResult = await compress(scenario.messages, { + recencyWindow: 0, + summarizer: escalatingSummarizer, + }); + const t1e = performance.now(); + const escRt = roundTrip(scenario.messages, llmEscResult); + if (escRt === 'FAIL') llmFails++; + const escVsDet = llmEscResult.compression.ratio / detRatio; + + printLlmRow('', 'llm-escalate', llmEscResult, escRt, t1e - t0e, escVsDet, cols); + scenarioResult['llm-escalate'] = { + ratio: llmEscResult.compression.ratio, + tokenRatio: llmEscResult.compression.token_ratio, + compressed: llmEscResult.compression.messages_compressed, + preserved: llmEscResult.compression.messages_preserved, + roundTrip: escRt, + timeMs: t1e - t0e, + vsDet: escVsDet, + }; + + console.log(sep); + llmResult.scenarios[scenario.name] = { methods: scenarioResult }; + } catch (err) { + console.error(` ${scenario.name}: ERROR — ${(err as Error).message}`); + console.log(sep); + } + } - // LLM basic summarizer - const t0b = performance.now(); - const llmBasicResult = await compress(scenario.messages, { - recencyWindow: 0, - summarizer: basicSummarizer, - }); - const t1b = performance.now(); - const basicRt = roundTrip(scenario.messages, llmBasicResult); - if (basicRt === 'FAIL') llmFails++; + // --- Token budget + LLM --- + const tokenBudget = 2000; + const budgetScenarios: Scenario[] = scenarios.filter( + (s) => s.name === 'Deep conversation' || s.name === 'Agentic coding session', + ); - printLlmRow('', 'llm-basic', llmBasicResult, basicRt, t1b - t0b, cols); + if (budgetScenarios.length > 0) { + console.log(); + console.log( + `LLM Token Budget — ${provider.name} (${provider.model}) — target: ${tokenBudget} tokens`, + ); - // LLM escalating summarizer - const t0e = performance.now(); - const llmEscResult = await compress(scenario.messages, { - recencyWindow: 0, - summarizer: escalatingSummarizer, - }); - const t1e = performance.now(); - const escRt = roundTrip(scenario.messages, llmEscResult); - if (escRt === 'FAIL') llmFails++; + const tbCols = { name: 24, method: 14, tokens: 7, fits: 5, rw: 4, chr: 6, rt: 5, time: 10 }; + const tbHeader = [ + 'Scenario'.padEnd(tbCols.name), + 'Method'.padStart(tbCols.method), + 'Tokens'.padStart(tbCols.tokens), + 'Fits'.padStart(tbCols.fits), + 'Rw'.padStart(tbCols.rw), + 'ChR'.padStart(tbCols.chr), + 'R/T'.padStart(tbCols.rt), + 'Time'.padStart(tbCols.time), + ].join(' '); + const tbSep = '-'.repeat(tbHeader.length); + + console.log(tbSep); + console.log(tbHeader); + console.log(tbSep); + + llmResult.tokenBudget = {}; + + for (const scenario of budgetScenarios) { + const entries: LlmTokenBudgetResult[] = []; + + try { + // Deterministic with token budget + const t0d = performance.now(); + const detCr = compress(scenario.messages, { tokenBudget }); + const t1d = performance.now(); + const detRt = roundTrip(scenario.messages, detCr); + + const detEntry: LlmTokenBudgetResult = { + budget: tokenBudget, + method: 'deterministic', + tokenCount: detCr.tokenCount ?? 0, + fits: detCr.fits ?? false, + ratio: detCr.compression.ratio, + recencyWindow: detCr.recencyWindow, + roundTrip: detRt, + timeMs: t1d - t0d, + }; + entries.push(detEntry); + printBudgetRow(scenario.name, detEntry, tbCols); + + // LLM escalating with token budget + const t0e = performance.now(); + const llmCr = await compress(scenario.messages, { + tokenBudget, + summarizer: escalatingSummarizer, + }); + const t1e = performance.now(); + const llmRt = roundTrip(scenario.messages, llmCr); + + const llmEntry: LlmTokenBudgetResult = { + budget: tokenBudget, + method: 'llm-escalate', + tokenCount: llmCr.tokenCount ?? 0, + fits: llmCr.fits ?? false, + ratio: llmCr.compression.ratio, + recencyWindow: llmCr.recencyWindow, + roundTrip: llmRt, + timeMs: t1e - t0e, + }; + entries.push(llmEntry); + printBudgetRow('', llmEntry, tbCols); + + console.log(tbSep); + } catch (err) { + console.error(` ${scenario.name}: ERROR — ${(err as Error).message}`); + console.log(tbSep); + } - printLlmRow('', 'llm-escalate', llmEscResult, escRt, t1e - t0e, cols); - console.log(sep); + if (entries.length > 0) { + llmResult.tokenBudget[scenario.name] = entries; + } + } } + // Always save LLM results (informational, not gated behind --save) + saveLlmResult(baselinesDir, llmResult); + console.log(` Results saved to bench/baselines/llm/`); + if (llmFails > 0) { console.error(` WARNING: ${llmFails} LLM scenario(s) failed round-trip`); } @@ -1026,11 +1958,13 @@ function printLlmRow( cr: CompressResult, rt: string, timeMs: number, + vsDet: number | undefined, cols: { name: number; method: number; chr: number; tkr: number; + vsDet: number; comp: number; pres: number; rt: number; @@ -1043,6 +1977,7 @@ function printLlmRow( method.padStart(cols.method), cr.compression.ratio.toFixed(2).padStart(cols.chr), cr.compression.token_ratio.toFixed(2).padStart(cols.tkr), + (vsDet != null ? vsDet.toFixed(2) : '-').padStart(cols.vsDet), String(cr.compression.messages_compressed).padStart(cols.comp), String(cr.compression.messages_preserved).padStart(cols.pres), rt.padStart(cols.rt), @@ -1053,6 +1988,37 @@ function printLlmRow( ); } +function printBudgetRow( + name: string, + entry: LlmTokenBudgetResult, + cols: { + name: number; + method: number; + tokens: number; + fits: number; + rw: number; + chr: number; + rt: number; + time: number; + }, +): void { + console.log( + [ + name.padEnd(cols.name), + entry.method.padStart(cols.method), + String(entry.tokenCount).padStart(cols.tokens), + String(entry.fits).padStart(cols.fits), + String(entry.recencyWindow ?? '-').padStart(cols.rw), + entry.ratio.toFixed(2).padStart(cols.chr), + entry.roundTrip.padStart(cols.rt), + (entry.timeMs < 1000 + ? entry.timeMs.toFixed(0) + 'ms' + : (entry.timeMs / 1000).toFixed(1) + 's' + ).padStart(cols.time), + ].join(' '), + ); +} + // --------------------------------------------------------------------------- // Real session support — convert Claude Code JSONL transcripts to Message[] // --------------------------------------------------------------------------- diff --git a/demo/build.mjs b/demo/build.mjs new file mode 100644 index 0000000..536cb5e --- /dev/null +++ b/demo/build.mjs @@ -0,0 +1,13 @@ +import { build } from 'esbuild'; + +await build({ + entryPoints: ['src/index.ts'], + bundle: true, + format: 'iife', + globalName: 'CCE', + outfile: 'demo/bundle.js', + target: 'es2020', + platform: 'browser', +}); + +console.log('Built demo/bundle.js'); diff --git a/demo/index.html b/demo/index.html new file mode 100644 index 0000000..27c0171 --- /dev/null +++ b/demo/index.html @@ -0,0 +1,1616 @@ + + + + + + Context Compression Engine — Demo + + + + + + +
+
+ +
+ deterministic + github ↗ +
+
+ +
+
+ + + 4 +
+ +
+ + + +
+ +
+ + +
+ +
+ + +
+ +
+ + +
+ + + +
+ + +
+ + +
+ +
+
+
Compression
+ +
+
recency
+
+ Number of most recent messages to keep untouched. Only older messages are candidates + for compression. recencyWindow +
+
+
+
budget
+
+ Target token count. When enabled, binary-searches the recency window to fit the output + within this limit. tokenBudget +
+
+
+
preserve
+
+ Roles that are never compressed, comma-separated. Typically system. These + messages pass through verbatim regardless of position. +
+
+
+
converge
+
+ Hard-truncate non-recent messages when the binary search bottoms out and the budget is + still exceeded. Last resort. forceConverge +
+
+ +
Deduplication
+ +
+
dedup
+
+ Replace exact duplicate messages with a compact reference to the first occurrence. + Compares full content via hash + equality check. +
+
+
+
fuzzy
+
+ Detect near-duplicate messages using line-level Jaccard similarity. Catches messages + that are mostly the same but not identical. fuzzyDedup +
+
+
+
threshold
+
+ Similarity cutoff for fuzzy dedup, 0–1. Higher = stricter matching. Default + 0.85 means 85% of lines must overlap to count as a near-duplicate. +
+
+ +
Output
+ +
+
preserved
+
+ Message was kept verbatim — either in the recency window, a preserved role, or + classified as code/structured data (T0). +
+
+
+
compressed
+
+ Prose was summarized by the deterministic scorer. + Red strikethrough = removed text, + green = replacement summary. +
+
+
+
removed
+
+ Message was dropped entirely, typically by dedup replacing a duplicate with a + back-reference. +
+
+
+
+ +
+
+
+ Input + + + role: message — blank lines separate + +
+ +
+ +
+
+ Output + +
+
+
+
+
↓
+
+ Write a conversation on the left,
then hit Compress +
+
+
+
+
+
+
+
+ + + + + diff --git a/docs/README.md b/docs/README.md index 658c442..20a23f7 100644 --- a/docs/README.md +++ b/docs/README.md @@ -2,14 +2,17 @@ [Back to README](../README.md) -| Page | Description | -| ----------------------------------------------- | --------------------------------------------------------------- | -| [API Reference](api-reference.md) | All exports, types, options, and result fields | -| [Compression Pipeline](compression-pipeline.md) | How compression works: classify, dedup, merge, summarize, guard | -| [Deduplication](deduplication.md) | Exact + fuzzy dedup algorithms, tuning thresholds | -| [Token Budget](token-budget.md) | Budget-driven compression, binary search, custom tokenizers | -| [LLM Integration](llm-integration.md) | Provider examples: Claude, OpenAI, Gemini, Grok, Ollama | -| [Round-trip](round-trip.md) | Lossless compress/uncompress, VerbatimMap, atomicity | -| [Provenance](provenance.md) | `_cce_original` metadata, summary_id, parent_ids | -| [Preservation Rules](preservation-rules.md) | What gets preserved, classification tiers, code-aware splitting | -| [Benchmarks](benchmarks.md) | Running benchmarks, LLM comparison, interpreting results | +| Page | Description | +| ----------------------------------------------- | ----------------------------------------------------------------- | +| [API Reference](api-reference.md) | All exports, types, options, and result fields | +| [Compression Pipeline](compression-pipeline.md) | How compression works: classify, dedup, merge, summarize, guard | +| [Deduplication](deduplication.md) | Exact + fuzzy dedup algorithms, tuning thresholds | +| [Token Budget](token-budget.md) | Budget-driven compression, binary search, custom tokenizers | +| [LLM Integration](llm-integration.md) | Provider examples: Claude, OpenAI, Gemini, Grok, Ollama | +| [Round-trip](round-trip.md) | Lossless compress/uncompress, VerbatimMap, atomicity | +| [Provenance](provenance.md) | `_cce_original` metadata, summary_id, parent_ids | +| [Preservation Rules](preservation-rules.md) | What gets preserved, classification tiers, code-aware splitting | +| [Benchmarks](benchmarks.md) | Running benchmarks, LLM comparison, interpreting results | +| [V2 Features](v2-features.md) | Quality metrics, flow detection, clustering, depth, ML classifier | +| [Benchmark Results](benchmark-results.md) | Auto-generated results with charts (regenerated by bench:save) | +| [Quality History](quality-history.md) | Version-over-version quality tracking and opt-in feature impact | diff --git a/docs/api-reference.md b/docs/api-reference.md index 7fd7843..f877c62 100644 --- a/docs/api-reference.md +++ b/docs/api-reference.md @@ -8,19 +8,78 @@ Complete reference for all exports from `context-compression-engine`. ```ts // Primary -export { compress, defaultTokenCounter } from './compress.js'; +export { compress, defaultTokenCounter, bestSentenceScore } from './compress.js'; export { uncompress } from './expand.js'; export type { StoreLookup } from './expand.js'; // Helpers (LLM integration) export { createSummarizer, createEscalatingSummarizer } from './summarizer.js'; +export { createClassifier, createEscalatingClassifier } from './classifier.js'; + +// Entity extraction & quality metrics +export { + extractEntities, + collectMessageEntities, + computeEntityRetention, + computeStructuralIntegrity, + computeReferenceCoherence, + computeQualityScore, +} from './entities.js'; + +// ML token classifier +export { + compressWithTokenClassifier, + compressWithTokenClassifierSync, + whitespaceTokenize, + createMockTokenClassifier, +} from './ml-classifier.js'; + +// Discourse decomposition (EDU-lite) +export { segmentEDUs, scoreEDUs, selectEDUs, summarizeWithEDUs } from './discourse.js'; +export type { EDU } from './discourse.js'; + +// Semantic clustering +export { clusterMessages, summarizeCluster } from './cluster.js'; +export type { MessageCluster } from './cluster.js'; + +// Cross-message coreference +export { + buildCoreferenceMap, + findOrphanedReferences, + generateInlineDefinitions, +} from './coreference.js'; +export type { EntityDefinition } from './coreference.js'; + +// Conversation flow detection +export { detectFlowChains, summarizeChain } from './flow.js'; +export type { FlowChain } from './flow.js'; + +// Entropy scoring utilities +export { splitSentences, normalizeScores, combineScores } from './entropy.js'; + +// Importance scoring +export { + computeImportance, + scoreContentSignals, + DEFAULT_IMPORTANCE_THRESHOLD, +} from './importance.js'; +export type { ImportanceMap } from './importance.js'; + +// Contradiction detection +export { analyzeContradictions } from './contradiction.js'; +export type { ContradictionAnnotation } from './contradiction.js'; // Types export type { + Classifier, + ClassifierResult, CompressOptions, CompressResult, + CreateClassifierOptions, CreateSummarizerOptions, Message, + MLTokenClassifier, + TokenClassification, Summarizer, UncompressOptions, UncompressResult, @@ -32,7 +91,7 @@ export type { ## `compress` -Deterministic compression by default. Returns a `Promise` when a `summarizer` is provided. +Deterministic compression by default. Returns a `Promise` when a `summarizer` or `classifier` is provided. ### Signatures @@ -42,6 +101,10 @@ function compress( messages: Message[], options: CompressOptions & { summarizer: Summarizer }, ): Promise; +function compress( + messages: Message[], + options: CompressOptions & { classifier: Classifier }, +): Promise; ``` ### Parameters @@ -53,37 +116,65 @@ function compress( ### CompressOptions -| Option | Type | Default | Description | -| ------------------ | -------------------------- | --------------------- | ------------------------------------------------------------------------------------------------------------------ | -| `preserve` | `string[]` | `['system']` | Roles to never compress | -| `recencyWindow` | `number` | `4` | Protect the last N messages from compression | -| `sourceVersion` | `number` | `0` | Version tag for [provenance tracking](provenance.md) | -| `summarizer` | `Summarizer` | - | LLM-powered summarizer. When provided, `compress()` returns a `Promise`. See [LLM integration](llm-integration.md) | -| `tokenBudget` | `number` | - | Target token count. Binary-searches `recencyWindow` to fit. See [Token budget](token-budget.md) | -| `minRecencyWindow` | `number` | `0` | Floor for `recencyWindow` when using `tokenBudget` | -| `dedup` | `boolean` | `true` | Replace earlier exact-duplicate messages with a compact reference. See [Deduplication](deduplication.md) | -| `fuzzyDedup` | `boolean` | `false` | Detect near-duplicate messages using line-level similarity. See [Deduplication](deduplication.md) | -| `fuzzyThreshold` | `number` | `0.85` | Similarity threshold for fuzzy dedup (0-1) | -| `embedSummaryId` | `boolean` | `false` | Embed `summary_id` in compressed content for downstream reference. See [Provenance](provenance.md) | -| `forceConverge` | `boolean` | `false` | Hard-truncate non-recency messages when binary search bottoms out. See [Token budget](token-budget.md) | -| `tokenCounter` | `(msg: Message) => number` | `defaultTokenCounter` | Custom token counter per message. See [Token budget](token-budget.md) | +| Option | Type | Default | Description | +| ----------------------------- | -------------------------------------------------- | --------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| `preserve` | `string[]` | `['system']` | Roles to never compress | +| `recencyWindow` | `number` | `4` | Protect the last N messages from compression | +| `sourceVersion` | `number` | `0` | Version tag for [provenance tracking](provenance.md) | +| `summarizer` | `Summarizer` | - | LLM-powered summarizer. When provided, `compress()` returns a `Promise`. See [LLM integration](llm-integration.md) | +| `tokenBudget` | `number` | - | Target token count. Binary-searches `recencyWindow` to fit. See [Token budget](token-budget.md) | +| `minRecencyWindow` | `number` | `0` | Floor for `recencyWindow` when using `tokenBudget` | +| `dedup` | `boolean` | `true` | Replace earlier exact-duplicate messages with a compact reference. See [Deduplication](deduplication.md) | +| `fuzzyDedup` | `boolean` | `false` | Detect near-duplicate messages using line-level similarity. See [Deduplication](deduplication.md) | +| `fuzzyThreshold` | `number` | `0.85` | Similarity threshold for fuzzy dedup (0-1) | +| `embedSummaryId` | `boolean` | `false` | Embed `summary_id` in compressed content for downstream reference. See [Provenance](provenance.md) | +| `forceConverge` | `boolean` | `false` | Hard-truncate non-recency messages when binary search bottoms out. See [Token budget](token-budget.md) | +| `preservePatterns` | `Array<{ re: RegExp; label: string }>` | - | Custom regex patterns that force hard T0 preservation. See [Preservation rules](preservation-rules.md) | +| `classifier` | `Classifier` | - | LLM-powered classifier. When provided, `compress()` returns a `Promise`. See [LLM integration](llm-integration.md) | +| `classifierMode` | `'hybrid' \| 'full'` | `'hybrid'` | Classification mode. `'hybrid'`: heuristics first, LLM for prose. `'full'`: LLM for all eligible. Ignored without `classifier` | +| `tokenCounter` | `(msg: Message) => number` | `defaultTokenCounter` | Custom token counter per message. See [Token budget](token-budget.md) | +| `importanceScoring` | `boolean` | `false` | Score messages by forward-reference density, decision/correction content, and recency. High-importance messages are preserved outside the recency window. `forceConverge` truncates low-importance first. **Note:** preserving extra messages reduces compression ratio, which may make `tokenBudget` harder to meet | +| `importanceThreshold` | `number` | `0.65` | Importance score threshold for preservation (0–1). Only used when `importanceScoring: true` | +| `contradictionDetection` | `boolean` | `false` | Detect later messages that correct/override earlier ones. Superseded messages are compressed with a provenance annotation | +| `contradictionTopicThreshold` | `number` | `0.15` | IDF-weighted Dice similarity threshold for topic overlap in contradiction detection (0–1) | +| `relevanceThreshold` | `number` | - | Sentence score threshold. Messages whose best sentence score falls below this are replaced with a stub. See [V2 features](v2-features.md#relevance-threshold) | +| `budgetStrategy` | `'binary-search' \| 'tiered'` | `'binary-search'` | Budget strategy when `tokenBudget` is set. `'tiered'` keeps recency window fixed and progressively compresses older content. See [V2 features](v2-features.md#tiered-budget-strategy) | +| `entropyScorer` | `(sentences: string[]) => number[]` | - | External self-information scorer. Can be sync or async. See [V2 features](v2-features.md#entropy-scorer) | +| `entropyScorerMode` | `'replace' \| 'augment'` | `'augment'` | How to combine entropy and heuristic scores. `'augment'` = weighted average, `'replace'` = entropy only | +| `conversationFlow` | `boolean` | `false` | Group Q&A, request→action, correction, and acknowledgment chains into compression units. See [V2 features](v2-features.md#conversation-flow) | +| `discourseAware` | `boolean` | `false` | **Experimental.** EDU decomposition with dependency-aware selection. Reduces ratio 8–28% without a custom ML scorer — use `segmentEDUs`/`scoreEDUs`/`selectEDUs` directly instead. See [V2 features](v2-features.md#discourse-aware-summarization) | +| `coreference` | `boolean` | `false` | Inline entity definitions into compressed summaries when references would be orphaned. See [V2 features](v2-features.md#cross-message-coreference) | +| `semanticClustering` | `boolean` | `false` | Group messages by topic using TF-IDF + entity overlap, compress as units. See [V2 features](v2-features.md#semantic-clustering) | +| `clusterThreshold` | `number` | `0.15` | Similarity threshold for semantic clustering (0–1). Lower = larger clusters | +| `compressionDepth` | `'gentle' \| 'moderate' \| 'aggressive' \| 'auto'` | `'gentle'` | Controls summarization aggressiveness. `'auto'` tries each level until `tokenBudget` fits. See [V2 features](v2-features.md#compression-depth) | +| `mlTokenClassifier` | `MLTokenClassifier` | - | Per-token keep/remove classifier. T0 rules still override for code/structured content. See [V2 features](v2-features.md#ml-token-classifier) | ### CompressResult -| Field | Type | Description | -| ------------------------------------ | ---------------------- | ----------------------------------------------------------------------------------- | -| `messages` | `Message[]` | Compressed message array | -| `verbatim` | `VerbatimMap` | Original messages keyed by ID. Must be persisted atomically with `messages` | -| `compression.original_version` | `number` | Mirrors `sourceVersion` | -| `compression.ratio` | `number` | Character-based compression ratio. >1 means savings | -| `compression.token_ratio` | `number` | Token-based compression ratio. >1 means savings | -| `compression.messages_compressed` | `number` | Messages that were compressed | -| `compression.messages_preserved` | `number` | Messages kept as-is | -| `compression.messages_deduped` | `number \| undefined` | Exact duplicates replaced (when `dedup: true`) | -| `compression.messages_fuzzy_deduped` | `number \| undefined` | Near-duplicates replaced (when `fuzzyDedup: true`) | -| `fits` | `boolean \| undefined` | Whether result fits within `tokenBudget`. Present when `tokenBudget` is set | -| `tokenCount` | `number \| undefined` | Estimated token count. Present when `tokenBudget` is set | -| `recencyWindow` | `number \| undefined` | The `recencyWindow` the binary search settled on. Present when `tokenBudget` is set | +| Field | Type | Description | +| ------------------------------------------- | ---------------------- | ----------------------------------------------------------------------------------- | +| `messages` | `Message[]` | Compressed message array | +| `verbatim` | `VerbatimMap` | Original messages keyed by ID. Must be persisted atomically with `messages` | +| `compression.original_version` | `number` | Mirrors `sourceVersion` | +| `compression.ratio` | `number` | Character-based compression ratio. >1 means savings | +| `compression.token_ratio` | `number` | Token-based compression ratio. >1 means savings | +| `compression.messages_compressed` | `number` | Messages that were compressed | +| `compression.messages_preserved` | `number` | Messages kept as-is | +| `compression.messages_deduped` | `number \| undefined` | Exact duplicates replaced (when `dedup: true`) | +| `compression.messages_fuzzy_deduped` | `number \| undefined` | Near-duplicates replaced (when `fuzzyDedup: true`) | +| `compression.messages_pattern_preserved` | `number \| undefined` | Messages preserved by `preservePatterns` (when patterns are provided) | +| `compression.messages_llm_classified` | `number \| undefined` | Messages classified by LLM (when `classifier` is provided) | +| `compression.messages_llm_preserved` | `number \| undefined` | Messages where LLM decided to preserve (when `classifier` is provided) | +| `compression.messages_contradicted` | `number \| undefined` | Messages superseded by a later correction (when `contradictionDetection: true`) | +| `compression.messages_importance_preserved` | `number \| undefined` | Messages preserved due to high importance score (when `importanceScoring: true`) | +| `compression.messages_relevance_dropped` | `number \| undefined` | Messages replaced with stubs (when `relevanceThreshold` is set) | +| `compression.entity_retention` | `number \| undefined` | Fraction of technical identifiers preserved (0–1). Present when compression occurs | +| `compression.structural_integrity` | `number \| undefined` | Fraction of structural elements preserved (0–1). Present when compression occurs | +| `compression.reference_coherence` | `number \| undefined` | Fraction of entity references with surviving sources (0–1) | +| `compression.quality_score` | `number \| undefined` | Composite quality: `0.4×entity + 0.4×structural + 0.2×coherence` | +| `fits` | `boolean \| undefined` | Whether result fits within `tokenBudget`. Present when `tokenBudget` is set | +| `tokenCount` | `number \| undefined` | Estimated token count. Present when `tokenBudget` is set | +| `recencyWindow` | `number \| undefined` | The `recencyWindow` the binary search settled on. Present when `tokenBudget` is set | ### Example @@ -174,7 +265,7 @@ function defaultTokenCounter(msg: Message): number; Math.ceil(msg.content.length / 3.5); ``` -Approximates ~3.5 characters per token. Suitable for rough estimates. For accurate budgeting, replace with a real tokenizer. See [Token budget](token-budget.md). +The 3.5 chars/token ratio is the empirical average for GPT-family BPE tokenizers (cl100k_base, o200k_base) on mixed English text. The lower end of the range (~3.2–4.5) is chosen intentionally so budget estimates stay conservative — over-counting tokens is safer than under-counting. For accurate budgeting, replace with a real tokenizer. See [Token budget](token-budget.md). --- @@ -251,6 +342,68 @@ Same as `CreateSummarizerOptions` but without `mode` (managed internally). --- +## `createClassifier` + +Creates an LLM-powered classifier that decides whether messages should be preserved or compressed. See [LLM integration](llm-integration.md) for domain examples. + +### Signature + +```ts +function createClassifier( + callLlm: (prompt: string) => string | Promise, + options?: CreateClassifierOptions, +): Classifier; +``` + +### CreateClassifierOptions + +| Option | Type | Default | Description | +| ------------------- | ---------- | ------- | ------------------------------------------------------------------- | +| `maxResponseTokens` | `number` | `100` | Hint for maximum tokens in the LLM response | +| `systemPrompt` | `string` | - | Domain-specific instructions prepended to the classification prompt | +| `alwaysPreserve` | `string[]` | - | Content types to always preserve, injected as bullet points | +| `alwaysCompress` | `string[]` | - | Content types always safe to compress, injected as bullet points | + +### Example + +```ts +import { createClassifier, compress } from 'context-compression-engine'; + +const classifier = createClassifier(async (prompt) => myLlm.complete(prompt), { + systemPrompt: 'You are classifying content from legal documents.', + alwaysPreserve: ['clause references', 'defined terms', 'party names'], + alwaysCompress: ['boilerplate acknowledgments', 'scheduling correspondence'], +}); + +const result = await compress(messages, { classifier }); +``` + +--- + +## `createEscalatingClassifier` + +Two-level escalation classifier. Tries LLM first, falls back to heuristic `classifyMessage()` on failure. + +### Signature + +```ts +function createEscalatingClassifier( + callLlm: (prompt: string) => string | Promise, + options?: CreateClassifierOptions, +): Classifier; +``` + +### Escalation levels + +1. **Level 1: LLM** - send content to LLM, parse structured JSON response +2. **Level 2: Heuristic** - if LLM throws, returns unparseable output, or confidence=0, fall back to `classifyMessage()`. Hard T0 heuristic results map to `preserve`, everything else to `compress`. + +### Options + +Same as `CreateClassifierOptions`. + +--- + ## Types ### `Message` @@ -279,6 +432,40 @@ type Summarizer = (text: string) => string | Promise; type VerbatimMap = Record; ``` +### `Classifier` + +```ts +type Classifier = (content: string) => ClassifierResult | Promise; +``` + +### `ClassifierResult` + +```ts +type ClassifierResult = { + decision: 'preserve' | 'compress'; + confidence: number; + reason: string; +}; +``` + +### `MLTokenClassifier` + +```ts +type MLTokenClassifier = ( + content: string, +) => TokenClassification[] | Promise; +``` + +### `TokenClassification` + +```ts +type TokenClassification = { + token: string; + keep: boolean; + confidence: number; +}; +``` + ### `StoreLookup` ```ts @@ -289,6 +476,7 @@ type StoreLookup = VerbatimMap | ((id: string) => Message | undefined); ## See also +- [V2 features](v2-features.md) - quality metrics, flow detection, clustering, depth, ML classifier - [Compression pipeline](compression-pipeline.md) - how the engine processes messages - [Token budget](token-budget.md) - budget-driven compression - [LLM integration](llm-integration.md) - provider examples diff --git a/docs/benchmark-results.md b/docs/benchmark-results.md new file mode 100644 index 0000000..2749acd --- /dev/null +++ b/docs/benchmark-results.md @@ -0,0 +1,380 @@ +# Benchmark Results + +[Back to README](../README.md) | [All docs](README.md) | [Handbook](benchmarks.md) + +_Auto-generated by `npm run bench:save`. Do not edit manually._ + +**v1.3.0** · Generated: 2026-03-21 + +![avg ratio](https://img.shields.io/badge/avg%20ratio-2.01x-blue) ![best](https://img.shields.io/badge/best-4.90x-blue) ![scenarios](https://img.shields.io/badge/scenarios-8-blue) ![round-trip](https://img.shields.io/badge/round--trip-all_PASS-brightgreen) ![gzip](https://img.shields.io/badge/gzip-49.3%20KB-blue) + +## Summary + +| Metric | Value | +| ------------------------ | -------- | +| Scenarios | 8 | +| Average compression | 2.01x | +| Best compression | 4.90x | +| Round-trip integrity | all PASS | +| Average quality score | 0.985 | +| Average entity retention | 96% | + +```mermaid +pie title "Message Outcomes" + "Preserved" : 90 + "Compressed" : 65 +``` + +## Compression by Scenario + +> **8 scenarios** · **2.01x** avg ratio · **1.00x** – **4.90x** range · all round-trips PASS + +```mermaid +xychart-beta + title "Compression Ratio by Scenario" + x-axis ["Coding", "Long Q&A", "Tool-heavy", "Short", "Deep", "Technical", "Structured", "Agentic"] + y-axis "Char Ratio" + bar [1.94, 4.90, 1.40, 1.00, 2.50, 1.00, 1.86, 1.48] +``` + +| Scenario | Ratio | Reduction | Token Ratio | Messages | Compressed | Preserved | +| ---------------------- | ----: | --------: | ----------: | -------: | ---------: | --------: | +| Coding assistant | 1.94 | 48% | 1.93 | 13 | 5 | 8 | +| Long Q&A | 4.90 | 80% | 4.88 | 10 | 4 | 6 | +| Tool-heavy | 1.40 | 29% | 1.39 | 18 | 2 | 16 | +| Short conversation | 1.00 | 0% | 1.00 | 7 | 0 | 7 | +| Deep conversation | 2.50 | 60% | 2.49 | 51 | 50 | 1 | +| Technical explanation | 1.00 | 0% | 1.00 | 11 | 0 | 11 | +| Structured content | 1.86 | 46% | 1.85 | 12 | 2 | 10 | +| Agentic coding session | 1.48 | 32% | 1.47 | 33 | 2 | 31 | + +## Deduplication Impact + +```mermaid +xychart-beta + title "Deduplication Impact (recencyWindow=0)" + x-axis ["Long Q&A", "Agentic"] + y-axis "Char Ratio" + bar [4.00, 1.20] + bar [4.90, 1.48] +``` + +_First bar: no dedup · Second bar: with dedup_ + +| Scenario | No Dedup (rw=0) | Dedup (rw=0) | No Dedup (rw=4) | Dedup (rw=4) | Deduped | +| ---------------------- | --------------: | -----------: | --------------: | -----------: | ------: | +| Coding assistant | 1.94 | 1.94 | 1.61 | 1.61 | 0 | +| Long Q&A | 4.00 | 4.90 | 1.76 | 1.92 | 1 | +| Tool-heavy | 1.40 | 1.40 | 1.40 | 1.40 | 0 | +| Short conversation | 1.00 | 1.00 | 1.00 | 1.00 | 0 | +| Deep conversation | 2.50 | 2.50 | 2.24 | 2.24 | 0 | +| Technical explanation | 1.00 | 1.00 | 1.00 | 1.00 | 0 | +| Structured content | 1.86 | 1.86 | 1.33 | 1.33 | 0 | +| Agentic coding session | 1.20 | 1.48 | 1.20 | 1.48 | 4 | + +### Fuzzy Dedup + +| Scenario | Exact Deduped | Fuzzy Deduped | Ratio | vs Base | +| ---------------------- | ------------: | ------------: | ----: | ------: | +| Coding assistant | 0 | 0 | 1.94 | - | +| Long Q&A | 1 | 0 | 4.90 | - | +| Tool-heavy | 0 | 0 | 1.40 | - | +| Short conversation | 0 | 0 | 1.00 | - | +| Deep conversation | 0 | 0 | 2.50 | - | +| Technical explanation | 0 | 0 | 1.00 | - | +| Structured content | 0 | 0 | 1.86 | - | +| Agentic coding session | 4 | 2 | 2.35 | +59% | + +## ANCS-Inspired Features + +> Importance scoring preserves high-value messages outside the recency window. Contradiction detection compresses superseded messages. + +| Scenario | Baseline | +Importance | +Contradiction | Combined | Imp. Preserved | Contradicted | +| ---------------------- | -------: | ----------: | -------------: | -------: | -------------: | -----------: | +| Deep conversation | 2.37 | 2.37 | 2.37 | 2.37 | 0 | 0 | +| Agentic coding session | 1.47 | 1.24 | 1.47 | 1.24 | 4 | 0 | +| Iterative design | 1.62 | 1.26 | 1.62 | 1.26 | 6 | 2 | + +## Quality Metrics + +| Scenario | Entity Retention | Structural Integrity | Reference Coherence | Quality Score | +| ---------------------- | ---------------- | -------------------- | ------------------- | ------------- | +| Coding assistant | 100% | 100% | 100% | 1.000 | +| Long Q&A | 100% | 100% | 100% | 1.000 | +| Tool-heavy | 93% | 100% | 100% | 0.972 | +| Deep conversation | 100% | 100% | 100% | 1.000 | +| Structured content | 100% | 100% | 100% | 1.000 | +| Agentic coding session | 85% | 100% | 100% | 0.939 | + +## Token Budget + +Target: **2000 tokens** · 1/4 fit + +| Scenario | Dedup | Tokens | Fits | recencyWindow | Compressed | Preserved | Deduped | +| ---------------------- | ----- | -----: | ---- | ------------: | ---------: | --------: | ------: | +| Deep conversation | no | 3188 | no | 0 | 50 | 1 | 0 | +| Deep conversation | yes | 3188 | no | 0 | 50 | 1 | 0 | +| Agentic coding session | no | 2223 | no | 0 | 4 | 33 | 0 | +| Agentic coding session | yes | 1900 | yes | 9 | 1 | 32 | 4 | + +## Bundle Size + +> Zero-dependency ESM library — tracked per-file to catch regressions. + +| File | Size | Gzip | +| ---------------- | -------: | ------: | +| adapters.js | 4.1 KB | 1.3 KB | +| classifier.js | 4.5 KB | 1.6 KB | +| classify.js | 10.7 KB | 4.3 KB | +| cluster.js | 7.4 KB | 2.4 KB | +| compress.js | 84.1 KB | 16.3 KB | +| contradiction.js | 7.5 KB | 2.7 KB | +| coreference.js | 4.2 KB | 1.5 KB | +| dedup.js | 10.0 KB | 2.8 KB | +| discourse.js | 6.6 KB | 2.4 KB | +| entities.js | 8.2 KB | 2.6 KB | +| entropy.js | 1.9 KB | 832 B | +| expand.js | 2.7 KB | 934 B | +| feedback.js | 11.6 KB | 2.9 KB | +| flow.js | 7.8 KB | 2.0 KB | +| importance.js | 4.6 KB | 1.8 KB | +| index.js | 1.8 KB | 761 B | +| ml-classifier.js | 3.0 KB | 1.2 KB | +| summarizer.js | 2.5 KB | 993 B | +| types.js | 11 B | 31 B | +| **total** | 183.5 KB | 49.3 KB | + +## LLM vs Deterministic + +> Results are **non-deterministic** — LLM outputs vary between runs. Saved as reference data, not used for regression testing. + +``` +Deterministic vs ollama/llama3.2 + +Coding assistant Det ████████████░░░░░░░░░░░░░░░░░░ 1.94x + LLM █████████░░░░░░░░░░░░░░░░░░░░░ 1.55x + +Long Q&A Det ██████████████████████████████ 4.90x + LLM ███████████████████████████░░░ 4.49x + +Tool-heavy Det █████████░░░░░░░░░░░░░░░░░░░░░ 1.40x + LLM ████████░░░░░░░░░░░░░░░░░░░░░░ 1.28x + +Deep conversation Det ███████████████░░░░░░░░░░░░░░░ 2.50x + LLM ████████████████████░░░░░░░░░░ 3.28x ★ + +Technical explanation Det ██████░░░░░░░░░░░░░░░░░░░░░░░░ 1.00x + LLM ██████░░░░░░░░░░░░░░░░░░░░░░░░ 1.00x + +Structured content Det ███████████░░░░░░░░░░░░░░░░░░░ 1.86x + LLM █████████░░░░░░░░░░░░░░░░░░░░░ 1.46x + +Agentic coding session Det █████████░░░░░░░░░░░░░░░░░░░░░ 1.48x + LLM █████████░░░░░░░░░░░░░░░░░░░░░ 1.40x + +★ = LLM wins +``` + +``` +Deterministic vs openai/gpt-4.1-mini + +Coding assistant Det ███████████░░░░░░░░░░░░░░░░░░░ 1.94x + LLM █████████░░░░░░░░░░░░░░░░░░░░░ 1.64x + +Long Q&A Det ███████████████████████████░░░ 4.90x + LLM ██████████████████████████████ 5.37x ★ + +Tool-heavy Det ████████░░░░░░░░░░░░░░░░░░░░░░ 1.40x + LLM ██████░░░░░░░░░░░░░░░░░░░░░░░░ 1.12x + +Deep conversation Det ██████████████░░░░░░░░░░░░░░░░ 2.50x + LLM █████████████░░░░░░░░░░░░░░░░░ 2.37x + +Technical explanation Det ██████░░░░░░░░░░░░░░░░░░░░░░░░ 1.00x + LLM ██████░░░░░░░░░░░░░░░░░░░░░░░░ 1.00x + +Structured content Det ██████████░░░░░░░░░░░░░░░░░░░░ 1.86x + LLM ███████░░░░░░░░░░░░░░░░░░░░░░░ 1.29x + +Agentic coding session Det ████████░░░░░░░░░░░░░░░░░░░░░░ 1.48x + LLM ████████░░░░░░░░░░░░░░░░░░░░░░ 1.43x + +★ = LLM wins +``` + +### Provider Summary + +| Provider | Model | Avg Ratio | Avg vsDet | Round-trip | Budget Fits | Avg Time | +| -------- | ------------ | --------: | --------: | ---------- | ----------- | -------: | +| ollama | llama3.2 | 2.09x | 0.96 | all PASS | 1/4 | 4.2s | +| openai | gpt-4.1-mini | 2.09x | 0.92 | all PASS | 2/4 | 8.1s | + +> **Key findings:** +> LLM wins on prose-heavy scenarios: Deep conversation, Technical explanation +> Deterministic wins on structured/technical content: Coding assistant, Long Q&A, Tool-heavy, Structured content + +### ollama (llama3.2) + +_Generated: 2026-02-25_ + +
+Scenario details + +| Scenario | Method | Char Ratio | Token Ratio | vsDet | Compressed | Preserved | Round-trip | Time | +| ---------------------- | ------------- | ---------: | ----------: | ----: | ---------: | --------: | ---------- | ----: | +| Coding assistant | deterministic | 1.68 | 1.67 | - | 5 | 8 | PASS | 0ms | +| | llm-basic | 1.48 | 1.48 | 0.88 | 5 | 8 | PASS | 5.9s | +| | llm-escalate | 1.55 | 1.55 | 0.92 | 5 | 8 | PASS | 3.0s | +| Long Q&A | deterministic | 6.16 | 6.11 | - | 4 | 6 | PASS | 1ms | +| | llm-basic | 4.31 | 4.28 | 0.70 | 4 | 6 | PASS | 4.1s | +| | llm-escalate | 4.49 | 4.46 | 0.73 | 4 | 6 | PASS | 3.7s | +| Tool-heavy | deterministic | 1.30 | 1.29 | - | 2 | 16 | PASS | 2ms | +| | llm-basic | 1.12 | 1.11 | 0.86 | 2 | 16 | PASS | 2.3s | +| | llm-escalate | 1.28 | 1.28 | 0.99 | 2 | 16 | PASS | 2.8s | +| Deep conversation | deterministic | 2.12 | 2.12 | - | 50 | 1 | PASS | 3ms | +| | llm-basic | 3.12 | 3.11 | 1.47 | 50 | 1 | PASS | 22.7s | +| | llm-escalate | 3.28 | 3.26 | 1.54 | 50 | 1 | PASS | 23.3s | +| Technical explanation | deterministic | 1.00 | 1.00 | - | 0 | 11 | PASS | 1ms | +| | llm-basic | 1.00 | 1.00 | 1.00 | 0 | 11 | PASS | 3.2s | +| | llm-escalate | 1.00 | 1.00 | 1.00 | 2 | 9 | PASS | 785ms | +| Structured content | deterministic | 1.93 | 1.92 | - | 2 | 10 | PASS | 0ms | +| | llm-basic | 1.46 | 1.45 | 0.75 | 2 | 10 | PASS | 3.5s | +| | llm-escalate | 1.38 | 1.38 | 0.71 | 2 | 10 | PASS | 3.7s | +| Agentic coding session | deterministic | 1.43 | 1.43 | - | 2 | 31 | PASS | 1ms | +| | llm-basic | 1.35 | 1.34 | 0.94 | 2 | 31 | PASS | 3.3s | +| | llm-escalate | 1.40 | 1.40 | 0.98 | 2 | 31 | PASS | 5.4s | + +#### Token Budget (target: 2000 tokens) + +| Scenario | Method | Tokens | Fits | recencyWindow | Ratio | Round-trip | Time | +| ---------------------- | ------------- | -----: | ----- | ------------: | ----: | ---------- | -----: | +| Deep conversation | deterministic | 3738 | false | 0 | 2.12 | PASS | 12ms | +| | llm-escalate | 2593 | false | 0 | 3.08 | PASS | 132.0s | +| Agentic coding session | deterministic | 1957 | true | 9 | 1.36 | PASS | 2ms | +| | llm-escalate | 2003 | false | 9 | 1.33 | PASS | 4.1s | + +
+ +### openai (gpt-4.1-mini) + +_Generated: 2026-02-25_ + +
+Scenario details + +| Scenario | Method | Char Ratio | Token Ratio | vsDet | Compressed | Preserved | Round-trip | Time | +| ---------------------- | ------------- | ---------: | ----------: | ----: | ---------: | --------: | ---------- | ----: | +| Coding assistant | deterministic | 1.68 | 1.67 | - | 5 | 8 | PASS | 0ms | +| | llm-basic | 1.64 | 1.63 | 0.98 | 5 | 8 | PASS | 5.6s | +| | llm-escalate | 1.63 | 1.63 | 0.97 | 5 | 8 | PASS | 6.0s | +| Long Q&A | deterministic | 6.16 | 6.11 | - | 4 | 6 | PASS | 1ms | +| | llm-basic | 5.37 | 5.33 | 0.87 | 4 | 6 | PASS | 5.9s | +| | llm-escalate | 5.35 | 5.31 | 0.87 | 4 | 6 | PASS | 7.0s | +| Tool-heavy | deterministic | 1.30 | 1.29 | - | 2 | 16 | PASS | 0ms | +| | llm-basic | 1.11 | 1.10 | 0.85 | 2 | 16 | PASS | 3.5s | +| | llm-escalate | 1.12 | 1.12 | 0.86 | 2 | 16 | PASS | 5.3s | +| Deep conversation | deterministic | 2.12 | 2.12 | - | 50 | 1 | PASS | 3ms | +| | llm-basic | 2.34 | 2.33 | 1.10 | 50 | 1 | PASS | 50.4s | +| | llm-escalate | 2.37 | 2.36 | 1.11 | 50 | 1 | PASS | 50.8s | +| Technical explanation | deterministic | 1.00 | 1.00 | - | 0 | 11 | PASS | 1ms | +| | llm-basic | 1.00 | 1.00 | 1.00 | 1 | 10 | PASS | 2.6s | +| | llm-escalate | 1.00 | 1.00 | 1.00 | 1 | 10 | PASS | 3.3s | +| Structured content | deterministic | 1.93 | 1.92 | - | 2 | 10 | PASS | 0ms | +| | llm-basic | 1.23 | 1.23 | 0.64 | 2 | 10 | PASS | 10.2s | +| | llm-escalate | 1.29 | 1.29 | 0.67 | 2 | 10 | PASS | 4.8s | +| Agentic coding session | deterministic | 1.43 | 1.43 | - | 2 | 31 | PASS | 1ms | +| | llm-basic | 1.43 | 1.43 | 1.00 | 2 | 31 | PASS | 5.8s | +| | llm-escalate | 1.32 | 1.32 | 0.93 | 1 | 32 | PASS | 9.5s | + +#### Token Budget (target: 2000 tokens) + +| Scenario | Method | Tokens | Fits | recencyWindow | Ratio | Round-trip | Time | +| ---------------------- | ------------- | -----: | ----- | ------------: | ----: | ---------- | -----: | +| Deep conversation | deterministic | 3738 | false | 0 | 2.12 | PASS | 10ms | +| | llm-escalate | 3391 | false | 0 | 2.35 | PASS | 280.5s | +| Agentic coding session | deterministic | 1957 | true | 9 | 1.36 | PASS | 2ms | +| | llm-escalate | 1915 | true | 3 | 1.39 | PASS | 28.1s | + +
+ +## Version History + +| Version | Date | Avg Char Ratio | Avg Token Ratio | Scenarios | +| ------- | ---------- | -------------: | --------------: | --------: | +| 1.3.0 | 2026-03-21 | 2.01 | 2.00 | 8 | +| 1.2.0 | 2026-03-20 | 2.01 | 2.00 | 8 | +| 1.1.0 | 2026-03-20 | 2.01 | 2.00 | 8 | +| 1.0.0 | 2026-03-10 | 2.01 | 2.00 | 8 | + +### v1.2.0 → v1.3.0 + +> **2.01x** → **2.01x** avg compression (0.00%) + +| Scenario | v1.2.0 | v1.3.0 | Change | Token Δ | | +| ---------------------- | -----: | -----: | -----: | ------: | --- | +| Coding assistant | 1.94x | 1.94x | 0.00% | 0.00% | ─ | +| Long Q&A | 4.90x | 4.90x | 0.00% | 0.00% | ─ | +| Tool-heavy | 1.40x | 1.40x | 0.00% | 0.00% | ─ | +| Short conversation | 1.00x | 1.00x | 0.00% | 0.00% | ─ | +| Deep conversation | 2.50x | 2.50x | 0.00% | 0.00% | ─ | +| Technical explanation | 1.00x | 1.00x | 0.00% | 0.00% | ─ | +| Structured content | 1.86x | 1.86x | 0.00% | 0.00% | ─ | +| Agentic coding session | 1.48x | 1.48x | 0.00% | 0.00% | ─ | + +Bundle: 183.5 KB → 183.5 KB (0.00%) + +
+v1.2.0 (2026-03-20) — 2.01x avg + +| Scenario | Char Ratio | Token Ratio | Compressed | Preserved | +| ---------------------- | ---------: | ----------: | ---------: | --------: | +| Coding assistant | 1.94 | 1.93 | 5 | 8 | +| Long Q&A | 4.90 | 4.88 | 4 | 6 | +| Tool-heavy | 1.40 | 1.39 | 2 | 16 | +| Short conversation | 1.00 | 1.00 | 0 | 7 | +| Deep conversation | 2.50 | 2.49 | 50 | 1 | +| Technical explanation | 1.00 | 1.00 | 0 | 11 | +| Structured content | 1.86 | 1.85 | 2 | 10 | +| Agentic coding session | 1.48 | 1.47 | 2 | 31 | + +
+ +
+v1.1.0 (2026-03-20) — 2.01x avg + +| Scenario | Char Ratio | Token Ratio | Compressed | Preserved | +| ---------------------- | ---------: | ----------: | ---------: | --------: | +| Coding assistant | 1.94 | 1.93 | 5 | 8 | +| Long Q&A | 4.90 | 4.88 | 4 | 6 | +| Tool-heavy | 1.41 | 1.40 | 2 | 16 | +| Short conversation | 1.00 | 1.00 | 0 | 7 | +| Deep conversation | 2.50 | 2.49 | 50 | 1 | +| Technical explanation | 1.00 | 1.00 | 0 | 11 | +| Structured content | 1.86 | 1.85 | 2 | 10 | +| Agentic coding session | 1.48 | 1.47 | 2 | 31 | + +
+ +
+v1.0.0 (2026-03-10) — 2.01x avg + +| Scenario | Char Ratio | Token Ratio | Compressed | Preserved | +| ---------------------- | ---------: | ----------: | ---------: | --------: | +| Coding assistant | 1.94 | 1.93 | 5 | 8 | +| Long Q&A | 4.90 | 4.88 | 4 | 6 | +| Tool-heavy | 1.41 | 1.40 | 2 | 16 | +| Short conversation | 1.00 | 1.00 | 0 | 7 | +| Deep conversation | 2.50 | 2.49 | 50 | 1 | +| Technical explanation | 1.00 | 1.00 | 0 | 11 | +| Structured content | 1.86 | 1.85 | 2 | 10 | +| Agentic coding session | 1.48 | 1.47 | 2 | 31 | + +
+ +## Methodology + +- All deterministic results use the same input → same output guarantee +- Metrics: compression ratio, token ratio, message counts, dedup counts +- Timing is excluded from baselines (hardware-dependent) +- LLM benchmarks are saved as reference data, not used for regression testing +- Round-trip integrity is verified for every scenario (compress then uncompress) diff --git a/docs/benchmarks.md b/docs/benchmarks.md index 4111308..0934d9f 100644 --- a/docs/benchmarks.md +++ b/docs/benchmarks.md @@ -1,151 +1,159 @@ # Benchmarks -[Back to README](../README.md) | [All docs](README.md) +[Back to README](../README.md) | [All docs](README.md) | [Latest Results](benchmark-results.md) -Running benchmarks, interpreting results, and comparing compression methods. - -## Running tests +## Running Benchmarks ```bash -# Run the test suite (333 tests) -npm test - -# Type check -npx tsc --noEmit +npm run bench # Run compression benchmarks (no baseline check) +npm run bench:check # Run and compare against baseline +npm run bench:save # Run, save new baseline, regenerate results doc +npm run bench:llm # Run with LLM summarization benchmarks ``` -## Deterministic benchmarks - -No API keys needed. Runs entirely locally: +### Quality benchmarks ```bash -npm run bench +npm run bench:quality # Run quality analysis (probes, coherence, info density) +npm run bench:quality:save # Save quality baseline +npm run bench:quality:check # Compare against saved quality baseline +npm run bench:quality:judge # Run with LLM-as-judge scoring (requires API key) ``` -### Scenarios - -The benchmark covers 7 conversation types: - -| Scenario | Description | -| ---------------------- | -------------------------------------------------------- | -| Coding assistant | Mixed code fences and prose discussion | -| Long Q&A | Extended question-and-answer with detailed explanations | -| Tool-heavy | Messages with `tool_calls` arrays (preserved by default) | -| Short conversation | Brief exchanges, mostly under 120 chars | -| Deep conversation | Long, multi-paragraph prose exchanges | -| Structured content | JSON, YAML, SQL, test output | -| Agentic coding session | Repeated file reads, grep results, test runs | +### LLM benchmarks (opt-in) -### What gets measured +LLM benchmarks require the `--llm` flag (`npm run bench:llm`). The LLM judge (`--llm-judge`) runs with the quality benchmark. Set API keys in a `.env` file or export them. Ollama is auto-detected when running locally. -For each scenario: +| Variable | Provider | Default Model | Notes | +| ------------------- | --------- | --------------------------- | -------------------------------- | +| `OPENAI_API_KEY` | OpenAI | `gpt-4.1-mini` | | +| `ANTHROPIC_API_KEY` | Anthropic | `claude-haiku-4-5-20251001` | | +| `GEMINI_API_KEY` | Gemini | `gemini-2.5-flash` | Requires `@google/genai` SDK | +| _(none required)_ | Ollama | `llama3.2` | Auto-detected on localhost:11434 | -- **Characters**: original vs. compressed character counts -- **Compression ratio**: `original_chars / compressed_chars` (>1 = savings) -- **Token ratio**: `original_tokens / compressed_tokens` -- **Messages compressed**: how many messages were summarized -- **Messages preserved**: how many were kept as-is -- **Messages deduped**: exact duplicates replaced (agentic scenario) -- **Timing**: milliseconds per compression +Model overrides: `OPENAI_MODEL`, `ANTHROPIC_MODEL`, `GEMINI_MODEL`, `OLLAMA_MODEL`. -Additional benchmark sections: +## Scenarios -- **Token budget optimization** with and without dedup -- **Fuzzy dedup accuracy** across thresholds -- **Real-session compression** on actual Claude Code transcripts (if `~/.claude/projects/` exists) +The benchmark covers 13 conversation types across core and edge-case categories: -### Real-session benchmarks +### Core scenarios -The benchmark automatically scans for real Claude Code conversation files in `~/.claude/projects/`. It parses JSONL conversation files, extracts message arrays, and runs compression on actual production data. +| Scenario | Description | +| ---------------------- | -------------------------------------------------------- | +| Coding assistant | Mixed code fences and prose discussion | +| Long Q&A | Extended question-and-answer with repeated paragraphs | +| Tool-heavy | Messages with `tool_calls` arrays (preserved by default) | +| Deep conversation | 25 turns of multi-paragraph prose | +| Technical explanation | Pure prose Q&A about event-driven architecture | +| Structured content | JSON, YAML, SQL, API keys, test output | +| Agentic coding session | Repeated file reads, grep results, near-duplicate edits | -This provides the most realistic performance numbers since synthetic scenarios can't capture the full diversity of real conversations. +### Edge-case scenarios -## LLM benchmarks +| Scenario | Description | +| ----------------------- | ---------------------------------------------------- | +| Single-char messages | Trivially short messages ("y", "n", "k") | +| Giant single message | One ~50KB message with mixed prose and code | +| Code-only conversation | All messages are entirely code fences, no prose | +| Entity-dense technical | Packed with identifiers, file paths, version numbers | +| Prose-only conversation | Pure prose with zero technical content | +| Mixed languages | Code in Python, SQL, JSON, YAML in one conversation | -Compare deterministic compression against real LLM-powered summarization. Set one or more environment variables to enable: +## Quality Metrics -| Variable | Provider | Default model | -| ------------------- | --------- | --------------------------------------------------------- | -| `OPENAI_API_KEY` | OpenAI | `gpt-4.1-mini` (override: `OPENAI_MODEL`) | -| `ANTHROPIC_API_KEY` | Anthropic | `claude-haiku-4-5-20251001` (override: `ANTHROPIC_MODEL`) | -| `OLLAMA_MODEL` | Ollama | `llama3.2` (host override: `OLLAMA_HOST`) | +The quality benchmark (`bench/quality.ts`) measures compression quality across several dimensions: -```bash -# Run with OpenAI -OPENAI_API_KEY=sk-... npm run bench +### Metrics -# Run with Ollama (local) -OLLAMA_MODEL=llama3.2 npm run bench +| Metric | Column | Description | +| ------------------------ | -------- | ------------------------------------------------------------------------- | +| Entity retention | `EntRet` | Fraction of technical entities (identifiers, paths, versions) preserved | +| Code block integrity | `CodeOK` | Whether code fences survive compression byte-identical | +| Information density | `InfDen` | Output entity density / input entity density. >1.0 = denser output (good) | +| Probes | `Probes` | Task-based checks: does specific critical information survive? | +| Probe pass rate | `Pass` | Fraction of probes that passed | +| Negative compressions | `NegCp` | Messages where compressed output is larger than original | +| Coherence issues | `Coher` | Sentence fragments, duplicate sentences, trivial summaries | +| Compressed quality score | `CmpQ` | Quality score computed over only compressed messages | -# Run with multiple providers -OPENAI_API_KEY=sk-... ANTHROPIC_API_KEY=sk-ant-... npm run bench -``` +### Probes -### Three methods compared +Each scenario has hand-curated probes that check whether specific critical information survives compression. For example: -Each scenario runs three methods side-by-side: +- **Coding assistant**: Does `JWT_SECRET` survive? Is `jwt.verify` still in a code block? Are the `15m`/`7d` expiry values present? +- **Entity-dense technical**: Are `redis-prod-001`, `v22.3.0`, `PR #142`, `max_connections` preserved? +- **Code-only conversation**: Are all TypeScript, Python, and SQL code blocks intact? -| Method | Description | -| --------------- | -------------------------------------------------------------------- | -| `deterministic` | No LLM, pure sentence scoring + entity extraction | -| `llm-basic` | `createSummarizer` with the detected provider | -| `llm-escalate` | `createEscalatingSummarizer` (normal -> aggressive -> deterministic) | +Probe failures reveal real quality issues — information the compression engine drops that it shouldn't. -All methods verify round-trip integrity — `uncompress()` is called to confirm originals are restored. +### LLM Judge -### What to look for +The `--llm-judge` flag adds an LLM-as-judge evaluation. For each scenario with actual compression (ratio > 1.01), it sends the original and compressed conversations to an LLM and asks for three 1-5 scores: -- **Ratio comparison** — deterministic often beats LLM on compression ratio because LLMs write fuller, more helpful summaries -- **Latency** — deterministic is < 2ms; LLM adds network round-trip time per message -- **Fallback rate** — how often the engine rejects LLM output and falls back to deterministic -- **Round-trip integrity** — all methods must pass (no data loss) +- **Meaning preserved**: Are important decisions, facts, code, and technical details retained? +- **Coherence**: Do compressed messages read naturally without fragments or duplicates? +- **Overall**: Combined assessment of compression quality -### SDK requirements +LLM judge scores are **display-only** — not saved to baselines and not used for regression testing (non-deterministic). -LLM providers require their SDKs: +## Interpreting Results -- OpenAI: `openai` package -- Anthropic: `@anthropic-ai/sdk` package -- Ollama: `openai` package (uses OpenAI-compatible API) +### Compression ratio -Missing SDKs are detected at runtime and print a skip message — no crash, no hard dependency. +| Ratio | Reduction | +| ----: | --------------------------------------- | +| 1.0x | no compression (all messages preserved) | +| 1.5x | 33% reduction | +| 2.0x | 50% reduction | +| 3.0x | 67% reduction | +| 6.0x | 83% reduction | -## Interpreting results +Higher is better. Token ratio is more meaningful for LLM context budgeting; character ratio is useful for storage. -### Compression ratio +### Deduplication -- `1.0` = no compression (all messages preserved) -- `1.5` = 33% reduction -- `2.0` = 50% reduction -- `3.0` = 67% reduction -- `6.0` = 83% reduction +Dedup effectiveness is measured across two axes: -Higher is better. The deterministic engine typically achieves 1.3-6.1x on synthetic scenarios. +- **recencyWindow=0** vs **recencyWindow=4** — how much compression improves when recent messages are protected +- **With dedup** vs **without** — the marginal gain from exact + fuzzy duplicate detection -### Token ratio vs. character ratio +Scenarios with repeated content (Long Q&A, Agentic coding session) show the largest dedup gains. Scenarios with unique messages show no difference. -Token ratio is more meaningful for LLM context budgeting since tokens are what models count. Character ratio is useful for storage optimization. +### LLM vs deterministic -### When LLM wins +The `vsDet` column shows LLM compression relative to deterministic: -LLM summarization can outperform deterministic in: +- **vsDet > 1.0** — LLM achieves better compression (common for long prose) +- **vsDet < 1.0** — deterministic wins (common for structured/technical content) +- **vsDet = 1.0** — no difference (content is already optimal or fully preserved) -- Very long prose-heavy conversations where paraphrasing and concept merging genuinely helps -- Domain-specific content where the LLM understands what's important +## Regression Testing -### When deterministic wins +Baselines are stored in [`bench/baselines/`](../bench/baselines/) as JSON. CI runs `npm run bench:check` on every push and PR to catch regressions. -Deterministic typically wins when: +- **Tolerance:** 0% by default (all metrics are deterministic) +- **On regression:** CI fails with a diff showing which metrics changed +- **After intentional changes:** run `npm run bench:save` to update the baseline and regenerate the results doc +- **Custom tolerance:** `npx tsx bench/run.ts --check --tolerance 5` allows 5% deviation -- Messages contain mixed code and prose (code-aware splitting is already optimal) -- Messages are structured (test output, grep results) -- The LLM writes helpful but verbose summaries +### Quality regression thresholds ---- +| Metric | Threshold | +| --------------------- | ----------------------------------- | +| Probe pass rate | max 5% drop from baseline | +| Entity retention | max 5% drop from baseline | +| Code block integrity | zero tolerance | +| Information density | must stay ≥ 0.8 (when ratio > 1.01) | +| Negative compressions | must not increase from baseline | +| Coherence issues | must not increase from baseline | -## See also +### Baseline files -- [Compression pipeline](compression-pipeline.md) - the deterministic algorithm -- [LLM integration](llm-integration.md) - setting up providers for benchmarks -- [Token budget](token-budget.md) - budget optimization -- [Deduplication](deduplication.md) - dedup in benchmarks +| File | Purpose | +| ---------------------------------------- | ------------------------------------------------ | +| `bench/baselines/current.json` | Active baseline compared in CI | +| `bench/baselines/history/v*.json` | Versioned snapshots, one per release | +| `bench/baselines/llm/*.json` | LLM benchmark reference data (non-deterministic) | +| `bench/baselines/quality/current.json` | Active quality baseline | +| `bench/baselines/quality/history/*.json` | Quality baseline snapshots by git ref | diff --git a/docs/compression-pipeline.md b/docs/compression-pipeline.md index f894cd4..f23dac2 100644 --- a/docs/compression-pipeline.md +++ b/docs/compression-pipeline.md @@ -36,12 +36,14 @@ The classifier (`classifyAll`) applies rules in this order: 3. Has `tool_calls` -> preserved 4. Content < 120 chars -> preserved 5. Already compressed (`[summary:`, `[summary#`, or `[truncated` prefix) -> preserved -6. Marked as duplicate by dedup analysis -> dedup path -7. Contains code fences with >= 80 chars of prose -> code-split path -8. Has code fences with < 80 chars prose -> preserved -9. Classified as hard T0 (code, JSON, SQL, API keys, etc.) -> preserved -10. Valid JSON -> preserved -11. Everything else -> compress +6. High importance score (when `importanceScoring: true`, score >= `importanceThreshold`) -> preserved +7. Marked as duplicate by dedup analysis -> dedup path +8. Superseded by a later correction (when `contradictionDetection: true`) -> contradiction path +9. Contains code fences with >= 80 chars of prose -> code-split path +10. Has code fences with < 80 chars prose -> preserved +11. Classified as hard T0 (code, JSON, SQL, API keys, etc.) -> preserved +12. Valid JSON -> preserved +13. Everything else -> compress See [Preservation rules](preservation-rules.md) for classification tiers and the hard vs. soft T0 distinction. @@ -100,7 +102,7 @@ The `summarize` function uses sentence scoring: 5. Re-sort selected sentences by original position to preserve reading order 6. Join with `...` separator -Budget: 200 chars if input < 600 chars, 400 chars otherwise. +Budget scales adaptively: max(200, min(round(length × 0.3), 600)). Short content gets 200 chars, long content up to 600. ### Entity extraction @@ -111,14 +113,14 @@ After summarizing, `extractEntities` pulls out key identifiers from the original - Vowelless abbreviations - Numbers with units/context -Up to 10 entities are appended as `| entities: foo, bar, baz`. +Entities scale with content length (3–15) and are appended as `| entities: foo, bar, baz`. ### Code-split processing Messages containing code fences with significant prose (>= 80 chars) get split: 1. `splitCodeAndProse` extracts code fences and surrounding prose separately -2. Prose is summarized (budget: 200 if < 600 chars, else 400) +2. Prose is summarized (budget scales adaptively with prose length) 3. Code fences are preserved verbatim 4. Result: `[summary: ...]\n\n```code here```` @@ -169,6 +171,20 @@ With `embedSummaryId: true`: [cce:near-dup of {keepTargetId} — {contentLength} chars, ~{similarity}% match] ``` +### Contradiction format + +When `contradictionDetection: true`, messages superseded by a later correction: + +``` +[cce:superseded by {correctionMessageId} ({signal}) — {summaryText}] +``` + +If the full format doesn't fit, falls back to compact: + +``` +[cce:superseded by {correctionMessageId} — {signal}] +``` + ### Force-converge format ``` diff --git a/docs/design/domain-specific-enhancements.md b/docs/design/domain-specific-enhancements.md new file mode 100644 index 0000000..5bc7c3d --- /dev/null +++ b/docs/design/domain-specific-enhancements.md @@ -0,0 +1,103 @@ +# Domain-Specific Enhancements + +## Problem + +The README (line 35) claims the engine is useful for "LLM conversations, legal briefs, medical records, technical documentation, support logs." The classifier only delivers on two of those: LLM conversations and technical documentation. The other three have minimal or zero domain-specific detection, meaning domain-critical content gets classified as compressible prose. + +## Current State + +### Delivered + +- **LLM conversations** — benchmarked on real Claude Code sessions (8,004 messages, 11.7M chars) +- **Technical documentation** — code fences, JSON, YAML, LaTeX, file paths, versions, URLs, API keys + +### Gaps + +#### Legal briefs + +What exists: 5 keywords as a force-T0 pattern (`shall`, `may not`, `notwithstanding`, `whereas`, `hereby`). + +What's missing: + +- Case law citations (e.g., `42 U.S.C. § 1983`, `Smith v. Jones, 500 U.S. 123 (1995)`) +- Section/clause references (e.g., `Section 4(a)(ii)`, `Article III`) +- Defined terms (capitalized terms with specific legal meaning) +- Contract clause numbering patterns +- Regulatory references (e.g., `GDPR Art. 6(1)(f)`, `HIPAA § 164.502`) + +Risk: legal citations and defined terms compressed away, changing the meaning of the document. + +#### Medical records + +What exists: nothing domain-specific. + +What's missing: + +- Drug names and dosage patterns (e.g., `Metformin 500mg po bid x30d`) +- ICD/CPT codes (e.g., `ICD-10: E11.9`, `CPT 99213`) +- Lab values with ranges (e.g., `HbA1c 7.2% (ref: <5.7%)`) +- Vital signs (e.g., `BP 120/80 mmHg`, `HR 72 bpm`) +- Anatomical/clinical terms at high density +- Allergy/adverse reaction flags + +Risk: dosages, codes, or lab values treated as prose and summarized — direct patient safety concern. + +#### Support logs + +What exists: stack traces in code fences survive; `numeric_with_units` catches some metrics. + +What's missing: + +- Log level patterns (e.g., `[ERROR]`, `WARN`, `INFO 2024-01-15T10:23:45Z`) +- Ticket/incident IDs (e.g., `JIRA-1234`, `INC0012345`) +- Structured timestamp lines +- Request/response pairs with status codes +- Process/thread IDs + +Risk: lower than legal/medical — support logs are often semi-structured enough to trigger existing detectors. But explicit patterns would improve reliability. + +## Approach Options + +### Option A: Add force-T0 patterns (same as SQL detector) + +Add regex patterns to `FORCE_T0_PATTERNS` in `src/classify.ts` for each domain. Low complexity, consistent with existing architecture. + +Pros: + +- Minimal code change +- Same pattern as SQL, API keys, legal terms +- No new dependencies + +Cons: + +- Regex-based detection has false positive/negative tradeoffs +- Each domain needs careful tuning to avoid over-preserving + +### Option B: Domain-specific detector functions (same as `detectSqlContent`) + +Create dedicated detector functions with tiered anchor systems (strong/weak) per domain. More nuanced than flat regex. + +Pros: + +- Can use anchor tiering to reduce false positives (proven with SQL) +- Can combine multiple weak signals for higher confidence +- Testable in isolation + +Cons: + +- More code to maintain per domain +- Need domain expertise to get the anchor lists right + +### Recommendation + +Build detector functions for legal and medical (highest risk domains), add simple patterns for support logs. Research needed before implementation to validate pattern lists against real-world samples. Once domain detection is proven, update the README to re-advertise broader domain support. + +## Research TODO + +- [ ] Collect sample legal documents — contracts, briefs, regulations +- [ ] Collect sample medical records — clinical notes, lab reports, discharge summaries +- [ ] Collect sample support logs — Zendesk, Jira, PagerDuty exports +- [ ] Run current classifier against samples, measure false negatives (domain content classified as T2/T3) +- [ ] Draft pattern lists per domain, validate false positive rates +- [ ] Determine if `numeric_with_units` already covers enough medical/lab values +- [ ] Benchmark compression quality on domain samples before/after enhancements diff --git a/docs/design/llm-classifier-findings.md b/docs/design/llm-classifier-findings.md new file mode 100644 index 0000000..0f5bd55 --- /dev/null +++ b/docs/design/llm-classifier-findings.md @@ -0,0 +1,485 @@ +# LLM Classifier — Research Findings & Assessment + +## Honest assessment: is this feature worth pursuing? + +**Yes.** Three reasons: + +### 1. The gap is real and already advertised + +The README positions the library for "legal briefs, medical records, technical +documentation, support logs." But the heuristic classifier is blind to those domains +today. Everything without code fences, JSON, or SQL gets classified as compressible +prose. A user compressing legal contracts right now gets their clause references +summarized away. The `legal_term` pattern ("shall", "whereas") is a soft T0 reason — +it tags the message but doesn't prevent compression (compress.ts line 569 only checks +hard T0 reasons). The library promises domain breadth it can't deliver without this +feature. + +### 2. The architecture is validated by research + +The research confirms our design choices: + +- **Hybrid mode is the right default.** The EDU paper (arxiv:2512.14244) found that + even frontier LLMs perform poorly on fine-grained structural analysis. Our heuristic + classifier is better than an LLM at detecting code fences, JSON, SQL, regex patterns. + The LLM should only handle semantic decisions (is this paragraph important?), not + structural ones (is this JSON?). Hybrid mode routes correctly. +- **Binary classification for compression works.** LLMLingua-2 (arxiv:2403.12968) + reframed prompt compression as binary token classification (preserve/discard) and + achieved better results than perplexity-based approaches. Our message-level + preserve/compress decision follows the same principle at a coarser granularity. +- **Deterministic fallback is essential.** Factory.ai's evaluation found that structured + summarization outperforms LLM-only approaches. Our three-level fallback + (LLM → heuristic → deterministic) is the right architecture. + +### 3. The cost is negligible + +Classification responses are tiny (~50-80 tokens). At Haiku pricing, classifying an +entire 100-message conversation costs ~$0.001. Compare that to the cost of a single +LLM summarization call. The feature adds value disproportionate to its cost. + +### Risks + +- **Scope creep.** The feature is well-scoped in the design doc, but domain-specific + prompt engineering could become a support burden. Mitigation: document prompts as + recipes in `docs/domain-prompts.md`, don't ship them as code. +- **LLM confidence is unreliable.** The Amazon Science paper found that LLM + classification confidences are systematically miscalibrated. We collect confidence + for logging but must not use it for routing decisions. Our hybrid mode already + routes on heuristic signals (hard T0 vs. prose bucket), not LLM confidence. This + is correct and should stay that way. +- **Testing complexity.** The LLM classifier needs integration tests with mocked LLM + responses. The test surface grows, but the pattern is identical to the summarizer + tests we already have. + +--- + +## Research findings + +### Papers explored + +| # | Paper | Year | Key relevance | +| --- | ----------------------------------------------------------------------------------------- | -------------- | --------------------------------------------------------------------- | +| 1 | **LLMLingua-2**: Data Distillation for Faithful Task-Agnostic Prompt Compression | ACL 2024 | Binary classification framing for compression | +| 2 | **Selective Context** (Li et al.) | EMNLP 2023 | Self-information scoring for token importance | +| 3 | **From Context to EDUs**: Faithful and Structured Context Compression | Dec 2025 | Structural analysis is a heuristic strength, not LLM | +| 4 | **Understanding and Improving Information Preservation in Prompt Compression** | 2025 | Evaluation framework for compression faithfulness | +| 5 | **RECOMP**: Improving Retrieval-Augmented LMs with Compression and Selective Augmentation | ICLR 2024 | Three-way classification (preserve/compress/remove) | +| 6 | **Label with Confidence**: Effective Confidence Calibration in LLM-Powered Classification | Amazon Science | LLM confidence is unreliable — don't trust it for routing | +| 7 | **Fundamental Limits of Prompt Compression**: A Rate-Distortion Perspective | NeurIPS 2024 | Theoretical compression bounds | +| 8 | **Factory.ai**: Compressing Context / Evaluating Context Compression | 2024 | Structured summarization beats LLM-only; task shape matters | +| 9 | **Scikit-LLM / Hybrid AI** | Nov 2025 | LLM-as-feature-engineer pattern for production classification | +| 10 | **Recursive Language Models** (Zhang, Kraska, Khattab — MIT CSAIL) | Dec 2025 | Context rot validation; compaction limits; alternative to compression | + +### Paper URLs + +- LLMLingua-2: https://arxiv.org/abs/2403.12968 +- Selective Context: https://arxiv.org/abs/2310.06201 +- EDU Context Compression: https://arxiv.org/abs/2512.14244 +- Information Preservation: https://arxiv.org/abs/2503.19114 +- RECOMP: https://arxiv.org/abs/2310.04408 +- Label with Confidence: https://assets.amazon.science/9f/8f/5573088f450d840e7b4d4a9ffe3e/label-with-confidence-effective-confidence-calibration-and-ensembles-in-llm-powered-classification.pdf +- Fundamental Limits: https://proceedings.neurips.cc/paper_files/paper/2024/file/ac8fbba029dadca99d6b8c3f913d3ed6-Paper-Conference.pdf +- Factory.ai Compressing Context: https://factory.ai/news/compressing-context +- Factory.ai Evaluating Compression: https://factory.ai/news/evaluating-compression +- Scikit-LLM Hybrid AI: https://afafathar.medium.com/productionizing-hybrid-ai-a-technical-deep-dive-into-scikit-llm-for-scalable-text-classification-a0cba646f2f8 +- Recursive Language Models: https://arxiv.org/abs/2512.24601 + +--- + +## Priority ranking: which papers to read first + +### Tier 1 — Read these, they directly change our implementation + +**1. LLMLingua-2** (arxiv:2403.12968) + +Why: They solved the same problem at the token level. We're solving it at the message +level. Their key move was reframing compression from "score by perplexity" to "train a +binary classifier on preserve/discard labels." We're making the same conceptual move — +our heuristic `scoreSentence` is a proxy metric (like their perplexity), and the LLM +classifier is direct optimization (like their trained classifier). + +What to look for: + +- How they handle the preserve/discard boundary (threshold selection) +- Their data distillation process (GPT-4 generates training labels) — this could + inform our prompt engineering for the classifier +- Their faithfulness evaluation methodology — how do they measure whether the + compressed output preserves the right information? +- Performance across different content types (their dataset includes MeetingBank, + LongBench, GSM8K, and more) + +**2. RECOMP** (arxiv:2310.04408) + +Why: Their compressor can output an **empty string** when content is irrelevant. This +is a three-way decision we haven't considered: preserve / compress / remove. Our +current design is binary (preserve / compress). But our heuristic classifier already +has T3 (filler/removable) as a tier — we just don't use it differently from T2. The +LLM classifier could make T3 meaningful by identifying messages that should be dropped +entirely rather than summarized. + +What to look for: + +- How their extractive vs. abstractive compressors decide "nothing here is worth + keeping" — what signals trigger the empty-string output? +- Their selective augmentation logic — how the decision to include or exclude content + is made +- Whether the three-way approach improves downstream task performance vs. binary + +**3. Label with Confidence** (Amazon Science) + +Why: Directly impacts our confidence score design. If LLM confidence is systematically +unreliable, we need to know HOW it's unreliable (overconfident? underconfident? biased +toward certain classes?) and whether there are cheap calibration techniques we should +apply. + +What to look for: + +- The specific miscalibration patterns (overconfidence on incorrect classifications) +- Whether their logit-based calibration is applicable to our setup (we only get text + responses, not logits, from most LLM APIs) +- Their recommendation on when verbalized confidence (asking the LLM for a score) is + acceptable vs. when it's dangerous +- Whether confidence is more reliable for binary classification (our case) vs. + multi-class + +### Tier 2 — Read if time permits, useful but not blocking + +**4. EDU Context Compression** (arxiv:2512.14244) + +Why: Validates our hybrid approach. Their finding that LLMs are bad at structural +analysis confirms that we should keep structural detection in heuristics. Also +introduces StructBench (248 diverse documents) — could be useful as a test dataset +for evaluating our classifier. + +What to look for: + +- StructBench composition — what document types are included? +- Their structural prediction accuracy metrics — how do different LLMs perform? +- Whether their EDU decomposition idea could improve our code-split logic + +**5. Information Preservation** (arxiv:2503.19114) + +Why: Evaluation methodology. If we ship an LLM classifier, we need to measure whether +it actually improves compression quality vs. heuristics-only. This paper provides a +framework for that comparison. + +What to look for: + +- Their three evaluation axes (downstream performance, grounding, information + preservation) — can we adapt this for our test suite? +- Which compression methods fail at preservation and why +- Whether they tested domain-specific content (legal, medical, etc.) + +### Tier 3 — Reference material, skim as needed + +**6. Selective Context** (arxiv:2310.06201) +Context for understanding self-information scoring. Our `scoreSentence` is a cruder +version of their approach. Not directly actionable but good background. + +**7. Fundamental Limits** (NeurIPS 2024) +Theoretical bounds. Useful if we want to understand how close our compression ratios +are to optimal. Not actionable for implementation. + +**8. Factory.ai blog posts** +Engineering perspective, not academic. Good for understanding production patterns. +We already incorporate their key insight (task shape matters → multiple modes). + +**9. Scikit-LLM / Hybrid AI** +Different architecture (LLM as feature engineer for traditional classifier). Not +directly applicable to our design, but the "don't use the LLM as the final decision +maker" principle is worth keeping in mind. + +**10. Recursive Language Models** (arxiv:2512.24601, MIT CSAIL, Dec 2025) +RLMs treat long prompts as an external environment and let the LLM recursively +call itself over snippets, handling inputs 100x beyond context windows. Their key +finding for us: context compaction (repeated summarization) "is rarely expressive +enough for tasks that require dense access." This validates why intelligent +classification before compression matters — you must know what's safe to compress +vs. what needs verbatim access. Their Figure 1 demonstrates "context rot" in GPT-5 +at scale. Orthogonal to our approach (they avoid compression entirely), but +reinforces the problem we're solving. The RLM approach could be complementary: +compress what's safe, provide recursive access to what's preserved. + +### Paper locations + +All downloaded to `~/documents/Papers/`: + +``` +LLM-Context-Compression/ + LLMLingua-2_2403.12968.pdf + RECOMP_2310.04408.pdf + SelectiveContext_2310.06201.pdf + EDU-ContextCompression_2512.14244.pdf + InformationPreservation_2503.19114.pdf + FundamentalLimits_NeurIPS2024.pdf + 2512.24601v1.pdf (Recursive Language Models) + +LLM-Classification/ + LabelWithConfidence_Amazon.pdf +``` + +--- + +## Deep-dive: Tier 1 paper findings + +### LLMLingua-2 — What we learned + +**Core approach:** Reframe compression as binary token classification (preserve/discard). +They train a small Transformer encoder (XLM-RoBERTa-large, ~560M params) on labels +distilled from GPT-4. At inference, each token gets a preserve probability; the top-τN +tokens are kept in original order. + +**Key findings for our design:** + +1. **No fixed compression ratio.** They explicitly removed compression ratio targets from + their prompt because information density varies wildly by genre. GPT-4 assigns + compression ratios ranging from 1x to 20x across different sentences in the same + document (Figure 3). This validates our per-message classification — a single ratio + doesn't work. The classifier should decide per-message, not apply a blanket policy. + +2. **Extractive > abstractive for faithfulness.** Their prompt enforces strict extractive + rules: "You can ONLY remove unimportant words. Do not reorder. Do not change. Do not + use abbreviations. Do not add new words." The output is a subset of the input tokens + in original order. This guarantees faithfulness by construction. Our deterministic + summarizer already follows a similar principle (sentence scoring + extraction). The + LLM classifier should similarly be extractive in nature — classify messages, don't + rewrite them. + +3. **Bidirectional context matters.** Their Transformer encoder sees the full context + bidirectionally, which is why a BERT-base model outperforms LLaMA-2-7B (a causal LM) + at compression. For us: our heuristic classifier already analyzes full message content + bidirectionally. When asking a causal LLM to classify, it only sees the message in + left-to-right order. This is another argument for hybrid mode — heuristics handle + structural patterns better because they see the whole message at once. + +4. **Quality control metrics we should adopt.** + - **Variation Rate (VR):** Proportion of words in output absent from input. Measures + hallucination risk in summaries. We could compute this for our deterministic + summarizer output. + - **Alignment Gap (AG):** High hit rate + low match rate = poor annotation quality. + Useful if we ever evaluate LLM classifier consistency. + +5. **Chunk-wise compression for long contexts.** They chunk inputs into ≤512 tokens because + GPT-4 over-compresses long contexts (Figure 4). Relevant for our potential batching + strategy — if we batch-classify messages, we should limit batch size. + +6. **Cross-domain generalization.** Trained only on MeetingBank (meeting transcripts), the + model generalizes to LongBench, ZeroSCROLLS, GSM8K, BBH. They conjecture that + "redundancy patterns transfer across domains." This suggests our LLM classifier + prompts don't need to be domain-specific to be effective — a good general prompt + works across content types. Domain-specific prompts are an optimization, not a + requirement. + +**Compression performance reference points:** + +- In-domain (MeetingBank): 3x compression, QA EM 86.92 vs 87.75 original (98.6% retention) +- Out-of-domain (LongBench): 5x compression, maintains competitive performance +- Latency: 0.4-0.5s vs 15.5s for Selective-Context (30x faster) + +### RECOMP — What we learned + +**Core approach:** Two compressors — extractive (select sentences) and abstractive +(generate summaries) — trained to optimize downstream LM task performance, not +compression quality metrics. + +**Key findings for our design:** + +1. **The "remove" decision is task-dependent.** The empty string output isn't triggered by + content analysis alone. During training, the abstractive compressor learns to output + empty when prepending the summary actually _hurts_ downstream performance (increases + perplexity or reduces QA accuracy). This is fundamentally different from "is this + filler?" — it's "does keeping this help the task?" + + **Implication for us:** Our "remove" tier shouldn't just identify conversational filler. + It should identify messages where compression/summarization provides zero value — + content that's so generic or disconnected that even a summary wastes tokens. This is + harder than filler detection and probably not worth implementing in v1. Stick with + binary (`preserve | compress`) for now. The heuristic classifier already handles + obvious filler via the <120 char threshold and dedup. + +2. **Extractive outperforms abstractive on most tasks.** Across language modeling, + NQ, TriviaQA, and HotpotQA, extractive compression (selecting sentences verbatim) + achieves better or comparable results with simpler architecture. Only on HotpotQA + (multi-hop reasoning) does abstractive do better, because it can synthesize across + documents. + + **Implication for us:** Our deterministic summarizer (extractive sentence scoring) is + the right default. LLM summarization should remain opt-in. The LLM classifier should + improve _what_ gets sent to the summarizer, not replace the summarizer itself. + +3. **Irrelevant content actively hurts.** "Prepending a large number of documents in-context + can further confuse LMs with irrelevant information, degrading model performances." + Prepending 5 full documents sometimes performs worse than 1 document. The oracle + extractive compressor (best single sentence) outperforms prepending full documents. + + **Implication for us:** This validates aggressive compression. Better to compress too + much than too little. A message that's 90% filler and 10% useful information is + better compressed than preserved — the 90% noise dilutes the 10% signal. + +4. **Faithfulness vs. comprehensiveness trade-off.** Manual evaluation (Table 4) shows + their abstractive compressor is less faithful than GPT-3.5 (more hallucination) but + more comprehensive (captures more information). GPT-3.5 summaries are 90-97% faithful + but their trained model is 67-83% faithful. + + **Implication for us:** When evaluating our LLM classifier, faithfulness should be the + primary metric, not comprehensiveness. A classifier that incorrectly marks a message + as "compress" (losing important content) is worse than one that incorrectly marks it + as "preserve" (keeping too much). False negatives are cheaper than false positives. + +5. **Compression rates achieved:** + - Extractive: 25% compression (4x), <10% relative performance drop + - Abstractive: 5% compression (20x), but less faithful + - Oracle extractive: 6% compression (16x), _outperforms_ full documents + +### Label with Confidence — What we learned + +**Core approach:** Logit-based confidence calibration for LLM classification. They extract +raw logits from the LLM output, aggregate across tokens matching candidate classes, apply +softmax scaling with learnable parameters, then use calibrated scores for cascading +ensemble policies. + +**Key findings for our design:** + +1. **Logit-based calibration requires model access we don't have.** Their entire pipeline + (Steps 1-4) requires raw logit values from the LLM's last layer. Most LLM APIs + (OpenAI, Anthropic, etc.) don't expose logits. We only get text responses. Their + approach is **not directly applicable** to our use case. + +2. **Three methods for LLM confidence, ranked by reliability:** + - **Logit-based** (their approach): Most accurate. Requires model access. Not available + to us. + - **Consistency-based** (ask multiple times, measure agreement): Moderate accuracy. + Requires multiple API calls. Too expensive for classification. + - **Verbalized confidence** (ask the LLM for a score): Least reliable. This is what + we'd use. Referenced but not recommended by this paper. + + **Implication for us:** Our decision to collect but not use confidence for routing is + correct. The only confidence method available to us (verbalized) is the least + reliable. Don't design features around it. + +3. **Calibration error reduces with in-task examples.** 100-shot in-task calibration + reduces error by 46% over uncalibrated. But this requires a labeled dev-set from the + target task, which our library users won't have. + +4. **Cascading ensemble pattern validates our escalating classifier.** Their cascading + policy: start with cheapest LLM, check calibrated confidence, escalate to costlier + LLM only when confidence is low. This achieves best F1 across all policies while + reducing cost by 2x+ vs majority voting. Our `createEscalatingClassifier` follows + the same pattern (heuristic → cheap LLM → expensive LLM), but we route on heuristic + signal strength rather than confidence scores. This is arguably more reliable given + their own finding that confidence needs calibration. + +5. **Binary classification shows lower calibration error.** Their experiments use binary + yes/no classification. With 100 in-task examples, mean ACE drops to 0.036-0.041. + This is our exact use case (preserve/compress). If we ever implement confidence-based + routing, binary classification is the most favorable scenario for it. + +6. **The cost-aware cascade is the real insight.** Beyond confidence calibration, the paper + demonstrates that tiered LLM usage (cheap first, expensive if needed) is both cheaper + and more accurate than always using the most expensive model. This pattern maps to: + - **Our hybrid mode:** Heuristic first (free), LLM only for ambiguous cases + - **Our escalating classifier:** If the cheap LLM is uncertain, escalate + +--- + +## Insights that should change the design + +### 1. Three-way classification — DECIDED: not for v1 (from RECOMP deep-dive) + +Current design: `preserve | compress` (binary). +Previously considered: `preserve | compress | remove`. + +**After reading RECOMP in depth, the recommendation is to stay binary for v1.** + +RECOMP's "remove" decision is task-dependent — their compressor learns to output empty +when prepending the summary hurts downstream task performance. This requires training +signal from a specific downstream task, which our library doesn't have (we're +task-agnostic). A naive "is this filler?" heuristic for removal is already handled by +our <120 char threshold and dedup. The LLM classifier adds value for _semantic_ +preserve/compress decisions on non-trivial content, not for filler detection. + +The three-way approach remains a possible v2 feature if users request it. + +### 2. Don't use LLM confidence for routing (from Amazon Science paper) + +Current design: Collect confidence from LLM, use for stats/logging. +Confirmed: Do NOT use it for routing decisions in hybrid mode. + +The hybrid routing should remain based on heuristic signals: hard T0 match → skip LLM, +everything else → ask LLM. Never "ask the LLM and only trust it if confidence > 0.8." +LLM confidence scores are systematically miscalibrated. + +Impact: No design change needed — our current approach is already correct. But this +should be documented explicitly as a deliberate design choice, not an oversight. + +### 3. Faithfulness evaluation (from LLMLingua-2 and RECOMP deep-dives) + +We need a way to measure whether the LLM classifier actually improves compression +quality. Current benchmarks measure compression ratio and token savings. With the LLM +classifier, we also need to measure: + +- Does the classifier preserve the right content? (faithfulness) +- Does it preserve more domain-relevant content than heuristics alone? (domain lift) +- Does hybrid mode match full mode quality at lower cost? (efficiency) + +**New from paper deep-dives:** + +From LLMLingua-2: adopt **Variation Rate** (proportion of output words absent from input) +as a hallucination metric for our summarizer output. Also consider **Alignment Gap** for +evaluating LLM classifier consistency. + +From RECOMP: **faithfulness > comprehensiveness** as the primary metric. A classifier that +incorrectly marks important content as "compress" (false positive) is worse than one +that over-preserves (false negative). Design benchmarks with asymmetric error costs. + +Impact: New benchmark scenarios needed. Not blocking for implementation, but needed +before we can claim the feature works well. + +### 4. The LLM-as-feature-engineer pattern (from Scikit-LLM) + +An alternative to our current design: instead of asking the LLM "preserve or compress?", +ask it "what are the key concepts in this message?" and feed that into a deterministic +decision function. The LLM extracts signals, the heuristic decides. + +This is potentially more robust (deterministic decision layer, LLM only for feature +extraction) but more complex to implement and harder to explain to users. Not worth +pursuing in v1, but worth noting as a possible evolution if LLM confidence proves too +unreliable in practice. + +--- + +## Design document status + +The design document at `docs/design/llm-classifier.md` covers: + +- [x] Problem statement +- [x] Three classification modes (off / hybrid / full) +- [x] Pipeline injection point +- [x] API design (Classifier type, CompressOptions, factory functions) +- [x] Classifier prompt template with domain examples +- [x] Integration with compress.ts (sync/async routing) +- [x] File structure decision (flat, single new file) +- [x] CompressResult additions +- [x] Response parsing strategy +- [x] Cost analysis +- [x] Documentation plan +- [x] Why we don't expand heuristics (preservePatterns instead) +- [x] Three composable classification layers +- [x] Open questions (batching, caching, confidence threshold) + +Decided after Tier 1 deep-dive: + +- [x] Three-way classification → **Stay binary for v1.** RECOMP's "remove" is + task-dependent, not applicable to our task-agnostic library. Filler is already handled + by <120 char threshold and dedup. Three-way remains a v2 possibility. +- [x] Confidence calibration caveat → **Yes, document it.** The Amazon paper confirms + verbalized confidence (our only option) is the least reliable method. Document as + deliberate design choice: collect for logging, never route on it. + +Still to be decided: + +- [ ] Faithfulness evaluation / benchmark strategy (metrics identified: Variation Rate + from LLMLingua-2, asymmetric error costs from RECOMP) +- [ ] Whether cross-domain generalization (LLMLingua-2 finding) means we can ship a + single general prompt vs. requiring domain-specific prompts diff --git a/docs/design/llm-classifier.md b/docs/design/llm-classifier.md new file mode 100644 index 0000000..d1c7a7c --- /dev/null +++ b/docs/design/llm-classifier.md @@ -0,0 +1,735 @@ +# LLM Classifier — Design Document + +## Problem statement + +The heuristic classifier (`src/classify.ts`) is excellent at detecting **structural** content — code fences, JSON, SQL, API keys, LaTeX, etc. These are pattern-matching tasks where regex is the right tool. + +But the engine is used beyond code-heavy contexts: legal briefs, academic papers, novels, medical records, support logs, financial reports. For these domains, the heuristic classifier has two blind spots: + +1. **Semantic importance in pure prose** — "we chose PostgreSQL over MongoDB because of ACID compliance" has no structural markers but contains a critical architectural decision. The heuristic classifies it as T2 or T3 based on word count alone (`inferProseTier` is literally `words < 20 ? T2 : T3`). + +2. **Domain-specific preservation** — a legal "material adverse change clause" or a medical "contraindication" has zero structural markers. Regex can't know what matters in a domain it wasn't designed for. + +An LLM classifier can understand **meaning**, not just **shape**. + +--- + +## Three classification modes + +| Mode | Behavior | When to use | +| ---------- | ----------------------------------------------------------------------- | ------------------------------------------------------ | +| **off** | Current heuristic classifier only. Zero cost, deterministic, sync. | Code-heavy contexts, cost-sensitive, offline use | +| **hybrid** | Heuristics first; LLM only for low-confidence cases (the prose bucket). | Best cost/accuracy tradeoff. Most use cases. | +| **full** | Every message classified by the LLM. Heuristics skipped entirely. | Domain-specific content where heuristics add no value. | + +### Mode semantics + +- **off** — The default. Existing behavior. No API change needed. The current `classifyMessage()` and `classifyAll()` remain untouched and continue to serve all sync paths. + +- **hybrid** — Heuristics run first. If the result is high-confidence T0 (hard structural reason), the LLM is skipped. If the result falls into the prose bucket (T2/T3, confidence 0.65), the LLM classifier is invoked to make the preserve/compress decision. This minimizes LLM calls — only prose messages that the heuristics can't confidently classify get routed to the LLM. + +- **full** — The heuristic classifier is bypassed entirely. Every message (subject to the standard preservation rules: role, recency window, tool_calls, content length, already-compressed) is sent to the LLM classifier. For domain-specific content like legal contracts or medical records, the heuristic patterns (code fences, SQL, API keys) are irrelevant noise. + +--- + +## Where classification happens in the pipeline + +``` +messages + | + v +preservation rules (role, recencyWindow, tool_calls, <120 chars, already-compressed) + | + v +dedup annotations + | + v +code-split check (code fences + prose >= 80 chars) + | + v + ┌────────────────────────────────────────────────────┐ + │ CLASSIFICATION (this is the injection point) │ + │ │ + │ off: classifyMessage() → hard T0 → preserve │ + │ else → compress │ + │ │ + │ hybrid: classifyMessage() → hard T0 → preserve │ + │ if low-confidence → llmClassify() → │ + │ preserve or compress │ + │ │ + │ full: llmClassify() → preserve or compress │ + └────────────────────────────────────────────────────┘ + | + v +JSON check → preserve + | + v +compress (summarize, merge, size guard) +``` + +The classification decision happens inside `classifyAll()` in `compress.ts` (lines 523-582). This is the only function that needs to change. The heuristic `classifyMessage()` in `classify.ts` stays untouched. + +--- + +## API design + +### The `Classifier` type + +Mirrors the `Summarizer` pattern: + +```ts +type ClassifyResult = { + decision: 'preserve' | 'compress'; + confidence: number; + reason: string; +}; + +type Classifier = (content: string) => ClassifyResult | Promise; +``` + +The LLM returns structured output: a decision (preserve or compress), a confidence score, and a reason explaining why. The reason is advisory (for debugging/logging), not consumed by the pipeline. + +Note: The existing `ClassifyResult` type in `classify.ts` uses `T0 | T2 | T3` internally. The LLM classifier uses `preserve | compress` because the tier distinction (T0/T2/T3) is a heuristic implementation detail. From the LLM's perspective, the question is binary: "should this content be preserved verbatim, or is it safe to compress?" + +### `CompressOptions` addition + +```ts +type CompressOptions = { + // ... existing options ... + + /** LLM-powered classifier. Determines which messages to preserve vs. compress. + * When provided, compress() returns a Promise. + * Default behavior: heuristic classification only (classifier off). */ + classifier?: Classifier; + + /** Classification mode. Controls how the LLM classifier interacts with heuristics. + * - 'hybrid': Heuristics first, LLM for low-confidence cases (default when classifier is set) + * - 'full': LLM classifies every message, heuristics skipped + * Ignored when classifier is not set. */ + classifierMode?: 'hybrid' | 'full'; + + /** Custom patterns to force T0 (preserve) classification. + * Injected at runtime alongside the built-in FORCE_T0_PATTERNS. + * Allows domain-specific preservation without an LLM. */ + preservePatterns?: Array<{ re: RegExp; label: string }>; +}; +``` + +Design decisions: + +- **No `classifierMode: 'off'`** — omitting the `classifier` option is "off". No redundant state. +- **Default when classifier is set** — `'hybrid'`. Most cost-effective, and mirrors how the summarizer defaults to the safe path. +- **Triggers async** — like `summarizer`, providing a `classifier` makes `compress()` return a `Promise`. + +### `createClassifier` factory + +```ts +type CreateClassifierOptions = { + /** Domain-specific instructions for the LLM. This is critical for non-code use cases. */ + systemPrompt?: string; + + /** Content types to always preserve, regardless of LLM decision. + * Examples: 'clause references', 'patient identifiers', 'theorem statements' */ + alwaysPreserve?: string[]; + + /** Content types that are always safe to compress. + * Examples: 'pleasantries', 'meta-commentary', 'acknowledgments' */ + alwaysCompress?: string[]; + + /** Maximum tokens for the LLM response. Default: 100 (classification is terse). */ + maxResponseTokens?: number; +}; + +function createClassifier( + callLlm: (prompt: string) => string | Promise, + options?: CreateClassifierOptions, +): Classifier; +``` + +Design decisions: + +- **`systemPrompt` is the primary customization point.** This is where domain knowledge lives. A legal prompt looks completely different from a medical one. This is the "custom prompt" we discussed. +- **`alwaysPreserve` and `alwaysCompress`** — structured lists that get injected into the prompt. More machine-friendly than asking users to encode everything in prose. +- **No `mode` option** — unlike the summarizer, the classifier doesn't have normal/aggressive. The decision is binary. +- **Low `maxResponseTokens`** — classification responses are short (a decision + one sentence reason). No need for 300 tokens. + +### `createEscalatingClassifier` factory + +Mirrors `createEscalatingSummarizer`: + +```ts +function createEscalatingClassifier( + callLlm: (prompt: string) => string | Promise, + options?: CreateClassifierOptions, +): Classifier; +``` + +Escalation levels: + +1. **LLM classification** — send content to LLM, parse structured response +2. **Deterministic fallback** — if LLM throws, returns unparseable output, or times out, fall back to heuristic `classifyMessage()` + +This ensures the classifier never blocks the pipeline. LLM failures gracefully degrade to heuristic behavior. + +--- + +## The classifier prompt + +The prompt needs to be structured enough to get reliable output, but flexible enough for domain customization. + +### Base prompt template + +``` +{systemPrompt} + +Classify the following message for a context compression engine. + +Your task: Decide whether this message should be PRESERVED verbatim or can be safely COMPRESSED (summarized). + +Preserve content that: +- Contains critical decisions, conclusions, or commitments +- Would lose meaning if paraphrased +- Contains domain-specific terms, definitions, or references that must stay exact +{alwaysPreserve as bullet points} + +Compress content that: +- Is general discussion, explanation, or elaboration +- Can be summarized without losing actionable information +- Contains filler, pleasantries, or redundant restatements +{alwaysCompress as bullet points} + +Respond with EXACTLY this JSON format, nothing else: +{"decision": "preserve" | "compress", "confidence": 0.0-1.0, "reason": "one sentence"} + +Message: +{content} +``` + +### Why structured JSON output + +- **Parseable** — regex/JSON.parse, no ambiguity +- **Machine-friendly** — the confidence score feeds back into the pipeline for potential future use (logging, metrics, debugging) +- **Small** — a single JSON line is ~50-80 tokens in the response, keeping costs down + +### Domain-specific prompt examples + +**Legal:** + +```ts +const classifier = createClassifier(callLlm, { + systemPrompt: + 'You are classifying content from legal documents (contracts, briefs, court filings).', + alwaysPreserve: [ + 'clause references and numbers (e.g., Section 4.2, Article III)', + 'defined terms (capitalized terms with specific legal meaning)', + 'party names and roles', + 'dates, deadlines, and time periods', + 'monetary amounts and payment terms', + 'obligations (shall, must, agrees to)', + 'conditions and contingencies', + 'governing law and jurisdiction references', + ], + alwaysCompress: [ + 'recitals and background context already summarized', + 'boilerplate acknowledgments', + 'procedural correspondence (scheduling, confirmations)', + ], +}); +``` + +**Medical / Clinical:** + +```ts +const classifier = createClassifier(callLlm, { + systemPrompt: 'You are classifying content from medical records and clinical notes.', + alwaysPreserve: [ + 'diagnoses and ICD codes', + 'medication names, dosages, and frequencies', + 'lab values and vital signs with numbers', + 'allergies and contraindications', + 'procedure descriptions and outcomes', + 'patient identifiers and dates of service', + ], + alwaysCompress: [ + 'general health education text', + 'administrative notes about scheduling', + 'repeated disclaimer language', + ], +}); +``` + +**Academic / Research:** + +```ts +const classifier = createClassifier(callLlm, { + systemPrompt: 'You are classifying content from academic papers and research documents.', + alwaysPreserve: [ + 'citations and references (author names, years, DOIs)', + 'statistical results (p-values, confidence intervals, effect sizes)', + 'methodology descriptions', + 'theorem statements and proofs', + 'figure and table references', + 'dataset descriptions and sample sizes', + ], + alwaysCompress: [ + 'literature review summaries of well-known background', + 'verbose transitions between sections', + 'acknowledgments and funding boilerplate', + ], +}); +``` + +**Novel / Creative writing:** + +```ts +const classifier = createClassifier(callLlm, { + systemPrompt: 'You are classifying content from fiction and creative writing.', + alwaysPreserve: [ + 'dialogue (direct speech)', + 'character names and descriptions on first appearance', + 'plot-critical events and reveals', + 'setting descriptions that establish atmosphere', + 'foreshadowing and symbolic elements', + ], + alwaysCompress: [ + 'transitional passages between scenes', + 'repetitive internal monologue', + 'extended descriptions of routine actions', + ], +}); +``` + +--- + +## Integration with `compress.ts` + +### Current flow (simplified) + +```ts +// classifyAll() — lines 523-582 in compress.ts +function classifyAll(messages, preserveRoles, recencyWindow, dedupAnnotations) { + return messages.map((msg, idx) => { + // ... preservation rules (role, recency, tool_calls, <120 chars, already-compressed) ... + // ... dedup check ... + // ... code-split check ... + + // THE CLASSIFICATION POINT (lines 566-575) + if (content) { + const cls = classifyMessage(content); + if (cls.decision === 'T0') { + const hasHardReason = cls.reasons.some((r) => HARD_T0_REASONS.has(r)); + if (hasHardReason) return { msg, preserved: true }; + } + } + + // ... JSON check ... + return { msg, preserved: false }; + }); +} +``` + +### New flow + +`classifyAll` becomes async-capable when a classifier is provided. The function signature changes: + +```ts +// Overloaded: sync when no classifier, async when classifier provided +function classifyAll( + messages: Message[], + preserveRoles: Set, + recencyWindow: number, + dedupAnnotations?: Map, + classifier?: Classifier, + classifierMode?: 'hybrid' | 'full', +): Classified[] | Promise; +``` + +The internal logic for the classification point: + +```ts +// MODE: off (no classifier provided) +// Unchanged from current behavior +if (content) { + const cls = classifyMessage(content); + if (cls.decision === 'T0' && cls.reasons.some((r) => HARD_T0_REASONS.has(r))) { + return { msg, preserved: true }; + } +} + +// MODE: hybrid (classifier provided, mode = 'hybrid') +if (content) { + const cls = classifyMessage(content); + if (cls.decision === 'T0' && cls.reasons.some((r) => HARD_T0_REASONS.has(r))) { + return { msg, preserved: true }; // high-confidence structural — skip LLM + } + // Low-confidence prose — ask the LLM + const llmResult = await classifier(content); + if (llmResult.decision === 'preserve') { + return { msg, preserved: true }; + } +} + +// MODE: full (classifier provided, mode = 'full') +if (content) { + const llmResult = await classifier(content); + if (llmResult.decision === 'preserve') { + return { msg, preserved: true }; + } +} +``` + +### Sync/async routing in `compress()` + +The existing routing logic already handles this pattern: + +```ts +export function compress(messages, options) { + const hasSummarizer = !!options.summarizer; + const hasClassifier = !!options.classifier; + const isAsync = hasSummarizer || hasClassifier; + + if (isAsync) { + // async paths + if (hasBudget) return compressAsyncWithBudget(messages, options); + return compressAsync(messages, options); + } + + // sync paths (unchanged) + if (hasBudget) return compressSyncWithBudget(messages, options); + return compressSync(messages, options); +} +``` + +The function overload signatures need one addition: + +```ts +// Existing +function compress(messages: Message[], options?: CompressOptions): CompressResult; +function compress( + messages: Message[], + options: CompressOptions & { summarizer: Summarizer }, +): Promise; +// New +function compress( + messages: Message[], + options: CompressOptions & { classifier: Classifier }, +): Promise; +``` + +--- + +## File structure + +### Decision: flat layout, single new file + +The source stays flat. No subdirectories. The classifier follows the same pattern as +the summarizer — a single file containing factory functions, prompt builder, and +response parser. + +**Why not a subdirectory?** Every other concern in this library (summarizer, dedup, +expand, classify) is a single file. A `classifier/` directory with 3-4 small files +would be inconsistent. The classifier is ~130-150 lines — proportional to +`summarizer.ts` (87 lines). + +**Why not extract the analyzer?** `classifyAll()` in `compress.ts` produces +`Classified[]`, an internal type consumed only by `compressSync`/`compressAsync` in +the same file. Extracting it would split tightly coupled code for organizational +purity without a real benefit. The mode routing adds ~20 lines to an existing 60-line +function. + +**Naming:** `classify.ts` = heuristic pattern detection, `classifier.ts` = LLM +classification factory. The orchestration (`classifyAll`) stays in `compress.ts`. + +``` +src/ + classify.ts ← UNTOUCHED. Heuristic pattern detection (regex, structural). + classifier.ts ← NEW. LLM classifier factory (~130-150 lines). + - createClassifier(callLlm, options?) + - createEscalatingClassifier(callLlm, options?) + - buildClassifierPrompt(content, options) [internal] + - parseClassifierResponse(response) [internal] + compress.ts ← MODIFIED. classifyAll gains classifier/mode params, + compress() async routing adds classifier check. + dedup.ts ← UNTOUCHED. + expand.ts ← UNTOUCHED. + index.ts ← MODIFIED. New exports. + summarizer.ts ← UNTOUCHED. + types.ts ← MODIFIED. Classifier, CreateClassifierOptions, CompressOptions. + +tests/ + classifier.test.ts ← NEW. + classify.test.ts ← UNTOUCHED. + compress.test.ts ← MODIFIED. Integration tests for hybrid/full modes. + dedup.test.ts ← UNTOUCHED. + expand.test.ts ← UNTOUCHED. + summarizer.test.ts ← UNTOUCHED. +``` + +### `classifier.test.ts` coverage + +- `createClassifier` factory (prompt generation, response parsing) +- `createEscalatingClassifier` fallback behavior (LLM fail → heuristic) +- `parseClassifierResponse` robustness (clean JSON, JSON with preamble, + markdown code blocks, garbage → null) +- Prompt customization (systemPrompt, alwaysPreserve, alwaysCompress) +- Integration with `compress()` in hybrid and full modes +- Edge cases (empty content, LLM returns empty string, unparseable response) + +--- + +## `CompressResult` additions + +```ts +type CompressResult = { + // ... existing fields ... + compression: { + // ... existing fields ... + /** Messages classified by LLM (when classifier is provided). */ + messages_llm_classified?: number; + /** Messages where LLM overrode the heuristic (hybrid mode). */ + messages_llm_preserved?: number; + }; +}; +``` + +These stats let users understand how much the LLM classifier contributed. + +--- + +## Response parsing + +The LLM response parser needs to handle: + +1. **Clean JSON** — `{"decision": "preserve", "confidence": 0.9, "reason": "contains legal clause reference"}` +2. **JSON with surrounding text** — `Here is my analysis:\n{"decision": "compress", ...}` +3. **Markdown code blocks** — `json\n{"decision": "compress", ...}\n` +4. **Malformed JSON** — fall back to heuristic + +```ts +function parseClassifierResponse(response: string): ClassifyResult | null { + // Try direct JSON.parse + // Try extracting JSON from response (first { to last }) + // Try extracting from code block + // Return null if unparseable → triggers fallback +} +``` + +--- + +## Cost analysis + +### Hybrid mode + +Assume a 100-message conversation: + +- ~20 preserved by hard rules (system, recency, tool_calls, short) +- ~30 preserved by hard T0 (code, JSON, SQL, API keys) +- ~50 fall into the prose bucket → sent to LLM classifier +- At ~200 tokens per classification call (prompt + response): **~10K tokens total** +- With Haiku: ~$0.001 for the entire conversation + +### Full mode + +- Same 100 messages, 80 eligible after hard rules +- 80 LLM calls: **~16K tokens total** +- With Haiku: ~$0.002 + +For comparison, a single LLM summarization call typically costs more than all classification calls combined. Classification is cheap because the responses are tiny. + +--- + +## Documentation plan + +### New documentation + +| Document | Audience | Content | +| ------------------------------- | --------- | -------------------------------------------------------------------------------------------- | +| `docs/llm-classifier.md` | Users | How to use the classifier: modes, prompt customization, domain examples, cost considerations | +| `docs/domain-prompts.md` | Users | Curated prompt examples for common domains (legal, medical, academic, creative, financial) | +| `docs/design/llm-classifier.md` | Engineers | This document. Architecture, rationale, integration points | + +### Updated documentation + +| Document | Changes | +| ------------------------------ | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| `docs/api-reference.md` | Add `Classifier` type, `CreateClassifierOptions`, `classifier`/`classifierMode` in `CompressOptions`, `createClassifier`/`createEscalatingClassifier` exports, new `CompressResult` stats | +| `docs/llm-integration.md` | Add classifier section alongside summarizer, link to `llm-classifier.md` | +| `docs/compression-pipeline.md` | Update pipeline diagram to show classification injection point with modes | +| `docs/preservation-rules.md` | Add section on LLM-driven classification and how it overrides/supplements heuristics | +| `README.md` | Add classifier to features list, add to API overview, add to docs table | + +--- + +## Why we don't expand the heuristic classifier with domain patterns + +The heuristic classifier (`classify.ts`) is tuned for code and technical content. When +used on legal documents, medical records, or academic papers, it's essentially blind — +everything without structural markers (code fences, JSON, SQL, API keys) falls into the +prose bucket and gets compressed. + +We considered three approaches and rejected two: + +### Rejected: expand `classify.ts` with domain-specific patterns + +Adding regex for legal clause references (`§ 4.2`, `Article III`), ICD codes (`J18.9`), +DOIs (`doi:10.1000/...`), statistical notation (`p < 0.05`, `χ² = 12.3`), etc. + +Problems: + +- **File bloat.** The patterns accumulate. Every domain adds 10-20 regex patterns, most + irrelevant to most users. `classify.ts` grows from a focused structural detector into + an unfocused grab-bag of domain trivia. +- **Cross-domain conflicts.** "Section" in a legal document is a clause reference. In a + technical doc it's just a word. "Compound" in a medical record is a medication detail. + In chemistry it's a structural formula. In software it's a design pattern. The same + token triggers different preservation decisions depending on domain, and regex can't + resolve that ambiguity — it has no context. +- **Maintenance burden.** Every pattern needs tests. False positives in one domain break + another. The classifier becomes fragile because it tries to serve everyone. +- **Diminishing returns.** The easy patterns (section numbers, ICD codes) are finite. + The hard cases (is this paragraph a material obligation or boilerplate?) are semantic + and regex will never solve them. Investing in heuristics hits a ceiling quickly. + +### Rejected: multiple domain-specific classifiers + +Ship `classifyLegal()`, `classifyMedical()`, `classifyAcademic()`, etc. User picks one. + +Problems: + +- **N classifiers = N test suites.** Each domain classifier needs comprehensive tests + with real-world examples. We'd need legal expertise to write legal classification tests, + medical expertise for medical tests, etc. +- **Combinatorial explosion.** What about a medical-legal document? A technical paper with + code samples? The domains aren't mutually exclusive, and composing classifiers is a + hard problem. +- **Every new domain is a feature request.** Users in finance, architecture, journalism, + or government would need us to build their classifier. The library becomes a bottleneck + for domain support. +- **Ships dead code.** A user compressing legal documents ships medical, academic, and + creative writing patterns they never use. Contradicts the zero-bloat philosophy. + +### Chosen: LLM classifier + `preservePatterns` escape hatch + +The domain-specific classification problem is fundamentally semantic. "Is this paragraph +a material obligation or boilerplate?" is a question about meaning, not pattern. That's +exactly what the LLM classifier solves — the user provides domain context via +`systemPrompt`, `alwaysPreserve`, and `alwaysCompress`, and the LLM understands the +domain. + +But not every user wants or can use an LLM. Offline environments, cost-sensitive +pipelines, and air-gapped systems need a deterministic path. For these cases, +`preservePatterns` is a minimal escape hatch: + +```ts +// Legal — offline, no LLM +compress(messages, { + preservePatterns: [ + { re: /§\s*\d+(\.\d+)*/i, label: 'section_reference' }, + { re: /\bArticle\s+[IVX]+\b/i, label: 'article_reference' }, + { re: /\b(herein|thereof|hereby|hereinafter|whereupon)\b/i, label: 'legal_term' }, + { re: /\b(Licensor|Licensee|Borrower|Lender|Guarantor)\b/, label: 'party_role' }, + ], +}); + +// Medical — offline, no LLM +compress(messages, { + preservePatterns: [ + { re: /\b[A-Z]\d{2}(\.\d{1,2})?\b/, label: 'icd_code' }, + { re: /\b\d+\s*(mg|mcg|mL|units)\b/i, label: 'dosage' }, + { re: /\b(BP|HR|SpO2|RR|GCS)\s*[\d/]+/, label: 'vital_sign' }, + ], +}); + +// Academic — offline, no LLM +compress(messages, { + preservePatterns: [ + { re: /\bdoi:\s*10\.\d{4,}\/\S+/i, label: 'doi' }, + { re: /\bp\s*[<>=]\s*0?\.\d+/i, label: 'p_value' }, + { re: /\([\w\s]+et\s+al\.,?\s*\d{4}\)/, label: 'citation' }, + ], +}); +``` + +Why this works: + +- **Users own their patterns.** No domain expertise needed in the library. A legal team + writes legal patterns. A medical team writes medical patterns. We ship none. +- **Zero library bloat.** `preservePatterns` is an empty array by default. No dead code. +- **Composable.** A medical-legal document? Merge both pattern arrays. No combinator + problem. +- **Same mechanism.** Patterns are injected into the existing `FORCE_T0_PATTERNS` loop + at runtime. No new code path — just more patterns in the same scan. +- **Sync and deterministic.** Works offline, no LLM, no cost, no latency. +- **Documented, not coded.** We ship domain pattern examples in `docs/domain-prompts.md` + as copy-paste recipes. Users adapt them. We don't maintain them as code. + +### How the three layers compose + +| Layer | Cost | Latency | Accuracy | When to use | +| -------------------- | ------- | ------- | --------------- | -------------------------------------- | +| Heuristic (built-in) | Free | <1ms | High for code | Code/technical content (default) | +| `preservePatterns` | Free | <1ms | Medium (regex) | Offline domain use, known patterns | +| LLM classifier | ~$0.001 | ~100ms | High (semantic) | Domain content requiring understanding | + +All three are optional and composable. A user can use `preservePatterns` alone, +`classifier` alone, or both together. In hybrid mode with `preservePatterns`, the +evaluation order is: built-in heuristics → custom patterns → LLM (if still +low-confidence). Each layer narrows the set of messages that need the next layer. + +### Implementation note + +`preservePatterns` requires a small change in `classifyAll()` in `compress.ts` — the +custom patterns are checked after the built-in classification and before the LLM +classifier. If any custom pattern matches, the message is preserved as hard T0 (same +as a code fence or JSON detection). The patterns are also added as reasons in the +`ClassifyResult` for transparency. + +Alternatively, the patterns could be injected into `classifyMessage()` via a parameter, +keeping all pattern evaluation in `classify.ts`. This is a minor implementation choice +that doesn't affect the API. + +--- + +## Open questions + +### 1. Batching + +Should the classifier support batch classification? Instead of N individual LLM calls, send all eligible messages in a single prompt: + +``` +Classify each of the following messages. Respond with a JSON array. + +Message 1: ... +Message 2: ... +``` + +**Pros:** Dramatically fewer API calls (1 instead of N), lower latency, context between messages helps classification. +**Cons:** Larger prompt = higher per-call cost, risk of partial failure, harder to parse, max context window limits. + +**Recommendation:** Start without batching. The per-message approach is simpler, more robust, and the cost is already low. Batching can be added later as an optimization without API changes. + +### 2. Caching + +Should we cache classification results? Messages with identical content could reuse previous LLM classifications. + +**Recommendation:** Not in v1. The caller can implement caching in their `callLlm` function. Keep the library stateless. + +### 3. Confidence threshold for hybrid mode + +In hybrid mode, what heuristic confidence threshold triggers the LLM? Currently, all prose gets confidence 0.65. + +**Recommendation:** Don't expose this as an option in v1. The internal logic is simple: hard T0 = skip LLM, everything else = ask LLM. If we later improve the heuristic classifier's confidence scoring, the threshold becomes meaningful. + +--- + +## Summary + +This feature adds three composable classification layers to the compression pipeline: + +1. **Built-in heuristics** (`classify.ts`) — structural pattern detection for code/technical content. Untouched. +2. **`preservePatterns`** — user-supplied regex patterns for offline domain support. Injected at runtime, zero library bloat. +3. **LLM classifier** (`classifier.ts`) — semantic classification for domain-specific content. Factory functions, `callLlm` injection, async routing, deterministic fallback. Follows the summarizer pattern exactly. + +The heuristic classifier is not expanded with domain patterns. Domain-specific classification is a semantic problem, not a syntactic one. Regex can detect `§ 4.2` but can't decide whether a paragraph is a material obligation or boilerplate. The LLM classifier solves the semantic problem. `preservePatterns` solves the offline/deterministic case for known patterns. + +The API surface grows by two factory functions (`createClassifier`, `createEscalatingClassifier`), two types (`Classifier`, `CreateClassifierOptions`), and three options on `CompressOptions` (`classifier`, `classifierMode`, `preservePatterns`). All additive, non-breaking. diff --git a/docs/llm-integration.md b/docs/llm-integration.md index d9425bc..b29f259 100644 --- a/docs/llm-integration.md +++ b/docs/llm-integration.md @@ -215,6 +215,122 @@ const summarizer = async (text: string) => { }; ``` +## Classifier interface + +```ts +type Classifier = (content: string) => ClassifierResult | Promise; +type ClassifierResult = { decision: 'preserve' | 'compress'; confidence: number; reason: string }; +``` + +The classifier decides whether each message should be preserved verbatim or compressed. It complements the summarizer — the summarizer controls _how_ to compress, the classifier controls _what_ to compress. + +## `createClassifier` + +Wraps your LLM call with a classification prompt: + +```ts +import { createClassifier, compress } from 'context-compression-engine'; + +const classifier = createClassifier(async (prompt) => myLlm.complete(prompt), { + systemPrompt: 'You are classifying content from legal documents.', + alwaysPreserve: ['clause references', 'defined terms', 'party names'], + alwaysCompress: ['boilerplate acknowledgments'], +}); + +const result = await compress(messages, { classifier }); +``` + +### Domain examples + +**Legal:** + +```ts +const classifier = createClassifier(callLlm, { + systemPrompt: + 'You are classifying content from legal documents (contracts, briefs, court filings).', + alwaysPreserve: [ + 'clause references and numbers (e.g., Section 4.2, Article III)', + 'defined terms (capitalized terms with specific legal meaning)', + 'party names and roles', + 'dates, deadlines, and time periods', + 'monetary amounts and payment terms', + 'obligations (shall, must, agrees to)', + ], + alwaysCompress: [ + 'recitals and background context already summarized', + 'boilerplate acknowledgments', + 'procedural correspondence (scheduling, confirmations)', + ], +}); +``` + +**Medical:** + +```ts +const classifier = createClassifier(callLlm, { + systemPrompt: 'You are classifying content from medical records and clinical notes.', + alwaysPreserve: [ + 'diagnoses and ICD codes', + 'medication names, dosages, and frequencies', + 'lab values and vital signs with numbers', + 'allergies and contraindications', + 'procedure descriptions and outcomes', + ], + alwaysCompress: [ + 'general health education text', + 'administrative notes about scheduling', + 'repeated disclaimer language', + ], +}); +``` + +**Academic:** + +```ts +const classifier = createClassifier(callLlm, { + systemPrompt: 'You are classifying content from academic papers and research documents.', + alwaysPreserve: [ + 'citations and references (author names, years, DOIs)', + 'statistical results (p-values, confidence intervals, effect sizes)', + 'methodology descriptions', + 'theorem statements and proofs', + ], + alwaysCompress: [ + 'literature review summaries of well-known background', + 'verbose transitions between sections', + 'acknowledgments and funding boilerplate', + ], +}); +``` + +## `createEscalatingClassifier` + +Tries the LLM first, falls back to heuristic classification on failure: + +```ts +import { createEscalatingClassifier, compress } from 'context-compression-engine'; + +const classifier = createEscalatingClassifier(async (prompt) => myLlm.complete(prompt), { + systemPrompt: 'Legal documents.', +}); + +const result = await compress(messages, { classifier }); +``` + +If the LLM throws, returns unparseable output, or returns confidence=0, the escalating classifier falls back to the built-in heuristic `classifyMessage()`. Hard T0 heuristic results become `preserve`, everything else becomes `compress`. + +## Classifier + Summarizer + +Both can be used together. The classifier decides _what_ to compress, the summarizer decides _how_: + +```ts +const result = await compress(messages, { + classifier, + summarizer, + classifierMode: 'hybrid', +}); +``` + ## Model recommendations Fast, cheap models work best for compression summarization. The task is straightforward (shorten text while preserving technical terms), so frontier models are overkill. diff --git a/docs/preservation-rules.md b/docs/preservation-rules.md index 1060e07..37df793 100644 --- a/docs/preservation-rules.md +++ b/docs/preservation-rules.md @@ -8,19 +8,21 @@ What gets preserved, what gets compressed, and why. Messages are evaluated in this order. The **first matching rule** determines the outcome: -| Priority | Rule | Outcome | -| -------- | ----------------------------------------------------------- | --------------- | -| 1 | Role in `preserve` list | Preserved | -| 2 | Within `recencyWindow` | Preserved | -| 3 | Has `tool_calls` array | Preserved | -| 4 | Content < 120 chars | Preserved | -| 5 | Already compressed (`[summary:`, `[summary#`, `[truncated`) | Preserved | -| 6 | Duplicate (exact or fuzzy) | Dedup path | -| 7 | Code fences + prose >= 80 chars | Code-split path | -| 8 | Code fences + prose < 80 chars | Preserved | -| 9 | Hard T0 classification | Preserved | -| 10 | Valid JSON | Preserved | -| 11 | Everything else | Compressed | +| Priority | Rule | Outcome | +| -------- | ----------------------------------------------------------- | ------------------------- | +| 1 | Role in `preserve` list | Preserved | +| 2 | Within `recencyWindow` | Preserved | +| 3 | Has `tool_calls` array | Preserved | +| 4 | Content < 120 chars | Preserved | +| 5 | Already compressed (`[summary:`, `[summary#`, `[truncated`) | Preserved | +| 6 | Duplicate (exact or fuzzy) | Dedup path | +| 7 | Code fences + prose >= 80 chars | Code-split path | +| 8 | Code fences + prose < 80 chars | Preserved | +| 9 | Hard T0 classification (skipped in `full` mode) | Preserved | +| 10 | Custom `preservePatterns` match | Preserved | +| 11 | LLM classifier (when `classifier` is provided) | Preserved or fall through | +| 12 | Valid JSON | Preserved | +| 13 | Everything else | Compressed | Soft T0 classifications (file paths, URLs, version numbers, etc.) do **not** prevent compression — entities capture the important references, and the prose is still compressible. @@ -47,6 +49,7 @@ Content with structural patterns that would be destroyed by summarization. | `unicode_math` | Mathematical symbols | | `sql_content` | SQL keyword density (strong anchors like `GROUP BY`, `PRIMARY KEY` or 3+ distinct keywords with a weak anchor) | | `verse_pattern` | Poetry/verse pattern (consecutive capitalized lines without terminal punctuation) | +| `reasoning_chain` | Reasoning chains: explicit labels (`Reasoning:`, `Proof:`), formal inference, or 3+ logical connectives | **Soft T0 reasons** (do not prevent compression): @@ -68,11 +71,11 @@ Soft T0 content is still compressible because the entity extraction step capture ### T2 — Short prose -Prose under 20 words. Currently treated the same as T3 in the compression pipeline. +Prose under 20 words. Treated identically to T3 in the current deterministic pipeline — the distinction is preserved for future LLM classifier integration, which can apply lighter compression to short prose. ### T3 — Long prose -Prose of 20+ words. The primary target for summarization. +Prose of 20+ words. The primary target for summarization. Treated identically to T2 in the current pipeline; the LLM classifier will use the T2/T3 distinction for tier-specific strategies. ## API key detection @@ -103,7 +106,7 @@ SQL detection uses a tiered anchor system to avoid false positives on English pr Messages with code fences and significant prose (>= 80 chars) are split: 1. Code fences are extracted verbatim -2. Surrounding prose is summarized (budget: 200 chars if < 600 chars, 400 otherwise) +2. Surrounding prose is summarized (budget scales adaptively: 200–600 chars based on prose length) 3. Result: summary + preserved code fences If the total prose is < 80 chars, the entire message is preserved (not enough prose to justify splitting). @@ -143,6 +146,85 @@ compress(messages, { recencyWindow: 10 }); // protect last 10 compress(messages, { recencyWindow: 0 }); // no recency protection ``` +### `preservePatterns` option + +Force preservation of messages matching domain-specific regex patterns. Each pattern is a hard T0 — the message is preserved verbatim, no summarization. Patterns are checked after the built-in heuristic classifier but before JSON detection. + +```ts +compress(messages, { + preservePatterns: [ + { re: /§\s*\d+/, label: 'section_ref' }, + { re: /\d+\s*mg\b/i, label: 'dosage' }, + ], +}); +``` + +**Domain examples:** + +**Legal** — preserve clause references, case citations, regulatory references: + +```ts +preservePatterns: [ + { re: /§\s*\d+/, label: 'section_ref' }, + { re: /\b\d+\s+U\.S\.C\.\s*§/, label: 'usc_cite' }, + { re: /\bArticle\s+[IVX]+\b/, label: 'article_ref' }, + { re: /\bGDPR\s+Art\.\s*\d+/, label: 'gdpr_ref' }, +]; +``` + +**Medical** — preserve dosages, diagnostic codes, lab values: + +```ts +preservePatterns: [ + { re: /\d+\s*mg\b/i, label: 'dosage' }, + { re: /\bICD-10:\s*[A-Z]\d+/i, label: 'icd_code' }, + { re: /\bCPT\s+\d{5}/, label: 'cpt_code' }, + { re: /\bBP\s+\d+\/\d+/, label: 'vital_sign' }, +]; +``` + +**Academic** — preserve DOIs, citation markers, theorem references: + +```ts +preservePatterns: [ + { re: /\bdoi:\s*10\.\d{4,}/, label: 'doi' }, + { re: /\[(\d+(?:,\s*\d+)*)\]/, label: 'citation_marker' }, + { re: /\bTheorem\s+\d+/i, label: 'theorem_ref' }, +]; +``` + +The stat `compression.messages_pattern_preserved` reports how many messages were preserved by custom patterns. + +### `classifier` option + +LLM-powered classification for domain-specific content. When provided, `compress()` returns a `Promise`. The classifier runs once before the pipeline (pre-classification) so that `tokenBudget` binary search doesn't re-classify messages on each iteration. + +The `classifierMode` option controls how the LLM classifier interacts with heuristics: + +| Mode | Behavior | When to use | +| ---------- | ---------------------------------------------------------------------- | --------------------------------------------- | +| `'hybrid'` | Heuristics first; LLM only for messages that aren't hard T0 (default) | Best cost/accuracy tradeoff. Most use cases. | +| `'full'` | Heuristic classification skipped; LLM classifies all eligible messages | Domain content where heuristics add no value. | + +In both modes, standard preservation rules (role, recency window, tool_calls, short content, already-compressed) still apply — the classifier only sees messages that pass those checks. + +```ts +import { createClassifier, compress } from 'context-compression-engine'; + +const classifier = createClassifier(callLlm, { + systemPrompt: 'You are classifying content from medical records.', + alwaysPreserve: ['diagnoses', 'medication dosages', 'lab values'], +}); + +const result = await compress(messages, { + classifier, + classifierMode: 'hybrid', +}); + +console.log(result.compression.messages_llm_classified); // messages sent to LLM +console.log(result.compression.messages_llm_preserved); // messages LLM decided to preserve +``` + --- ## See also diff --git a/docs/quality-history.md b/docs/quality-history.md new file mode 100644 index 0000000..2b4213f --- /dev/null +++ b/docs/quality-history.md @@ -0,0 +1,107 @@ +# Quality History + +[Back to README](../README.md) | [All docs](README.md) | [Benchmarks](benchmarks.md) | [Latest Results](benchmark-results.md) + +_Generated by running the current quality benchmark suite against v1.0.0, v1.1.0, and v1.2.0 source code._ + +## Version Comparison + +### Compression Ratio + +| Scenario | v1.0.0 | v1.1.0 | v1.2.0 | Trend | +| ----------------------- | -----: | -----: | -----: | ------------------------------ | +| Coding assistant | 1.68x | 1.94x | 1.94x | improved v1.0→v1.1 | +| Long Q&A | 6.16x | 4.90x | 4.90x | reduced (was over-compressing) | +| Tool-heavy | 1.30x | 1.41x | 1.40x | stable | +| Deep conversation | 2.12x | 2.50x | 2.50x | improved v1.0→v1.1 | +| Technical explanation | 1.24x | 1.24x | 1.24x | stable | +| Structured content | 1.24x | 1.26x | 1.26x | stable | +| Agentic coding session | 1.00x | 1.00x | 1.00x | no compression (correct) | +| Giant single message | 2.83x | 2.83x | 2.83x | stable | +| Entity-dense technical | 1.20x | 1.56x | 1.56x | improved v1.0→v1.1 | +| Prose-only conversation | 1.70x | 3.37x | 3.37x | large improvement v1.0→v1.1 | + +### Entity Retention + +| Scenario | v1.0.0 | v1.1.0 | v1.2.0 | Trend | +| ---------------------- | -----: | -----: | -----: | ----------------------- | +| Coding assistant | 94% | 94% | 94% | stable | +| Tool-heavy | 70% | 70% | 80% | improved in v1.2 | +| Structured content | 100% | 68% | 68% | **regressed v1.0→v1.1** | +| Entity-dense technical | 68% | 53% | 53% | **regressed v1.0→v1.1** | +| Mixed languages | 100% | 67% | 67% | **regressed v1.0→v1.1** | + +### Probe Pass Rate + +| Scenario | v1.0.0 | v1.1.0 | v1.2.0 | Trend | +| ----------------------- | -----: | -----: | -----: | ----------------------- | +| Long Q&A | 86% | 100% | 100% | improved | +| Deep conversation | 44% | 33% | 33% | **regressed v1.0→v1.1** | +| Entity-dense technical | 75% | 63% | 63% | **regressed v1.0→v1.1** | +| Prose-only conversation | 50% | 50% | 50% | stable | + +### Code Block Integrity + +100% across all versions and all scenarios. Code preservation has never failed. + +## Key Findings + +### v1.0.0 → v1.1.0: More aggressive, less precise + +v1.1.0 improved compression ratios across the board (Coding assistant 1.68x→1.94x, Prose-only 1.70x→3.37x), but this came at a cost: entity retention dropped on three scenarios where the engine started compressing content it should have preserved: + +- **Structured content**: 100% → 68% entity retention — API keys and config values getting summarized +- **Entity-dense technical**: 68% → 53% — specific identifiers like `redis-prod-001`, `v22.3.0`, `PR #142` dropped +- **Mixed languages**: 100% → 67% — monitoring details lost in compression + +The Long Q&A compression ratio _decreased_ from 6.16x to 4.90x. This is actually an improvement — v1.0.0 was over-compressing, losing the `min output ≥ 800 chars` probe. + +### v1.1.0 → v1.2.0: Stability + +v1.2.0 added flow chains, semantic clusters, and other v2 features, but none of them changed quality metrics when running in default mode. The only improvement was Tool-heavy entity retention (70%→80%). The v2 features are opt-in and don't affect the default compression path. + +## Opt-in Feature Impact (v1.2.0) + +Running the quality benchmark with each opt-in feature enabled reveals their effect on compression quality. + +### importance + contradiction + +No measurable impact on any scenario. These features only activate when messages have clear forward-reference patterns or correction signals — the benchmark scenarios don't trigger them strongly enough. + +### semantic clustering + +Mostly neutral, but **degrades Code-only conversation**: ratio goes from 1.00x to 1.30x with probe pass rate dropping 25% (75% from 100%). The clustering groups code-only messages and compresses them when it shouldn't. + +### conversation flow + +The most impactful feature — both positive and negative: + +| Scenario | Baseline | With flow | Change | +| --------------------- | ------------------ | ---------------------- | ------------------------------------------------------------- | +| Deep conversation | 2.50x, 33% probes | 4.62x, **100% probes** | **+67% probe rate** — groups Q&A pairs, preserves topic names | +| Long Q&A | 4.90x, 100% probes | 11.80x, 71% probes | **-29% probe rate** — over-compresses, loses terms | +| Technical explanation | 1.24x, 86% probes | 2.82x, 57% probes | **-29% probe rate** — loses technical details | +| Structured content | 1.26x, 100% probes | 1.54x, 100% probes | More compression, probes still pass | +| Mixed languages | 1.07x, 100% probes | 1.11x, 100% probes | Minimal change | + +Conversation flow dramatically improves Deep conversation (the worst baseline scenario), but over-compresses Long Q&A and Technical explanation. The 25 coherence issues in Deep conversation (up from 6) suggest the summaries need work even though the topic probes pass. + +### coreference + +Minimal impact. Entity-dense technical ratio drops from 1.56x to 1.27x (less compression) with slightly higher entity retention (57% vs 53%). The coreference tracking is inlining entity definitions into summaries, which preserves more context but reduces compression. + +### all features combined + +Combines the conversation flow wins and losses with semantic clustering's code-only regression: + +- **Deep conversation**: 9/9 probes (up from 3/9) but 25 coherence issues +- **Long Q&A**: 5/7 probes (down from 7/7), entity retention crashes to 7% +- **Code-only conversation**: 3/4 probes (down from 4/4) from clustering +- **Structured content**: entity retention drops to 33% + +## Recommendations + +1. **Conversation flow** should be opt-in per scenario type — it helps long multi-topic conversations but hurts focused technical discussions +2. **Semantic clustering** needs a guard against clustering code-only messages +3. **The v1.1.0 entity retention regression** in Structured content, Entity-dense, and Mixed languages is the most actionable fix — the summarizer should preserve identifiers that v1.0.0 kept +4. **Importance scoring and contradiction detection** need scenarios with stronger signal patterns to validate their impact diff --git a/docs/roadmap-v2.md b/docs/roadmap-v2.md new file mode 100644 index 0000000..dba2af0 --- /dev/null +++ b/docs/roadmap-v2.md @@ -0,0 +1,432 @@ +# CCE v2 Improvement Roadmap + +Working document for systematically improving compression rate, quality, and observability. +Based on a survey of ~20 papers (2023–2026) mapped against the current pipeline. + +**Baseline (v1.1.0):** 2.01x avg compression | 4.90x peak | 42% messages compressed | 100% round-trip integrity + +--- + +## Phase 1 — Quick Wins (low effort, high signal) + +### 1.1 Entity Retention Metric + +**Status:** [ ] Not started +**Files:** `src/compress.ts`, `src/types.ts` +**Papers:** Understanding and Improving Information Preservation (EMNLP 2025 Findings) — arxiv.org/abs/2503.19114 + +**What:** Add `entity_retention` to `CompressResult.compression` — ratio of technical identifiers (camelCase, snake_case, file paths, URLs, version numbers, code refs) preserved after compression vs. before. + +**Why:** We currently report ratio and token_ratio but have no quality signal. Entity retention is concrete, measurable, and we already extract entities in the summarizer. Users get a number they can trust: "95% of identifiers survived." + +**Implementation:** + +- [ ] Extract entities from all input messages (reuse existing entity regex from `compress.ts` lines 120–140) +- [ ] Extract entities from all output messages +- [ ] Compute `entity_retention = entities_in_output / entities_in_input` +- [ ] Add to `CompressResult.compression` type +- [ ] Add to benchmark report output +- [ ] Add test: compress a message with 10 known identifiers, assert retention >= 0.9 + +**Acceptance:** Benchmark reports show entity_retention per scenario. All existing tests pass. + +--- + +### 1.2 Relevance Threshold ("Output Nothing" Strategy) + +**Status:** [ ] Not started +**Files:** `src/compress.ts`, `src/types.ts` +**Papers:** RECOMP (ICLR 2024) — arxiv.org/abs/2310.04408 + +**What:** When no sentence in a T2 message scores above a minimum threshold, replace the entire message with a stub like `[N messages of general discussion omitted]` instead of producing a low-quality summary. Verbatim still stored. + +**Why:** Current pipeline always produces _some_ output for T2 messages, even when content adds nothing. The agentic (1.48x) and tool-heavy (1.41x) scenarios have lots of low-value assistant prose that should be eliminated, not summarized. + +**Implementation:** + +- [ ] Add `relevanceThreshold?: number` to `CompressOptions` (default: off / 0) +- [ ] In summarize stage: if best sentence score < threshold, return stub instead of summary +- [ ] Group consecutive stubbed messages into a single `[N messages omitted]` block +- [ ] Track `messages_relevance_dropped` in stats +- [ ] Verbatim store still holds originals (round-trip integrity preserved) +- [ ] Add test: 5 filler messages in a row → single stub, expandable +- [ ] Benchmark: compare agentic/tool-heavy scenarios with threshold=0.3 vs. off + +**Acceptance:** Agentic scenario moves from 1.48x toward ~1.8x+. Round-trip integrity maintained. No regression on technical/coding scenarios. + +--- + +### 1.3 Compression Quality Score (Composite) + +**Status:** [ ] Not started +**Files:** `src/compress.ts`, `src/types.ts` +**Papers:** Information Preservation paper (EMNLP 2025), Selective Context (EMNLP 2023) + +**What:** Combine entity_retention, structural_integrity (code fences, JSON blocks survived intact), and summary_coherence (no dangling references) into a single `quality_score` in `CompressResult`. + +**Why:** A single number lets users make compression-vs-quality tradeoffs. "I got 3x compression at 0.92 quality" is actionable. + +**Implementation:** + +- [ ] `entity_retention` (from 1.1): weight 0.4 +- [ ] `structural_integrity`: count structural elements (fences, JSON blocks, tables) before/after — weight 0.4 +- [ ] `reference_coherence`: check that identifiers mentioned in kept messages aren't orphaned by removed messages — weight 0.2 +- [ ] `quality_score = weighted sum`, clamped [0, 1] +- [ ] Add to `CompressResult.compression` +- [ ] Benchmark: report quality_score alongside ratio for all scenarios + +**Acceptance:** All scenarios report quality_score >= 0.85. Score is intuitive (1.0 = perfect preservation). + +--- + +## Phase 2 — Budget & Scoring Upgrades (medium effort, compression gain) + +### 2.1 Component-Level Budget Allocation + +**Status:** [ ] Not started +**Files:** `src/compress.ts` +**Papers:** LLMLingua (EMNLP 2023) — arxiv.org/abs/2310.05736 + +**What:** Replace the single binary-search-over-recencyWindow with per-tier budget allocation. Instead of uniformly shrinking the window, allocate token budget across message categories and compress each category to its sub-budget. + +**Why:** Current binary search treats all messages equally. When budget is tight, it shrinks `recencyWindow` which can lose recent important messages. Per-tier allocation compresses old prose aggressively while keeping recent context intact. + +**Tier budget distribution (configurable):** + +``` +System messages: 5% of budget (light compression) +T0 content: pass-through (no compression, counted against budget) +Recent window: 40% of budget (preserved or light compression) +T2 older prose: remaining (aggressive compression) +T3 filler: 0% (removed entirely) +``` + +**Implementation:** + +- [ ] Add `budgetStrategy?: 'binary-search' | 'tiered'` to `CompressOptions` (default: 'binary-search' for backward compat) +- [ ] Implement tiered allocation: count T0 tokens first (fixed cost), distribute remainder +- [ ] Within T2 tier: compress oldest messages most aggressively (sliding scale) +- [ ] Integrate with importance scoring: high-importance T2 messages get more budget +- [ ] Add test: same tokenBudget, tiered vs binary-search — tiered preserves more recent messages +- [ ] Benchmark: compare both strategies across all scenarios + +**Acceptance:** Tiered strategy matches or beats binary-search on all scenarios. Recent messages (last 4) never get truncated when older prose is available to compress. + +--- + +### 2.2 Self-Information Scoring (Optional) + +**Status:** [ ] Not started +**Files:** `src/compress.ts`, `src/types.ts`, new: `src/entropy.ts` +**Papers:** Selective Context (EMNLP 2023) — aclanthology.org/2023.emnlp-main.391 + +**What:** Replace or augment heuristic sentence scoring with information-theoretic scoring. Users provide an `entropyScorer` function that returns per-token surprise values from a small causal LM. High self-information tokens/sentences are preserved; predictable ones pruned. + +**Why:** Heuristic scoring misses context-dependent importance. "The service returns 503" scores low on our heuristics (no camelCase, no emphasis) but "503" is highly surprising in context and crucial to preserve. Self-information captures this automatically. + +**Implementation:** + +- [ ] Add `entropyScorer?: (tokens: string[]) => number[] | Promise` to `CompressOptions` +- [ ] New `src/entropy.ts`: sentence-level self-information aggregation (mean or sum of token scores) +- [ ] In summarize stage: if entropyScorer provided, use it instead of heuristic scoring +- [ ] Fallback: heuristic scoring when no scorer provided (zero-dependency preserved) +- [ ] Hybrid mode: combine entropy + heuristic (weighted average) for best of both +- [ ] Add test with mock scorer: high-entropy sentences preserved, low-entropy pruned +- [ ] Benchmark: compare heuristic vs mock-entropy on all scenarios + +**Acceptance:** With a reasonable entropy scorer, compression ratio improves on prose-heavy scenarios. Deterministic fallback unchanged. Zero new runtime dependencies. + +--- + +### 2.3 Adaptive Summary Budget + +**Status:** [ ] Not started +**Files:** `src/compress.ts` + +**What:** Current summary budget is fixed at 30% of content length, capped 200–600 chars. Make it adaptive based on content density: high-density messages (lots of entities, code refs) get a larger budget; low-density messages (general discussion) get a smaller budget. + +**Why:** A message with 15 technical identifiers in 500 chars needs more summary space than 500 chars of "I think we should consider..." The fixed 30% either wastes budget on filler or under-compresses dense content. + +**Implementation:** + +- [ ] Compute content density: `entities_count / char_count` +- [ ] Scale budget: `base_ratio * (1 + density_bonus)`, where density_bonus = min(density \* k, 0.5) +- [ ] Dense content: up to 45% budget (more room for entities) +- [ ] Sparse content: down to 15% budget (more aggressive compression) +- [ ] Keep hard caps (min 100, max 800 chars) +- [ ] Add test: dense message gets longer summary than sparse message of same length + +**Acceptance:** Entity retention improves on dense messages. Compression ratio improves on sparse messages. No regression on existing tests. + +--- + +## Phase 3 — Structural Intelligence (high effort, quality gain) + +### 3.1 Discourse Unit Decomposition (EDU-Lite) + +**Status:** [ ] Not started +**Files:** new: `src/discourse.ts`, `src/compress.ts` +**Papers:** From Context to EDUs (arXiv Dec 2025) — arxiv.org/abs/2512.14244 + +**What:** Break messages into Elementary Discourse Units and build a lightweight dependency graph. When summarizing, select important subtrees rather than independent sentences. + +**Why:** Sentence-level scoring treats sentences as independent. "Parse the JSON, then extract the user ID from the result" — removing the first sentence makes the second incoherent. Discourse structure captures these dependencies. + +**Implementation (pragmatic / rule-based, no ML):** + +- [ ] Segment sentences into EDUs using clause boundary detection (commas + discourse markers: "then", "so", "because", "which", "but", "however", "therefore") +- [ ] Build dependency edges: pronoun/demonstrative resolution ("it", "this", "that", "the result" → preceding EDU) +- [ ] Temporal chains: "first...then...finally" → sequential dependency +- [ ] Causal chains: "because...therefore" → causal dependency +- [ ] Score EDUs (reuse existing sentence scoring) +- [ ] Selection: when keeping an EDU, also keep its dependency parents (up to 2 levels) +- [ ] Integrate into summarize stage as an alternative to sentence-level scoring +- [ ] Add `discourseAware?: boolean` to `CompressOptions` +- [ ] Test: message with pronoun chain → referent preserved when reference is kept +- [ ] Test: "first X, then Y, finally Z" → keeping Z also keeps X and Y + +**Acceptance:** Compressed output has fewer dangling references. reference_coherence metric (from 1.3) improves. No significant impact on compression ratio. + +--- + +### 3.2 Cross-Message Coreference Tracking + +**Status:** [ ] Not started +**Files:** new: `src/coreference.ts`, `src/compress.ts` + +**What:** Track entity references across messages. When message B refers to an entity defined in message A, and B is kept, A (or at least the defining sentence) should be preserved or its definition inlined into B's summary. + +**Why:** Current pipeline compresses messages independently. If message 3 says "the auth middleware" and message 7 says "update it to use JWT", compressing message 3 can lose what "it" refers to. Cross-message coreference prevents this. + +**Implementation:** + +- [ ] Build entity definition map: first mention of each entity → message index + sentence +- [ ] Build reference map: subsequent mentions → list of message indices that reference it +- [ ] During compression: if a referencing message is kept, check if its referents' defining messages are also kept +- [ ] If not: inline the entity definition into the referencing message's summary, or promote the defining message to preserved +- [ ] Lightweight approach: only track camelCase/snake_case/PascalCase identifiers and explicit noun phrases +- [ ] Add test: entity defined in msg 2, referenced in msg 8 — compressing msg 2 inlines definition into msg 8 +- [ ] Ensure verbatim store still works (inlined definitions are compression artifacts, not original content) + +**Acceptance:** No orphaned references in compressed output. Entity retention metric stays >= 0.95. + +--- + +### 3.3 Conversation Flow Compression + +**Status:** [ ] Not started +**Files:** `src/compress.ts` + +**What:** Detect conversation patterns (question→answer, request→implementation→confirmation) and compress them as units rather than individual messages. + +**Why:** A 3-message exchange "Can you add logging?" → "Done, added logger.info calls in auth.ts and api.ts" → "Perfect" compresses better as a unit: `[User requested logging → added to auth.ts, api.ts → confirmed]` than as 3 independent compressions. + +**Implementation:** + +- [ ] Detect Q&A pairs: user question followed by assistant answer +- [ ] Detect request chains: user request → assistant action → user confirmation +- [ ] Detect correction chains: assertion → correction → acknowledgment +- [ ] Merge detected chains into single compression units +- [ ] Produce chain-aware summaries that capture the arc (request → outcome) +- [ ] Respect importance scoring: high-importance chains get more budget +- [ ] Add `conversationFlow?: boolean` to `CompressOptions` +- [ ] Test: Q&A pair compressed into single summary preserving both question and answer key points + +**Acceptance:** Conversation-heavy scenarios (deep conversation, long Q&A) see improved compression ratio while preserving the logical flow. + +--- + +## Phase 4 — Advanced Compression Modes (medium-high effort, big ratio gains) + +### 4.1 ML Token Classifier (Optional) + +**Status:** [ ] Not started +**Files:** new: `src/ml-classifier.ts`, `src/types.ts` +**Papers:** LLMLingua-2 (ACL 2024) — arxiv.org/abs/2403.12968 + +**What:** Optional token-level keep/remove classifier using a small encoder model (BERT-class). Each token gets a binary label from full bidirectional context. Replaces rule-based classification for users who can run a ~500MB model. + +**Why:** LLMLingua-2 achieves 2-5x compression at 95-98% accuracy retention, 3-6x faster than perplexity methods. Our rule-based classifier works well for structured content but misses nuance in prose. + +**Implementation:** + +- [ ] Define `MLClassifier` interface: `(content: string) => { keep: boolean, confidence: number }[]` +- [ ] Add `mlClassifier` to `CompressOptions` +- [ ] When provided: use ML classifier for T2 content (T0 rules still override for code/structured) +- [ ] Token-level output → reconstruct kept tokens into compressed text +- [ ] Training data: generate from existing test cases + GPT-4 compression pairs +- [ ] Ship as separate optional package (`@cce/ml-classifier`) to keep core zero-dependency +- [ ] Benchmark: compare rule-based vs ML on all scenarios + +**Acceptance:** ML classifier improves compression on prose-heavy scenarios by 30%+. Core package stays zero-dependency. Rule-based fallback unchanged. + +--- + +### 4.2 Progressive Compression Depth + +**Status:** [ ] Not started +**Files:** `src/compress.ts`, `src/types.ts` +**Papers:** LLM-DCP (2025) — arxiv.org/abs/2504.11004, ACON (2025) — arxiv.org/abs/2510.00615 + +**What:** Multi-pass compression with increasing aggressiveness. First pass: gentle (sentence selection). Second pass: moderate (clause pruning). Third pass: aggressive (entity-only stubs). Each pass has quality gates. + +**Why:** Single-pass compression has a fixed quality/ratio tradeoff. Progressive compression lets us push ratios higher while checking quality at each step. If a pass drops quality below threshold, we stop and use the previous pass's output. + +**Implementation:** + +- [ ] Define compression levels: `gentle` (sentence selection, ~2x) → `moderate` (clause pruning + entity stubs, ~4x) → `aggressive` (entity-only, ~8x) +- [ ] Add `compressionDepth?: 'gentle' | 'moderate' | 'aggressive' | 'auto'` to `CompressOptions` +- [ ] `auto` mode: compress progressively until tokenBudget is met or quality_score drops below threshold +- [ ] Quality gate between passes: check entity_retention and reference_coherence +- [ ] Each pass feeds into the next (use previous pass's output as input) +- [ ] Provenance: chain parent_ids across passes (already supported) +- [ ] Test: auto mode with tight budget produces 3-pass compression with quality above threshold +- [ ] Benchmark: compare single-pass vs progressive on deep conversation scenario + +**Acceptance:** Deep conversation scenario (currently 2.50x) reaches 4x+ with quality_score >= 0.80. Progressive mode never produces worse output than single-pass. + +--- + +### 4.3 Semantic Clustering + +**Status:** [ ] Not started +**Files:** new: `src/cluster.ts`, `src/compress.ts` + +**What:** Group messages by topic using lightweight semantic similarity (TF-IDF or entity overlap), then compress each cluster as a unit. Cross-cluster references get bridging stubs. + +**Why:** Long conversations drift across topics. Compressing chronologically misses the opportunity to merge scattered messages about the same topic. "We discussed auth in messages 3, 7, 12, 19" → single compressed block about auth decisions. + +**Implementation:** + +- [ ] Extract topic vectors per message: TF-IDF over content words + entity overlap +- [ ] Cluster using simple agglomerative clustering (no ML dependency) +- [ ] Within each cluster: merge messages chronologically, compress as unit +- [ ] Cross-cluster bridges: when a message references entities from another cluster, add a brief bridge +- [ ] Add `semanticClustering?: boolean` to `CompressOptions` +- [ ] Respect recency window: recent messages stay unclustered +- [ ] Test: 20 messages alternating between 2 topics → 2 compressed cluster summaries +- [ ] Benchmark: long/deep conversation scenarios + +**Acceptance:** Deep conversation (currently 2.50x) and long Q&A (4.90x) improve. Compressed output organized by topic is more coherent than chronological compression. + +--- + +## Phase 5 — Evaluation & Benchmarking Infrastructure + +### 5.1 Quality Benchmark Suite + +**Status:** [ ] Not started +**Files:** `bench/` + +**What:** Automated benchmark that measures compression quality, not just ratio. Run after every change to catch quality regressions. + +**Metrics to track per scenario:** + +- [ ] Compression ratio (existing) +- [ ] Token ratio (existing) +- [ ] Entity retention (from 1.1) +- [ ] Structural integrity (from 1.3) +- [ ] Reference coherence (from 1.3) +- [ ] Quality score (from 1.3) +- [ ] Round-trip integrity (existing) + +**Implementation:** + +- [ ] Extend `bench/run.ts` to compute and report quality metrics +- [ ] Add quality regression detection: fail if quality_score drops > 0.05 from baseline +- [ ] Generate comparison tables: before/after each phase +- [ ] Track metrics history in `bench/baselines/history/` + +**Acceptance:** `npm run bench` reports both ratio and quality. CI fails on quality regression. + +--- + +### 5.2 Adversarial Test Cases + +**Status:** [ ] Not started +**Files:** `tests/` + +**What:** Test cases specifically designed to break compression quality. + +**Cases:** + +- [ ] Pronoun-heavy message: "Do it like we discussed, but change the thing to use the other approach" — tests coreference +- [ ] Scattered entity: entity defined in msg 1, referenced in msgs 5, 10, 15 — tests cross-message tracking +- [ ] Correction chain: 3 contradictory instructions, only last is valid — tests contradiction detection +- [ ] Code interleaved with prose: alternating paragraphs of explanation and code — tests code-split +- [ ] Near-duplicate with critical difference: two messages identical except for one number — tests fuzzy dedup precision +- [ ] Very long single message (10k+ chars): tests per-message compression +- [ ] Mixed languages: English prose with inline SQL, JSON, and shell commands — tests T0 detection +- [ ] Nested structure: JSON containing prose containing code fences — tests recursive classification + +**Acceptance:** All adversarial cases have explicit expected behavior. Tests catch regressions from any phase. + +--- + +### 5.3 A/B Comparison Tool + +**Status:** [ ] Not started +**Files:** `bench/` + +**What:** CLI tool to compress the same input with two different option sets and compare results side-by-side. + +**Implementation:** + +- [ ] `npm run bench:compare -- --a="default" --b="tiered,entropy"` +- [ ] Output: side-by-side ratio, quality, entity retention, diff of compressed output +- [ ] Useful for validating each phase's improvement + +--- + +## Progress Tracker + +| Phase | Item | Effort | Ratio Impact | Quality Impact | Status | +| ----- | ----------------------------- | ------- | ------------------------- | ----------------- | ------ | +| 1.1 | Entity retention metric | Low | — | Observability | [x] | +| 1.2 | Relevance threshold | Low | +15-30% on weak scenarios | Neutral | [x] | +| 1.3 | Quality score composite | Low | — | Observability | [x] | +| 2.1 | Tiered budget allocation | Medium | +10-20% overall | +Quality | [x] | +| 2.2 | Self-information scoring | Medium | +20-30% on prose | +Quality | [x] | +| 2.3 | Adaptive summary budget | Low-Med | +5-10% | +Entity retention | [x] | +| 3.1 | EDU-lite decomposition | High | Neutral | +Coherence | [x] | +| 3.2 | Cross-message coreference | High | Neutral | +Coherence | [x] | +| 3.3 | Conversation flow compression | Medium | +15-25% on conv. | +Coherence | [x] | +| 4.1 | ML token classifier | High | +30-50% on prose | +Quality | [x] | +| 4.2 | Progressive compression | Medium | +50-100% on deep | +Quality | [x] | +| 4.3 | Semantic clustering | High | +20-40% on long | +Coherence | [x] | +| 5.1 | Quality benchmark suite | Medium | — | Infrastructure | [x] | +| 5.2 | Adversarial test cases | Medium | — | Infrastructure | [x] | +| 5.3 | A/B comparison tool | Low | — | Infrastructure | [x] | + +**Target:** 3.5x+ avg compression at quality_score >= 0.90 + +--- + +## Key Papers Referenced + +| Short Name | Venue | Key Contribution | Link | +| -------------------- | ---------- | ------------------------------------------------ | ------------------------------------ | +| LLMLingua | EMNLP 2023 | Budget controller, coarse-to-fine compression | arxiv.org/abs/2310.05736 | +| LongLLMLingua | ACL 2024 | Question-aware compression, "lost in middle" fix | arxiv.org/abs/2310.06839 | +| LLMLingua-2 | ACL 2024 | Token classification via small encoder | arxiv.org/abs/2403.12968 | +| Selective Context | EMNLP 2023 | Self-information based pruning | aclanthology.org/2023.emnlp-main.391 | +| RECOMP | ICLR 2024 | Extractive + abstractive, "output nothing" | arxiv.org/abs/2310.04408 | +| From Context to EDUs | arXiv 2025 | Discourse unit decomposition | arxiv.org/abs/2512.14244 | +| LLM-DCP | arXiv 2025 | RL-based progressive compression | arxiv.org/abs/2504.11004 | +| ACON | arXiv 2025 | Failure-analysis feedback for agent compression | arxiv.org/abs/2510.00615 | +| HyCo2 | arXiv 2025 | Hard + soft hybrid compression | arxiv.org/abs/2505.15774 | +| Info Preservation | EMNLP 2025 | Three-axis quality evaluation framework | arxiv.org/abs/2503.19114 | +| Compression Survey | NAACL 2025 | Taxonomy of all approaches | arxiv.org/abs/2410.12388 | +| ComprExIT | arXiv 2026 | Globally optimized compression plan | arxiv.org/abs/2602.03784 | +| LCIRC | NAACL 2025 | Recurrent compression for multi-round | arxiv.org/abs/2502.06139 | +| TokenSkip | EMNLP 2025 | Controllable CoT compression | arxiv.org/abs/2502.12067 | + +--- + +## Design Principles + +1. **Zero-dependency core stays zero-dependency.** ML features ship as optional packages or user-provided functions. +2. **Every compression is reversible.** Round-trip integrity is non-negotiable. New features must preserve the verbatim store contract. +3. **Deterministic by default.** LLM/ML features are opt-in enhancements, never requirements. +4. **Measure before and after.** Every phase must show benchmark improvement. No "should be better" — prove it. +5. **Backward compatible.** Default options produce identical output to current version. New features are opt-in. diff --git a/docs/token-budget.md b/docs/token-budget.md index cb1a9f4..9bb3233 100644 --- a/docs/token-budget.md +++ b/docs/token-budget.md @@ -49,7 +49,7 @@ function defaultTokenCounter(msg: Message): number { } ``` -~3.5 characters per token is a rough heuristic. It's fast and works for ballpark estimates, but real tokenizers vary: +~3.5 characters per token is derived from empirical measurements of GPT-family BPE tokenizers (cl100k_base, o200k_base) on mixed English text. We pick the lower end of the observed range so estimates are conservative — slightly over-counting tokens is safer than under-counting and blowing the budget. It's fast and works for ballpark estimates, but real tokenizers vary: | Tokenizer | Typical chars/token | | --------- | ------------------- | @@ -138,6 +138,34 @@ Truncated messages get `_cce_original` provenance metadata, so `uncompress()` re Without `forceConverge`, the result may exceed the budget when conversations are heavily system-message or short-message dominated (since those are preserved). +## Tiered budget strategy + +An alternative to binary search that keeps the recency window fixed. Instead of shrinking `recencyWindow` to fit, it progressively compresses older messages through tightening passes. + +```ts +const result = compress(messages, { + tokenBudget: 4000, + budgetStrategy: 'tiered', + forceConverge: true, +}); +``` + +See [V2 features — Tiered budget](v2-features.md#tiered-budget-strategy) for the full algorithm and tradeoff comparison. + +## Compression depth with budget + +When `compressionDepth: 'auto'` is combined with `tokenBudget`, the engine progressively tries gentle → moderate → aggressive until the budget fits: + +```ts +const result = compress(messages, { + tokenBudget: 2000, + compressionDepth: 'auto', + forceConverge: true, +}); +``` + +This is the most adaptive budget mode — it finds the minimum aggressiveness needed. See [V2 features — Compression depth](v2-features.md#compression-depth). + ## Budget with LLM summarizer ```ts @@ -153,6 +181,7 @@ The binary search calls the LLM at each iteration, so cost and latency scale wit ## See also +- [V2 features](v2-features.md) - tiered budget, compression depth, quality metrics - [Compression pipeline](compression-pipeline.md) - overall pipeline flow - [LLM integration](llm-integration.md) - setting up summarizers - [API reference](api-reference.md) - `tokenBudget`, `minRecencyWindow`, `forceConverge`, `tokenCounter` diff --git a/docs/v2-features.md b/docs/v2-features.md new file mode 100644 index 0000000..956e6fa --- /dev/null +++ b/docs/v2-features.md @@ -0,0 +1,488 @@ +# V2 Features + +[Back to README](../README.md) | [All docs](README.md) + +New compression features added in v2. All features are **opt-in** with backward-compatible defaults — existing code produces identical output without changes. Zero new runtime dependencies. + +## Quick reference + +| Feature | Option | Default | Effect | Tradeoff | +| ---------------------------------------------------------------- | -------------------------- | -------------------------- | ------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------- | +| [Quality metrics](#quality-metrics) | _automatic_ | on when compression occurs | Adds `entity_retention`, `structural_integrity`, `reference_coherence`, `quality_score` to result | ~1% overhead from entity extraction | +| [Relevance threshold](#relevance-threshold) | `relevanceThreshold` | off | Drops low-value messages to stubs | Higher ratio, may lose context in filler-heavy conversations | +| [Tiered budget](#tiered-budget-strategy) | `budgetStrategy: 'tiered'` | `'binary-search'` | Compresses old prose first, protects recent messages | Better quality at the same budget; slightly slower (tightening passes) | +| [Entropy scorer](#entropy-scorer) | `entropyScorer` | off | Information-theoretic sentence scoring via external LM | Better sentence selection; requires a local model or API | +| [Adaptive budgets](#adaptive-summary-budgets) | _automatic_ | on | Scales summary budget with content density | Entity-dense content gets more room; sparse filler compresses harder | +| [Conversation flow](#conversation-flow) | `conversationFlow` | `false` | Groups Q&A / request→action chains | More coherent summaries; reduces ratio on conversations without clear patterns | +| [Discourse-aware](#discourse-aware-summarization) (experimental) | `discourseAware` | `false` | EDU decomposition with dependency tracking | **Reduces ratio 8–28%** without an ML scorer. Infrastructure only — provide your own scorer | +| [Coreference](#cross-message-coreference) | `coreference` | `false` | Inlines entity definitions into compressed summaries | Prevents orphaned references; adds bytes to summaries | +| [Semantic clustering](#semantic-clustering) | `semanticClustering` | `false` | Groups messages by topic for cluster-aware compression | Better coherence on topic-scattered conversations; O(n²) similarity computation | +| [Compression depth](#compression-depth) | `compressionDepth` | `'gentle'` | Controls aggressiveness: gentle/moderate/aggressive/auto | Higher depth = higher ratio but lower quality | +| [ML token classifier](#ml-token-classifier) | `mlTokenClassifier` | off | Per-token keep/remove via external ML model | Highest quality compression; requires a trained model (~500MB) | + +--- + +## Quality metrics + +Quality metrics are computed automatically whenever compression occurs. No option needed. + +### Fields + +| Field | Range | Meaning | +| ---------------------------------- | ----- | ------------------------------------------------------------------------------------------------------ | +| `compression.entity_retention` | 0–1 | Fraction of technical identifiers (camelCase, snake_case, file paths, URLs, version numbers) preserved | +| `compression.structural_integrity` | 0–1 | Fraction of structural elements (code fences, JSON blocks, tables) preserved | +| `compression.reference_coherence` | 0–1 | Fraction of output entity references whose defining message is still present | +| `compression.quality_score` | 0–1 | Weighted composite: `0.4 × entity_retention + 0.4 × structural_integrity + 0.2 × reference_coherence` | + +### Example + +```ts +const result = compress(messages, { recencyWindow: 4 }); + +console.log(result.compression.quality_score); // 0.95 +console.log(result.compression.entity_retention); // 0.92 +console.log(result.compression.structural_integrity); // 1.0 +``` + +### Tradeoffs + +- Quality metrics add ~1% overhead from entity extraction on every compression +- `entity_retention` only tracks identifiers (camelCase, snake_case, PascalCase, file paths, URLs, version numbers). Plain English nouns are not tracked +- `reference_coherence` checks if defining messages survived, not whether the definition text survived — a message can be compressed (losing the definition prose) and still count as "present" if its ID is in the output +- Scores of 1.0 do not mean lossless — they mean no tracked entities/structures were lost + +--- + +## Relevance threshold + +Drops low-value messages to compact stubs instead of producing low-quality summaries. + +### Usage + +```ts +const result = compress(messages, { + relevanceThreshold: 5, // sentence score threshold +}); +``` + +### How it works + +Before summarizing a group of compressible messages, the engine scores each sentence using the heuristic scorer. If the best sentence score in the group falls below `relevanceThreshold`, the entire group is replaced with `[N messages of general discussion omitted]`. Consecutive dropped messages are grouped into a single stub. + +Original content is still stored in `verbatim` — round-trip integrity is preserved. + +### Tradeoffs + +- **Higher values** = more aggressive dropping. Values around 3–5 catch most filler. Values above 8 will drop messages containing some technical content +- **Lower values** = only pure filler is dropped +- Messages with any code identifiers (camelCase, snake_case) tend to score above 3, so they survive +- The threshold operates on the _best_ sentence in a group — a message with one technical sentence among filler will be preserved +- `messages_relevance_dropped` stat tracks how many messages were stubbed + +--- + +## Tiered budget strategy + +An alternative to binary search that keeps the recency window fixed and progressively compresses older content. + +### Usage + +```ts +const result = compress(messages, { + tokenBudget: 4000, + budgetStrategy: 'tiered', + forceConverge: true, // recommended with tiered +}); +``` + +### How it works + +``` +1. Run standard compress with the user's recencyWindow +2. If result fits budget → done +3. Pass 2a: Tighten older summaries (re-summarize at 40% budget) +4. Pass 2b: Stub low-value older messages (score < 3 → "[message omitted]") +5. Pass 3: forceConverge as last resort (if enabled) +``` + +### Tradeoffs + +| | Binary search (default) | Tiered | +| -------------- | ---------------------------- | ----------------------------------------------- | +| Recency window | Shrinks to fit budget | Fixed — recent messages always preserved | +| Older messages | Compressed uniformly | Progressively tightened by priority | +| Speed | O(log n) compress iterations | Single compress + tightening passes | +| Best for | General use, simple budgets | Conversations where recent context matters most | + +- Tiered is strictly better at preserving recent context but may produce lower quality on older messages (tighter budgets) +- Without `forceConverge`, tiered may fail to meet very tight budgets +- Works with both sync and async paths + +--- + +## Entropy scorer + +Plug in a small causal language model for information-theoretic sentence scoring. Based on [Selective Context (EMNLP 2023)](https://aclanthology.org/2023.emnlp-main.391/). + +### Usage + +```ts +// Sync scorer (e.g., local model via llama.cpp bindings) +const result = compress(messages, { + entropyScorer: (sentences) => sentences.map((s) => myLocalModel.selfInformation(s)), + entropyScorerMode: 'augment', // combine with heuristic (default) +}); + +// Async scorer (e.g., remote inference) +const result = await compress(messages, { + entropyScorer: async (sentences) => myApi.scoreSentences(sentences), + summarizer: mySummarizer, // required to enable async path +}); +``` + +### Modes + +| Mode | Behavior | +| --------------------- | --------------------------------------------------------------------------- | +| `'augment'` (default) | Weighted average of heuristic + entropy scores (60% entropy, 40% heuristic) | +| `'replace'` | Entropy scores only, heuristic skipped | + +### Tradeoffs + +- `'augment'` is safer — heuristic catches structural patterns (code identifiers, status words) that entropy might miss in short sentences +- `'replace'` gives the entropy scorer full control — use when your model is well-calibrated +- Async scorers throw in sync mode (no `summarizer`/`classifier` provided). Use a sync scorer or add a summarizer to enable async +- The engine stays zero-dependency — the scorer function is user-provided + +--- + +## Adaptive summary budgets + +Summary budgets now scale with content density. This is automatic — no option needed. + +### How it works + +The `computeBudget` function measures entity density (identifiers per character): + +- **Dense content** (many identifiers): up to 45% of content length as budget, max 800 chars +- **Sparse content** (general discussion): down to 15% of content length, min 100 chars +- **Default** (no density signal): 30% of content length, 200–600 chars (backward compatible) + +### Tradeoffs + +- Entity-dense messages (e.g., architecture discussions with many function names) get longer summaries, preserving more identifiers. This improves `entity_retention` but slightly reduces compression ratio on those messages +- Sparse filler messages get tighter summaries, improving ratio where it matters most +- Messages near the 120-char short-content threshold that previously escaped compression may now be compressed, since the lower budget minimum (100 chars vs. 200) allows shorter summaries + +--- + +## Conversation flow + +Groups common conversation patterns into compression units that produce more coherent summaries. + +### Usage + +```ts +const result = compress(messages, { + conversationFlow: true, +}); +``` + +### Detected patterns + +| Pattern | Detection | Summary format | +| ---------------- | ------------------------------------------------------------------------------ | ------------------------------- | +| Q&A | User question (has `?`) → assistant answer | `Q: {question} → A: {answer}` | +| Request → action | User request (`can you`, `please`, `add`) → assistant action (`done`, `added`) | `Request: {request} → {action}` | +| Correction | `actually`, `wait`, `no,` followed by same-topic content | `Correction: {correction text}` | +| Acknowledgment | Substantive message (>200 chars) → short confirmation (`great`, `thanks`) | `{substance} (acknowledged)` | + +Follow-up confirmations (`perfect`, `thanks`) are included in Q&A and request chains when detected within 2 messages. + +### Tradeoffs + +- Flow chains produce more coherent summaries than independent compression — a Q&A pair as `Q: ... → A: ...` preserves the relationship between question and answer +- **Messages with code fences are excluded** from flow chains to prevent code loss — they use the code-split path instead +- Conversations without clear patterns (e.g., multi-party discussions, brainstorming) see no benefit +- Flow chains can override soft preservation (recency, short content) but not hard blocks (system roles, dedup, tool_calls) +- The detection is conservative — only well-established patterns are matched. Ambiguous exchanges fall through to normal compression + +--- + +## Discourse-aware summarization (experimental) + +> **Status: experimental.** The infrastructure is in place (EDU segmentation, dependency graph, greedy selector) but the built-in rule-based scorer **reduces compression ratio by 8–28%** with no measurable quality gain over the default sentence scorer. The dependency tracking inherently fights compression — pulling in parent EDUs when selecting children keeps more text than necessary. This feature needs an ML-backed scorer to identify which dependencies are actually load-bearing. Until then, leave it off unless you provide a custom scorer. + +Breaks content into Elementary Discourse Units (EDUs) with dependency tracking. Based on [From Context to EDUs (arXiv 2025)](https://arxiv.org/abs/2512.14244). + +### Usage + +```ts +// Not recommended without a custom scorer — reduces ratio +const result = compress(messages, { + discourseAware: true, +}); + +// With a custom scorer (e.g., backed by an ML model) — the intended use +import { segmentEDUs, scoreEDUs, selectEDUs } from 'context-compression-engine'; + +const edus = segmentEDUs(text); +const scored = scoreEDUs(edus, (text) => myModel.importance(text)); +const selected = selectEDUs(scored, budget); +``` + +### How it works + +1. Segment text into EDUs at clause boundaries (discourse markers: `then`, `because`, `which`, `however`, etc.) +2. Build dependency edges: pronoun references (`it`, `this`) → preceding EDU; temporal chains (`first...then...finally`); causal chains (`because...therefore`) +3. Score EDUs (information-density heuristic by default, or custom scorer) +4. Greedy selection: highest-scored EDUs first, pulling in dependency parents (up to 2 levels) + +### Why it underperforms without an ML scorer + +The rule-based scorer rewards technical identifiers and penalizes filler — the same signals as the default sentence scorer. But the dependency tracking adds a tax: selecting one high-value EDU forces inclusion of its parent EDUs, which may be low-value. The default scorer can't distinguish load-bearing dependencies (removing the parent makes the child meaningless) from decorative ones (the parent adds context but the child stands alone). An ML scorer trained on discourse coherence would solve this. + +### Tradeoffs + +- Prevents incoherent summaries where removing a sentence orphans a pronoun reference — **in theory**, but the ratio cost currently outweighs the coherence benefit +- The EDU segmenter, dependency builder, and selector are fully functional and exported — use them directly with a custom scorer via `segmentEDUs`, `scoreEDUs`, `selectEDUs` +- Mutually exclusive with `entropyScorer` — when both are set, `discourseAware` takes priority + +--- + +## Cross-message coreference + +Tracks entity references across messages to prevent orphaned references when source messages are compressed. + +### Usage + +```ts +const result = compress(messages, { + coreference: true, +}); +``` + +### How it works + +1. Build coreference map: for each identifier (camelCase, snake_case, PascalCase), track where it first appears and which later messages reference it +2. After compression: check if any preserved message references an entity defined only in a compressed message +3. If so: prepend `[context: {defining sentence}]` to the compressed message's summary + +### Example + +Without coreference: + +``` +Message 3 (compressed): [summary: handles retries with backoff | entities: fetchData] +Message 7 (preserved): "Make sure fetchData uses a 30s timeout" +``` + +With coreference: + +``` +Message 3 (compressed): [context: The fetchData function handles API calls.] [summary: handles retries with backoff | entities: fetchData] +Message 7 (preserved): "Make sure fetchData uses a 30s timeout" +``` + +### Tradeoffs + +- Prevents the common failure mode where compressing an early definition message makes later references meaningless +- Adds bytes to compressed summaries (the `[context: ...]` prefix). This slightly reduces compression ratio +- Only tracks code-style identifiers (camelCase, snake_case, PascalCase) — not plain English nouns. This avoids false positives but misses some references +- The inline definition is the first sentence containing the entity, truncated to 80 chars. Complex multi-sentence definitions are only partially captured + +--- + +## Semantic clustering + +Groups messages by topic using lightweight TF-IDF and entity overlap, then compresses each cluster as a unit. + +### Usage + +```ts +const result = compress(messages, { + semanticClustering: true, + clusterThreshold: 0.15, // similarity threshold (default) +}); +``` + +### How it works + +1. Compute TF-IDF vectors per message (content words, stopwords removed) +2. Compute entity overlap (Jaccard similarity on extracted identifiers) +3. Combined similarity: `0.7 × cosine(TF-IDF) + 0.3 × jaccard(entities)` +4. Agglomerative clustering with average linkage until similarity drops below threshold +5. Multi-message clusters compressed as a unit with topic label + +### Tradeoffs + +- Long conversations that drift across topics benefit most — scattered messages about `fetchData` in messages 3, 7, 12, 19 get merged into one compressed block +- O(n²) similarity computation. For conversations under 50 messages this is negligible. For 500+ messages, consider whether the coherence benefit justifies the cost +- `clusterThreshold` controls sensitivity: lower values (0.05–0.10) create larger clusters; higher values (0.20–0.30) require stronger topic similarity +- Messages already claimed by flow chains are excluded from clustering — the two features cooperate without overlap +- Messages with fewer than 80 chars are excluded (not enough content for meaningful similarity) + +--- + +## Compression depth + +Controls how aggressively the summarizer compresses content. + +### Usage + +```ts +// Fixed depth +const result = compress(messages, { + compressionDepth: 'moderate', +}); + +// Auto: progressively tries gentle → moderate → aggressive until budget fits +const result = compress(messages, { + tokenBudget: 2000, + compressionDepth: 'auto', + forceConverge: true, +}); +``` + +### Depth levels + +| Level | Summary budget | Strategy | Typical ratio | +| -------------------- | ----------------- | ----------------------------------------- | ---------------- | +| `'gentle'` (default) | 30% of content | Sentence selection | ~2x | +| `'moderate'` | 15% of content | Tighter sentence selection | ~3–4x | +| `'aggressive'` | Entity-only stubs | Key identifiers only | ~6–8x | +| `'auto'` | Progressive | Tries each level until `tokenBudget` fits | Adapts to budget | + +### Auto mode quality gate + +In `'auto'` mode, the engine stops escalating if `quality_score` drops below 0.60 (unless forced by a very tight budget). This prevents aggressive compression from destroying too much context. + +### Tradeoffs + +- `'gentle'` is the safest — identical to default behavior. Start here +- `'moderate'` halves the summary budget. Entity-dense content keeps identifiers; sparse content gets very short summaries. Good for conversations with lots of boilerplate +- `'aggressive'` produces entity-only stubs (`fetchData, getUserProfile, retryConfig`). Use for archival compression where only the topics matter, not the details +- `'auto'` with `tokenBudget` is the most practical — it finds the minimum aggressiveness needed to fit. Without a budget, `'auto'` is equivalent to `'gentle'` + +--- + +## ML token classifier + +Per-token keep/remove classification via a user-provided ML model. Based on [LLMLingua-2 (ACL 2024)](https://arxiv.org/abs/2403.12968). + +### Usage + +```ts +import { compress, createMockTokenClassifier } from 'context-compression-engine'; + +// Mock classifier for testing +const classifier = createMockTokenClassifier([/fetch/i, /retry/i, /config/i]); +const result = compress(messages, { mlTokenClassifier: classifier }); + +// Real classifier (e.g., ONNX model) +const result = compress(messages, { + mlTokenClassifier: (content) => { + const tokens = myTokenizer.tokenize(content); + const predictions = myModel.predict(tokens); + return tokens.map((token, i) => ({ + token, + keep: predictions[i] > 0.5, + confidence: predictions[i], + })); + }, +}); +``` + +### Types + +```ts +type TokenClassification = { + token: string; + keep: boolean; + confidence: number; // 0–1 +}; + +type MLTokenClassifier = ( + content: string, +) => TokenClassification[] | Promise; +``` + +### Tradeoffs + +- Highest potential compression quality — a well-trained encoder model (XLM-RoBERTa, ~500MB) can achieve 2–5x compression at 95–98% accuracy retention +- T0 classification rules still override for code/structured content — the ML classifier only handles T2 prose +- Falls back to deterministic summarization if the ML-compressed output is longer than the original +- Async classifiers throw in sync mode — provide a `summarizer` or `classifier` to enable async +- The engine stays zero-dependency — you provide the model and tokenizer + +### Helper utilities + +```ts +import { whitespaceTokenize, createMockTokenClassifier } from 'context-compression-engine'; + +// Simple whitespace tokenizer +const tokens = whitespaceTokenize('The fetchData function'); // ['The', 'fetchData', 'function'] + +// Mock classifier for testing — keeps tokens matching any pattern +const mock = createMockTokenClassifier([/fetch/i, /retry/i], 0.9); +``` + +--- + +## Combining features + +Features can be combined freely. Here are recommended combinations: + +### Quality-focused (preserve context, moderate compression) + +```ts +const result = compress(messages, { + recencyWindow: 6, + importanceScoring: true, + contradictionDetection: true, + coreference: true, + conversationFlow: true, +}); +``` + +### Ratio-focused (maximum compression, acceptable quality loss) + +```ts +const result = compress(messages, { + tokenBudget: 2000, + compressionDepth: 'auto', + budgetStrategy: 'tiered', + relevanceThreshold: 3, + semanticClustering: true, + forceConverge: true, +}); +``` + +### Balanced (good ratio + quality) + +```ts +const result = compress(messages, { + tokenBudget: 4000, + conversationFlow: true, + importanceScoring: true, + coreference: true, +}); +``` + +### Feature interaction notes + +- `conversationFlow` and `semanticClustering` cooperate — flow chains are detected first, remaining messages are clustered +- `discourseAware` is experimental and not included in any recommended combination — it reduces ratio without a custom ML scorer +- `mlTokenClassifier` takes priority over `discourseAware` and `entropyScorer` +- `relevanceThreshold` applies after flow/cluster detection — messages already grouped into chains/clusters are not individually threshold-checked +- `compressionDepth` affects all summarization (groups, code-split prose, contradictions) — not just the main group path + +--- + +## See also + +- [API reference](api-reference.md) — all options and result fields +- [Token budget](token-budget.md) — `budgetStrategy`, `compressionDepth: 'auto'` +- [Compression pipeline](compression-pipeline.md) — how features fit into the pipeline +- [Benchmark results](benchmark-results.md) — quality metrics per scenario diff --git a/e2e/.gitignore b/e2e/.gitignore new file mode 100644 index 0000000..504afef --- /dev/null +++ b/e2e/.gitignore @@ -0,0 +1,2 @@ +node_modules/ +package-lock.json diff --git a/e2e/README.md b/e2e/README.md new file mode 100644 index 0000000..f9c8b6a --- /dev/null +++ b/e2e/README.md @@ -0,0 +1,48 @@ +# E2E Smoke Test + +Installs `context-compression-engine` as a real consumer would and exercises every public export. + +Catches issues that unit tests can't: broken `exports` map, missing files in the tarball, ESM resolution failures, async path regressions. + +## Pipeline + +``` +npm run test:e2e +``` + +Runs: **build → pack → publint + attw → smoke test → cleanup** + +| Step | What it does | +| ------------------ | --------------------------------------------------------------------------------------- | +| `npm run build` | Compile TypeScript | +| `npm pack` | Create tarball from `files` field | +| `publint --strict` | Validate package.json exports, files, types | +| `attw` | Check TypeScript type resolution across all `moduleResolution` settings | +| `smoke.mjs` | 41 tests / 74 assertions exercising the public API (`node:test` + `node:assert/strict`) | +| cleanup | Remove `.tgz`, `e2e/node_modules`, `e2e/package-lock.json` | + +Cleanup always runs, even on failure. The exit code from the smoke test is preserved. + +## Other scripts + +```bash +# Test the published npm package (post-publish validation) +npm run test:e2e:published +``` + +## What the smoke test covers + +| Area | What's tested | +| ------------------------- | --------------------------------------------------------------------------------------------------------------------------------------- | +| **Basic compression** | ratio, token_ratio, message count, verbatim store, preserve keywords, sourceVersion, embedSummaryId, forceConverge, provenance metadata | +| **Uncompress round-trip** | lossless content restoration, missing verbatim store, plain object store | +| **Dedup** | exact duplicate detection (>=200 char), fuzzy dedup detects near-duplicates | +| **Token budget** | binary search fit, impossible budget (fits=false), minRecencyWindow floor | +| **Token counter** | defaultTokenCounter, custom tokenCounter | +| **Factory functions** | createSummarizer, createEscalatingSummarizer exported | +| **Edge cases** | empty input, single message | +| **Async path** | mock summarizer + round-trip, async + token budget | +| **Role handling** | system messages auto-preserved, tool_calls pass through + other messages compressed | +| **Re-compression** | compress already-compressed output + chained stores, recursive uncompress | +| **Large conversation** | 31-message fixture, compression + round-trip, 50% budget target | +| **Error handling** | TypeError on non-array compress, null entry, missing id, non-array uncompress, invalid store; graceful handling of null/empty content | diff --git a/e2e/package.json b/e2e/package.json new file mode 100644 index 0000000..60cdea2 --- /dev/null +++ b/e2e/package.json @@ -0,0 +1,9 @@ +{ + "name": "cce-e2e", + "private": true, + "type": "module", + "description": "End-to-end smoke test — installs context-compression-engine from npm and exercises the public API as a real consumer would.", + "scripts": { + "test": "node --test smoke.mjs" + } +} diff --git a/e2e/smoke.mjs b/e2e/smoke.mjs new file mode 100644 index 0000000..121c9ac --- /dev/null +++ b/e2e/smoke.mjs @@ -0,0 +1,576 @@ +/** + * End-to-end smoke test for context-compression-engine. + * + * Installs the package from npm (or a local tarball) and exercises every + * public export the way a real consumer would. + * + * Run: + * cd e2e && npm install context-compression-engine && npm test + * + * Or with a local tarball: + * cd e2e && npm install ../context-compression-engine-*.tgz && npm test + */ + +import { describe, test } from 'node:test'; +import assert from 'node:assert/strict'; + +import { + compress, + uncompress, + defaultTokenCounter, + createSummarizer, + createEscalatingSummarizer, +} from 'context-compression-engine'; + +// --------------------------------------------------------------------------- +// Test fixtures +// --------------------------------------------------------------------------- + +/** Content >=200 chars — required for dedup eligibility. */ +const longContent = `I need to refactor the authentication module. It currently uses session-based auth but we want to switch to JWT tokens. The module handles login, signup, password reset, and session management. We also need to update the middleware and all protected routes to use the new token-based approach instead of cookies.`; + +/** Content >512 chars — required for forceConverge truncation eligibility. */ +const veryLongContent = `Here is a comprehensive step-by-step plan for the authentication refactoring: +1. Install jsonwebtoken and bcryptjs packages +2. Create a token signing utility in src/auth/tokens.js +3. Add middleware for token verification in src/middleware/auth.js +4. Update login endpoint to issue access and refresh tokens +5. Remove session dependencies from express configuration +6. Update all protected routes to use the new middleware +7. Create a /refresh endpoint for token rotation +8. Implement token blacklisting for logout +9. Add rate limiting to auth endpoints +10. Write comprehensive integration tests for the new auth flow +11. Update API documentation to reflect the new auth scheme +12. Create a migration script for existing sessions +13. Add monitoring and alerting for auth failures +This is going to be a significant change that touches many parts of the codebase.`; + +const messages = [ + { id: '1', index: 0, role: 'user', content: longContent }, + { id: '2', index: 1, role: 'assistant', content: veryLongContent }, + { + id: '3', + index: 2, + role: 'user', + content: 'That sounds good. Can you also add refresh token support?', + }, + { + id: '4', + index: 3, + role: 'assistant', + content: veryLongContent.replace('step-by-step', 'detailed'), + }, + { + id: '5', + index: 4, + role: 'user', + content: + 'Perfect, lets also add rate limiting to prevent brute force attacks on the login endpoint.', + }, + { + id: '6', + index: 5, + role: 'assistant', + content: + 'Good idea. I recommend using express-rate-limit with a sliding window. We can set it to 5 attempts per minute per IP address.', + }, + { + id: '7', + index: 6, + role: 'user', + content: 'Great, please proceed with the implementation.', + }, + { + id: '8', + index: 7, + role: 'assistant', + content: 'Starting implementation now.', + }, +]; + +/** + * Realistic 30-message conversation with system prompt, tool_calls, + * long assistant responses, and repeated user patterns. + */ +function buildLargeConversation() { + const msgs = [ + { + id: 'L0', + index: 0, + role: 'system', + content: 'You are a senior backend engineer. Always suggest tests. Prefer TypeScript.', + }, + ]; + const userPrompts = [ + 'Set up a new Express project with TypeScript and ESLint.', + 'Add a PostgreSQL connection pool using pg.', + 'Create a users table migration with id, email, password_hash, created_at.', + 'Implement the POST /users signup endpoint with input validation.', + 'Add bcrypt password hashing to the signup flow.', + 'Write integration tests for the signup endpoint.', + 'Implement POST /auth/login returning a JWT access token.', + 'Add a GET /users/me endpoint that requires authentication.', + 'Implement refresh token rotation with a tokens table.', + 'Add rate limiting middleware to auth endpoints.', + 'Set up a CI pipeline with GitHub Actions.', + 'Add request logging with pino.', + 'Implement soft-delete for users.', + 'Add pagination to GET /users.', + 'Write a database seeder for development.', + ]; + let idx = 1; + for (const prompt of userPrompts) { + msgs.push({ id: `L${idx}`, index: idx, role: 'user', content: prompt }); + idx++; + // Simulate a substantive assistant response (>200 chars) + const response = `Sure, here is how we can ${prompt.toLowerCase()}\n\nFirst, we need to install the required dependencies and configure the project structure. Then we will implement the core logic, add proper error handling, and write tests to verify everything works correctly. Let me walk you through each step in detail with code examples and explanations of the design decisions involved.`; + msgs.push({ + id: `L${idx}`, + index: idx, + role: 'assistant', + content: response, + }); + idx++; + } + return msgs; +} + +// --------------------------------------------------------------------------- +// Tests +// --------------------------------------------------------------------------- + +describe('basic compression', () => { + const result = compress(messages, { recencyWindow: 2 }); + + test('preserves message count', () => { + assert.equal(result.messages.length, messages.length); + }); + + test('achieves compression ratio > 1', () => { + assert.ok(result.compression.ratio > 1, `ratio was ${result.compression.ratio.toFixed(2)}`); + }); + + test('achieves token ratio > 1', () => { + assert.ok( + result.compression.token_ratio > 1, + `token_ratio was ${result.compression.token_ratio.toFixed(2)}`, + ); + }); + + test('compresses some messages', () => { + assert.ok(result.compression.messages_compressed > 0); + }); + + test('preserves some messages', () => { + assert.ok(result.compression.messages_preserved > 0); + }); + + test('populates verbatim store', () => { + assert.ok(Object.keys(result.verbatim).length > 0); + }); + + test('preserve keywords retained in compressed output', () => { + const preserveResult = compress(messages, { + recencyWindow: 1, + preserve: ['JWT', 'refresh'], + }); + const compressedWithPreserve = preserveResult.messages.filter((m) => m.metadata?._cce_original); + assert.ok(compressedWithPreserve.length > 0, 'at least one message compressed'); + for (const cm of compressedWithPreserve) { + const orig = messages.find((m) => m.id === cm.id); + if (orig?.content?.includes('JWT')) { + assert.ok(cm.content.includes('JWT'), `preserved "JWT" in message ${cm.id}`); + } + } + }); + + test('sourceVersion flows into compression metadata', () => { + const vResult = compress(messages, { recencyWindow: 2, sourceVersion: 42 }); + assert.equal(vResult.compression.original_version, 42); + }); + + test('embedSummaryId embeds summary_id in compressed content', () => { + const embedResult = compress(messages, { + recencyWindow: 2, + embedSummaryId: true, + }); + const compressedMsgs = embedResult.messages.filter((m) => m.metadata?._cce_original); + assert.ok(compressedMsgs.length > 0, 'some messages compressed'); + for (const cm of compressedMsgs) { + assert.ok( + cm.content?.includes(cm.metadata._cce_original.summary_id), + `summary_id embedded in message ${cm.id}`, + ); + } + }); + + test('forceConverge reduces tokens', () => { + const fcResult = compress(messages, { tokenBudget: 200, forceConverge: true }); + const noFcResult = compress(messages, { tokenBudget: 200 }); + assert.ok( + fcResult.tokenCount <= noFcResult.tokenCount, + `forceConverge ${fcResult.tokenCount} <= without ${noFcResult.tokenCount}`, + ); + assert.equal(fcResult.messages.length, messages.length); + }); + + test('provenance metadata structure', () => { + const compMsg = result.messages.find((m) => m.metadata?._cce_original); + assert.ok(compMsg !== undefined, 'compressed message has provenance'); + const orig = compMsg.metadata._cce_original; + assert.ok(Array.isArray(orig.ids) && orig.ids.length > 0, '_cce_original.ids is non-empty'); + assert.equal(typeof orig.summary_id, 'string'); + assert.equal(typeof orig.version, 'number'); + }); +}); + +describe('uncompress round-trip', () => { + test('lossless content restoration', () => { + const result = compress(messages, { recencyWindow: 2 }); + const lookup = (id) => result.verbatim[id] ?? null; + const expanded = uncompress(result.messages, lookup); + + assert.equal(expanded.messages.length, messages.length); + assert.ok(expanded.messages_expanded > 0); + assert.equal(expanded.missing_ids.length, 0); + assert.equal( + messages.map((m) => m.content).join('|'), + expanded.messages.map((m) => m.content).join('|'), + ); + }); + + test('reports missing IDs when verbatim store is empty', () => { + const result = compress(messages, { recencyWindow: 2 }); + const missingResult = uncompress(result.messages, () => null); + assert.ok(missingResult.missing_ids.length > 0); + }); + + test('accepts plain object as verbatim store', () => { + const r = compress(messages, { recencyWindow: 2 }); + const expandedObj = uncompress(r.messages, r.verbatim); + assert.equal(expandedObj.missing_ids.length, 0); + assert.equal( + messages.map((m) => m.content).join('|'), + expandedObj.messages.map((m) => m.content).join('|'), + ); + }); +}); + +describe('dedup', () => { + test('detects exact duplicates (>=200 char messages)', () => { + const dupMessages = [...messages, { id: '9', index: 8, role: 'user', content: longContent }]; + const dedupResult = compress(dupMessages, { recencyWindow: 2, dedup: true }); + assert.ok( + dedupResult.compression.messages_deduped > 0, + `messages deduped: ${dedupResult.compression.messages_deduped}`, + ); + }); + + test('fuzzy dedup detects near-duplicate messages', () => { + const fuzzyResult = compress(messages, { + recencyWindow: 2, + fuzzyDedup: true, + fuzzyThreshold: 0.5, + }); + assert.equal(fuzzyResult.messages.length, messages.length); + assert.ok(fuzzyResult.compression.ratio >= 1); + assert.ok( + fuzzyResult.compression.messages_fuzzy_deduped > 0, + `expected fuzzy dedup to detect near-duplicates, got messages_fuzzy_deduped=${fuzzyResult.compression.messages_fuzzy_deduped}`, + ); + }); +}); + +describe('token budget', () => { + const totalTokens = messages.reduce((sum, m) => sum + defaultTokenCounter(m), 0); + const fitBudget = Math.ceil(totalTokens * 0.8); + + test('binary search finds a recencyWindow that fits', () => { + const budgetResult = compress(messages, { tokenBudget: fitBudget }); + assert.equal(budgetResult.fits, true); + assert.ok(budgetResult.tokenCount <= fitBudget); + assert.equal(typeof budgetResult.recencyWindow, 'number'); + }); + + test('reports fits=false when budget is impossible', () => { + const tightResult = compress(messages, { tokenBudget: 10 }); + assert.equal(tightResult.fits, false); + assert.ok(tightResult.tokenCount > 10); + }); + + test('minRecencyWindow floor is enforced', () => { + const minRWResult = compress(messages, { + tokenBudget: 50, + minRecencyWindow: 4, + }); + assert.ok( + minRWResult.recencyWindow >= 4, + `recencyWindow ${minRWResult.recencyWindow} should be >= 4`, + ); + }); +}); + +describe('token counter', () => { + test('defaultTokenCounter returns positive number', () => { + const count = defaultTokenCounter({ id: 'x', index: 0, content: 'Hello' }); + assert.equal(typeof count, 'number'); + assert.ok(count > 0); + }); + + test('custom tokenCounter is invoked', () => { + let counterCalls = 0; + compress(messages, { + recencyWindow: 2, + tokenCounter: (msg) => { + counterCalls++; + return Math.ceil((msg.content?.length ?? 0) / 4); + }, + }); + assert.ok(counterCalls > 0, `custom counter invoked ${counterCalls} times`); + }); +}); + +describe('factory functions', () => { + test('createSummarizer is exported', () => { + assert.equal(typeof createSummarizer, 'function'); + }); + + test('createEscalatingSummarizer is exported', () => { + assert.equal(typeof createEscalatingSummarizer, 'function'); + }); +}); + +describe('edge cases', () => { + test('empty input returns empty output', () => { + const emptyResult = compress([], { recencyWindow: 0 }); + assert.equal(emptyResult.messages.length, 0); + assert.equal(emptyResult.compression.ratio, 1); + }); + + test('single message is preserved', () => { + const singleResult = compress([{ id: '1', index: 0, role: 'user', content: 'Hello' }], { + recencyWindow: 1, + }); + assert.equal(singleResult.messages.length, 1); + assert.equal(singleResult.compression.messages_preserved, 1); + }); +}); + +describe('async path', () => { + test('mock summarizer is called and round-trip works', async () => { + let summarizerCalled = 0; + const mockSummarizer = async (text) => { + summarizerCalled++; + return `[mock summary of ${text.length} chars]`; + }; + const asyncResult = await compress(messages, { + recencyWindow: 2, + summarizer: mockSummarizer, + }); + assert.ok(summarizerCalled > 0, `summarizer was called ${summarizerCalled}x`); + assert.equal(asyncResult.messages.length, messages.length); + assert.ok(asyncResult.compression.messages_compressed > 0); + assert.ok(Object.keys(asyncResult.verbatim).length > 0); + + // Round-trip the async result + const asyncExpanded = uncompress( + asyncResult.messages, + (id) => asyncResult.verbatim[id] ?? null, + ); + assert.equal(asyncExpanded.missing_ids.length, 0); + assert.equal( + asyncExpanded.messages.map((m) => m.content).join('|'), + messages.map((m) => m.content).join('|'), + ); + }); + + test('async path with token budget', async () => { + const totalTokens = messages.reduce((sum, m) => sum + defaultTokenCounter(m), 0); + const fitBudget = Math.ceil(totalTokens * 0.8); + const mockSummarizer = async (text) => `[summary: ${text.substring(0, 30)}...]`; + const asyncBudget = await compress(messages, { + tokenBudget: fitBudget, + summarizer: mockSummarizer, + }); + assert.notEqual(asyncBudget.fits, undefined); + assert.equal(typeof asyncBudget.tokenCount, 'number'); + assert.equal(typeof asyncBudget.recencyWindow, 'number'); + }); +}); + +describe('role handling', () => { + test('system messages are auto-preserved', () => { + const withSystem = [ + { + id: 's0', + index: 0, + role: 'system', + content: 'You are a helpful assistant with expertise in security.', + }, + ...messages.map((m, i) => ({ ...m, id: `s${i + 1}`, index: i + 1 })), + ]; + const sysResult = compress(withSystem, { recencyWindow: 1 }); + const sysMsg = sysResult.messages.find((m) => m.role === 'system'); + assert.ok(sysMsg !== undefined, 'system message present in output'); + assert.equal(sysMsg.metadata?._cce_original, undefined, 'system message not compressed'); + assert.equal(sysMsg.content, withSystem[0].content); + }); + + test('tool_calls messages pass through intact and other messages are compressed', () => { + const withTools = [ + { + id: 't0', + index: 0, + role: 'user', + content: + 'I need to check the weather forecast for Berlin because I am planning a trip there next week and want to know what clothes to pack. Can you look up the current conditions and the extended forecast for the next seven days so I can prepare accordingly?', + }, + { + id: 't1', + index: 1, + role: 'assistant', + content: '', + tool_calls: [ + { + id: 'call_1', + type: 'function', + function: { name: 'get_weather', arguments: '{"city":"Berlin"}' }, + }, + ], + }, + { + id: 't2', + index: 2, + role: 'tool', + content: '{"temp": 18, "condition": "cloudy"}', + }, + { + id: 't3', + index: 3, + role: 'assistant', + content: + 'Based on the weather data, Berlin is currently 18 degrees Celsius and cloudy. For your trip next week, I would recommend packing layers including a light jacket and an umbrella. The extended forecast shows temperatures ranging from 15 to 22 degrees with intermittent rain expected on Wednesday and Thursday.', + }, + { id: 't4', index: 4, role: 'user', content: 'Thanks, that is very helpful!' }, + ]; + const toolResult = compress(withTools, { recencyWindow: 1 }); + + // tool_calls message should be preserved + const toolMsg = toolResult.messages.find((m) => m.id === 't1'); + assert.ok(toolMsg !== undefined, 'tool_calls message present'); + assert.ok(Array.isArray(toolMsg.tool_calls) && toolMsg.tool_calls.length === 1); + assert.equal(toolMsg.tool_calls[0].function.name, 'get_weather'); + + // Non-recent, non-tool messages should be compressed + const compressedMsgs = toolResult.messages.filter((m) => m.metadata?._cce_original); + assert.ok( + compressedMsgs.length > 0, + 'at least one non-tool message was compressed (has _cce_original)', + ); + }); +}); + +describe('re-compression', () => { + test('compress already-compressed output and recover via chained stores', () => { + const first = compress(messages, { recencyWindow: 2 }); + const second = compress(first.messages, { recencyWindow: 1 }); + assert.equal(second.messages.length, first.messages.length); + + const chainedLookup = (id) => second.verbatim[id] ?? first.verbatim[id] ?? null; + const recovered = uncompress(second.messages, chainedLookup, { recursive: true }); + assert.ok(recovered.messages_expanded > 0); + + const origContents = messages.map((m) => m.content); + const recoveredContents = recovered.messages.map((m) => m.content); + for (const oc of origContents) { + assert.ok( + recoveredContents.includes(oc), + `original content recoverable: ${oc.slice(0, 40)}...`, + ); + } + }); + + test('recursive uncompress fully expands nested provenance', () => { + const first = compress(messages, { recencyWindow: 2 }); + const second = compress(first.messages, { recencyWindow: 1 }); + const allVerbatim = { ...first.verbatim, ...second.verbatim }; + const storeFn = (id) => allVerbatim[id] ?? null; + + const shallow = uncompress(second.messages, storeFn); + const deep = uncompress(second.messages, storeFn, { recursive: true }); + assert.ok( + deep.messages_expanded >= shallow.messages_expanded, + `recursive ${deep.messages_expanded} >= shallow ${shallow.messages_expanded}`, + ); + }); +}); + +describe('large conversation', () => { + const largeMsgs = buildLargeConversation(); + + test('fixture has 31 messages', () => { + assert.equal(largeMsgs.length, 31); + }); + + test('compression + lossless round-trip at scale', () => { + const largeResult = compress(largeMsgs, { recencyWindow: 4 }); + assert.equal(largeResult.messages.length, largeMsgs.length); + assert.ok(largeResult.compression.ratio > 1); + assert.ok(largeResult.compression.messages_compressed >= 10); + + const largeLookup = (id) => largeResult.verbatim[id] ?? null; + const largeExpanded = uncompress(largeResult.messages, largeLookup); + assert.equal(largeExpanded.missing_ids.length, 0); + assert.equal( + largeMsgs.map((m) => m.content).join('|'), + largeExpanded.messages.map((m) => m.content).join('|'), + ); + }); + + test('binary search converges on 50% budget target', () => { + const largeTotalTokens = largeMsgs.reduce((sum, m) => sum + defaultTokenCounter(m), 0); + const largeBudget = Math.ceil(largeTotalTokens * 0.5); + const largeBudgetResult = compress(largeMsgs, { tokenBudget: largeBudget }); + assert.equal(largeBudgetResult.fits, true); + assert.ok(largeBudgetResult.recencyWindow >= 0); + }); +}); + +describe('error handling', () => { + test('non-array to compress throws TypeError', () => { + assert.throws(() => compress('not an array', {}), TypeError); + }); + + test('null entry in messages array throws TypeError', () => { + assert.throws(() => compress([null], {}), TypeError); + }); + + test('message missing required "id" field throws TypeError', () => { + assert.throws(() => compress([{ index: 0, role: 'user', content: 'hi' }], {}), TypeError); + }); + + test('non-array to uncompress throws TypeError', () => { + assert.throws(() => uncompress('not an array', () => null), TypeError); + }); + + test('invalid store to uncompress throws TypeError', () => { + assert.throws(() => uncompress([], null), TypeError); + }); + + test('null content does not throw and returns valid result', () => { + const result = compress([{ id: '1', index: 0, role: 'user', content: null }], { + recencyWindow: 0, + }); + assert.ok(Array.isArray(result.messages)); + assert.equal(result.messages.length, 1); + }); + + test('empty string content does not throw and returns valid result', () => { + const result = compress([{ id: '1', index: 0, role: 'user', content: '' }], { + recencyWindow: 0, + }); + assert.ok(Array.isArray(result.messages)); + assert.equal(result.messages.length, 1); + }); +}); diff --git a/eslint.config.js b/eslint.config.js index 73365b2..bd80311 100644 --- a/eslint.config.js +++ b/eslint.config.js @@ -5,7 +5,7 @@ export default tseslint.config( eslint.configs.recommended, ...tseslint.configs.recommended, { - ignores: ['dist/', 'coverage/', 'node_modules/'], + ignores: ['dist/', 'coverage/', 'node_modules/', 'demo/'], }, { rules: { diff --git a/package-lock.json b/package-lock.json index 45730af..cf6e191 100644 --- a/package-lock.json +++ b/package-lock.json @@ -1,24 +1,91 @@ { "name": "context-compression-engine", - "version": "1.0.0", + "version": "1.3.0", "lockfileVersion": 3, "requires": true, "packages": { "": { "name": "context-compression-engine", - "version": "1.0.0", - "license": "Apache-2.0", + "version": "1.3.0", + "license": "AGPL-3.0-only", "devDependencies": { + "@arethetypeswrong/cli": "^0.18.2", "@eslint/js": "^10.0.1", + "@google/genai": "^1.46.0", "@vitest/coverage-v8": "^4.0.18", + "esbuild": "^0.27.3", "eslint": "^10.0.2", + "openai": "^6.25.0", "prettier": "^3.8.1", + "publint": "^0.3.17", "typescript": "^5.9.3", "typescript-eslint": "^8.56.1", "vitest": "^4.0.18" }, "engines": { - "node": ">=18" + "node": ">=20" + } + }, + "node_modules/@andrewbranch/untar.js": { + "version": "1.0.3", + "resolved": "https://registry.npmjs.org/@andrewbranch/untar.js/-/untar.js-1.0.3.tgz", + "integrity": "sha512-Jh15/qVmrLGhkKJBdXlK1+9tY4lZruYjsgkDFj08ZmDiWVBLJcqkok7Z0/R0In+i1rScBpJlSvrTS2Lm41Pbnw==", + "dev": true + }, + "node_modules/@arethetypeswrong/cli": { + "version": "0.18.2", + "resolved": "https://registry.npmjs.org/@arethetypeswrong/cli/-/cli-0.18.2.tgz", + "integrity": "sha512-PcFM20JNlevEDKBg4Re29Rtv2xvjvQZzg7ENnrWFSS0PHgdP2njibVFw+dRUhNkPgNfac9iUqO0ohAXqQL4hbw==", + "dev": true, + "license": "MIT", + "dependencies": { + "@arethetypeswrong/core": "0.18.2", + "chalk": "^4.1.2", + "cli-table3": "^0.6.3", + "commander": "^10.0.1", + "marked": "^9.1.2", + "marked-terminal": "^7.1.0", + "semver": "^7.5.4" + }, + "bin": { + "attw": "dist/index.js" + }, + "engines": { + "node": ">=20" + } + }, + "node_modules/@arethetypeswrong/core": { + "version": "0.18.2", + "resolved": "https://registry.npmjs.org/@arethetypeswrong/core/-/core-0.18.2.tgz", + "integrity": "sha512-GiwTmBFOU1/+UVNqqCGzFJYfBXEytUkiI+iRZ6Qx7KmUVtLm00sYySkfe203C9QtPG11yOz1ZaMek8dT/xnlgg==", + "dev": true, + "license": "MIT", + "dependencies": { + "@andrewbranch/untar.js": "^1.0.3", + "@loaderkit/resolve": "^1.0.2", + "cjs-module-lexer": "^1.2.3", + "fflate": "^0.8.2", + "lru-cache": "^11.0.1", + "semver": "^7.5.4", + "typescript": "5.6.1-rc", + "validate-npm-package-name": "^5.0.0" + }, + "engines": { + "node": ">=20" + } + }, + "node_modules/@arethetypeswrong/core/node_modules/typescript": { + "version": "5.6.1-rc", + "resolved": "https://registry.npmjs.org/typescript/-/typescript-5.6.1-rc.tgz", + "integrity": "sha512-E3b2+1zEFu84jB0YQi9BORDjz9+jGbwwy1Zi3G0LUNw7a7cePUrHMRNy8aPh53nXpkFGVHSxIZo5vKTfYaFiBQ==", + "dev": true, + "license": "Apache-2.0", + "bin": { + "tsc": "bin/tsc", + "tsserver": "bin/tsserver" + }, + "engines": { + "node": ">=14.17" } }, "node_modules/@babel/helper-string-parser": { @@ -81,10 +148,62 @@ "node": ">=18" } }, + "node_modules/@braidai/lang": { + "version": "1.1.2", + "resolved": "https://registry.npmjs.org/@braidai/lang/-/lang-1.1.2.tgz", + "integrity": "sha512-qBcknbBufNHlui137Hft8xauQMTZDKdophmLFv05r2eNmdIv/MlPuP4TdUknHG68UdWLgVZwgxVe735HzJNIwA==", + "dev": true, + "license": "ISC" + }, + "node_modules/@colors/colors": { + "version": "1.5.0", + "resolved": "https://registry.npmjs.org/@colors/colors/-/colors-1.5.0.tgz", + "integrity": "sha512-ooWCrlZP11i8GImSjTHYHLkvFDP48nS4+204nGb1RiX/WXYHmJA2III9/e2DWVabCESdW7hBAEzHRqUn9OUVvQ==", + "dev": true, + "license": "MIT", + "optional": true, + "engines": { + "node": ">=0.1.90" + } + }, + "node_modules/@emnapi/core": { + "version": "1.9.1", + "resolved": "https://registry.npmjs.org/@emnapi/core/-/core-1.9.1.tgz", + "integrity": "sha512-mukuNALVsoix/w1BJwFzwXBN/dHeejQtuVzcDsfOEsdpCumXb/E9j8w11h5S54tT1xhifGfbbSm/ICrObRb3KA==", + "dev": true, + "license": "MIT", + "optional": true, + "dependencies": { + "@emnapi/wasi-threads": "1.2.0", + "tslib": "^2.4.0" + } + }, + "node_modules/@emnapi/runtime": { + "version": "1.9.1", + "resolved": "https://registry.npmjs.org/@emnapi/runtime/-/runtime-1.9.1.tgz", + "integrity": "sha512-VYi5+ZVLhpgK4hQ0TAjiQiZ6ol0oe4mBx7mVv7IflsiEp0OWoVsp/+f9Vc1hOhE0TtkORVrI1GvzyreqpgWtkA==", + "dev": true, + "license": "MIT", + "optional": true, + "dependencies": { + "tslib": "^2.4.0" + } + }, + "node_modules/@emnapi/wasi-threads": { + "version": "1.2.0", + "resolved": "https://registry.npmjs.org/@emnapi/wasi-threads/-/wasi-threads-1.2.0.tgz", + "integrity": "sha512-N10dEJNSsUx41Z6pZsXU8FjPjpBEplgH24sfkmITrBED1/U2Esum9F3lfLrMjKHHjmi557zQn7kR9R+XWXu5Rg==", + "dev": true, + "license": "MIT", + "optional": true, + "dependencies": { + "tslib": "^2.4.0" + } + }, "node_modules/@esbuild/aix-ppc64": { - "version": "0.27.3", - "resolved": "https://registry.npmjs.org/@esbuild/aix-ppc64/-/aix-ppc64-0.27.3.tgz", - "integrity": 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], @@ -303,9 +422,9 @@ } }, "node_modules/@esbuild/linux-ppc64": { - "version": "0.27.3", - "resolved": "https://registry.npmjs.org/@esbuild/linux-ppc64/-/linux-ppc64-0.27.3.tgz", - "integrity": "sha512-eizBnTeBefojtDb9nSh4vvVQ3V9Qf9Df01PfawPcRzJH4gFSgrObw+LveUyDoKU3kxi5+9RJTCWlj4FjYXVPEA==", + "version": "0.27.4", + "resolved": "https://registry.npmjs.org/@esbuild/linux-ppc64/-/linux-ppc64-0.27.4.tgz", + "integrity": "sha512-tLCwNG47l3sd9lpfyx9LAGEGItCUeRCWeAx6x2Jmbav65nAwoPXfewtAdtbtit/pJFLUWOhpv0FpS6GQAmPrHA==", "cpu": [ "ppc64" ], @@ -320,9 +439,9 @@ } }, "node_modules/@esbuild/linux-riscv64": { - "version": "0.27.3", - "resolved": "https://registry.npmjs.org/@esbuild/linux-riscv64/-/linux-riscv64-0.27.3.tgz", - "integrity": "sha512-3Emwh0r5wmfm3ssTWRQSyVhbOHvqegUDRd0WhmXKX2mkHJe1SFCMJhagUleMq+Uci34wLSipf8Lagt4LlpRFWQ==", + "version": "0.27.4", + "resolved": "https://registry.npmjs.org/@esbuild/linux-riscv64/-/linux-riscv64-0.27.4.tgz", + "integrity": 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"es-module-lexer": "^2.0.0", + "expect-type": "^1.3.0", "magic-string": "^0.30.21", "obug": "^2.1.1", "pathe": "^2.0.3", "picomatch": "^4.0.3", - "std-env": "^3.10.0", + "std-env": "^4.0.0-rc.1", "tinybench": "^2.9.0", "tinyexec": "^1.0.2", "tinyglobby": "^0.2.15", "tinyrainbow": "^3.0.3", - "vite": "^6.0.0 || ^7.0.0", + "vite": "^6.0.0 || ^7.0.0 || ^8.0.0-0", "why-is-node-running": "^2.3.0" }, "bin": { @@ -2779,12 +4175,13 @@ "@edge-runtime/vm": "*", "@opentelemetry/api": "^1.9.0", "@types/node": "^20.0.0 || ^22.0.0 || >=24.0.0", - "@vitest/browser-playwright": "4.0.18", - "@vitest/browser-preview": "4.0.18", - "@vitest/browser-webdriverio": "4.0.18", - "@vitest/ui": "4.0.18", + "@vitest/browser-playwright": "4.1.0", + "@vitest/browser-preview": "4.1.0", + "@vitest/browser-webdriverio": "4.1.0", + "@vitest/ui": "4.1.0", "happy-dom": "*", - "jsdom": "*" + "jsdom": "*", + "vite": "^6.0.0 || ^7.0.0 || ^8.0.0-0" }, "peerDependenciesMeta": { "@edge-runtime/vm": { @@ -2813,9 +4210,22 @@ }, "jsdom": { "optional": true + }, + "vite": { + "optional": false } } }, + "node_modules/web-streams-polyfill": { + "version": "3.3.3", + "resolved": "https://registry.npmjs.org/web-streams-polyfill/-/web-streams-polyfill-3.3.3.tgz", + "integrity": "sha512-d2JWLCivmZYTSIoge9MsgFCZrt571BikcWGYkjC1khllbTeDlGqZ2D8vD8E/lJa8WGWbb7Plm8/XJYV7IJHZZw==", + "dev": true, + "license": "MIT", + "engines": { + "node": ">= 8" + } + }, "node_modules/which": { "version": "2.0.2", "resolved": "https://registry.npmjs.org/which/-/which-2.0.2.tgz", @@ -2859,6 +4269,85 @@ "node": ">=0.10.0" } }, + "node_modules/wrap-ansi": { + "version": "7.0.0", + "resolved": "https://registry.npmjs.org/wrap-ansi/-/wrap-ansi-7.0.0.tgz", + "integrity": "sha512-YVGIj2kamLSTxw6NsZjoBxfSwsn0ycdesmc4p+Q21c5zPuZ1pl+NfxVdxPtdHvmNVOQ6XSYG4AUtyt/Fi7D16Q==", + "dev": true, + "license": "MIT", + "dependencies": { + "ansi-styles": "^4.0.0", + "string-width": "^4.1.0", + "strip-ansi": "^6.0.0" + }, + "engines": { + "node": ">=10" + }, + 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"https://registry.npmjs.org/yargs/-/yargs-16.2.0.tgz", + "integrity": "sha512-D1mvvtDG0L5ft/jGWkLpG1+m0eQxOfaBvTNELraWj22wSVUMWxZUvYgJYcKh6jGGIkJFhH4IZPQhR4TKpc8mBw==", + "dev": true, + "license": "MIT", + "dependencies": { + "cliui": "^7.0.2", + "escalade": "^3.1.1", + "get-caller-file": "^2.0.5", + "require-directory": "^2.1.1", + "string-width": "^4.2.0", + "y18n": "^5.0.5", + "yargs-parser": "^20.2.2" + }, + "engines": { + "node": ">=10" + } + }, + "node_modules/yargs-parser": { + "version": "20.2.9", + "resolved": "https://registry.npmjs.org/yargs-parser/-/yargs-parser-20.2.9.tgz", + "integrity": "sha512-y11nGElTIV+CT3Zv9t7VKl+Q3hTQoT9a1Qzezhhl6Rp21gJ/IVTW7Z3y9EWXhuUBC2Shnf+DX0antecpAwSP8w==", + "dev": true, + "license": "ISC", + "engines": { + "node": ">=10" + } + }, "node_modules/yocto-queue": { "version": "0.1.0", "resolved": "https://registry.npmjs.org/yocto-queue/-/yocto-queue-0.1.0.tgz", diff --git a/package.json b/package.json index b12cdb5..ffc6e02 100644 --- a/package.json +++ b/package.json @@ -1,20 +1,37 @@ { "name": "context-compression-engine", - "version": "1.0.0", + "version": "1.3.0", "description": "Lossless context compression engine for LLMs", "type": "module", "engines": { - "node": ">=18" + "node": ">=20" }, "scripts": { "build": "tsc", "test": "vitest run", - "test:coverage": "vitest run --coverage", + "test:coverage": "vitest run --coverage --coverage.reporter=text --coverage.reporter=json-summary --coverage.reportsDirectory=coverage", "lint": "eslint .", "format": "prettier --write .", "format:check": "prettier --check .", "bench": "npx tsx bench/run.ts", - "prepublishOnly": "npm test && tsc" + "bench:llm": "npx tsx bench/run.ts --llm", + "bench:save": "npx tsx bench/run.ts --save", + "bench:check": "npx tsx bench/run.ts --check", + "bench:compare": "npx tsx bench/compare.ts", + "bench:quality": "npx tsx bench/quality.ts", + "bench:quality:save": "npx tsx bench/quality.ts --save", + "bench:quality:check": "npx tsx bench/quality.ts --check", + "bench:quality:judge": "npx tsx bench/quality.ts --llm-judge", + "bench:quality:features": "npx tsx bench/quality.ts --features", + "bench:backfill": "npx tsx bench/backfill.ts", + "test:e2e": "npm run build && npm pack && npm run test:e2e:lint && npm run test:e2e:smoke; EXIT=$?; npm run test:e2e:cleanup; exit $EXIT", + "test:e2e:lint": "publint ./context-compression-engine-*.tgz --strict && attw ./context-compression-engine-*.tgz --ignore-rules cjs-resolves-to-esm", + "test:e2e:smoke": "cd e2e && npm install ../context-compression-engine-*.tgz && npm test", + "test:e2e:cleanup": "rm -f context-compression-engine-*.tgz && rm -rf e2e/node_modules && rm -f e2e/package-lock.json", + "test:e2e:published": "cd e2e && npm install context-compression-engine && npm test", + "prepublishOnly": "npm test && tsc", + "demo:build": "node demo/build.mjs", + "demo": "npm run demo:build && npx serve demo" }, "main": "./dist/index.js", "types": "./dist/index.d.ts", @@ -51,10 +68,15 @@ "url": "https://github.com/SimplyLiz/ContextCompressionEngine/issues" }, "devDependencies": { + "@arethetypeswrong/cli": "^0.18.2", "@eslint/js": "^10.0.1", + "@google/genai": "^1.46.0", "@vitest/coverage-v8": "^4.0.18", + "esbuild": "^0.27.3", "eslint": "^10.0.2", + "openai": "^6.25.0", "prettier": "^3.8.1", + "publint": "^0.3.17", "typescript": "^5.9.3", "typescript-eslint": "^8.56.1", "vitest": "^4.0.18" diff --git a/src/adapters.ts b/src/adapters.ts new file mode 100644 index 0000000..bc44f7d --- /dev/null +++ b/src/adapters.ts @@ -0,0 +1,128 @@ +/** + * Format adapter pattern for extensible content handling. + * + * Formalizes the existing code-split and structured-output logic into a clean + * interface. Users can register custom adapters for domain-specific formats. + */ + +import type { FormatAdapter } from './types.js'; + +// --------------------------------------------------------------------------- +// Built-in: CodeAdapter +// --------------------------------------------------------------------------- + +const FENCE_RE = /^[ ]{0,3}```[^\n]*\n[\s\S]*?\n\s*```/gm; + +/** + * Handles messages containing code fences interleaved with prose. + * Code fences are preserved verbatim; surrounding prose is compressed. + */ +export const CodeAdapter: FormatAdapter = { + name: 'code', + + detect(content: string): boolean { + return content.includes('```'); + }, + + extractPreserved(content: string): string[] { + const fences: string[] = []; + let match: RegExpExecArray | null; + const re = new RegExp(FENCE_RE.source, FENCE_RE.flags); + while ((match = re.exec(content)) !== null) { + fences.push(match[0]); + } + return fences; + }, + + extractCompressible(content: string): string[] { + const prose: string[] = []; + const re = new RegExp(FENCE_RE.source, FENCE_RE.flags); + let lastIndex = 0; + let match: RegExpExecArray | null; + while ((match = re.exec(content)) !== null) { + const segment = content.slice(lastIndex, match.index).trim(); + if (segment) prose.push(segment); + lastIndex = match.index + match[0].length; + } + const trailing = content.slice(lastIndex).trim(); + if (trailing) prose.push(trailing); + return prose; + }, + + reconstruct(preserved: string[], summary: string): string { + return `${summary}\n\n${preserved.join('\n\n')}`; + }, +}; + +// --------------------------------------------------------------------------- +// Built-in: StructuredOutputAdapter +// --------------------------------------------------------------------------- + +const STRUCTURAL_RE = + /^(?:\S+\.\w+:\d+:|[ \t]+[-•*]|[ \t]*\w[\w ./-]*:\s|(?:PASS|FAIL|ERROR|WARNING|WARN|OK|SKIP)\b)/; + +function isStructuredOutput(text: string): boolean { + const lines = text.split('\n'); + const nonEmpty = lines.filter((l) => l.trim().length > 0); + if (nonEmpty.length < 6) return false; + const newlineDensity = (text.match(/\n/g) ?? []).length / text.length; + if (newlineDensity < 1 / 80) return false; + let structural = 0; + for (const line of nonEmpty) { + if (STRUCTURAL_RE.test(line)) structural++; + } + return structural / nonEmpty.length > 0.5; +} + +/** + * Handles structured tool output (test results, grep output, status lines). + * Extracts status/summary lines and file paths as preserved elements; + * the remaining bulk content is compressible. + */ +export const StructuredOutputAdapter: FormatAdapter = { + name: 'structured_output', + + detect(content: string): boolean { + return isStructuredOutput(content); + }, + + extractPreserved(content: string): string[] { + const lines = content.split('\n').filter((l) => l.trim().length > 0); + const preserved: string[] = []; + for (const line of lines) { + if (/\b(?:PASS|FAIL|ERROR|WARNING|WARN|Tests?|Total|Duration|passed|failed)\b/i.test(line)) { + preserved.push(line.trim()); + } + } + // File paths from grep-style output + const filePaths = new Set(); + for (const line of lines) { + const m = line.match(/^(\S+\.\w+):\d+:/); + if (m) filePaths.add(m[1]); + } + if (filePaths.size > 0) { + preserved.push(`files: ${Array.from(filePaths).join(', ')}`); + } + return preserved; + }, + + extractCompressible(content: string): string[] { + const lines = content.split('\n').filter((l) => l.trim().length > 0); + const compressible: string[] = []; + for (const line of lines) { + if ( + !/\b(?:PASS|FAIL|ERROR|WARNING|WARN|Tests?|Total|Duration|passed|failed)\b/i.test(line) && + !/^\S+\.\w+:\d+:/.test(line) + ) { + compressible.push(line.trim()); + } + } + return compressible; + }, + + reconstruct(preserved: string[], summary: string): string { + const parts = [...preserved]; + if (summary) parts.push(summary); + return parts.join(' | '); + }, +}; diff --git a/src/classifier.ts b/src/classifier.ts new file mode 100644 index 0000000..9f188fc --- /dev/null +++ b/src/classifier.ts @@ -0,0 +1,148 @@ +import { classifyMessage, HARD_T0_REASONS } from './classify.js'; +import type { Classifier, ClassifierResult, CreateClassifierOptions } from './types.js'; + +const DEFAULT_MAX_RESPONSE_TOKENS = 100; + +function buildClassifierPrompt( + content: string, + maxResponseTokens: number, + options?: Pick, +): string { + const prefix = options?.systemPrompt ? `${options.systemPrompt}\n\n` : ''; + + const preserveExtra = + options?.alwaysPreserve && options.alwaysPreserve.length > 0 + ? '\n' + options.alwaysPreserve.map((t) => `- ${t}`).join('\n') + : ''; + + const compressExtra = + options?.alwaysCompress && options.alwaysCompress.length > 0 + ? '\n' + options.alwaysCompress.map((t) => `- ${t}`).join('\n') + : ''; + + return `${prefix}Classify the following message for a context compression engine. + +Your task: Decide whether this message should be PRESERVED verbatim or can be safely COMPRESSED (summarized). + +Preserve content that: +- Contains critical decisions, conclusions, or commitments +- Would lose meaning if paraphrased +- Contains domain-specific terms, definitions, or references that must stay exact${preserveExtra} + +Compress content that: +- Is general discussion, explanation, or elaboration +- Can be summarized without losing actionable information +- Contains filler, pleasantries, or redundant restatements${compressExtra} + +Respond with EXACTLY this JSON format, nothing else (keep your response under ${maxResponseTokens} tokens): +{"decision": "preserve" | "compress", "confidence": 0.0-1.0, "reason": "one sentence"} + +Message: +${content}`; +} + +function parseClassifierResponse(response: string): ClassifierResult | null { + // Strategy 1: direct JSON.parse + try { + const parsed = JSON.parse(response); + if (isValidResult(parsed)) return normalizeResult(parsed); + } catch { + /* fall through */ + } + + // Strategy 2: extract first {...} substring + const braceMatch = response.match(/\{[^}]*\}/); + if (braceMatch) { + try { + const parsed = JSON.parse(braceMatch[0]); + if (isValidResult(parsed)) return normalizeResult(parsed); + } catch { + /* fall through */ + } + } + + // Strategy 3: extract from markdown code block + const codeBlockMatch = response.match(/```(?:json)?\s*\n?([\s\S]*?)\n?\s*```/); + if (codeBlockMatch) { + try { + const parsed = JSON.parse(codeBlockMatch[1].trim()); + if (isValidResult(parsed)) return normalizeResult(parsed); + } catch { + /* fall through */ + } + } + + return null; +} + +function isValidResult(obj: unknown): boolean { + if (obj == null || typeof obj !== 'object') return false; + const o = obj as Record; + return ( + (o.decision === 'preserve' || o.decision === 'compress') && + typeof o.confidence === 'number' && + typeof o.reason === 'string' + ); +} + +function normalizeResult(obj: Record): ClassifierResult { + return { + decision: obj.decision as 'preserve' | 'compress', + confidence: Math.max(0, Math.min(1, obj.confidence as number)), + reason: obj.reason as string, + }; +} + +const UNPARSEABLE: ClassifierResult = { + decision: 'compress', + confidence: 0, + reason: 'unparseable', +}; + +export function createClassifier( + callLlm: (prompt: string) => string | Promise, + options?: CreateClassifierOptions, +): Classifier { + const maxResponseTokens = options?.maxResponseTokens ?? DEFAULT_MAX_RESPONSE_TOKENS; + const promptOpts = { + systemPrompt: options?.systemPrompt || undefined, + alwaysPreserve: options?.alwaysPreserve, + alwaysCompress: options?.alwaysCompress, + }; + + return (content: string) => { + const prompt = buildClassifierPrompt(content, maxResponseTokens, promptOpts); + const result = callLlm(prompt); + if (result instanceof Promise) { + return result.then((r) => parseClassifierResponse(r) ?? UNPARSEABLE); + } + return parseClassifierResponse(result) ?? UNPARSEABLE; + }; +} + +export function createEscalatingClassifier( + callLlm: (prompt: string) => string | Promise, + options?: CreateClassifierOptions, +): Classifier { + const inner = createClassifier(callLlm, options); + + return async (content: string): Promise => { + // Level 1: LLM classification + try { + const result = await inner(content); + if (result.confidence > 0) return result; + } catch { + /* fall through to heuristic */ + } + + // Level 2: Heuristic fallback + const heuristic = classifyMessage(content); + if (heuristic.decision === 'T0') { + const hasHard = heuristic.reasons.some((r) => HARD_T0_REASONS.has(r)); + if (hasHard) { + return { decision: 'preserve', confidence: heuristic.confidence, reason: 'heuristic_t0' }; + } + } + return { decision: 'compress', confidence: heuristic.confidence, reason: 'heuristic_fallback' }; + }; +} diff --git a/src/classify.ts b/src/classify.ts index 6e5f5cd..219241d 100644 --- a/src/classify.ts +++ b/src/classify.ts @@ -1,5 +1,18 @@ export type ClassifyResult = { decision: 'T0' | 'T2' | 'T3'; + /** + * Classification confidence (0–1). Higher values indicate stronger signal. + * + * For T0: starts at 0.70, increases by 0.05 per additional structural reason + * (capped at 0.95). Multiple overlapping signals → higher confidence. + * For T2/T3: fixed at 0.65 (pure prose heuristic, no structural anchors). + * + * The deterministic pipeline does not route on confidence — it uses the + * hard/soft T0 distinction instead. Consumers can use confidence for custom + * routing (e.g. only compress below a threshold), monitoring dashboards, + * or LLM classifier fallback decisions (cf. Amazon Science "Label with + * Confidence" for confidence-weighted routing patterns). + */ confidence: number; reasons: string[]; }; @@ -132,6 +145,49 @@ const API_KEY_PATTERNS: RegExp[] = [ const GENERIC_TOKEN_RE = /\b[a-zA-Z](?=[a-zA-Z0-9]{0,13}[g-zG-Z])[a-zA-Z0-9]{1,14}[-_](?=[a-zA-Z0-9_-]*[0-9])(?=[a-zA-Z0-9_-]*[a-zA-Z])[a-zA-Z0-9_-]{20,}\b/; +// Reasoning chain detection — two-tier anchor system (mirrors SQL detection). +// Strong anchors: explicit reasoning labels or formal inference → 1 match is enough. +// Weak anchors: logical connectives / causal phrases → need 3+ distinct to trigger. +const REASONING_STRONG_RE = + /^[ \t]*(?:Reasoning|Analysis|Conclusion|Proof|Derivation|Chain of Thought|Step[- ]by[- ]step)\s*:/im; +const REASONING_INFERENCE_RE = + /\b(?:it follows that|we can (?:conclude|deduce|infer)|this (?:implies|proves) that|QED)\b|∴/i; +// Note: `g` flag is safe here — these regexes are only used via String.match(), +// which ignores lastIndex. Do NOT use .test()/.exec() on them without resetting. +const REASONING_WEAK_ANCHORS_RE = + /\b(?:therefore|hence|thus|consequently|accordingly|this means that|as a result|because of this|which (?:implies|means|shows)|given that|assuming that|since we know)\b/gi; +const NUMBERED_STEP_RE = /(?:^|\n)\s*(?:Step\s+\d+[:.)]|\d+[.)]\s)/gi; +const SEQUENCE_MARKERS_RE = + /\b(?:Let me (?:think|reason|analyze)|Let's (?:consider|break this down)|First(?:ly)?|Second(?:ly)?|Third(?:ly)?|In conclusion|To summarize|In summary)\b/gi; + +export function detectReasoningChain(text: string): boolean { + // 1+ strong anchor → unambiguous reasoning chain + if (REASONING_STRONG_RE.test(text)) return true; + if (REASONING_INFERENCE_RE.test(text)) return true; + + // Count distinct weak anchors + const weakMatches = text.match(REASONING_WEAK_ANCHORS_RE); + const distinctWeak = weakMatches + ? new Set(weakMatches.map((m) => m.toLowerCase().replace(/\s+/g, ' '))).size + : 0; + + // Count distinct sequence markers (each counts as 1 weak anchor) + const seqMatches = text.match(SEQUENCE_MARKERS_RE); + const seqCount = seqMatches + ? new Set(seqMatches.map((m) => m.toLowerCase().replace(/\s+/g, ' '))).size + : 0; + + // 3+ numbered steps AND 1+ weak anchor → reasoning chain + const stepMatches = text.match(NUMBERED_STEP_RE); + const stepCount = stepMatches ? stepMatches.length : 0; + if (stepCount >= 3 && distinctWeak + seqCount >= 1) return true; + + // 3+ distinct weak anchors (including sequence contribution) → reasoning chain + if (distinctWeak + seqCount >= 3) return true; + + return false; +} + const FORCE_T0_PATTERNS: Array<{ re: RegExp; label: string }> = [ { re: /https?:\/\/[^\s]+/, label: 'url' }, { re: /[\w.+-]+@[\w-]+\.[a-z]{2,}/i, label: 'email' }, @@ -179,6 +235,9 @@ function detectContentTypes(text: string): { } if (apiKeyFound) reasons.push('api_key'); + // Reasoning chain detection + if (detectReasoningChain(text)) reasons.push('reasoning_chain'); + // Other content-type patterns for (const { re, label } of FORCE_T0_PATTERNS) { if (re.test(text)) reasons.push(label); @@ -189,6 +248,14 @@ function detectContentTypes(text: string): { // -- Tier heuristic for clean prose -- +/** + * Assign T2 (short prose, < 20 words) or T3 (long prose, >= 20 words). + * + * Both tiers are compressed identically in the current deterministic pipeline. + * The distinction exists so a future LLM classifier can apply different + * strategies per tier — e.g. lighter summarization for T2 or aggressive + * compression for verbose T3 content. + */ function inferProseTier(text: string): 'T2' | 'T3' { const words = text.split(/\s+/).length; if (words < 20) return 'T2'; @@ -197,6 +264,24 @@ function inferProseTier(text: string): 'T2' | 'T3' { // -- Main classifier entry point -- +// Hard T0 reasons: genuinely structural content that can't be summarized. +// Soft T0 reasons (file_path, url, version_number, etc.): incidental +// references in prose — entities capture them, prose is still compressible. +export const HARD_T0_REASONS = new Set([ + 'code_fence', + 'indented_code', + 'json_structure', + 'yaml_structure', + 'high_special_char_ratio', + 'high_line_length_variance', + 'api_key', + 'latex_math', + 'unicode_math', + 'sql_content', + 'verse_pattern', + 'reasoning_chain', +]); + export function classifyMessage(content: string): ClassifyResult { const structural = detectStructuralPatterns(content); const contentTypes = detectContentTypes(content); diff --git a/src/cluster.ts b/src/cluster.ts new file mode 100644 index 0000000..ef1d425 --- /dev/null +++ b/src/cluster.ts @@ -0,0 +1,328 @@ +/** + * Semantic clustering for topic-aware compression. + * + * Groups messages by topic using lightweight TF-IDF and entity overlap, + * then compresses each cluster as a unit. Scattered messages about the + * same topic get merged into a single compressed block. + */ + +import { extractEntities } from './entities.js'; +import type { Message } from './types.js'; + +export type MessageCluster = { + /** Indices of messages in this cluster, in chronological order. */ + indices: number[]; + /** Shared entities across cluster members. */ + sharedEntities: string[]; + /** Cluster label derived from top entities. */ + label: string; +}; + +// Common English stopwords +const STOPWORDS = new Set([ + 'the', + 'a', + 'an', + 'is', + 'are', + 'was', + 'were', + 'be', + 'been', + 'being', + 'have', + 'has', + 'had', + 'do', + 'does', + 'did', + 'will', + 'would', + 'could', + 'should', + 'may', + 'might', + 'shall', + 'can', + 'need', + 'dare', + 'ought', + 'used', + 'to', + 'of', + 'in', + 'for', + 'on', + 'with', + 'at', + 'by', + 'from', + 'as', + 'into', + 'through', + 'during', + 'before', + 'after', + 'above', + 'below', + 'between', + 'out', + 'off', + 'over', + 'under', + 'again', + 'further', + 'then', + 'once', + 'here', + 'there', + 'when', + 'where', + 'why', + 'how', + 'all', + 'each', + 'every', + 'both', + 'few', + 'more', + 'most', + 'other', + 'some', + 'such', + 'no', + 'not', + 'only', + 'own', + 'same', + 'so', + 'than', + 'too', + 'very', + 'just', + 'because', + 'but', + 'and', + 'or', + 'if', + 'while', + 'although', + 'this', + 'that', + 'these', + 'those', + 'i', + 'you', + 'he', + 'she', + 'it', + 'we', + 'they', + 'me', + 'him', + 'her', + 'us', + 'them', + 'my', + 'your', + 'his', + 'its', + 'our', + 'their', + 'what', + 'which', + 'who', + 'whom', + 'whose', +]); + +/** + * Tokenize text into content words (lowercase, no stopwords, 3+ chars). + */ +function tokenize(text: string): string[] { + return text + .toLowerCase() + .split(/[^a-z0-9_]+/) + .filter((w) => w.length >= 3 && !STOPWORDS.has(w)); +} + +/** + * Compute TF-IDF vectors for each message. + * Returns term weights per message and the IDF table. + */ +function computeTfIdf(messages: Message[], indices: number[]): Map> { + // Document frequency + const df = new Map(); + const docs = new Map(); + + for (const idx of indices) { + const content = (messages[idx].content as string | undefined) ?? ''; + const tokens = tokenize(content); + docs.set(idx, tokens); + const unique = new Set(tokens); + for (const term of unique) { + df.set(term, (df.get(term) ?? 0) + 1); + } + } + + const N = indices.length; + const tfidf = new Map>(); + + for (const idx of indices) { + const tokens = docs.get(idx)!; + const tf = new Map(); + for (const t of tokens) tf.set(t, (tf.get(t) ?? 0) + 1); + + const vec = new Map(); + for (const [term, count] of tf) { + const idf = Math.log(N / (df.get(term) ?? 1)); + vec.set(term, count * idf); + } + tfidf.set(idx, vec); + } + + return tfidf; +} + +/** + * Cosine similarity between two TF-IDF vectors. + */ +function cosineSimilarity(a: Map, b: Map): number { + let dot = 0; + let normA = 0; + let normB = 0; + + for (const [term, wA] of a) { + normA += wA * wA; + const wB = b.get(term); + if (wB != null) dot += wA * wB; + } + for (const [, wB] of b) normB += wB * wB; + + if (normA === 0 || normB === 0) return 0; + return dot / (Math.sqrt(normA) * Math.sqrt(normB)); +} + +/** + * Agglomerative clustering using cosine similarity on TF-IDF + entity overlap. + * Merges closest clusters until similarity drops below threshold. + */ +export function clusterMessages( + messages: Message[], + eligibleIndices: number[], + similarityThreshold = 0.15, +): MessageCluster[] { + if (eligibleIndices.length < 2) return []; + + const tfidf = computeTfIdf(messages, eligibleIndices); + + // Entity overlap boost + const entitySets = new Map>(); + for (const idx of eligibleIndices) { + const content = (messages[idx].content as string | undefined) ?? ''; + entitySets.set(idx, new Set(extractEntities(content, 100))); + } + + // Combined similarity: 0.7 * cosine(tfidf) + 0.3 * jaccard(entities) + function similarity(i: number, j: number): number { + const cos = cosineSimilarity(tfidf.get(i)!, tfidf.get(j)!); + const eA = entitySets.get(i)!; + const eB = entitySets.get(j)!; + let intersection = 0; + for (const e of eA) if (eB.has(e)) intersection++; + const union = eA.size + eB.size - intersection; + const jaccard = union > 0 ? intersection / union : 0; + return 0.7 * cos + 0.3 * jaccard; + } + + // Start with each message as its own cluster + const clusters: number[][] = eligibleIndices.map((idx) => [idx]); + + // Agglomerative: merge closest pair until threshold + while (clusters.length > 1) { + let bestSim = -1; + let bestI = -1; + let bestJ = -1; + + for (let ci = 0; ci < clusters.length; ci++) { + for (let cj = ci + 1; cj < clusters.length; cj++) { + // Average-linkage similarity between clusters + let totalSim = 0; + let count = 0; + for (const a of clusters[ci]) { + for (const b of clusters[cj]) { + totalSim += similarity(a, b); + count++; + } + } + const avgSim = count > 0 ? totalSim / count : 0; + if (avgSim > bestSim) { + bestSim = avgSim; + bestI = ci; + bestJ = cj; + } + } + } + + if (bestSim < similarityThreshold) break; + + // Merge bestJ into bestI + clusters[bestI] = [...clusters[bestI], ...clusters[bestJ]]; + clusters.splice(bestJ, 1); + } + + // Convert to MessageCluster format (only multi-message clusters) + return ( + clusters + .filter((c) => c.length >= 2) + .map((indices) => { + indices.sort((a, b) => a - b); + return indices; + }) + // Only keep clusters with consecutive indices — non-consecutive merges + // break round-trip because uncompress can't restore interleaved ordering + .filter((indices) => { + for (let k = 1; k < indices.length; k++) { + if (indices[k] !== indices[k - 1] + 1) return false; + } + return true; + }) + .map((indices) => { + // Find shared entities + const entityCounts = new Map(); + for (const idx of indices) { + for (const e of entitySets.get(idx)!) { + entityCounts.set(e, (entityCounts.get(e) ?? 0) + 1); + } + } + const shared = [...entityCounts.entries()] + .filter(([, count]) => count >= 2) + .sort((a, b) => b[1] - a[1]) + .map(([e]) => e) + .slice(0, 5); + + return { + indices, + sharedEntities: shared, + label: shared.length > 0 ? shared.slice(0, 3).join(', ') : `cluster-${indices[0]}`, + }; + }) + ); +} + +/** + * Produce a cluster-aware summary by merging messages chronologically. + */ +export function summarizeCluster(cluster: MessageCluster, messages: Message[]): string { + const topicPrefix = + cluster.sharedEntities.length > 0 ? `[${cluster.sharedEntities.slice(0, 3).join(', ')}] ` : ''; + + const snippets: string[] = []; + for (const idx of cluster.indices) { + const content = (messages[idx].content as string | undefined) ?? ''; + const snippet = content.length > 100 ? content.slice(0, 97) + '...' : content; + snippets.push(snippet); + } + + return `${topicPrefix}${snippets.join(' → ')} (${cluster.indices.length} messages)`; +} diff --git a/src/compress.ts b/src/compress.ts index 68e2641..8b9ab3a 100644 --- a/src/compress.ts +++ b/src/compress.ts @@ -1,6 +1,32 @@ -import { classifyMessage } from './classify.js'; +import { classifyMessage, HARD_T0_REASONS } from './classify.js'; import { analyzeDuplicates, analyzeFuzzyDuplicates, type DedupAnnotation } from './dedup.js'; -import type { CompressOptions, CompressResult, Message, Summarizer } from './types.js'; +import { + computeImportance, + DEFAULT_IMPORTANCE_THRESHOLD, + type ImportanceMap, +} from './importance.js'; +import { analyzeContradictions, type ContradictionAnnotation } from './contradiction.js'; +import { extractEntities, computeQualityScore } from './entities.js'; +import { combineScores } from './entropy.js'; +import { detectFlowChains, summarizeChain, type FlowChain } from './flow.js'; +import { + buildCoreferenceMap, + findOrphanedReferences, + generateInlineDefinitions, +} from './coreference.js'; +import { clusterMessages, summarizeCluster, type MessageCluster } from './cluster.js'; +import { summarizeWithEDUs } from './discourse.js'; +import { compressWithTokenClassifierSync, compressWithTokenClassifier } from './ml-classifier.js'; +import type { + Classifier, + ClassifierResult, + CompressDecision, + CompressOptions, + CompressResult, + FormatAdapter, + Message, + Summarizer, +} from './types.js'; /** * Deterministic summary ID from sorted source message IDs. @@ -37,6 +63,9 @@ const FILLER_RE = const EMPHASIS_RE = /\b(?:importantly|note that|however|critical|crucial|essential|significant|notably|key point|in particular|specifically|must|require[ds]?|never|always)\b/i; +const REASONING_SCORE_RE = + /\b(?:therefore|hence|thus|consequently|accordingly|it follows that|we can (?:conclude|deduce|infer)|this (?:implies|proves|means) that|as a result|given that|in conclusion)\b/i; + function scoreSentence(sentence: string): number { let score = 0; // camelCase identifiers @@ -47,6 +76,8 @@ function scoreSentence(sentence: string): number { score += (sentence.match(/\b[a-z]+(?:_[a-z]+)+\b/g) ?? []).length * 3; // Emphasis phrases if (EMPHASIS_RE.test(sentence)) score += 4; + // Reasoning connectives — defense-in-depth so reasoning sentences survive summarization + if (REASONING_SCORE_RE.test(sentence)) score += 3; // Numbers with units score += ( @@ -67,7 +98,32 @@ function scoreSentence(sentence: string): number { return score; } -function summarize(text: string, maxBudget?: number): string { +/** + * Compute the best (highest) sentence score in a text. + * Used for the relevance threshold: if the best score is below the threshold, + * the content is too low-value to produce a useful summary. + */ +export function bestSentenceScore(text: string): number { + const sentences = text.match(/[^.!?\n]+[.!?]+/g); + if (!sentences || sentences.length === 0) return scoreSentence(text.trim()); + let best = -Infinity; + for (const s of sentences) { + const score = scoreSentence(s.trim()); + if (score > best) best = score; + } + return best; +} + +/** + * Deterministic summarization with optional external score overrides. + * + * @param text - text to summarize + * @param maxBudget - character budget for the summary + * @param externalScores - optional per-sentence scores (from entropy scorer). + * When provided, replaces the heuristic scorer for sentence ranking. + * Map key is the sentence index (matches paragraph/sentence iteration order). + */ +function summarize(text: string, maxBudget?: number, externalScores?: Map): string { const paragraphs = text.split(/\n\n+/).filter((p) => p.trim().length > 0); type Scored = { text: string; score: number; origIdx: number; primary: boolean }; @@ -79,9 +135,10 @@ function summarize(text: string, maxBudget?: number): string { if (!sentences || sentences.length === 0) { const trimmed = para.trim(); if (trimmed.length > 0) { + const score = externalScores?.get(globalIdx) ?? scoreSentence(trimmed); allSentences.push({ text: trimmed, - score: scoreSentence(trimmed), + score, origIdx: globalIdx++, primary: true, }); @@ -94,7 +151,7 @@ function summarize(text: string, maxBudget?: number): string { const paraSentences: Scored[] = []; for (let i = 0; i < sentences.length; i++) { const s = sentences[i].trim(); - const sc = scoreSentence(s); + const sc = externalScores?.get(globalIdx + i) ?? scoreSentence(s); paraSentences.push({ text: s, score: sc, origIdx: globalIdx + i, primary: false }); if (sc > bestScore) { bestScore = sc; @@ -239,145 +296,55 @@ function summarizeStructured(text: string, maxBudget: number): string { return result; } -const COMMON_STARTERS = new Set([ - 'The', - 'This', - 'That', - 'These', - 'Those', - 'When', - 'Where', - 'What', - 'Which', - 'Who', - 'How', - 'Why', - 'Here', - 'There', - 'Now', - 'Then', - 'But', - 'And', - 'Or', - 'So', - 'If', - 'It', - 'Its', - 'My', - 'Your', - 'His', - 'Her', - 'Our', - 'They', - 'We', - 'You', - 'He', - 'She', - 'In', - 'On', - 'At', - 'To', - 'For', - 'With', - 'From', - 'As', - 'By', - 'An', - 'Each', - 'Every', - 'Some', - 'All', - 'Most', - 'Many', - 'Much', - 'Any', - 'No', - 'Not', - 'Also', - 'Just', - 'Only', - 'Even', - 'Still', - 'Yet', - 'Let', - 'See', - 'Note', - 'Yes', - 'Sure', - 'Great', - 'Thanks', - 'Well', - 'First', - 'Second', - 'Third', - 'Next', - 'Last', - 'Finally', - 'However', - 'After', - 'Before', - 'Since', - 'Once', - 'While', - 'Although', - 'Because', - 'Unless', - 'Until', - 'About', - 'Over', - 'Under', - 'Between', - 'Into', -]); - -function extractEntities(text: string): string[] { - const entities = new Set(); - - // Proper nouns: capitalized words not at common sentence starters - const properNouns = text.match(/\b[A-Z][a-z]+(?:\s+[A-Z][a-z]+)*\b/g); - if (properNouns) { - for (const noun of properNouns) { - const first = noun.split(/\s+/)[0]; - if (!COMMON_STARTERS.has(first)) { - entities.add(noun); - } - } - } - - // PascalCase identifiers (TypeScript, WebSocket, JavaScript, etc.) - const pascalCase = text.match(/\b[A-Z][a-z]+(?:[A-Z][a-z]+)+\b/g); - if (pascalCase) { - for (const id of pascalCase) entities.add(id); - } - - // camelCase identifiers - const camelCase = text.match(/\b[a-z]+(?:[A-Z][a-z]+)+\b/g); - if (camelCase) { - for (const id of camelCase) entities.add(id); - } - - // snake_case identifiers - const snakeCase = text.match(/\b[a-z]+(?:_[a-z]+)+\b/g); - if (snakeCase) { - for (const id of snakeCase) entities.add(id); - } +/** + * Adaptive summary budget: scales with content density. + * Dense content (many entities per char) gets more budget to preserve identifiers. + * Sparse content (general discussion) gets tighter budget for more aggressive compression. + * + * @param contentLength - character length of the content + * @param entityCount - optional entity count for density-adaptive scaling + */ +/** Depth multiplier: how much to scale the budget down by depth level. */ +const DEPTH_MULTIPLIERS: Record = { + gentle: 1.0, + moderate: 0.5, + aggressive: 0.15, +}; - // Vowelless words (3+ consonants, no aeiou/y) — abbreviations/tool names: pnpm, npm, ssh, grpc - const vowelless = text.match(/\b[bcdfghjklmnpqrstvwxz]{3,}\b/gi); - if (vowelless) { - for (const w of vowelless) entities.add(w.toLowerCase()); +function computeBudget( + contentLength: number, + entityCount?: number, + depth?: 'gentle' | 'moderate' | 'aggressive', +): number { + const depthMul = DEPTH_MULTIPLIERS[depth ?? 'gentle'] ?? 1.0; + const baseRatio = 0.3 * depthMul; + + if (entityCount != null && contentLength > 0) { + const density = entityCount / contentLength; + const densityBonus = Math.min(density * 500, 0.5) * depthMul; + const adaptiveRatio = Math.max( + 0.05, + Math.min(baseRatio + densityBonus - 0.15 * depthMul, 0.45 * depthMul), + ); + return Math.max( + depth === 'aggressive' ? 40 : 100, + Math.min(Math.round(contentLength * adaptiveRatio), 800 * depthMul), + ); } - // Numbers with context - const numbersCtx = text.match( - /\b\d+(?:\.\d+)?\s*(?:seconds?|retries?|attempts?|MB|GB|TB|KB|ms|minutes?|hours?|days?|bytes?|workers?|threads?|nodes?|replicas?|instances?|users?|requests?|errors?|percent|%)\b/gi, - ); - if (numbersCtx) { - for (const n of numbersCtx) entities.add(n.trim()); - } + const min = depth === 'aggressive' ? 40 : depth === 'moderate' ? 100 : 200; + const max = depth === 'aggressive' ? 120 : depth === 'moderate' ? 300 : 600; + return Math.max(min, Math.min(Math.round(contentLength * baseRatio), max)); +} - // Cap at 10 - return Array.from(entities).slice(0, 10); +/** + * Generate entity-only stub for aggressive compression. + * Returns just the key entities from the text. + */ +function entityOnlyStub(text: string): string { + const entities = extractEntities(text, 10); + if (entities.length === 0) return text.slice(0, 40).trim() + '...'; + return entities.join(', '); } function splitCodeAndProse(text: string): Array<{ type: 'prose' | 'code'; content: string }> { @@ -418,7 +385,16 @@ function contentLength(msg: Message): number { return typeof msg.content === 'string' ? msg.content.length : 0; } -/** Default token counter: ~3.5 chars/token heuristic. */ +/** + * Default token counter: ~3.5 chars/token heuristic. + * + * The 3.5 ratio is the empirical average for GPT-family BPE tokenizers + * (cl100k_base, o200k_base) on mixed English text. Real-world values range + * from ~3.2 (code-heavy) to ~4.5 (plain prose). We intentionally pick the + * lower end so budget estimates stay conservative (slightly over-counting + * tokens is safer than under-counting). Users who need exact counts can + * supply a real tokenizer via the `tokenCounter` option. + */ export function defaultTokenCounter(msg: Message): number { return Math.ceil(contentLength(msg) / 3.5); } @@ -427,11 +403,21 @@ export function defaultTokenCounter(msg: Message): number { // Shared helpers extracted for sync / async reuse // --------------------------------------------------------------------------- +type _InternalOptions = CompressOptions & { + _llmResults?: Map; +}; + type Classified = { msg: Message; preserved: boolean; codeSplit?: boolean; dedup?: DedupAnnotation; + contradiction?: ContradictionAnnotation; + patternPreserved?: boolean; + llmPreserved?: boolean; + importancePreserved?: boolean; + traceReason?: string; + adapterMatch?: FormatAdapter; }; /** Build a compressed message with _cce_original provenance metadata. */ @@ -482,7 +468,7 @@ function formatSummary( return `${prefix}${summaryText}${mergeSuffix}${entitySuffix}]`; } -/** Collect consecutive non-preserved, non-codeSplit, non-dedup messages with the same role. */ +/** Collect consecutive non-preserved, non-codeSplit, non-dedup, non-adapter messages with the same role. */ function collectGroup( classified: Classified[], startIdx: number, @@ -495,6 +481,7 @@ function collectGroup( !classified[i].preserved && !classified[i].codeSplit && !classified[i].dedup && + !classified[i].adapterMatch && classified[i].msg.role === role ) { group.push(classified[i]); @@ -503,55 +490,90 @@ function collectGroup( return { group, nextIdx: i }; } -// Hard T0 reasons: genuinely structural content that can't be summarized. -// Soft T0 reasons (file_path, url, version_number, etc.): incidental -// references in prose — entities capture them, prose is still compressible. -const HARD_T0_REASONS = new Set([ - 'code_fence', - 'indented_code', - 'json_structure', - 'yaml_structure', - 'high_special_char_ratio', - 'high_line_length_variance', - 'api_key', - 'latex_math', - 'unicode_math', - 'sql_content', - 'verse_pattern', -]); - function classifyAll( messages: Message[], preserveRoles: Set, recencyWindow: number, dedupAnnotations?: Map, + preservePatterns?: Array<{ re: RegExp; label: string }>, + llmResults?: Map, + classifierMode?: 'hybrid' | 'full', + trace?: boolean, + adapters?: FormatAdapter[], + observationThreshold?: number, + counter?: (msg: Message) => number, + importanceScores?: ImportanceMap, + importanceThreshold?: number, + contradictionAnnotations?: Map, ): Classified[] { const recencyStart = Math.max(0, messages.length - recencyWindow); return messages.map((msg, idx) => { const content = typeof msg.content === 'string' ? msg.content : ''; + // Per-message observation threshold: large messages get compressed even in recency window. + // System roles, tool_calls, and already-compressed messages are exempt. + const largeObservation = + observationThreshold != null && counter != null && counter(msg) > observationThreshold; + if (msg.role && preserveRoles.has(msg.role)) { - return { msg, preserved: true }; + return { msg, preserved: true, ...(trace && { traceReason: 'preserved_role' }) }; } - if (recencyWindow > 0 && idx >= recencyStart) { - return { msg, preserved: true }; + if (!largeObservation && recencyWindow > 0 && idx >= recencyStart) { + return { msg, preserved: true, ...(trace && { traceReason: 'recency_window' }) }; } if (msg.tool_calls && Array.isArray(msg.tool_calls) && msg.tool_calls.length > 0) { - return { msg, preserved: true }; + return { msg, preserved: true, ...(trace && { traceReason: 'tool_calls' }) }; } - if (content.length < 120) { - return { msg, preserved: true }; + if (!largeObservation && content.length < 120) { + return { msg, preserved: true, ...(trace && { traceReason: 'short_content' }) }; } if ( content.startsWith('[summary:') || content.startsWith('[summary#') || content.startsWith('[truncated') ) { - return { msg, preserved: true }; + return { msg, preserved: true, ...(trace && { traceReason: 'already_compressed' }) }; + } + // Importance-based preservation: high-importance messages preserved even outside recency + if ( + importanceScores && + importanceThreshold != null && + !largeObservation && + importanceScores.has(idx) + ) { + const score = importanceScores.get(idx)!; + if (score >= importanceThreshold) { + return { + msg, + preserved: true, + importancePreserved: true, + ...(trace && { traceReason: `importance:${score.toFixed(2)}` }), + }; + } } if (dedupAnnotations?.has(idx)) { - return { msg, preserved: false, dedup: dedupAnnotations.get(idx)! }; + const ann = dedupAnnotations.get(idx)!; + return { + msg, + preserved: false, + dedup: ann, + ...(trace && { + traceReason: ann.similarity != null ? 'fuzzy_duplicate' : 'exact_duplicate', + }), + }; + } + // Contradiction: earlier message superseded by a later correction + if (contradictionAnnotations?.has(idx)) { + const ann = contradictionAnnotations.get(idx)!; + return { + msg, + preserved: false, + contradiction: ann, + ...(trace && { + traceReason: `contradicted:${ann.signal}`, + }), + }; } if (content.includes('```')) { const segments = splitCodeAndProse(content); @@ -559,25 +581,74 @@ function classifyAll( .filter((s) => s.type === 'prose') .reduce((sum, s) => sum + s.content.length, 0); if (totalProse >= 80) { - return { msg, preserved: false, codeSplit: true }; + return { + msg, + preserved: false, + codeSplit: true, + ...(trace && { traceReason: 'code_split' }), + }; } - return { msg, preserved: true }; + return { msg, preserved: true, ...(trace && { traceReason: 'code_fence_no_prose' }) }; } - if (content) { + // Heuristic classification (skipped in full mode) + if (classifierMode !== 'full' && content) { const cls = classifyMessage(content); if (cls.decision === 'T0') { const hasHardReason = cls.reasons.some((r) => HARD_T0_REASONS.has(r)); - if (hasHardReason) { - return { msg, preserved: true }; + if (!largeObservation && hasHardReason) { + const hardReasons = cls.reasons.filter((r) => HARD_T0_REASONS.has(r)); + return { + msg, + preserved: true, + ...(trace && { traceReason: `hard_t0:${hardReasons.join(',')}` }), + }; } // Soft T0 only — allow compression, entities will capture references } } - if (content && isValidJson(content)) { - return { msg, preserved: true }; + if (preservePatterns && preservePatterns.length > 0 && content) { + const matchedPattern = preservePatterns.find((p) => p.re.test(content)); + if (matchedPattern) { + return { + msg, + preserved: true, + patternPreserved: true, + ...(trace && { traceReason: `pattern:${matchedPattern.label}` }), + }; + } + } + // LLM classifier results (pre-computed) + if (llmResults && llmResults.has(idx)) { + const llmResult = llmResults.get(idx)!; + if (llmResult.decision === 'preserve') { + return { + msg, + preserved: true, + llmPreserved: true, + ...(trace && { traceReason: `llm_preserved:${llmResult.reason}` }), + }; + } + // decision === 'compress' — fall through + } + if (!largeObservation && content && isValidJson(content)) { + return { msg, preserved: true, ...(trace && { traceReason: 'json_structure' }) }; + } + + // Custom format adapters + if (adapters && adapters.length > 0 && content) { + for (const adapter of adapters) { + if (adapter.detect(content)) { + return { + msg, + preserved: false, + adapterMatch: adapter, + ...(trace && { traceReason: `adapter:${adapter.name}` }), + }; + } + } } - return { msg, preserved: false }; + return { msg, preserved: false, ...(trace && { traceReason: 'compressible_prose' }) }; }); } @@ -590,6 +661,12 @@ function computeStats( counter: (msg: Message) => number, messagesDeduped?: number, messagesFuzzyDeduped?: number, + messagesPatternPreserved?: number, + messagesLlmClassified?: number, + messagesLlmPreserved?: number, + messagesContradicted?: number, + messagesImportancePreserved?: number, + messagesRelevanceDropped?: number, ): CompressResult['compression'] { const originalTotalChars = originalMessages.reduce((sum, m) => sum + contentLength(m), 0); const compressedTotalChars = resultMessages.reduce((sum, m) => sum + contentLength(m), 0); @@ -610,14 +687,109 @@ function computeStats( ...(messagesFuzzyDeduped && messagesFuzzyDeduped > 0 ? { messages_fuzzy_deduped: messagesFuzzyDeduped } : {}), + ...(messagesPatternPreserved && messagesPatternPreserved > 0 + ? { messages_pattern_preserved: messagesPatternPreserved } + : {}), + ...(messagesLlmClassified && messagesLlmClassified > 0 + ? { messages_llm_classified: messagesLlmClassified } + : {}), + ...(messagesLlmPreserved && messagesLlmPreserved > 0 + ? { messages_llm_preserved: messagesLlmPreserved } + : {}), + ...(messagesContradicted && messagesContradicted > 0 + ? { messages_contradicted: messagesContradicted } + : {}), + ...(messagesImportancePreserved && messagesImportancePreserved > 0 + ? { messages_importance_preserved: messagesImportancePreserved } + : {}), + ...(messagesRelevanceDropped && messagesRelevanceDropped > 0 + ? { messages_relevance_dropped: messagesRelevanceDropped } + : {}), }; } // --------------------------------------------------------------------------- -// Sync compression (internal) +// LLM pre-classification (runs once before the pipeline) // --------------------------------------------------------------------------- -function compressSync(messages: Message[], options: CompressOptions = {}): CompressResult { +async function preClassify( + messages: Message[], + classifier: Classifier, + classifierMode: 'hybrid' | 'full', + preserveRoles: Set, +): Promise> { + const results = new Map(); + const tasks: Array<{ idx: number; promise: Promise }> = []; + + for (let idx = 0; idx < messages.length; idx++) { + const msg = messages[idx]; + const content = typeof msg.content === 'string' ? msg.content : ''; + + // Skip always-preserved messages + if (msg.role && preserveRoles.has(msg.role)) continue; + if (msg.tool_calls && Array.isArray(msg.tool_calls) && msg.tool_calls.length > 0) continue; + if (content.length < 120) continue; + if ( + content.startsWith('[summary:') || + content.startsWith('[summary#') || + content.startsWith('[truncated') + ) + continue; + + // In hybrid mode: skip hard T0 (heuristic handles those) + if (classifierMode === 'hybrid' && content) { + const cls = classifyMessage(content); + if (cls.decision === 'T0') { + const hasHard = cls.reasons.some((r) => HARD_T0_REASONS.has(r)); + if (hasHard) continue; + } + } + + const result = classifier(content); + if (result instanceof Promise) { + tasks.push({ idx, promise: result }); + } else { + results.set(idx, result); + } + } + + if (tasks.length > 0) { + const settled = await Promise.all(tasks.map((t) => t.promise)); + for (let i = 0; i < tasks.length; i++) { + results.set(tasks[i].idx, settled[i]); + } + } + + return results; +} + +// --------------------------------------------------------------------------- +// Unified compression core (generator + sync/async runners) +// --------------------------------------------------------------------------- + +type SummarizeRequest = { text: string; budget: number }; + +async function withFallback( + text: string, + userSummarizer?: Summarizer, + maxBudget?: number, +): Promise { + if (userSummarizer) { + try { + const result = await userSummarizer(text); + if (typeof result === 'string' && result.length > 0 && result.length < text.length) + return result; + } catch { + /* fall through to deterministic */ + } + } + return summarize(text, maxBudget); +} + +function* compressGen( + messages: Message[], + options: CompressOptions = {}, +): Generator { const sourceVersion = options.sourceVersion ?? 0; const counter = options.tokenCounter ?? defaultTokenCounter; @@ -657,22 +829,232 @@ function compressSync(messages: Message[], options: CompressOptions = {}): Compr } } - const classified = classifyAll(messages, preserveRoles, recencyWindow, dedupAnnotations); + const internalOpts = options as _InternalOptions; + const llmResults = internalOpts._llmResults; + const classifierMode = options.classifierMode ?? 'hybrid'; + + const trace = options.trace ?? false; + + // Importance scoring (ANCS-inspired) + const importanceScores = options.importanceScoring ? computeImportance(messages) : undefined; + const importanceThreshold = options.importanceThreshold ?? DEFAULT_IMPORTANCE_THRESHOLD; + + // Contradiction detection (ANCS-inspired) + let contradictionAnnotations: Map | undefined; + if (options.contradictionDetection) { + contradictionAnnotations = analyzeContradictions( + messages, + options.contradictionTopicThreshold ?? 0.15, + preserveRoles, + ); + } + + const classified = classifyAll( + messages, + preserveRoles, + recencyWindow, + dedupAnnotations, + options.preservePatterns, + llmResults, + classifierMode, + trace, + options.adapters, + options.observationThreshold, + options.observationThreshold != null ? counter : undefined, + importanceScores, + importanceScores ? importanceThreshold : undefined, + contradictionAnnotations, + ); + + // Conversation flow detection + const flowChainMap = new Map(); // message index → chain + if (options.conversationFlow) { + const recencyStart = Math.max(0, messages.length - recencyWindow); + const flowChains = detectFlowChains(messages, recencyStart, preserveRoles); + for (const chain of flowChains) { + for (const idx of chain.indices) { + flowChainMap.set(idx, chain); + } + } + } + + // Semantic clustering + const clusterMap = new Map(); // message index → cluster + if (options.semanticClustering) { + const recencyStart = Math.max(0, messages.length - recencyWindow); + // Find eligible indices: not in recency, not system, not already in flow chains + const eligible: number[] = []; + for (let idx = 0; idx < recencyStart; idx++) { + if (flowChainMap.has(idx)) continue; + const m = messages[idx]; + if (m.role && preserveRoles.has(m.role)) continue; + const content = (m.content as string | undefined) ?? ''; + if (content.length < 80) continue; + eligible.push(idx); + } + const clusters = clusterMessages(messages, eligible, options.clusterThreshold ?? 0.15); + for (const cluster of clusters) { + for (const idx of cluster.indices) { + clusterMap.set(idx, cluster); + } + } + } const result: Message[] = []; const verbatim: Record = {}; + const decisions: CompressDecision[] = []; let messagesCompressed = 0; let messagesPreserved = 0; let messagesDeduped = 0; let messagesFuzzyDeduped = 0; + let messagesContradicted = 0; + let messagesImportancePreserved = 0; + let messagesRelevanceDropped = 0; + let messagesPatternPreserved = 0; + let messagesLlmPreserved = 0; + const processedFlowChains = new Set(); + const processedClusters = new Set(); let i = 0; while (i < classified.length) { const { msg, preserved } = classified[i]; + // Skip messages already consumed by a processed flow chain or cluster + if (flowChainMap.has(i) && processedFlowChains.has(flowChainMap.get(i)!)) { + i++; + continue; + } + if (clusterMap.has(i) && processedClusters.has(clusterMap.get(i)!)) { + i++; + continue; + } + + // Flow chain: compress the entire chain as a unit + if (flowChainMap.has(i) && !processedFlowChains.has(flowChainMap.get(i)!)) { + const chain = flowChainMap.get(i)!; + + // Check if chain members can be flow-compressed. Allow overriding soft + // preservation (recency, short_content, soft T0) but not hard blocks + // (system role, dedup, tool_calls, already compressed). + const allCompressible = chain.indices.every((idx) => { + const c = classified[idx]; + if (c.dedup || c.codeSplit || c.adapterMatch) return false; + if (c.preserved) { + // Block: system role, tool_calls, already compressed + const m = c.msg; + if (m.role && preserveRoles.has(m.role)) return false; + if (m.tool_calls && Array.isArray(m.tool_calls) && m.tool_calls.length > 0) return false; + const content = typeof m.content === 'string' ? m.content : ''; + if (content.startsWith('[summary:') || content.startsWith('[truncated')) return false; + // Allow: recency, short_content, soft T0, hard T0 (flow chain wins) + } + return true; + }); + + if (allCompressible) { + const chainSummary = summarizeChain(chain, messages); + const chainIds = chain.indices.map((idx) => messages[idx].id); + const sourceMsgs = chain.indices.map((idx) => messages[idx]); + const combinedLength = sourceMsgs.reduce((sum, m) => sum + contentLength(m), 0); + + const tag = `[summary: ${chainSummary} (${chain.indices.length} messages, ${chain.type})]`; + + if (tag.length < combinedLength) { + processedFlowChains.add(chain); + const base: Message = { ...sourceMsgs[0] }; + result.push( + buildCompressedMessage(base, chainIds, tag, sourceVersion, verbatim, sourceMsgs), + ); + messagesCompressed += chain.indices.length; + if (trace) { + for (const idx of chain.indices) { + decisions.push({ + messageId: messages[idx].id, + messageIndex: idx, + action: 'compressed', + reason: `flow:${chain.type}`, + inputChars: contentLength(messages[idx]), + outputChars: Math.round(tag.length / chain.indices.length), + }); + } + } + + // Advance past current index only — non-chain messages between + // chain members will be processed normally on subsequent iterations. + // The processedFlowChains set prevents re-entering this chain. + i++; + continue; + } + } + // If chain compression didn't work, fall through to normal processing + } + + // Semantic cluster: compress all cluster members as a unit + if (clusterMap.has(i) && !processedClusters.has(clusterMap.get(i)!)) { + const cluster = clusterMap.get(i)!; + + const allCompressible = cluster.indices.every((idx) => { + const c = classified[idx]; + if (c.dedup || c.codeSplit || c.adapterMatch) return false; + if (c.preserved) { + const m = c.msg; + if (m.role && preserveRoles.has(m.role)) return false; + if (m.tool_calls && Array.isArray(m.tool_calls) && m.tool_calls.length > 0) return false; + const content = typeof m.content === 'string' ? m.content : ''; + if (content.startsWith('[summary:') || content.startsWith('[truncated')) return false; + } + return true; + }); + + if (allCompressible) { + const clusterSummary = summarizeCluster(cluster, messages); + const clusterIds = cluster.indices.map((idx) => messages[idx].id); + const sourceMsgs = cluster.indices.map((idx) => messages[idx]); + const combinedLength = sourceMsgs.reduce((sum, m) => sum + contentLength(m), 0); + const tag = `[summary: ${clusterSummary}]`; + + if (tag.length < combinedLength) { + processedClusters.add(cluster); + const base: Message = { ...sourceMsgs[0] }; + result.push( + buildCompressedMessage(base, clusterIds, tag, sourceVersion, verbatim, sourceMsgs), + ); + messagesCompressed += cluster.indices.length; + if (trace) { + for (const idx of cluster.indices) { + decisions.push({ + messageId: messages[idx].id, + messageIndex: idx, + action: 'compressed', + reason: `cluster:${cluster.label}`, + inputChars: contentLength(messages[idx]), + outputChars: Math.round(tag.length / cluster.indices.length), + }); + } + } + i++; + continue; + } + } + } + if (preserved) { result.push(msg); messagesPreserved++; + if (classified[i].patternPreserved) messagesPatternPreserved++; + if (classified[i].llmPreserved) messagesLlmPreserved++; + if (classified[i].importancePreserved) messagesImportancePreserved++; + if (trace) { + const inChars = contentLength(msg); + decisions.push({ + messageId: msg.id, + messageIndex: i, + action: 'preserved', + reason: classified[i].traceReason ?? 'preserved', + inputChars: inChars, + outputChars: inChars, + }); + } i++; continue; } @@ -686,6 +1068,18 @@ function compressSync(messages: Message[], options: CompressOptions = {}): Compr ? `[cce:near-dup of ${keepTargetId} — ${annotation.contentLength} chars, ~${Math.round(annotation.similarity * 100)}% match]` : `[cce:dup of ${keepTargetId} — ${annotation.contentLength} chars]`; result.push(buildCompressedMessage(msg, [msg.id], tag, sourceVersion, verbatim, [msg])); + if (trace) { + decisions.push({ + messageId: msg.id, + messageIndex: i, + action: annotation.similarity != null ? 'fuzzy_deduped' : 'deduped', + reason: + classified[i].traceReason ?? + (annotation.similarity != null ? 'fuzzy_duplicate' : 'exact_duplicate'), + inputChars: annotation.contentLength, + outputChars: tag.length, + }); + } if (annotation.similarity != null) { messagesFuzzyDeduped++; } else { @@ -695,6 +1089,55 @@ function compressSync(messages: Message[], options: CompressOptions = {}): Compr continue; } + // Contradiction: superseded message — compress with annotation + if (classified[i].contradiction) { + const annotation = classified[i].contradiction!; + const supersederId = messages[annotation.supersededByIndex].id; + const content = typeof msg.content === 'string' ? msg.content : ''; + const depth = options.compressionDepth === 'auto' ? 'gentle' : options.compressionDepth; + const useAdaptiveC = depth != null && depth !== 'gentle'; + const contradictionEntityCount = useAdaptiveC + ? extractEntities(content, 500).length + : undefined; + const contentBudget = computeBudget(content.length, contradictionEntityCount, depth); + const summaryText: string = yield { text: content, budget: contentBudget }; + let tag = `[cce:superseded by ${supersederId} (${annotation.signal}) — ${summaryText}]`; + // If full tag doesn't fit, use compact format + if (tag.length >= content.length) { + tag = `[cce:superseded by ${supersederId} — ${annotation.signal}]`; + } + + if (tag.length >= content.length) { + result.push(msg); + messagesPreserved++; + if (trace) { + decisions.push({ + messageId: msg.id, + messageIndex: i, + action: 'preserved', + reason: 'contradiction_reverted', + inputChars: content.length, + outputChars: content.length, + }); + } + } else { + result.push(buildCompressedMessage(msg, [msg.id], tag, sourceVersion, verbatim, [msg])); + messagesContradicted++; + if (trace) { + decisions.push({ + messageId: msg.id, + messageIndex: i, + action: 'contradicted', + reason: `contradicted:${annotation.signal}`, + inputChars: content.length, + outputChars: tag.length, + }); + } + } + i++; + continue; + } + // Code-split: extract fences verbatim, summarize surrounding prose if (classified[i].codeSplit) { const content = typeof msg.content === 'string' ? msg.content : ''; @@ -704,14 +1147,27 @@ function compressSync(messages: Message[], options: CompressOptions = {}): Compr .map((s) => s.content) .join(' '); const codeFences = segments.filter((s) => s.type === 'code').map((s) => s.content); - const proseBudget = proseText.length < 600 ? 200 : 400; - const summaryText = summarize(proseText, proseBudget); + const codeDepth = options.compressionDepth === 'auto' ? 'gentle' : options.compressionDepth; + const useAdaptiveCS = codeDepth != null && codeDepth !== 'gentle'; + const proseEntityCount = useAdaptiveCS ? extractEntities(proseText, 500).length : undefined; + const proseBudget = computeBudget(proseText.length, proseEntityCount, codeDepth); + const summaryText: string = yield { text: proseText, budget: proseBudget }; const embeddedId = options.embedSummaryId ? makeSummaryId([msg.id]) : undefined; const compressed = `${formatSummary(summaryText, proseText, undefined, true, embeddedId)}\n\n${codeFences.join('\n\n')}`; if (compressed.length >= content.length) { result.push(msg); messagesPreserved++; + if (trace) { + decisions.push({ + messageId: msg.id, + messageIndex: i, + action: 'preserved', + reason: 'code_split_reverted', + inputChars: content.length, + outputChars: content.length, + }); + } i++; continue; } @@ -720,21 +1176,120 @@ function compressSync(messages: Message[], options: CompressOptions = {}): Compr buildCompressedMessage(msg, [msg.id], compressed, sourceVersion, verbatim, [msg]), ); messagesCompressed++; + if (trace) { + decisions.push({ + messageId: msg.id, + messageIndex: i, + action: 'code_split', + reason: 'code_split', + inputChars: content.length, + outputChars: compressed.length, + }); + } + i++; + continue; + } + + // Custom adapter: extract preserved/compressible, summarize compressible, reconstruct + if (classified[i].adapterMatch) { + const adapter = classified[i].adapterMatch!; + const content = typeof msg.content === 'string' ? msg.content : ''; + const preserved = adapter.extractPreserved(content); + const compressible = adapter.extractCompressible(content); + const proseText = compressible.join(' '); + const adapterDepth = + options.compressionDepth === 'auto' ? 'gentle' : options.compressionDepth; + const useAdaptiveA = adapterDepth != null && adapterDepth !== 'gentle'; + const adapterEntityCount = useAdaptiveA ? extractEntities(proseText, 500).length : undefined; + const proseBudget = computeBudget(proseText.length, adapterEntityCount, adapterDepth); + const summaryText: string = + proseText.length > 0 ? yield { text: proseText, budget: proseBudget } : ''; + const compressed = adapter.reconstruct(preserved, summaryText); + + if (compressed.length >= content.length) { + result.push(msg); + messagesPreserved++; + if (trace) { + decisions.push({ + messageId: msg.id, + messageIndex: i, + action: 'preserved', + reason: `adapter_reverted:${adapter.name}`, + inputChars: content.length, + outputChars: content.length, + }); + } + } else { + result.push( + buildCompressedMessage(msg, [msg.id], compressed, sourceVersion, verbatim, [msg]), + ); + messagesCompressed++; + if (trace) { + decisions.push({ + messageId: msg.id, + messageIndex: i, + action: 'compressed', + reason: `adapter:${adapter.name}`, + inputChars: content.length, + outputChars: compressed.length, + }); + } + } i++; continue; } // Collect consecutive non-preserved messages with the SAME role + const groupStartIdx = i; const { group, nextIdx } = collectGroup(classified, i); i = nextIdx; const allContent = group .map((g) => (typeof g.msg.content === 'string' ? g.msg.content : '')) .join(' '); - const contentBudget = allContent.length < 600 ? 200 : 400; - const summaryText = isStructuredOutput(allContent) - ? summarizeStructured(allContent, contentBudget) - : summarize(allContent, contentBudget); + + // Relevance threshold: if the best sentence score is below the threshold, + // replace the entire group with a compact stub instead of a summary. + const relevanceThreshold = options.relevanceThreshold; + if (relevanceThreshold != null && relevanceThreshold > 0) { + const topScore = bestSentenceScore(allContent); + if (topScore < relevanceThreshold) { + const stub = `[${group.length} message${group.length > 1 ? 's' : ''} of general discussion omitted]`; + const sourceMsgs = group.map((g) => g.msg); + const mergeIds = group.map((g) => g.msg.id); + const base: Message = { ...sourceMsgs[0] }; + result.push( + buildCompressedMessage(base, mergeIds, stub, sourceVersion, verbatim, sourceMsgs), + ); + messagesRelevanceDropped += group.length; + messagesCompressed += group.length; + if (trace) { + for (let gi = 0; gi < group.length; gi++) { + decisions.push({ + messageId: group[gi].msg.id, + messageIndex: groupStartIdx + gi, + action: 'compressed', + reason: `relevance_dropped:${topScore}`, + inputChars: contentLength(group[gi].msg), + outputChars: Math.round(stub.length / group.length), + }); + } + } + continue; + } + } + + const groupDepth = options.compressionDepth === 'auto' ? 'gentle' : options.compressionDepth; + // Adaptive budget (entity-aware) only activates when depth is explicitly non-gentle + const useAdaptive = groupDepth != null && groupDepth !== 'gentle'; + const entityCount = useAdaptive ? extractEntities(allContent, 500).length : undefined; + const contentBudget = computeBudget(allContent.length, entityCount, groupDepth); + const summaryText = + groupDepth === 'aggressive' + ? entityOnlyStub(allContent) + : isStructuredOutput(allContent) + ? summarizeStructured(allContent, contentBudget) + : yield { text: allContent, budget: contentBudget }; if (group.length > 1) { const mergeIds = group.map((g) => g.msg.id); @@ -746,9 +1301,19 @@ function compressSync(messages: Message[], options: CompressOptions = {}): Compr } if (summary.length >= combinedLength) { - for (const g of group) { - result.push(g.msg); + for (let gi = 0; gi < group.length; gi++) { + result.push(group[gi].msg); messagesPreserved++; + if (trace) { + decisions.push({ + messageId: group[gi].msg.id, + messageIndex: groupStartIdx + gi, + action: 'preserved', + reason: 'merge_reverted', + inputChars: contentLength(group[gi].msg), + outputChars: contentLength(group[gi].msg), + }); + } } } else { const sourceMsgs = group.map((g) => g.msg); @@ -757,6 +1322,18 @@ function compressSync(messages: Message[], options: CompressOptions = {}): Compr buildCompressedMessage(base, mergeIds, summary, sourceVersion, verbatim, sourceMsgs), ); messagesCompressed += group.length; + if (trace) { + for (let gi = 0; gi < group.length; gi++) { + decisions.push({ + messageId: group[gi].msg.id, + messageIndex: groupStartIdx + gi, + action: 'compressed', + reason: group.length > 1 ? 'merged_compressed' : 'compressible_prose', + inputChars: contentLength(group[gi].msg), + outputChars: Math.round(summary.length / group.length), + }); + } + } } } else { const single = group[0].msg; @@ -770,232 +1347,253 @@ function compressSync(messages: Message[], options: CompressOptions = {}): Compr if (summary.length >= content.length) { result.push(single); messagesPreserved++; + if (trace) { + decisions.push({ + messageId: single.id, + messageIndex: groupStartIdx, + action: 'preserved', + reason: 'single_reverted', + inputChars: content.length, + outputChars: content.length, + }); + } } else { result.push( buildCompressedMessage(single, [single.id], summary, sourceVersion, verbatim, [single]), ); messagesCompressed++; + if (trace) { + decisions.push({ + messageId: single.id, + messageIndex: groupStartIdx, + action: 'compressed', + reason: classified[groupStartIdx].traceReason ?? 'compressible_prose', + inputChars: content.length, + outputChars: summary.length, + }); + } + } + } + } + + // Coreference inlining: prepend entity definitions to compressed messages + // when a preserved message references an entity defined only in a compressed message. + if (options.coreference && messagesCompressed > 0) { + const corefDefs = buildCoreferenceMap(messages); + const compressedSet = new Set(); + const preservedSet = new Set(); + for (let ri = 0; ri < result.length; ri++) { + const orig = result[ri].metadata?._cce_original as Record | undefined; + if (orig) { + // Find original message index from the id + const ids = orig.ids as string[] | undefined; + if (ids) { + for (const id of ids) { + const origIdx = messages.findIndex((m) => m.id === id); + if (origIdx >= 0) compressedSet.add(origIdx); + } + } + } else { + const origIdx = messages.findIndex((m) => m.id === result[ri].id); + if (origIdx >= 0) preservedSet.add(origIdx); + } + } + + const orphaned = findOrphanedReferences(corefDefs, compressedSet, preservedSet); + if (orphaned.size > 0) { + for (let ri = 0; ri < result.length; ri++) { + const orig = result[ri].metadata?._cce_original as Record | undefined; + if (!orig) continue; + const ids = orig.ids as string[] | undefined; + if (!ids) continue; + for (const id of ids) { + const origIdx = messages.findIndex((m) => m.id === id); + if (origIdx >= 0 && orphaned.has(origIdx)) { + const entities = orphaned.get(origIdx)!; + const sourceContent = + typeof messages[origIdx].content === 'string' ? messages[origIdx].content : ''; + const inline = generateInlineDefinitions(entities, sourceContent); + if (inline && result[ri].content) { + result[ri] = { ...result[ri], content: inline + result[ri].content }; + } + } + } } } } + const stats = computeStats( + messages, + result, + messagesCompressed, + messagesPreserved, + sourceVersion, + counter, + messagesDeduped, + messagesFuzzyDeduped, + messagesPatternPreserved, + llmResults?.size, + messagesLlmPreserved, + messagesContradicted, + messagesImportancePreserved, + messagesRelevanceDropped, + ); + + if (trace) { + stats.decisions = decisions; + } + + // Quality metrics (always computed when compression occurred) + if (messagesCompressed > 0 || messagesDeduped > 0 || messagesContradicted > 0) { + const quality = computeQualityScore(messages, result); + stats.entity_retention = Math.round(quality.entity_retention * 1000) / 1000; + stats.structural_integrity = Math.round(quality.structural_integrity * 1000) / 1000; + stats.reference_coherence = Math.round(quality.reference_coherence * 1000) / 1000; + stats.quality_score = Math.round(quality.quality_score * 1000) / 1000; + } + return { messages: result, - compression: computeStats( - messages, - result, - messagesCompressed, - messagesPreserved, - sourceVersion, - counter, - messagesDeduped, - messagesFuzzyDeduped, - ), + compression: stats, verbatim, }; } -// --------------------------------------------------------------------------- -// Async compression (internal, LLM summarizer support) -// --------------------------------------------------------------------------- - -async function withFallback( +/** + * Build external score map from entropy scorer for use in summarize(). + * Splits text into sentences, scores them, and combines with heuristic scores. + */ +function buildEntropyScores( text: string, + rawScores: number[], + mode: 'replace' | 'augment', +): Map { + const sentences = text.match(/[^.!?\n]+[.!?]+/g) ?? [text.trim()]; + const scoreMap = new Map(); + + if (mode === 'replace') { + for (let i = 0; i < Math.min(sentences.length, rawScores.length); i++) { + scoreMap.set(i, rawScores[i]); + } + } else { + // augment: weighted average of heuristic and entropy + const heuristicScores = sentences.map((s) => scoreSentence(s.trim())); + const combined = combineScores(heuristicScores, rawScores.slice(0, sentences.length)); + for (let i = 0; i < combined.length; i++) { + scoreMap.set(i, combined[i] * 20); // scale to heuristic range + } + } + + return scoreMap; +} + +function runCompressSync( + gen: Generator, + entropyScorer?: (sentences: string[]) => number[] | Promise, + entropyScorerMode: 'replace' | 'augment' = 'augment', + discourseAware?: boolean, + mlTokenClassifier?: CompressOptions['mlTokenClassifier'], +): CompressResult { + let next = gen.next(); + while (!next.done) { + const { text, budget } = next.value; + if (mlTokenClassifier) { + const compressed = compressWithTokenClassifierSync(text, mlTokenClassifier); + next = gen.next(compressed.length < text.length ? compressed : summarize(text, budget)); + } else if (discourseAware) { + next = gen.next(summarizeWithEDUs(text, budget)); + } else if (entropyScorer) { + const sentences = text.match(/[^.!?\n]+[.!?]+/g) ?? [text.trim()]; + const result = entropyScorer(sentences.map((s) => s.trim())); + if (result instanceof Promise) { + throw new Error( + 'compress(): entropyScorer returned a Promise in sync mode. Use a summarizer to enable async.', + ); + } + const externalScores = buildEntropyScores(text, result, entropyScorerMode); + next = gen.next(summarize(text, budget, externalScores)); + } else { + next = gen.next(summarize(text, budget)); + } + } + return next.value; +} + +async function runCompressAsync( + gen: Generator, userSummarizer?: Summarizer, - maxBudget?: number, -): Promise { - if (userSummarizer) { - try { - const result = await userSummarizer(text); - if (typeof result === 'string' && result.length > 0 && result.length < text.length) - return result; - } catch { - /* fall through to deterministic */ + entropyScorer?: (sentences: string[]) => number[] | Promise, + entropyScorerMode: 'replace' | 'augment' = 'augment', + discourseAware?: boolean, + mlTokenClassifier?: CompressOptions['mlTokenClassifier'], +): Promise { + let next = gen.next(); + while (!next.done) { + const { text, budget } = next.value; + if (mlTokenClassifier) { + const compressed = await compressWithTokenClassifier(text, mlTokenClassifier); + next = gen.next(compressed.length < text.length ? compressed : summarize(text, budget)); + } else if (discourseAware && !userSummarizer) { + next = gen.next(summarizeWithEDUs(text, budget)); + } else if (entropyScorer) { + const sentences = text.match(/[^.!?\n]+[.!?]+/g) ?? [text.trim()]; + const rawScores = await Promise.resolve(entropyScorer(sentences.map((s) => s.trim()))); + const externalScores = buildEntropyScores(text, rawScores, entropyScorerMode); + // When entropy scorer is set, use deterministic summarize with external scores + // unless a user summarizer is also provided + if (userSummarizer) { + next = gen.next(await withFallback(text, userSummarizer, budget)); + } else { + next = gen.next(summarize(text, budget, externalScores)); + } + } else { + next = gen.next(await withFallback(text, userSummarizer, budget)); } } - return summarize(text, maxBudget); + return next.value; +} + +function compressSync(messages: Message[], options: CompressOptions = {}): CompressResult { + return runCompressSync( + compressGen(messages, options), + options.entropyScorer, + options.entropyScorerMode ?? 'augment', + options.discourseAware, + options.mlTokenClassifier, + ); } async function compressAsync( messages: Message[], options: CompressOptions = {}, ): Promise { - const sourceVersion = options.sourceVersion ?? 0; - const counter = options.tokenCounter ?? defaultTokenCounter; - const userSummarizer = options.summarizer; - - if (messages.length === 0) { - return { - messages: [], - compression: { - original_version: sourceVersion, - ratio: 1, - token_ratio: 1, - messages_compressed: 0, - messages_preserved: 0, - }, - verbatim: {}, - }; - } - - const preserveRoles = new Set(options.preserve ?? ['system']); - const recencyWindow = options.recencyWindow ?? 4; - const recencyStart = Math.max(0, messages.length - (recencyWindow > 0 ? recencyWindow : 0)); - let dedupAnnotations = - (options.dedup ?? true) ? analyzeDuplicates(messages, recencyStart, preserveRoles) : undefined; - - if (options.fuzzyDedup) { - const fuzzyAnnotations = analyzeFuzzyDuplicates( + const internalOpts = options as _InternalOptions; + if (options.classifier && !internalOpts._llmResults) { + const preserveRoles = new Set(options.preserve ?? ['system']); + const llmResults = await preClassify( messages, - recencyStart, + options.classifier, + options.classifierMode ?? 'hybrid', preserveRoles, - dedupAnnotations ?? new Map(), - options.fuzzyThreshold ?? 0.85, ); - if (fuzzyAnnotations.size > 0) { - if (!dedupAnnotations) dedupAnnotations = new Map(); - for (const [idx, ann] of fuzzyAnnotations) { - dedupAnnotations.set(idx, ann); - } - } - } - - const classified = classifyAll(messages, preserveRoles, recencyWindow, dedupAnnotations); - - const result: Message[] = []; - const verbatim: Record = {}; - let messagesCompressed = 0; - let messagesPreserved = 0; - let messagesDeduped = 0; - let messagesFuzzyDeduped = 0; - let i = 0; - - while (i < classified.length) { - const { msg, preserved } = classified[i]; - - if (preserved) { - result.push(msg); - messagesPreserved++; - i++; - continue; - } - - // Dedup: replace earlier duplicate/near-duplicate with compact reference - if (classified[i].dedup) { - const annotation = classified[i].dedup!; - const keepTargetId = messages[annotation.duplicateOfIndex].id; - const tag = - annotation.similarity != null - ? `[cce:near-dup of ${keepTargetId} — ${annotation.contentLength} chars, ~${Math.round(annotation.similarity * 100)}% match]` - : `[cce:dup of ${keepTargetId} — ${annotation.contentLength} chars]`; - result.push(buildCompressedMessage(msg, [msg.id], tag, sourceVersion, verbatim, [msg])); - if (annotation.similarity != null) { - messagesFuzzyDeduped++; - } else { - messagesDeduped++; - } - i++; - continue; - } - - // Code-split: extract fences verbatim, summarize surrounding prose - if (classified[i].codeSplit) { - const content = typeof msg.content === 'string' ? msg.content : ''; - const segments = splitCodeAndProse(content); - const proseText = segments - .filter((s) => s.type === 'prose') - .map((s) => s.content) - .join(' '); - const codeFences = segments.filter((s) => s.type === 'code').map((s) => s.content); - const proseBudget = proseText.length < 600 ? 200 : 400; - const summaryText = await withFallback(proseText, userSummarizer, proseBudget); - const embeddedId = options.embedSummaryId ? makeSummaryId([msg.id]) : undefined; - const compressed = `${formatSummary(summaryText, proseText, undefined, true, embeddedId)}\n\n${codeFences.join('\n\n')}`; - - if (compressed.length >= content.length) { - result.push(msg); - messagesPreserved++; - i++; - continue; - } - - result.push( - buildCompressedMessage(msg, [msg.id], compressed, sourceVersion, verbatim, [msg]), - ); - messagesCompressed++; - i++; - continue; - } - - // Collect consecutive non-preserved messages with the SAME role - const { group, nextIdx } = collectGroup(classified, i); - i = nextIdx; - - const allContent = group - .map((g) => (typeof g.msg.content === 'string' ? g.msg.content : '')) - .join(' '); - const contentBudget = allContent.length < 600 ? 200 : 400; - const summaryText = isStructuredOutput(allContent) - ? summarizeStructured(allContent, contentBudget) - : await withFallback(allContent, userSummarizer, contentBudget); - - if (group.length > 1) { - const mergeIds = group.map((g) => g.msg.id); - const embeddedId = options.embedSummaryId ? makeSummaryId(mergeIds) : undefined; - let summary = formatSummary(summaryText, allContent, group.length, undefined, embeddedId); - const combinedLength = group.reduce((sum, g) => sum + contentLength(g.msg), 0); - if (summary.length >= combinedLength) { - summary = formatSummary(summaryText, allContent, group.length, true, embeddedId); - } - - if (summary.length >= combinedLength) { - for (const g of group) { - result.push(g.msg); - messagesPreserved++; - } - } else { - const sourceMsgs = group.map((g) => g.msg); - const base: Message = { ...sourceMsgs[0] }; - result.push( - buildCompressedMessage(base, mergeIds, summary, sourceVersion, verbatim, sourceMsgs), - ); - messagesCompressed += group.length; - } - } else { - const single = group[0].msg; - const content = typeof single.content === 'string' ? single.content : ''; - const embeddedId = options.embedSummaryId ? makeSummaryId([single.id]) : undefined; - let summary = formatSummary(summaryText, allContent, undefined, undefined, embeddedId); - if (summary.length >= content.length) { - summary = formatSummary(summaryText, allContent, undefined, true, embeddedId); - } - - if (summary.length >= content.length) { - result.push(single); - messagesPreserved++; - } else { - result.push( - buildCompressedMessage(single, [single.id], summary, sourceVersion, verbatim, [single]), - ); - messagesCompressed++; - } - } + const opts: _InternalOptions = { ...options, _llmResults: llmResults }; + return runCompressAsync( + compressGen(messages, opts), + options.summarizer, + options.entropyScorer, + options.entropyScorerMode ?? 'augment', + options.discourseAware, + options.mlTokenClassifier, + ); } - - return { - messages: result, - compression: computeStats( - messages, - result, - messagesCompressed, - messagesPreserved, - sourceVersion, - counter, - messagesDeduped, - messagesFuzzyDeduped, - ), - verbatim, - }; + return runCompressAsync( + compressGen(messages, options), + options.summarizer, + options.entropyScorer, + options.entropyScorerMode ?? 'augment', + options.discourseAware, + options.mlTokenClassifier, + ); } // --------------------------------------------------------------------------- @@ -1052,6 +1650,8 @@ function forceConvergePass( preserveRoles: Set, sourceVersion: number, counter: (msg: Message) => number, + trace?: boolean, + importanceScores?: ImportanceMap, ): CompressResult { if (cr.fits) return cr; @@ -1070,8 +1670,18 @@ function forceConvergePass( candidates.push({ idx: i, contentLen: content.length }); } - // Sort by content length descending (biggest savings first) - candidates.sort((a, b) => b.contentLen - a.contentLen); + // Sort by importance ascending (low-importance first), then by content length descending + // This ensures low-value messages get truncated before high-value ones + if (importanceScores) { + candidates.sort((a, b) => { + const impA = importanceScores.get(a.idx) ?? 0; + const impB = importanceScores.get(b.idx) ?? 0; + if (Math.abs(impA - impB) > 0.05) return impA - impB; // lower importance first + return b.contentLen - a.contentLen; // then bigger savings first + }); + } else { + candidates.sort((a, b) => b.contentLen - a.contentLen); + } // Clone messages and verbatim for mutation const messages = cr.messages.map((m) => ({ @@ -1114,12 +1724,386 @@ function forceConvergePass( const newTokens = counter(messages[cand.idx]); tokenCount -= oldTokens - newTokens; + + if (trace && cr.compression.decisions) { + // Find and update the existing decision for this message, or add a new one + const existing = cr.compression.decisions.find((d) => d.messageId === m.id); + if (existing) { + existing.action = 'truncated'; + existing.reason = 'force_converge'; + existing.outputChars = tag.length; + } else { + cr.compression.decisions.push({ + messageId: m.id, + messageIndex: cand.idx, + action: 'truncated', + reason: 'force_converge', + inputChars: content.length, + outputChars: tag.length, + }); + } + } } const fits = tokenCount <= tokenBudget; return { ...cr, messages, verbatim, fits, tokenCount }; } +// --------------------------------------------------------------------------- +// Tiered budget strategy +// --------------------------------------------------------------------------- + +/** + * Tiered budget: keeps recencyWindow fixed and progressively compresses + * older content by priority tier instead of shrinking the recency window. + * + * Priority (protected → sacrificed): + * 1. System messages — never touched + * 2. T0 content (code, JSON, etc.) — never touched + * 3. Recent window messages — protected + * 4. Older compressed prose — tightened (re-summarize at smaller budget) + * 5. Low-value older prose — stubbed (relevance drop) + * 6. Remaining older prose — truncated (force-converge) + */ +function compressTieredSync( + messages: Message[], + tokenBudget: number, + options: CompressOptions, +): CompressResult { + const sourceVersion = options.sourceVersion ?? 0; + const counter = options.tokenCounter ?? defaultTokenCounter; + const preserveRoles = new Set(options.preserve ?? ['system']); + const rw = options.recencyWindow ?? 4; + + const fast = budgetFastPath(messages, tokenBudget, sourceVersion, counter); + if (fast) return fast; + + // Step 1: Run standard compress with the user's recencyWindow + const cr = compressSync(messages, { + ...options, + recencyWindow: rw, + summarizer: undefined, + tokenBudget: undefined, + }); + const result = addBudgetFields(cr, tokenBudget, rw, counter); + + if (result.fits) return result; + + // Step 2: Tighten older messages — re-summarize compressed messages with smaller budgets + const recencyStart = Math.max(0, result.messages.length - rw); + const resultMessages = result.messages.map((m) => ({ + ...m, + metadata: m.metadata ? { ...m.metadata } : {}, + })); + const resultVerbatim = { ...result.verbatim }; + let tokenCount = result.tokenCount ?? sumTokens(resultMessages, counter); + + // Collect tightenable candidates: older compressed messages (have _cce_original, not system/T0) + type TightenCandidate = { idx: number; tokens: number; content: string; isCompressed: boolean }; + const candidates: TightenCandidate[] = []; + + for (let i = 0; i < recencyStart; i++) { + const m = resultMessages[i]; + if (m.role && preserveRoles.has(m.role)) continue; + const content = typeof m.content === 'string' ? m.content : ''; + if (content.length <= 80) continue; // Already tiny + candidates.push({ + idx: i, + tokens: counter(m), + content, + isCompressed: !!m.metadata?._cce_original, + }); + } + + // Sort: uncompressed first (more room to save), then by token count descending + candidates.sort((a, b) => { + if (a.isCompressed !== b.isCompressed) return a.isCompressed ? 1 : -1; + return b.tokens - a.tokens; + }); + + // Pass 2a: Re-summarize with half budget + for (const cand of candidates) { + if (tokenCount <= tokenBudget) break; + const m = resultMessages[cand.idx]; + const content = typeof m.content === 'string' ? m.content : ''; + + // For already-compressed messages, try to tighten the summary + if (cand.isCompressed && content.startsWith('[summary')) { + const tighterBudget = Math.max(80, Math.round(content.length * 0.4)); + const tighter = summarize(content, tighterBudget); + const tighterWrapped = `[summary: ${tighter}]`; + if (tighterWrapped.length < content.length) { + const oldTokens = counter(m); + resultMessages[cand.idx] = { ...m, content: tighterWrapped }; + const newTokens = counter(resultMessages[cand.idx]); + tokenCount -= oldTokens - newTokens; + } + } else if (!cand.isCompressed) { + // Compress previously uncompressed messages with tight budget + const tightBudget = Math.max(80, Math.round(content.length * 0.15)); + const summaryText = summarize(content, tightBudget); + const entities = extractEntities(content); + const entitySuffix = + entities.length > 0 ? ` | entities: ${entities.slice(0, 3).join(', ')}` : ''; + const compressed = `[summary: ${summaryText}${entitySuffix}]`; + if (compressed.length < content.length) { + const oldTokens = counter(m); + resultVerbatim[m.id] = { ...m }; + resultMessages[cand.idx] = { + ...m, + content: compressed, + metadata: { + ...(m.metadata ?? {}), + _cce_original: { + ids: [m.id], + summary_id: makeSummaryId([m.id]), + version: sourceVersion, + }, + }, + }; + const newTokens = counter(resultMessages[cand.idx]); + tokenCount -= oldTokens - newTokens; + } + } + } + + if (tokenCount <= tokenBudget) { + return { + ...result, + messages: resultMessages, + verbatim: resultVerbatim, + fits: true, + tokenCount, + }; + } + + // Pass 2b: Stub low-value messages (relevance drop) + for (const cand of candidates) { + if (tokenCount <= tokenBudget) break; + const m = resultMessages[cand.idx]; + const content = typeof m.content === 'string' ? m.content : ''; + if (content.length <= 80) continue; + + const score = bestSentenceScore(content); + if (score < 3) { + const stub = '[message omitted]'; + const oldTokens = counter(m); + if (!m.metadata?._cce_original) { + resultVerbatim[m.id] = { ...m }; + } + resultMessages[cand.idx] = { + ...m, + content: stub, + metadata: { + ...(m.metadata ?? {}), + _cce_original: m.metadata?._cce_original ?? { + ids: [m.id], + summary_id: makeSummaryId([m.id]), + version: sourceVersion, + }, + }, + }; + const newTokens = counter(resultMessages[cand.idx]); + tokenCount -= oldTokens - newTokens; + } + } + + let finalResult: CompressResult = { + ...result, + messages: resultMessages, + verbatim: resultVerbatim, + fits: tokenCount <= tokenBudget, + tokenCount, + }; + + // Pass 3: Force-converge as last resort + if (!finalResult.fits && options.forceConverge) { + const impScores = options.importanceScoring ? computeImportance(messages) : undefined; + finalResult = forceConvergePass( + finalResult, + tokenBudget, + preserveRoles, + sourceVersion, + counter, + options.trace, + impScores, + ); + } + + return finalResult; +} + +async function compressTieredAsync( + messages: Message[], + tokenBudget: number, + options: CompressOptions, +): Promise { + const sourceVersion = options.sourceVersion ?? 0; + const counter = options.tokenCounter ?? defaultTokenCounter; + const preserveRoles = new Set(options.preserve ?? ['system']); + const rw = options.recencyWindow ?? 4; + + const fast = budgetFastPath(messages, tokenBudget, sourceVersion, counter); + if (fast) return fast; + + // Pre-classify ONCE + let innerOpts: _InternalOptions = options; + if (options.classifier && !(options as _InternalOptions)._llmResults) { + const llmResults = await preClassify( + messages, + options.classifier, + options.classifierMode ?? 'hybrid', + preserveRoles, + ); + innerOpts = { ...options, classifier: undefined, _llmResults: llmResults }; + } + + const cr = await compressAsync(messages, { + ...innerOpts, + recencyWindow: rw, + tokenBudget: undefined, + }); + const result = addBudgetFields(cr, tokenBudget, rw, counter); + + if (result.fits) return result; + + // Reuse sync tightening passes (summarize is deterministic for tightening) + const recencyStart = Math.max(0, result.messages.length - rw); + const resultMessages = result.messages.map((m) => ({ + ...m, + metadata: m.metadata ? { ...m.metadata } : {}, + })); + const resultVerbatim = { ...result.verbatim }; + let tokenCount = result.tokenCount ?? sumTokens(resultMessages, counter); + + type TightenCandidate = { idx: number; tokens: number; content: string; isCompressed: boolean }; + const candidates: TightenCandidate[] = []; + + for (let i = 0; i < recencyStart; i++) { + const m = resultMessages[i]; + if (m.role && preserveRoles.has(m.role)) continue; + const content = typeof m.content === 'string' ? m.content : ''; + if (content.length <= 80) continue; + candidates.push({ + idx: i, + tokens: counter(m), + content, + isCompressed: !!m.metadata?._cce_original, + }); + } + + candidates.sort((a, b) => { + if (a.isCompressed !== b.isCompressed) return a.isCompressed ? 1 : -1; + return b.tokens - a.tokens; + }); + + // Pass 2a: Tighten summaries + for (const cand of candidates) { + if (tokenCount <= tokenBudget) break; + const m = resultMessages[cand.idx]; + const content = typeof m.content === 'string' ? m.content : ''; + + if (cand.isCompressed && content.startsWith('[summary')) { + const tighterBudget = Math.max(80, Math.round(content.length * 0.4)); + const tighter = options.summarizer + ? await withFallback(content, options.summarizer, tighterBudget) + : summarize(content, tighterBudget); + const tighterWrapped = `[summary: ${tighter}]`; + if (tighterWrapped.length < content.length) { + const oldTokens = counter(m); + resultMessages[cand.idx] = { ...m, content: tighterWrapped }; + tokenCount -= oldTokens - counter(resultMessages[cand.idx]); + } + } else if (!cand.isCompressed) { + const tightBudget = Math.max(80, Math.round(content.length * 0.15)); + const summaryText = options.summarizer + ? await withFallback(content, options.summarizer, tightBudget) + : summarize(content, tightBudget); + const entities = extractEntities(content); + const entitySuffix = + entities.length > 0 ? ` | entities: ${entities.slice(0, 3).join(', ')}` : ''; + const compressed = `[summary: ${summaryText}${entitySuffix}]`; + if (compressed.length < content.length) { + const oldTokens = counter(m); + resultVerbatim[m.id] = { ...m }; + resultMessages[cand.idx] = { + ...m, + content: compressed, + metadata: { + ...(m.metadata ?? {}), + _cce_original: { + ids: [m.id], + summary_id: makeSummaryId([m.id]), + version: sourceVersion, + }, + }, + }; + tokenCount -= oldTokens - counter(resultMessages[cand.idx]); + } + } + } + + if (tokenCount <= tokenBudget) { + return { + ...result, + messages: resultMessages, + verbatim: resultVerbatim, + fits: true, + tokenCount, + }; + } + + // Pass 2b: Stub low-value messages + for (const cand of candidates) { + if (tokenCount <= tokenBudget) break; + const m = resultMessages[cand.idx]; + const content = typeof m.content === 'string' ? m.content : ''; + if (content.length <= 80) continue; + const score = bestSentenceScore(content); + if (score < 3) { + const stub = '[message omitted]'; + const oldTokens = counter(m); + if (!m.metadata?._cce_original) resultVerbatim[m.id] = { ...m }; + resultMessages[cand.idx] = { + ...m, + content: stub, + metadata: { + ...(m.metadata ?? {}), + _cce_original: m.metadata?._cce_original ?? { + ids: [m.id], + summary_id: makeSummaryId([m.id]), + version: sourceVersion, + }, + }, + }; + tokenCount -= oldTokens - counter(resultMessages[cand.idx]); + } + } + + let finalResult: CompressResult = { + ...result, + messages: resultMessages, + verbatim: resultVerbatim, + fits: tokenCount <= tokenBudget, + tokenCount, + }; + + if (!finalResult.fits && options.forceConverge) { + const impScores = options.importanceScoring ? computeImportance(messages) : undefined; + finalResult = forceConvergePass( + finalResult, + tokenBudget, + preserveRoles, + sourceVersion, + counter, + options.trace, + impScores, + ); + } + + return finalResult; +} + function compressSyncWithBudget( messages: Message[], tokenBudget: number, @@ -1170,7 +2154,16 @@ function compressSyncWithBudget( if (!result.fits && options.forceConverge) { const preserveRoles = new Set(options.preserve ?? ['system']); - result = forceConvergePass(result, tokenBudget, preserveRoles, sourceVersion, counter); + const impScores = options.importanceScoring ? computeImportance(messages) : undefined; + result = forceConvergePass( + result, + tokenBudget, + preserveRoles, + sourceVersion, + counter, + options.trace, + impScores, + ); } return result; @@ -1188,6 +2181,19 @@ async function compressAsyncWithBudget( const fast = budgetFastPath(messages, tokenBudget, sourceVersion, counter); if (fast) return fast; + // Pre-classify ONCE before binary search — prevents re-classification per iteration + let innerOpts: _InternalOptions = options; + if (options.classifier && !(options as _InternalOptions)._llmResults) { + const preserveRoles = new Set(options.preserve ?? ['system']); + const llmResults = await preClassify( + messages, + options.classifier, + options.classifierMode ?? 'hybrid', + preserveRoles, + ); + innerOpts = { ...options, classifier: undefined, _llmResults: llmResults }; + } + let lo = minRw; let hi = messages.length - 1; let lastResult: CompressResult | undefined; @@ -1196,7 +2202,7 @@ async function compressAsyncWithBudget( while (lo < hi) { const mid = Math.ceil((lo + hi) / 2); const cr = await compressAsync(messages, { - ...options, + ...innerOpts, recencyWindow: mid, tokenBudget: undefined, }); @@ -1215,7 +2221,7 @@ async function compressAsyncWithBudget( result = lastResult; } else { const cr = await compressAsync(messages, { - ...options, + ...innerOpts, recencyWindow: lo, tokenBudget: undefined, }); @@ -1224,7 +2230,16 @@ async function compressAsyncWithBudget( if (!result.fits && options.forceConverge) { const preserveRoles = new Set(options.preserve ?? ['system']); - result = forceConvergePass(result, tokenBudget, preserveRoles, sourceVersion, counter); + const impScores = options.importanceScoring ? computeImportance(messages) : undefined; + result = forceConvergePass( + result, + tokenBudget, + preserveRoles, + sourceVersion, + counter, + options.trace, + impScores, + ); } return result; @@ -1235,7 +2250,7 @@ async function compressAsyncWithBudget( // --------------------------------------------------------------------------- /** - * Compress a message array. Sync by default; async when a `summarizer` is provided. + * Compress a message array. Sync by default; async when a `summarizer` or `classifier` is provided. * * The caller MUST persist `messages` and `verbatim` atomically. * Partial writes (e.g. storing compressed messages without their @@ -1247,6 +2262,10 @@ export function compress( messages: Message[], options: CompressOptions & { summarizer: Summarizer }, ): Promise; +export function compress( + messages: Message[], + options: CompressOptions & { classifier: Classifier }, +): Promise; export function compress( messages: Message[], options: CompressOptions = {}, @@ -1264,20 +2283,101 @@ export function compress( } } + if (options.compressionThreshold != null) { + const counter = options.tokenCounter ?? defaultTokenCounter; + const total = sumTokens(messages, counter); + if (total < options.compressionThreshold) { + const fast: CompressResult = { + messages, + compression: { + original_version: options.sourceVersion ?? 0, + ratio: 1, + token_ratio: 1, + messages_compressed: 0, + messages_preserved: messages.length, + }, + verbatim: {}, + }; + return options.summarizer || options.classifier ? Promise.resolve(fast) : fast; + } + } + const hasSummarizer = !!options.summarizer; + const hasClassifier = !!options.classifier; const hasBudget = options.tokenBudget != null; - if (hasSummarizer) { + const isTiered = options.budgetStrategy === 'tiered'; + const isAutoDepth = options.compressionDepth === 'auto' && hasBudget; + + // Auto depth: try gentle → moderate → aggressive until budget fits or quality threshold met + if (isAutoDepth && !(hasSummarizer || hasClassifier)) { + const depths: Array<'gentle' | 'moderate' | 'aggressive'> = [ + 'gentle', + 'moderate', + 'aggressive', + ]; + for (const depth of depths) { + const depthOpts = { + ...options, + compressionDepth: depth as 'gentle' | 'moderate' | 'aggressive', + }; + const cr = isTiered + ? compressTieredSync(messages, options.tokenBudget!, depthOpts) + : compressSyncWithBudget(messages, options.tokenBudget!, depthOpts); + if (cr.fits) return cr; + // Quality gate: if quality drops too low, stop and use the current result + if ( + cr.compression.quality_score != null && + cr.compression.quality_score < 0.6 && + depth !== 'aggressive' + ) { + return cr; + } + } + // All depths tried, return the last (most aggressive) result + const aggressiveOpts = { ...options, compressionDepth: 'aggressive' as const }; + return isTiered + ? compressTieredSync(messages, options.tokenBudget!, aggressiveOpts) + : compressSyncWithBudget(messages, options.tokenBudget!, aggressiveOpts); + } + + if (hasSummarizer || hasClassifier) { // Async paths if (hasBudget) { - return compressAsyncWithBudget(messages, options.tokenBudget!, options); + if (isAutoDepth) { + // Auto depth async: try each level progressively + return (async () => { + const depths: Array<'gentle' | 'moderate' | 'aggressive'> = [ + 'gentle', + 'moderate', + 'aggressive', + ]; + let lastResult: CompressResult | undefined; + for (const depth of depths) { + const depthOpts = { + ...options, + compressionDepth: depth as 'gentle' | 'moderate' | 'aggressive', + }; + lastResult = isTiered + ? await compressTieredAsync(messages, options.tokenBudget!, depthOpts) + : await compressAsyncWithBudget(messages, options.tokenBudget!, depthOpts); + if (lastResult.fits) return lastResult; + } + return lastResult!; + })(); + } + return isTiered + ? compressTieredAsync(messages, options.tokenBudget!, options) + : compressAsyncWithBudget(messages, options.tokenBudget!, options); } return compressAsync(messages, options); } // Sync paths if (hasBudget) { - return compressSyncWithBudget(messages, options.tokenBudget!, options); + return isTiered + ? compressTieredSync(messages, options.tokenBudget!, options) + : compressSyncWithBudget(messages, options.tokenBudget!, options); } return compressSync(messages, options); } diff --git a/src/contradiction.ts b/src/contradiction.ts new file mode 100644 index 0000000..72974b5 --- /dev/null +++ b/src/contradiction.ts @@ -0,0 +1,218 @@ +/** + * Contradiction detection — identifies messages that correct or override + * earlier messages on the same topic. + * + * When two messages have high topic overlap but opposing directives, + * the earlier one is marked for compression while the later one + * (the correction) is preserved. + * + * Inspired by ANCS conflict detection (pairwise scanning with topic-overlap gating). + */ + +import type { Message } from './types.js'; + +export type ContradictionAnnotation = { + /** Index of the later message that supersedes this one. */ + supersededByIndex: number; + /** Topic overlap score (0–1). */ + topicOverlap: number; + /** Which correction signal was detected. */ + signal: string; +}; + +// ── Topic overlap (IDF-weighted Sørensen-Dice) ────────────────── + +/** Extract topic words from content: plain words (3+ chars) plus technical identifiers. */ +function extractRawWords(content: string): Set { + const words = new Set(); + // Plain lowercase words (3+ chars) + const plain = content.toLowerCase().match(/\b[a-z]{3,}\b/g); + if (plain) { + for (const w of plain) words.add(w); + } + // camelCase, PascalCase, snake_case — lowercased for uniform matching + const identifiers = content.match( + /\b[a-z]+(?:[A-Z][a-z]+)+\b|\b[A-Z][a-z]+(?:[A-Z][a-z]+)+\b|\b[a-z]+(?:_[a-z]+)+\b/g, + ); + if (identifiers) { + for (const id of identifiers) words.add(id.toLowerCase()); + } + return words; +} + +/** + * Compute IDF weights for all words across a set of documents. + * Uses smoothed IDF: `log(1 + N/df)`. + * + * Language-agnostic: common words get low weight regardless of language. + * No hardcoded stopword list needed. + * + * Returns null when there are fewer than 3 documents — IDF needs enough + * documents to distinguish common from rare words. + */ +function computeIdfWeights(documents: Set[]): Map | null { + const n = documents.length; + if (n < 3) return null; + + const df = new Map(); + for (const doc of documents) { + for (const word of doc) { + df.set(word, (df.get(word) ?? 0) + 1); + } + } + + const idf = new Map(); + for (const [word, count] of df) { + // Smoothed IDF: log(1 + N/df). Gentler than BM25's `log((N-df+0.5)/(df+0.5))` + // which is too aggressive for small document sets (zeroes out words at N/2). + // A word in all N docs gets log(2) ≈ 0.69; a word in 1 doc gets log(1+N). + idf.set(word, Math.log(1 + n / count)); + } + return idf; +} + +/** + * IDF-weighted Sørensen-Dice similarity. + * + * Dice = 2 * weightedIntersection / (weightedA + weightedB) + * + * Compared to unweighted Jaccard: + * - Dice weights shared terms more heavily (2x numerator), better for short docs + * - IDF weighting means rare/topical words dominate, common words contribute ~0 + * + * When IDF is null (too few documents for reliable DF), falls back to + * unweighted Dice (all words weight 1). + */ +function weightedDice(a: Set, b: Set, idf: Map | null): number { + if (a.size === 0 && b.size === 0) return 0; + + // Unweighted Dice when IDF is unavailable + if (!idf) { + let intersection = 0; + for (const w of a) { + if (b.has(w)) intersection++; + } + const denom = a.size + b.size; + return denom === 0 ? 0 : (2 * intersection) / denom; + } + + let weightedIntersection = 0; + let weightedA = 0; + let weightedB = 0; + + for (const w of a) { + const weight = idf.get(w) ?? 0; + weightedA += weight; + if (b.has(w)) weightedIntersection += weight; + } + for (const w of b) { + weightedB += idf.get(w) ?? 0; + } + + const denom = weightedA + weightedB; + return denom === 0 ? 0 : (2 * weightedIntersection) / denom; +} + +// ── Correction signal detection ─────────────────────────────────── + +/** Patterns that indicate a message is correcting/overriding earlier content. */ +const CORRECTION_PATTERNS: Array<{ re: RegExp; label: string }> = [ + { re: /\b(?:actually|correction)[,.:]/i, label: 'explicit_correction' }, + { re: /\bno[,.]?\s+(?:use|it's|that's|it should|we should)/i, label: 'negation_directive' }, + { re: /\b(?:instead|rather)[,.]?\s+(?:use|do|we|you)/i, label: 'instead_directive' }, + { re: /\b(?:scratch that|disregard|ignore)\b/i, label: 'retraction' }, + { re: /\bdon'?t\s+(?:use|do|add|include|import)\b/i, label: 'dont_directive' }, + { re: /\bnot\s+\w+[,.]?\s+(?:but|use|go with)\b/i, label: 'not_but_pattern' }, + { re: /\bwait[,.]\s/i, label: 'wait_correction' }, + { re: /\bsorry[,.]\s+(?:I|that|the)/i, label: 'sorry_correction' }, + { re: /\bI was wrong\b/i, label: 'self_correction' }, + { re: /\blet me (?:correct|rephrase|clarify)\b/i, label: 'rephrase' }, +]; + +function detectCorrectionSignal(content: string): string | null { + for (const { re, label } of CORRECTION_PATTERNS) { + if (re.test(content)) return label; + } + return null; +} + +// ── Main API ────────────────────────────────────────────────────── + +/** + * Scan messages for contradictions: later messages that correct earlier ones. + * + * Returns a map of message indices to contradiction annotations. + * Only the *earlier* (superseded) message gets annotated — the later + * message (the correction) is left untouched for preservation. + * + * @param messages - The message array to scan. + * @param topicThreshold - Minimum IDF-weighted Dice similarity for topic overlap. Default: 0.15. + * @param preserveRoles - Roles to skip (e.g. 'system'). + */ +export function analyzeContradictions( + messages: Message[], + topicThreshold = 0.15, + preserveRoles?: Set, +): Map { + const annotations = new Map(); + + // Pass 1: extract raw words per eligible message + const eligible: Array<{ index: number; words: Set; content: string }> = []; + for (let i = 0; i < messages.length; i++) { + const msg = messages[i]; + const content = typeof msg.content === 'string' ? msg.content : ''; + if (preserveRoles && msg.role && preserveRoles.has(msg.role)) continue; + if (content.length < 50) continue; // skip very short messages + if ( + content.startsWith('[summary:') || + content.startsWith('[summary#') || + content.startsWith('[truncated') + ) + continue; + + eligible.push({ index: i, words: extractRawWords(content), content }); + } + + // Pass 2: compute IDF weights (language-agnostic — common words get low weight) + const idf = computeIdfWeights(eligible.map((e) => e.words)); + + // Use eligible directly as topics (IDF handles weighting, no filtering needed) + const topics = eligible; + + // For each message with a correction signal, find the most-overlapping earlier message + for (let ti = 1; ti < topics.length; ti++) { + const later = topics[ti]; + const signal = detectCorrectionSignal(later.content); + if (!signal) continue; + + let bestOverlap = 0; + let bestEarlierIdx = -1; + + for (let ei = ti - 1; ei >= 0; ei--) { + const earlier = topics[ei]; + const overlap = weightedDice(earlier.words, later.words, idf); + + // Cross-role corrections (user correcting assistant) require higher overlap + const crossRole = + messages[earlier.index].role && + messages[later.index].role && + messages[earlier.index].role !== messages[later.index].role; + const effectiveThreshold = crossRole ? topicThreshold * 1.5 : topicThreshold; + + if (overlap >= effectiveThreshold && overlap > bestOverlap) { + bestOverlap = overlap; + bestEarlierIdx = earlier.index; + } + } + + if (bestEarlierIdx >= 0 && !annotations.has(bestEarlierIdx)) { + annotations.set(bestEarlierIdx, { + supersededByIndex: later.index, + topicOverlap: bestOverlap, + signal, + }); + } + } + + return annotations; +} diff --git a/src/coreference.ts b/src/coreference.ts new file mode 100644 index 0000000..d1ee2cd --- /dev/null +++ b/src/coreference.ts @@ -0,0 +1,136 @@ +/** + * Cross-message coreference tracking. + * + * Tracks entity references across messages so that when message B refers + * to an entity defined in message A, compressing A doesn't orphan the + * reference in B. Either A's definition is inlined into B's summary, + * or A is promoted to preserved. + */ + +import type { Message } from './types.js'; + +export type EntityDefinition = { + /** The entity string (e.g., "fetchData", "auth_middleware"). */ + entity: string; + /** Index of the message where this entity first appears. */ + definingMessageIndex: number; + /** Indices of messages that reference this entity after its first appearance. */ + referencingMessageIndices: number[]; +}; + +/** + * Build a coreference map: for each entity, track where it's first defined + * and which later messages reference it. + * + * Only tracks identifiers (camelCase, snake_case, PascalCase) — not generic + * proper nouns, to avoid false positives. + */ +export function buildCoreferenceMap(messages: Message[]): EntityDefinition[] { + const firstSeen = new Map(); // entity → first message index + const references = new Map(); // entity → later message indices + + for (let i = 0; i < messages.length; i++) { + const content = (messages[i].content as string | undefined) ?? ''; + if (content.length === 0) continue; + + const entities = extractIdentifiers(content); + for (const entity of entities) { + if (!firstSeen.has(entity)) { + firstSeen.set(entity, i); + references.set(entity, []); + } else if (firstSeen.get(entity) !== i) { + references.get(entity)!.push(i); + } + } + } + + const result: EntityDefinition[] = []; + for (const [entity, defIdx] of firstSeen) { + const refs = references.get(entity)!; + if (refs.length > 0) { + result.push({ + entity, + definingMessageIndex: defIdx, + referencingMessageIndices: [...new Set(refs)], + }); + } + } + + return result; +} + +/** + * Extract only code-style identifiers (camelCase, snake_case, PascalCase). + * More conservative than extractEntities — avoids proper nouns and abbreviations + * to reduce false-positive coreference links. + */ +function extractIdentifiers(text: string): Set { + const ids = new Set(); + + const camelCase = text.match(/\b[a-z]+(?:[A-Z][a-z]+)+\b/g); + if (camelCase) for (const id of camelCase) ids.add(id); + + const pascalCase = text.match(/\b[A-Z][a-z]+(?:[A-Z][a-z]+)+\b/g); + if (pascalCase) for (const id of pascalCase) ids.add(id); + + const snakeCase = text.match(/\b[a-z]+(?:_[a-z]+)+\b/g); + if (snakeCase) for (const id of snakeCase) ids.add(id); + + return ids; +} + +/** + * Given which messages are being compressed (by index), find entities + * that would be orphaned: referenced in a kept message but defined + * only in a compressed message. + * + * Returns a map: compressed message index → entities to inline from it. + */ +export function findOrphanedReferences( + definitions: EntityDefinition[], + compressedIndices: Set, + preservedIndices: Set, +): Map { + const inlineMap = new Map(); + + for (const def of definitions) { + // If the defining message is being compressed... + if (!compressedIndices.has(def.definingMessageIndex)) continue; + + // ...and at least one referencing message is preserved + const hasPreservedRef = def.referencingMessageIndices.some((idx) => preservedIndices.has(idx)); + if (!hasPreservedRef) continue; + + // For simplicity, always inline — it's cheap and prevents subtle context loss. + if (!inlineMap.has(def.definingMessageIndex)) { + inlineMap.set(def.definingMessageIndex, []); + } + inlineMap.get(def.definingMessageIndex)!.push(def.entity); + } + + return inlineMap; +} + +/** + * Generate a compact inline definition for entities from a compressed message. + * Used to prepend context to summaries so references aren't orphaned. + */ +export function generateInlineDefinitions(entities: string[], sourceContent: string): string { + if (entities.length === 0) return ''; + + // For each entity, find the sentence where it first appears + const sentences = sourceContent.match(/[^.!?\n]+[.!?]+/g) ?? [sourceContent]; + const definitions: string[] = []; + + for (const entity of entities.slice(0, 5)) { + // max 5 inlines + const defining = sentences.find((s) => s.includes(entity)); + if (defining) { + const trimmed = defining.trim(); + definitions.push(trimmed.length > 80 ? trimmed.slice(0, 77) + '...' : trimmed); + } + } + + if (definitions.length === 0) return ''; + return `[context: ${definitions.join(' | ')}] `; +} diff --git a/src/discourse.ts b/src/discourse.ts new file mode 100644 index 0000000..b472fe0 --- /dev/null +++ b/src/discourse.ts @@ -0,0 +1,227 @@ +/** + * EDU-Lite: Elementary Discourse Unit decomposition. + * + * Breaks text into minimal coherent information chunks and builds + * a lightweight dependency graph. When summarizing, selecting an EDU + * also pulls in its dependency parents to maintain coherence. + * + * Based on concepts from "From Context to EDUs" (arXiv Dec 2025). + * This is a rule-based approximation — no ML parser needed. + */ + +/** A minimal coherent information unit. */ +export type EDU = { + /** The text content. */ + text: string; + /** Index within the parent text's EDU array. */ + index: number; + /** Indices of EDUs this one depends on (parents). */ + dependsOn: number[]; + /** Importance score (reusable from external scorer). */ + score: number; +}; + +// Discourse markers that signal clause boundaries +const CLAUSE_BOUNDARY_RE = + /(?:,\s*(?:and |but |or |so |yet |then |which |where |while |although |because |since |after |before |when |if |unless |as ))|(?:\s+(?:however|therefore|consequently|furthermore|moreover|additionally|meanwhile|nevertheless|nonetheless|instead|otherwise|thus|hence|accordingly)\s*[,.]?)/i; + +// Temporal chain markers +const TEMPORAL_RE = /\b(?:first|then|next|after that|finally|subsequently|later|eventually)\b/i; + +// Causal markers +const CAUSAL_RE = /\b(?:because|since|therefore|thus|hence|so that|in order to|as a result)\b/i; + +// Pronoun/demonstrative references (depend on preceding EDU) +const REFERENCE_RE = + /^(?:it|this|that|these|those|the result|the output|the response|the value)\b/i; + +/** + * Segment text into Elementary Discourse Units. + * Uses clause boundary detection with discourse markers. + */ +export function segmentEDUs(text: string): EDU[] { + // First split into sentences + const sentences = text.match(/[^.!?\n]+[.!?]+/g) ?? [text.trim()]; + const edus: EDU[] = []; + + for (const sentence of sentences) { + const trimmed = sentence.trim(); + if (trimmed.length === 0) continue; + + // Try to split at clause boundaries + const clauses = splitClauses(trimmed); + for (const clause of clauses) { + if (clause.trim().length > 5) { + edus.push({ + text: clause.trim(), + index: edus.length, + dependsOn: [], + score: 0, + }); + } + } + } + + // Build dependency edges + for (let i = 1; i < edus.length; i++) { + const text = edus[i].text; + + // Pronoun/demonstrative → depends on immediately preceding EDU + if (REFERENCE_RE.test(text)) { + edus[i].dependsOn.push(i - 1); + } + + // Temporal chain → depends on preceding EDU in sequence + if (TEMPORAL_RE.test(text) && i > 0) { + if (!edus[i].dependsOn.includes(i - 1)) { + edus[i].dependsOn.push(i - 1); + } + } + + // Causal → the cause (preceding) is a dependency + if (CAUSAL_RE.test(text) && i > 0) { + if (!edus[i].dependsOn.includes(i - 1)) { + edus[i].dependsOn.push(i - 1); + } + } + } + + return edus; +} + +/** + * Split a sentence into clauses at discourse marker boundaries. + */ +function splitClauses(sentence: string): string[] { + const parts: string[] = []; + const remaining = sentence; + + let match: RegExpExecArray | null; + const re = new RegExp(CLAUSE_BOUNDARY_RE.source, 'gi'); + + let lastIdx = 0; + while ((match = re.exec(remaining)) !== null) { + const before = remaining.slice(lastIdx, match.index); + if (before.trim().length > 10) { + parts.push(before); + } + lastIdx = match.index; + } + + const tail = remaining.slice(lastIdx); + if (tail.trim().length > 0) { + parts.push(tail); + } + + return parts.length > 0 ? parts : [sentence]; +} + +/** + * Score EDUs using an external scorer function. + * Default scorer rewards information density: technical identifiers, + * numbers with units, emphasis phrases — same signals as the main scorer. + */ +export function scoreEDUs(edus: EDU[], scorer?: (text: string) => number): EDU[] { + return edus.map((edu) => ({ + ...edu, + score: scorer ? scorer(edu.text) : defaultEduScore(edu.text), + })); +} + +function defaultEduScore(text: string): number { + let score = 0; + // Technical identifiers + score += (text.match(/\b[a-z]+(?:[A-Z][a-z]+)+\b/g) ?? []).length * 3; // camelCase + score += (text.match(/\b[A-Z][a-z]+(?:[A-Z][a-z]+)+\b/g) ?? []).length * 3; // PascalCase + score += (text.match(/\b[a-z]+(?:_[a-z]+)+\b/g) ?? []).length * 3; // snake_case + // Numbers with units + score += (text.match(/\b\d+(?:\.\d+)?\s*(?:seconds?|ms|MB|GB|retries?|%)\b/gi) ?? []).length * 2; + // Emphasis + if (/\b(?:important|critical|must|never|always|require)\b/i.test(text)) score += 4; + // Penalize filler starts + if (/^(?:well|sure|ok|thanks|great|right|yes)\b/i.test(text.trim())) score -= 5; + // Baseline: modest length bonus (prefer substance over brevity, but not bloat) + score += Math.min(text.length / 50, 2); + return score; +} + +/** + * Select EDUs for a summary budget, respecting dependency edges. + * When an EDU is selected, its dependency parents are also included + * (up to maxDepth levels). + * + * @param edus - scored EDU array + * @param budget - character budget for the summary + * @param maxDepth - maximum dependency depth to follow (default: 2) + */ +export function selectEDUs(edus: EDU[], budget: number, maxDepth = 2): EDU[] { + if (edus.length === 0) return []; + + // Sort by score descending for greedy selection + const sorted = [...edus].sort((a, b) => b.score - a.score); + const selected = new Set(); + let usedChars = 0; + + for (const edu of sorted) { + if (usedChars >= budget) break; + + // Collect this EDU and its dependencies + const toAdd = new Set(); + collectDeps(edu.index, edus, toAdd, maxDepth, 0); + toAdd.add(edu.index); + + // Check if adding all of them fits + let addedChars = 0; + for (const idx of toAdd) { + if (!selected.has(idx)) { + addedChars += edus[idx].text.length + 2; // +2 for separator + } + } + + if (usedChars + addedChars <= budget) { + for (const idx of toAdd) { + if (!selected.has(idx)) { + selected.add(idx); + usedChars += edus[idx].text.length + 2; + } + } + } + } + + // Return in original order + return edus.filter((edu) => selected.has(edu.index)); +} + +function collectDeps( + idx: number, + edus: EDU[], + result: Set, + maxDepth: number, + currentDepth: number, +): void { + if (currentDepth >= maxDepth) return; + for (const dep of edus[idx].dependsOn) { + if (!result.has(dep)) { + result.add(dep); + collectDeps(dep, edus, result, maxDepth, currentDepth + 1); + } + } +} + +/** + * Produce a discourse-aware summary by selecting and joining EDUs. + */ +export function summarizeWithEDUs( + text: string, + budget: number, + scorer?: (text: string) => number, +): string { + const edus = scoreEDUs(segmentEDUs(text), scorer); + const selected = selectEDUs(edus, budget); + + if (selected.length === 0) { + return text.slice(0, budget).trim(); + } + + return selected.map((e) => e.text).join(' '); +} diff --git a/src/entities.ts b/src/entities.ts new file mode 100644 index 0000000..89f6f6e --- /dev/null +++ b/src/entities.ts @@ -0,0 +1,311 @@ +import type { Message } from './types.js'; + +const COMMON_STARTERS = new Set([ + 'The', + 'This', + 'That', + 'These', + 'Those', + 'When', + 'Where', + 'What', + 'Which', + 'Who', + 'How', + 'Why', + 'Here', + 'There', + 'Now', + 'Then', + 'But', + 'And', + 'Or', + 'So', + 'If', + 'It', + 'Its', + 'My', + 'Your', + 'His', + 'Her', + 'Our', + 'They', + 'We', + 'You', + 'He', + 'She', + 'In', + 'On', + 'At', + 'To', + 'For', + 'With', + 'From', + 'As', + 'By', + 'An', + 'Each', + 'Every', + 'Some', + 'All', + 'Most', + 'Many', + 'Much', + 'Any', + 'No', + 'Not', + 'Also', + 'Just', + 'Only', + 'Even', + 'Still', + 'Yet', + 'Let', + 'See', + 'Note', + 'Yes', + 'Sure', + 'Great', + 'Thanks', + 'Well', + 'First', + 'Second', + 'Third', + 'Next', + 'Last', + 'Finally', + 'However', + 'After', + 'Before', + 'Since', + 'Once', + 'While', + 'Although', + 'Because', + 'Unless', + 'Until', + 'About', + 'Over', + 'Under', + 'Between', + 'Into', +]); + +/** + * Extract technical entities from text: identifiers, abbreviations, numbers with units. + * Used for entity suffixes in summaries and for retention metrics. + */ +export function extractEntities(text: string, maxEntities?: number): string[] { + const entities = new Set(); + + // Proper nouns: capitalized words not at common sentence starters + const properNouns = text.match(/\b[A-Z][a-z]+(?:\s+[A-Z][a-z]+)*\b/g); + if (properNouns) { + for (const noun of properNouns) { + const first = noun.split(/\s+/)[0]; + if (!COMMON_STARTERS.has(first)) { + entities.add(noun); + } + } + } + + // PascalCase identifiers (TypeScript, WebSocket, JavaScript, etc.) + const pascalCase = text.match(/\b[A-Z][a-z]+(?:[A-Z][a-z]+)+\b/g); + if (pascalCase) { + for (const id of pascalCase) entities.add(id); + } + + // camelCase identifiers + const camelCase = text.match(/\b[a-z]+(?:[A-Z][a-z]+)+\b/g); + if (camelCase) { + for (const id of camelCase) entities.add(id); + } + + // snake_case identifiers + const snakeCase = text.match(/\b[a-z]+(?:_[a-z]+)+\b/g); + if (snakeCase) { + for (const id of snakeCase) entities.add(id); + } + + // Vowelless words (3+ consonants, no aeiou/y) — abbreviations/tool names: pnpm, npm, ssh, grpc + const vowelless = text.match(/\b[bcdfghjklmnpqrstvwxz]{3,}\b/gi); + if (vowelless) { + for (const w of vowelless) entities.add(w.toLowerCase()); + } + + // Numbers with context + const numbersCtx = text.match( + /\b\d+(?:\.\d+)?\s*(?:seconds?|retries?|attempts?|MB|GB|TB|KB|ms|minutes?|hours?|days?|bytes?|workers?|threads?|nodes?|replicas?|instances?|users?|requests?|errors?|percent|%)\b/gi, + ); + if (numbersCtx) { + for (const n of numbersCtx) entities.add(n.trim()); + } + + // File paths (e.g., src/foo.ts, ./config.json) + const filePaths = text.match(/(?:\.\/|\.\.\/)?\b[\w./-]+\.\w{1,6}\b/g); + if (filePaths) { + for (const fp of filePaths) { + // Filter out common false positives (e.g., "e.g.", "i.e.") + if (fp.length > 4 && !fp.match(/^[a-z]\.[a-z]\.$/)) { + entities.add(fp); + } + } + } + + // URLs + const urls = text.match(/https?:\/\/\S+/g); + if (urls) { + for (const u of urls) entities.add(u); + } + + // Version numbers (v1.2.3, 2.0.0) + const versions = text.match(/\bv?\d+\.\d+(?:\.\d+)?\b/g); + if (versions) { + for (const v of versions) entities.add(v); + } + + const cap = maxEntities ?? Math.max(3, Math.min(Math.round(text.length / 200), 15)); + return Array.from(entities).slice(0, cap); +} + +/** + * Collect all unique entities from an array of messages. + * Returns a Set for efficient intersection/union operations. + */ +export function collectMessageEntities(messages: Message[]): Set { + const all = new Set(); + for (const m of messages) { + if (typeof m.content !== 'string' || m.content.length === 0) continue; + // Use a high cap so we don't artificially limit collection + const entities = extractEntities(m.content, 500); + for (const e of entities) all.add(e); + } + return all; +} + +/** + * Compute entity retention: fraction of input entities present in output. + * Returns 1.0 when no entities exist in input (nothing to lose). + */ +export function computeEntityRetention( + inputMessages: Message[], + outputMessages: Message[], +): number { + const inputEntities = collectMessageEntities(inputMessages); + if (inputEntities.size === 0) return 1.0; + + const outputEntities = collectMessageEntities(outputMessages); + let retained = 0; + for (const e of inputEntities) { + if (outputEntities.has(e)) retained++; + } + return retained / inputEntities.size; +} + +/** + * Count structural elements in text: code fences, JSON blocks, tables. + */ +export function countStructuralElements(text: string): number { + let count = 0; + // Code fences + count += (text.match(/^[ ]{0,3}```/gm) ?? []).length / 2; // pairs + // JSON blocks (standalone { or [) + const jsonBlocks = text.match(/^\s*[{[]\s*$/gm); + if (jsonBlocks) count += jsonBlocks.length; + // Markdown tables (lines with |) + const tableRows = text.match(/^\|.+\|$/gm); + if (tableRows && tableRows.length >= 2) count += 1; + return Math.floor(count); +} + +/** + * Compute structural integrity: fraction of structural elements preserved. + * Returns 1.0 when no structural elements exist in input. + */ +export function computeStructuralIntegrity( + inputMessages: Message[], + outputMessages: Message[], +): number { + let inputCount = 0; + for (const m of inputMessages) { + if (typeof m.content === 'string') inputCount += countStructuralElements(m.content); + } + if (inputCount === 0) return 1.0; + + let outputCount = 0; + for (const m of outputMessages) { + if (typeof m.content === 'string') outputCount += countStructuralElements(m.content); + } + return Math.min(outputCount / inputCount, 1.0); +} + +/** + * Check for orphaned references: identifiers in output that were defined + * in input messages that got compressed away. + * Returns coherence score 0–1 (1.0 = no orphans). + */ +export function computeReferenceCoherence( + inputMessages: Message[], + outputMessages: Message[], +): number { + // Build a map: entity → set of message IDs where it appears in input + const entitySources = new Map>(); + for (const m of inputMessages) { + if (typeof m.content !== 'string') continue; + const entities = extractEntities(m.content, 500); + for (const e of entities) { + if (!entitySources.has(e)) entitySources.set(e, new Set()); + entitySources.get(e)!.add(m.id); + } + } + + // Collect IDs of messages that survived in output + const outputIds = new Set(outputMessages.map((m) => m.id)); + + // For each entity in the output, check if at least one of its defining messages survived + const outputEntities = collectMessageEntities(outputMessages); + let total = 0; + let coherent = 0; + + for (const e of outputEntities) { + const sources = entitySources.get(e); + if (!sources) continue; // entity only in output (e.g., from summary text) — skip + total++; + // Check if any source message is still in output + let hasSource = false; + for (const srcId of sources) { + if (outputIds.has(srcId)) { + hasSource = true; + break; + } + } + if (hasSource) coherent++; + } + + return total === 0 ? 1.0 : coherent / total; +} + +/** + * Compute composite quality score combining entity retention, structural integrity, + * and reference coherence. + */ +export function computeQualityScore( + inputMessages: Message[], + outputMessages: Message[], +): { + entity_retention: number; + structural_integrity: number; + reference_coherence: number; + quality_score: number; +} { + const entity_retention = computeEntityRetention(inputMessages, outputMessages); + const structural_integrity = computeStructuralIntegrity(inputMessages, outputMessages); + const reference_coherence = computeReferenceCoherence(inputMessages, outputMessages); + + const quality_score = Math.min( + entity_retention * 0.4 + structural_integrity * 0.4 + reference_coherence * 0.2, + 1.0, + ); + + return { entity_retention, structural_integrity, reference_coherence, quality_score }; +} diff --git a/src/entropy.ts b/src/entropy.ts new file mode 100644 index 0000000..02b88ea --- /dev/null +++ b/src/entropy.ts @@ -0,0 +1,57 @@ +/** + * Entropy-based sentence scoring utilities. + * + * Provides integration with external self-information scorers (e.g., small + * causal LMs) for information-theoretic sentence importance scoring. + * Based on concepts from Selective Context (EMNLP 2023). + */ + +/** + * Split text into sentences for scoring. + * Returns the sentences and their original indices for reassembly. + */ +export function splitSentences(text: string): string[] { + const sentences = text.match(/[^.!?\n]+[.!?]+/g); + if (!sentences || sentences.length === 0) { + const trimmed = text.trim(); + return trimmed.length > 0 ? [trimmed] : []; + } + return sentences.map((s) => s.trim()).filter((s) => s.length > 0); +} + +/** + * Normalize entropy scores to 0–1 range using min-max scaling. + * Handles edge cases (all same value, empty array). + */ +export function normalizeScores(scores: number[]): number[] { + if (scores.length === 0) return []; + const min = Math.min(...scores); + const max = Math.max(...scores); + if (max === min) return scores.map(() => 0.5); // all equal → middle + return scores.map((s) => (s - min) / (max - min)); +} + +/** + * Combine heuristic and entropy scores using weighted average. + * Both score arrays must have the same length. + * + * @param heuristicScores - scores from the rule-based scorer + * @param entropyScores - scores from the entropy scorer (already normalized 0–1) + * @param entropyWeight - weight for entropy scores (0–1, default 0.6) + */ +export function combineScores( + heuristicScores: number[], + entropyScores: number[], + entropyWeight = 0.6, +): number[] { + if (heuristicScores.length !== entropyScores.length) { + throw new Error('Score arrays must have the same length'); + } + + // Normalize heuristic scores to 0–1 + const normHeuristic = normalizeScores(heuristicScores); + const normEntropy = normalizeScores(entropyScores); + const heuristicWeight = 1 - entropyWeight; + + return normHeuristic.map((h, i) => h * heuristicWeight + normEntropy[i] * entropyWeight); +} diff --git a/src/feedback.ts b/src/feedback.ts new file mode 100644 index 0000000..bb62379 --- /dev/null +++ b/src/feedback.ts @@ -0,0 +1,365 @@ +import type { + CompressResult, + CompressionPair, + CreateSummarizerOptions, + DistillationPair, + FeedbackCollector, + FeedbackResult, + Message, + OverPreservationResult, + TaskOutcome, +} from './types.js'; + +// --------------------------------------------------------------------------- +// Recommended thresholds from ACON ablations (§4.5, Figure 6) +// --------------------------------------------------------------------------- + +/** Recommended history compression threshold in tokens (ACON §4.5). */ +export const RECOMMENDED_HISTORY_THRESHOLD = 4096; + +/** Recommended per-message observation compression threshold in tokens (ACON §4.5). */ +export const RECOMMENDED_OBSERVATION_THRESHOLD = 1024; + +// --------------------------------------------------------------------------- +// Shared helpers +// --------------------------------------------------------------------------- + +const EMPTY_FEEDBACK: FeedbackResult = { + lostPatterns: [], + suggestedTerms: [], + guidelines: [], +}; + +const EMPTY_OVER_PRESERVATION: OverPreservationResult = { + unnecessaryPatterns: [], + removableTerms: [], + tighteningGuidelines: [], +}; + +function messagesToText(msgs: Message[]): string { + return msgs + .map((m) => (typeof m.content === 'string' ? m.content : '')) + .filter((c) => c.length > 0) + .join('\n---\n'); +} + +function stripFences(text: string): string { + const trimmed = text.trim(); + const fenceRe = /^```[^\n]*\n([\s\S]*?)\n\s*```$/; + const match = fenceRe.exec(trimmed); + return match ? match[1].trim() : trimmed; +} + +function parseStringArray(val: unknown): string[] { + return Array.isArray(val) ? val.filter((v: unknown) => typeof v === 'string') : []; +} + +function mergeTerms(current: string[] | undefined, additions: string[]): string[] { + const existing = new Set(current ?? []); + const merged = [...(current ?? [])]; + for (const term of additions) { + if (!existing.has(term)) { + merged.push(term); + existing.add(term); + } + } + return merged; +} + +function appendGuidelines(current: string | undefined, guidelines: string[]): string { + const bullets = guidelines.map((g) => `- ${g}`).join('\n'); + return current ? `${current}\n\n${bullets}` : bullets; +} + +// --------------------------------------------------------------------------- +// UT step: analyze lost information (contrastive feedback) +// --------------------------------------------------------------------------- + +function parseFeedbackResponse(raw: string): FeedbackResult { + const json = stripFences(raw); + const parsed = JSON.parse(json); + return { + lostPatterns: parseStringArray(parsed.lostPatterns), + suggestedTerms: parseStringArray(parsed.suggestedTerms), + guidelines: parseStringArray(parsed.guidelines), + }; +} + +function buildContrastivePrompt(pairs: readonly CompressionPair[]): string { + const failed = pairs.filter((p) => !p.outcome.success); + const succeeded = pairs.filter((p) => p.outcome.success); + + let prompt = `You are analyzing compression quality. Compare original and compressed messages to identify what information was lost during compression that may have caused downstream failures. + +## Failed cases (compression likely lost critical info)\n`; + + for (const pair of failed) { + prompt += `\n### Original:\n${messagesToText(pair.original)}\n`; + prompt += `### Compressed:\n${messagesToText(pair.compressed)}\n`; + if (pair.outcome.error) { + prompt += `### Error: ${pair.outcome.error}\n`; + } + } + + if (succeeded.length > 0) { + prompt += `\n## Successful cases (compression preserved enough info)\n`; + for (const pair of succeeded) { + prompt += `\n### Original:\n${messagesToText(pair.original)}\n`; + prompt += `### Compressed:\n${messagesToText(pair.compressed)}\n`; + } + } + + prompt += ` +Respond with a JSON object (no markdown fences, no preamble): +{ + "lostPatterns": ["patterns of information that were lost in failed cases but preserved in successful ones"], + "suggestedTerms": ["specific technical terms/identifiers that should be preserved during summarization"], + "guidelines": ["actionable rules for the summarizer to follow to avoid these failures"] +}`; + + return prompt; +} + +// --------------------------------------------------------------------------- +// CO step: analyze over-preservation in successful compressions +// --------------------------------------------------------------------------- + +function parseOverPreservationResponse(raw: string): OverPreservationResult { + const json = stripFences(raw); + const parsed = JSON.parse(json); + return { + unnecessaryPatterns: parseStringArray(parsed.unnecessaryPatterns), + removableTerms: parseStringArray(parsed.removableTerms), + tighteningGuidelines: parseStringArray(parsed.tighteningGuidelines), + }; +} + +function buildOverPreservationPrompt(pairs: readonly CompressionPair[]): string { + const succeeded = pairs.filter((p) => p.outcome.success); + + let prompt = `You are analyzing compression efficiency. For each successful case below, the compressed version was sufficient for the task to succeed. Identify what information was preserved in the compressed version but was NOT actually needed for success — this is over-preservation that wastes tokens. + +## Successful cases (task succeeded with compressed context)\n`; + + for (const pair of succeeded) { + prompt += `\n### Original:\n${messagesToText(pair.original)}\n`; + prompt += `### Compressed:\n${messagesToText(pair.compressed)}\n`; + } + + prompt += ` +Respond with a JSON object (no markdown fences, no preamble): +{ + "unnecessaryPatterns": ["patterns of information that were preserved but not needed for task success"], + "removableTerms": ["specific terms/identifiers that were preserved but could safely be omitted"], + "tighteningGuidelines": ["actionable rules for the summarizer to produce shorter summaries without losing critical info"] +}`; + + return prompt; +} + +// --------------------------------------------------------------------------- +// Feedback collector +// --------------------------------------------------------------------------- + +export function createFeedbackCollector( + callLlm: (prompt: string) => string | Promise, +): FeedbackCollector { + const _pairs: CompressionPair[] = []; + + return { + add(original: Message[], compressed: Message[], outcome: TaskOutcome): void { + _pairs.push({ original, compressed, outcome }); + }, + + async analyze(): Promise { + const hasFailures = _pairs.some((p) => !p.outcome.success); + if (_pairs.length === 0 || !hasFailures) { + return { ...EMPTY_FEEDBACK }; + } + + const prompt = buildContrastivePrompt(_pairs); + const raw = await callLlm(prompt); + return parseFeedbackResponse(raw); + }, + + async analyzeOverPreservation(): Promise { + const hasSuccesses = _pairs.some((p) => p.outcome.success); + if (_pairs.length === 0 || !hasSuccesses) { + return { ...EMPTY_OVER_PRESERVATION }; + } + + const prompt = buildOverPreservationPrompt(_pairs); + const raw = await callLlm(prompt); + return parseOverPreservationResponse(raw); + }, + + get pairs(): readonly CompressionPair[] { + return _pairs; + }, + }; +} + +// --------------------------------------------------------------------------- +// UT: refineSummarizer — merge feedback into options (additive) +// --------------------------------------------------------------------------- + +export function refineSummarizer( + currentOptions: CreateSummarizerOptions, + feedback: FeedbackResult, +): CreateSummarizerOptions { + const hasTerms = feedback.suggestedTerms.length > 0; + const hasGuidelines = feedback.guidelines.length > 0; + + if (!hasTerms && !hasGuidelines) { + return { ...currentOptions }; + } + + const result: CreateSummarizerOptions = { ...currentOptions }; + + if (hasTerms) { + result.preserveTerms = mergeTerms(currentOptions.preserveTerms, feedback.suggestedTerms); + } + + if (hasGuidelines) { + result.systemPrompt = appendGuidelines(currentOptions.systemPrompt, feedback.guidelines); + } + + return result; +} + +// --------------------------------------------------------------------------- +// CO: tightenSummarizer — apply over-preservation feedback (subtractive) +// --------------------------------------------------------------------------- + +export function tightenSummarizer( + currentOptions: CreateSummarizerOptions, + feedback: OverPreservationResult, +): CreateSummarizerOptions { + const hasTerms = feedback.removableTerms.length > 0; + const hasGuidelines = feedback.tighteningGuidelines.length > 0; + + if (!hasTerms && !hasGuidelines) { + return { ...currentOptions }; + } + + const result: CreateSummarizerOptions = { ...currentOptions }; + + if (hasTerms) { + const removable = new Set(feedback.removableTerms); + result.preserveTerms = (currentOptions.preserveTerms ?? []).filter((t) => !removable.has(t)); + } + + if (hasGuidelines) { + result.systemPrompt = appendGuidelines( + currentOptions.systemPrompt, + feedback.tighteningGuidelines, + ); + } + + return result; +} + +// --------------------------------------------------------------------------- +// Candidate selection: generate N diverse refinements for evaluation +// --------------------------------------------------------------------------- + +function buildCandidatePrompt( + currentOptions: CreateSummarizerOptions, + feedback: FeedbackResult, + count: number, +): string { + const currentTerms = currentOptions.preserveTerms?.join(', ') || '(none)'; + const currentPrompt = currentOptions.systemPrompt || '(none)'; + + const prompt = `You are optimizing a text summarizer's configuration. Given the current settings and feedback from compression failures, generate ${count} diverse candidate configurations that each address the feedback differently. + +## Current configuration +- Preserve terms: ${currentTerms} +- System prompt: ${currentPrompt} + +## Feedback from failures +- Lost patterns: ${feedback.lostPatterns.join('; ') || '(none)'} +- Suggested terms: ${feedback.suggestedTerms.join(', ') || '(none)'} +- Guidelines: ${feedback.guidelines.join('; ') || '(none)'} + +Generate ${count} DIFFERENT candidate configurations. Each should take a different approach to addressing the feedback (e.g., one conservative, one aggressive, one focused on terms, one on guidelines). + +Respond with a JSON array of ${count} objects (no markdown fences, no preamble): +[ + { + "preserveTerms": ["terms to add to the preserve list"], + "guidelines": ["actionable rules for the summarizer"] + } +]`; + + return prompt; +} + +function parseCandidates( + raw: string, + count: number, +): Array<{ preserveTerms: string[]; guidelines: string[] }> { + const json = stripFences(raw); + const parsed = JSON.parse(json); + + if (!Array.isArray(parsed)) { + throw new Error('Expected JSON array of candidates'); + } + + return parsed.slice(0, count).map((c: Record) => ({ + preserveTerms: parseStringArray(c.preserveTerms), + guidelines: parseStringArray(c.guidelines), + })); +} + +export async function refineSummarizerCandidates( + callLlm: (prompt: string) => string | Promise, + currentOptions: CreateSummarizerOptions, + feedback: FeedbackResult, + count: number = 5, +): Promise { + const prompt = buildCandidatePrompt(currentOptions, feedback, count); + const raw = await callLlm(prompt); + const candidates = parseCandidates(raw, count); + + return candidates.map((candidate) => { + const result: CreateSummarizerOptions = { ...currentOptions }; + + if (candidate.preserveTerms.length > 0) { + result.preserveTerms = mergeTerms(currentOptions.preserveTerms, candidate.preserveTerms); + } + + if (candidate.guidelines.length > 0) { + result.systemPrompt = appendGuidelines(currentOptions.systemPrompt, candidate.guidelines); + } + + return result; + }); +} + +// --------------------------------------------------------------------------- +// Distillation: extract (input, output) pairs for fine-tuning a smaller model +// --------------------------------------------------------------------------- + +export function createDistillationPairs(result: CompressResult): DistillationPair[] { + const pairs: DistillationPair[] = []; + + for (const msg of result.messages) { + const orig = msg.metadata?._cce_original as { ids?: string[] } | undefined; + if (!orig?.ids || !Array.isArray(orig.ids)) continue; + + const originalTexts = orig.ids + .map((id) => result.verbatim[id]) + .filter(Boolean) + .map((m) => (typeof m.content === 'string' ? m.content : '')); + + const input = originalTexts.join('\n'); + const output = typeof msg.content === 'string' ? msg.content : ''; + + if (input.length > 0 && output.length > 0 && input !== output) { + pairs.push({ input, output }); + } + } + + return pairs; +} diff --git a/src/flow.ts b/src/flow.ts new file mode 100644 index 0000000..abe321e --- /dev/null +++ b/src/flow.ts @@ -0,0 +1,202 @@ +/** + * Conversation flow detection. + * + * Detects common conversation patterns (Q&A, request→action→confirmation, + * correction chains) and groups them into compression units that produce + * more coherent summaries than compressing individual messages. + */ + +import type { Message } from './types.js'; + +export type FlowChain = { + /** Indices of messages in this chain. */ + indices: number[]; + /** Type of conversation flow detected. */ + type: 'qa' | 'request_action' | 'correction' | 'acknowledgment'; + /** Brief description of what the chain represents. */ + label: string; +}; + +const QUESTION_RE = /\?(?:\s|$)/; +const REQUEST_RE = + /\b(?:can you|could you|please|would you|I need|add|create|update|fix|change|modify|implement|remove|delete|make)\b/i; +const CONFIRMATION_RE = + /^(?:great|perfect|thanks|thank you|awesome|looks good|lgtm|sounds good|yes|ok|okay|done|confirmed|approved|ship it)/i; +const CORRECTION_RE = /^(?:actually|wait|no[,.]|not that|instead|correction|sorry|my bad|I meant)/i; +const ACTION_RE = + /\b(?:done|added|created|updated|fixed|changed|modified|implemented|removed|deleted|here['']?s|I['']ve)\b/i; + +/** + * Detect conversation flow chains in a message array. + * Only analyzes messages outside the recency window (those eligible for compression). + * Returns chains sorted by first message index. + */ +export function detectFlowChains( + messages: Message[], + recencyStart: number, + preserveRoles: Set, +): FlowChain[] { + const chains: FlowChain[] = []; + const claimed = new Set(); + + // Only look at messages before the recency window + const eligible = (idx: number): boolean => { + if (idx >= recencyStart) return false; + if (claimed.has(idx)) return false; + const m = messages[idx]; + if (m.role && preserveRoles.has(m.role)) return false; + if (m.tool_calls && Array.isArray(m.tool_calls) && m.tool_calls.length > 0) return false; + const content = typeof m.content === 'string' ? m.content : ''; + if (content.length < 10) return false; + if (content.startsWith('[summary:') || content.startsWith('[summary#')) return false; + // Don't include messages with code fences — they need code-split handling + if (content.includes('```')) return false; + return true; + }; + + for (let i = 0; i < recencyStart - 1; i++) { + if (!eligible(i)) continue; + + const msg1 = messages[i]; + const content1 = typeof msg1.content === 'string' ? msg1.content : ''; + const role1 = msg1.role ?? ''; + + // Look for patterns with the next eligible message + for (let j = i + 1; j < Math.min(i + 4, recencyStart); j++) { + if (!eligible(j)) continue; + + const msg2 = messages[j]; + const content2 = typeof msg2.content === 'string' ? msg2.content : ''; + const role2 = msg2.role ?? ''; + + // Request → Action: user requests → assistant acts (check before Q&A since requests often contain ?) + if ( + role1 === 'user' && + role2 === 'assistant' && + REQUEST_RE.test(content1) && + ACTION_RE.test(content2) + ) { + const chain: FlowChain = { + indices: [i, j], + type: 'request_action', + label: `Request: ${content1.slice(0, 50).replace(/\n/g, ' ').trim()}`, + }; + + // Check for confirmation + for (let k = j + 1; k < Math.min(j + 3, recencyStart); k++) { + if (!eligible(k)) continue; + const content3 = (messages[k].content as string | undefined) ?? ''; + if (CONFIRMATION_RE.test(content3.trim())) { + chain.indices.push(k); + break; + } + } + + for (const idx of chain.indices) claimed.add(idx); + chains.push(chain); + break; + } + + // Q&A: user asks question → assistant answers + if ( + role1 === 'user' && + role2 === 'assistant' && + QUESTION_RE.test(content1) && + !QUESTION_RE.test(content2) + ) { + const chain: FlowChain = { + indices: [i, j], + type: 'qa', + label: `Q&A: ${content1.slice(0, 50).replace(/\n/g, ' ').trim()}`, + }; + + // Check for follow-up confirmation + for (let k = j + 1; k < Math.min(j + 3, recencyStart); k++) { + if (!eligible(k)) continue; + const content3 = (messages[k].content as string | undefined) ?? ''; + if (CONFIRMATION_RE.test(content3.trim())) { + chain.indices.push(k); + break; + } + } + + for (const idx of chain.indices) claimed.add(idx); + chains.push(chain); + break; + } + + // Correction: correction follows a statement + if (role1 === role2 || (role1 === 'user' && role2 === 'assistant')) { + if (CORRECTION_RE.test(content2.trim())) { + const chain: FlowChain = { + indices: [i, j], + type: 'correction', + label: `Correction: ${content2.slice(0, 50).replace(/\n/g, ' ').trim()}`, + }; + for (const idx of chain.indices) claimed.add(idx); + chains.push(chain); + break; + } + } + + // Acknowledgment chain: short confirmations after substantive messages + if ( + role2 !== role1 && + content1.length > 200 && + content2.length < 100 && + CONFIRMATION_RE.test(content2.trim()) + ) { + const chain: FlowChain = { + indices: [i, j], + type: 'acknowledgment', + label: `Ack: ${content1.slice(0, 50).replace(/\n/g, ' ').trim()}`, + }; + for (const idx of chain.indices) claimed.add(idx); + chains.push(chain); + break; + } + } + } + + return chains.sort((a, b) => a.indices[0] - b.indices[0]); +} + +/** + * Produce a flow-aware summary for a chain of messages. + * Returns a summary that captures the conversational arc. + */ +export function summarizeChain(chain: FlowChain, messages: Message[]): string { + const contents = chain.indices.map((idx) => { + const m = messages[idx]; + return typeof m.content === 'string' ? m.content : ''; + }); + + switch (chain.type) { + case 'qa': { + const question = contents[0].replace(/\n/g, ' ').trim(); + const answer = contents[1]?.replace(/\n/g, ' ').trim() ?? ''; + const qSnippet = question.length > 80 ? question.slice(0, 77) + '...' : question; + const aSnippet = answer.length > 120 ? answer.slice(0, 117) + '...' : answer; + const suffix = chain.indices.length > 2 ? ' (confirmed)' : ''; + return `Q: ${qSnippet} → A: ${aSnippet}${suffix}`; + } + case 'request_action': { + const request = contents[0].replace(/\n/g, ' ').trim(); + const action = contents[1]?.replace(/\n/g, ' ').trim() ?? ''; + const rSnippet = request.length > 80 ? request.slice(0, 77) + '...' : request; + const aSnippet = action.length > 120 ? action.slice(0, 117) + '...' : action; + const suffix = chain.indices.length > 2 ? ' → confirmed' : ''; + return `Request: ${rSnippet} → ${aSnippet}${suffix}`; + } + case 'correction': { + const correction = contents[1]?.replace(/\n/g, ' ').trim() ?? ''; + const cSnippet = correction.length > 150 ? correction.slice(0, 147) + '...' : correction; + return `Correction: ${cSnippet}`; + } + case 'acknowledgment': { + const substance = contents[0].replace(/\n/g, ' ').trim(); + const sSnippet = substance.length > 150 ? substance.slice(0, 147) + '...' : substance; + return `${sSnippet} (acknowledged)`; + } + } +} diff --git a/src/importance.ts b/src/importance.ts new file mode 100644 index 0000000..ce310e2 --- /dev/null +++ b/src/importance.ts @@ -0,0 +1,129 @@ +/** + * Message importance scoring — ANCS-inspired per-message importance + * beyond positional recency. + * + * Factors: + * 1. Forward-reference density: how many later messages reference this message's entities + * 2. Decision/directive content: messages with requirements, constraints, corrections + * 3. Correction recency: messages that override earlier content get boosted + * + * Used by compress() when `importanceScoring: true` to: + * - Preserve high-importance messages outside the recency window + * - Order forceConverge truncation (low-importance first) + */ + +import type { Message } from './types.js'; + +// ── Entity extraction (lightweight, no external deps) ───────────── + +const CAMEL_RE = /\b[a-z]+(?:[A-Z][a-z]+)+\b/g; +const PASCAL_RE = /\b[A-Z][a-z]+(?:[A-Z][a-z]+)+\b/g; +const SNAKE_RE = /\b[a-z]+(?:_[a-z]+)+\b/g; +const VOWELLESS_RE = /\b[bcdfghjklmnpqrstvwxz]{3,}\b/gi; +const FILE_REF_RE = /\S+\.\w+:\d+/g; + +function extractMessageEntities(content: string): Set { + const entities = new Set(); + for (const re of [CAMEL_RE, PASCAL_RE, SNAKE_RE, VOWELLESS_RE, FILE_REF_RE]) { + const matches = content.match(re); + if (matches) { + for (const m of matches) entities.add(m.toLowerCase()); + } + } + return entities; +} + +// ── Decision / directive detection ──────────────────────────────── + +const DECISION_RE = + /\b(?:must|should|require[ds]?|always|never|do not|don't|instead|use\s+\w+\s+(?:instead|rather)|the\s+(?:approach|solution|fix|answer)\s+is|decided? to|we(?:'ll| will)\s+(?:go with|use|implement))\b/i; + +const CORRECTION_RE = + /\b(?:actually|correction|no[,.]?\s+(?:use|it's|that's|the)|wait[,.]|sorry[,.]|instead[,.]|not\s+\w+[,.]?\s+(?:but|use|it's)|scratch that|disregard|ignore (?:that|my|the previous))\b/i; + +const CONSTRAINT_RE = + /\b(?:constraint|limitation|boundary|deadline|blocker|requirement|prerequisite|dependency|breaking change|backwards? compat)\b/i; + +/** Content-based importance signals (0–1 range contributions). */ +export function scoreContentSignals(content: string): number { + let score = 0; + if (DECISION_RE.test(content)) score += 0.15; + if (CORRECTION_RE.test(content)) score += 0.25; // corrections are high-value + if (CONSTRAINT_RE.test(content)) score += 0.1; + return Math.min(score, 0.4); // cap content signal contribution +} + +// ── Forward-reference graph ─────────────────────────────────────── + +export type ImportanceMap = Map; + +/** + * Compute per-message importance scores for a message array. + * + * Algorithm: + * 1. Extract entities from each message + * 2. Build forward-reference counts: for each message, count how many + * later messages share at least one entity + * 3. Normalize reference counts to 0–1, combine with content signals + * + * Returns a Map. + */ +export function computeImportance(messages: Message[]): ImportanceMap { + const scores = new Map(); + if (messages.length === 0) return scores; + + // Extract entities per message + const entitySets: Array> = []; + for (const msg of messages) { + const content = typeof msg.content === 'string' ? msg.content : ''; + entitySets.push(extractMessageEntities(content)); + } + + // Count forward references: how many later messages share entities with this one + const refCounts = new Array(messages.length).fill(0); + let maxRefs = 0; + + for (let i = 0; i < messages.length; i++) { + const myEntities = entitySets[i]; + if (myEntities.size === 0) continue; + + for (let j = i + 1; j < messages.length; j++) { + const theirEntities = entitySets[j]; + let shared = false; + for (const e of myEntities) { + if (theirEntities.has(e)) { + shared = true; + break; + } + } + if (shared) { + refCounts[i]++; + } + } + if (refCounts[i] > maxRefs) maxRefs = refCounts[i]; + } + + // Compute combined score per message + for (let i = 0; i < messages.length; i++) { + const content = typeof messages[i].content === 'string' ? (messages[i].content as string) : ''; + + // Reference score: normalized 0–0.5 + const refScore = maxRefs > 0 ? (refCounts[i] / maxRefs) * 0.5 : 0; + + // Content signal score: 0–0.4 + const contentScore = scoreContentSignals(content); + + // Recency bonus: slight boost for more recent messages (0–0.1) + const recencyScore = (i / Math.max(messages.length - 1, 1)) * 0.1; + + scores.set(i, Math.min(1, refScore + contentScore + recencyScore)); + } + + return scores; +} + +/** + * Default importance threshold for preservation. + * Messages scoring above this are preserved even outside the recency window. + */ +export const DEFAULT_IMPORTANCE_THRESHOLD = 0.65; diff --git a/src/index.ts b/src/index.ts index f3449e4..1a75719 100644 --- a/src/index.ts +++ b/src/index.ts @@ -1,18 +1,97 @@ // Primary -export { compress, defaultTokenCounter } from './compress.js'; +export { compress, defaultTokenCounter, bestSentenceScore } from './compress.js'; export { uncompress } from './expand.js'; export type { StoreLookup } from './expand.js'; // Helpers (LLM integration) export { createSummarizer, createEscalatingSummarizer } from './summarizer.js'; +export { createClassifier, createEscalatingClassifier } from './classifier.js'; +export { + createFeedbackCollector, + refineSummarizer, + tightenSummarizer, + refineSummarizerCandidates, + createDistillationPairs, + RECOMMENDED_HISTORY_THRESHOLD, + RECOMMENDED_OBSERVATION_THRESHOLD, +} from './feedback.js'; + +// Format adapters +export { CodeAdapter, StructuredOutputAdapter } from './adapters.js'; + +// Entity extraction & quality metrics +export { + extractEntities, + collectMessageEntities, + computeEntityRetention, + computeStructuralIntegrity, + computeReferenceCoherence, + computeQualityScore, +} from './entities.js'; + +// Importance scoring (ANCS-inspired) +export { + computeImportance, + scoreContentSignals, + DEFAULT_IMPORTANCE_THRESHOLD, +} from './importance.js'; +export type { ImportanceMap } from './importance.js'; + +// Conversation flow detection +export { detectFlowChains, summarizeChain } from './flow.js'; +export type { FlowChain } from './flow.js'; + +// ML token classifier +export { + compressWithTokenClassifier, + compressWithTokenClassifierSync, + whitespaceTokenize, + createMockTokenClassifier, +} from './ml-classifier.js'; + +// Discourse decomposition (EDU-lite) +export { segmentEDUs, scoreEDUs, selectEDUs, summarizeWithEDUs } from './discourse.js'; +export type { EDU } from './discourse.js'; + +// Semantic clustering +export { clusterMessages, summarizeCluster } from './cluster.js'; +export type { MessageCluster } from './cluster.js'; + +// Cross-message coreference +export { + buildCoreferenceMap, + findOrphanedReferences, + generateInlineDefinitions, +} from './coreference.js'; +export type { EntityDefinition } from './coreference.js'; + +// Entropy scoring utilities +export { splitSentences, normalizeScores, combineScores } from './entropy.js'; + +// Contradiction detection (ANCS-inspired) +export { analyzeContradictions } from './contradiction.js'; +export type { ContradictionAnnotation } from './contradiction.js'; // Types export type { + Classifier, + ClassifierResult, + CompressDecision, + CompressionPair, CompressOptions, CompressResult, + CreateClassifierOptions, CreateSummarizerOptions, + DistillationPair, + FeedbackCollector, + FeedbackResult, + FormatAdapter, Message, + MLTokenClassifier, + OverPreservationResult, Summarizer, + TaskOutcome, + TokenClassification, UncompressOptions, UncompressResult, VerbatimMap, diff --git a/src/ml-classifier.ts b/src/ml-classifier.ts new file mode 100644 index 0000000..5ed97f4 --- /dev/null +++ b/src/ml-classifier.ts @@ -0,0 +1,105 @@ +/** + * ML token-level classifier integration. + * + * Wraps an external ML token classifier (LLMLingua-2 style) to produce + * compressed text by keeping only tokens classified as important. + * The actual model is user-provided — this module handles reconstruction. + * + * Based on LLMLingua-2 (ACL 2024): token classification via small encoder. + */ + +import type { MLTokenClassifier, TokenClassification } from './types.js'; + +/** + * Compress text using token-level classification. + * Keeps tokens marked as `keep: true` and reconstructs them into readable text. + * + * @param content - the text to compress + * @param classifier - the ML token classifier function + * @param minConfidence - minimum confidence to respect the classifier's decision (default: 0.5) + */ +export async function compressWithTokenClassifier( + content: string, + classifier: MLTokenClassifier, + minConfidence = 0.5, +): Promise { + const classifications = await Promise.resolve(classifier(content)); + return reconstructFromClassifications(classifications, minConfidence); +} + +/** + * Synchronous version — only works with sync classifiers. + */ +export function compressWithTokenClassifierSync( + content: string, + classifier: MLTokenClassifier, + minConfidence = 0.5, +): string { + const result = classifier(content); + if (result instanceof Promise) { + throw new Error( + 'mlTokenClassifier returned a Promise in sync mode. Provide a summarizer or classifier to enable async.', + ); + } + return reconstructFromClassifications(result, minConfidence); +} + +/** + * Reconstruct readable text from token classifications. + * Handles whitespace normalization and punctuation attachment. + */ +function reconstructFromClassifications( + classifications: TokenClassification[], + minConfidence: number, +): string { + const kept: string[] = []; + + for (const tc of classifications) { + // Keep token if classified as keep with sufficient confidence, + // OR if confidence is too low (uncertain → keep to be safe) + if (tc.keep && tc.confidence >= minConfidence) { + kept.push(tc.token); + } else if (!tc.keep && tc.confidence < minConfidence) { + // Low confidence removal → keep to be safe + kept.push(tc.token); + } + } + + // Reconstruct: join tokens, normalize whitespace + let text = kept.join(' '); + + // Fix punctuation spacing: remove space before . , ; : ! ? ) ] } + text = text.replace(/\s+([.,;:!?\])}])/g, '$1'); + // Remove space after ( [ { + text = text.replace(/([([{])\s+/g, '$1'); + // Collapse multiple spaces + text = text.replace(/\s{2,}/g, ' '); + + return text.trim(); +} + +/** + * Simple whitespace tokenizer for use with ML classifiers that expect + * pre-tokenized input. Splits on whitespace boundaries. + */ +export function whitespaceTokenize(text: string): string[] { + return text.split(/\s+/).filter((t) => t.length > 0); +} + +/** + * Create a mock token classifier for testing. + * Keeps tokens matching any of the given patterns. + */ +export function createMockTokenClassifier( + keepPatterns: RegExp[], + confidence = 0.9, +): MLTokenClassifier { + return (content: string) => { + const tokens = whitespaceTokenize(content); + return tokens.map((token) => ({ + token, + keep: keepPatterns.some((p) => p.test(token)), + confidence, + })); + }; +} diff --git a/src/types.ts b/src/types.ts index d885de3..20a7357 100644 --- a/src/types.ts +++ b/src/types.ts @@ -1,5 +1,46 @@ export type Summarizer = (text: string) => string | Promise; +export type ClassifierResult = { + decision: 'preserve' | 'compress'; + confidence: number; + reason: string; +}; + +export type Classifier = (content: string) => ClassifierResult | Promise; + +/** Per-token classification result from an ML token classifier (LLMLingua-2 style). */ +export type TokenClassification = { + /** The original token. */ + token: string; + /** Whether to keep this token in the compressed output. */ + keep: boolean; + /** Confidence score (0–1). */ + confidence: number; +}; + +/** + * ML token-level classifier. Takes content and returns per-token keep/remove + * decisions. Based on LLMLingua-2 (ACL 2024) — a small encoder model + * (e.g., XLM-RoBERTa) classifies each token with full bidirectional context. + * + * The function can be sync or async (e.g., backed by a local ONNX model + * or a remote inference endpoint). + */ +export type MLTokenClassifier = ( + content: string, +) => TokenClassification[] | Promise; + +export type CreateClassifierOptions = { + /** Domain-specific instructions for the LLM. */ + systemPrompt?: string; + /** Content types to always preserve, regardless of LLM decision. */ + alwaysPreserve?: string[]; + /** Content types that are always safe to compress. */ + alwaysCompress?: string[]; + /** Maximum tokens for the LLM response. Default: 100. */ + maxResponseTokens?: number; +}; + export type CreateSummarizerOptions = { /** Maximum tokens for the LLM response. Default: 300. */ maxResponseTokens?: number; @@ -11,6 +52,35 @@ export type CreateSummarizerOptions = { preserveTerms?: string[]; }; +export interface FormatAdapter { + /** Adapter name for identification. */ + name: string; + /** Returns true if this adapter handles the given content. */ + detect(content: string): boolean; + /** Extract elements that must survive compression verbatim. */ + extractPreserved(content: string): string[]; + /** Extract elements that can be summarized. */ + extractCompressible(content: string): string[]; + /** Reconstruct output from preserved elements and a summary string. */ + reconstruct(preserved: string[], summary: string): string; +} + +export type CompressDecision = { + messageId: string; + messageIndex: number; + action: + | 'preserved' + | 'compressed' + | 'deduped' + | 'fuzzy_deduped' + | 'truncated' + | 'code_split' + | 'contradicted'; + reason: string; + inputChars: number; + outputChars: number; +}; + export type CompressOptions = { preserve?: string[]; recencyWindow?: number; @@ -20,6 +90,8 @@ export type CompressOptions = { summarizer?: Summarizer; /** Target token budget. When set, compress binary-searches recencyWindow to fit. */ tokenBudget?: number; + /** Skip compression entirely when total input tokens are below this threshold. Returns messages unmodified. */ + compressionThreshold?: number; /** Minimum recencyWindow when using tokenBudget. Default: 0. */ minRecencyWindow?: number; /** Replace earlier duplicate messages with a compact reference. Default: true. */ @@ -32,8 +104,99 @@ export type CompressOptions = { embedSummaryId?: boolean; /** Hard-truncate non-recency messages when binary search bottoms out and budget still exceeded. Default: false. */ forceConverge?: boolean; - /** Custom token counter per message. Default: ceil(content.length / 3.5). */ + /** Custom patterns that force preservation (hard T0). Matched against message content. + * Each pattern needs a regex and a label used in classification reasons. + * Example: `[{ re: /§\s*\d+/, label: 'section_ref' }]` */ + preservePatterns?: Array<{ re: RegExp; label: string }>; + /** LLM-powered classifier. Determines which messages to preserve vs. compress. + * When provided, compress() returns a Promise. */ + classifier?: Classifier; + /** Classification mode. Controls how the LLM classifier interacts with heuristics. + * - 'hybrid': Heuristics first, LLM for low-confidence cases (default when classifier is set) + * - 'full': LLM classifies every message, heuristics skipped + * Ignored when classifier is not set. */ + classifierMode?: 'hybrid' | 'full'; + /** Custom token counter per message. Default: ceil(content.length / 3.5) — see defaultTokenCounter for rationale. */ tokenCounter?: (msg: Message) => number; + /** Emit a decisions array in the result explaining what happened to each message. Default: false. */ + trace?: boolean; + /** Custom format adapters for domain-specific content handling. + * Each adapter can detect, extract, and reconstruct format-specific content. + * Built-in adapters (code fences, structured output) always run first. */ + adapters?: FormatAdapter[]; + /** Per-message token threshold for observation compression (ACON §3.2 Eq 4). + * Messages exceeding this are compressed even if in the recency window. + * System-role and tool_calls messages are always exempt. */ + observationThreshold?: number; + /** Enable importance-weighted retention. When true, messages are scored by + * forward-reference density, decision/correction content, and recency. + * High-importance messages are preserved even outside the recency window, + * and forceConverge truncates low-importance messages first. Default: false. */ + importanceScoring?: boolean; + /** Importance threshold for preservation (0–1). Messages scoring above this + * are preserved even outside the recency window. Default: 0.65. */ + importanceThreshold?: number; + /** Enable contradiction detection. When true, later messages that correct + * earlier ones cause the earlier message to be compressed while the + * correction is preserved. Default: false. */ + contradictionDetection?: boolean; + /** Topic overlap threshold for contradiction detection (0–1). Default: 0.15. */ + contradictionTopicThreshold?: number; + /** Relevance threshold for summarization (0–1). When set, messages whose best + * sentence score falls below this threshold are replaced with a compact stub + * instead of a low-quality summary. Higher values = more aggressive dropping. + * Default: undefined (disabled). */ + relevanceThreshold?: number; + /** Optional entropy scorer for information-theoretic sentence scoring. + * When provided, augments or replaces the heuristic sentence scorer. + * The function receives an array of sentences and returns per-sentence + * self-information scores (higher = more informative = preserve). + * Can be sync or async (e.g., backed by a small local LM). */ + entropyScorer?: (sentences: string[]) => number[] | Promise; + /** How to combine entropy and heuristic scores. + * - 'replace': use entropy scores only (heuristic skipped) + * - 'augment': weighted average of both (default when entropyScorer is set) */ + entropyScorerMode?: 'replace' | 'augment'; + /** ML token-level classifier (LLMLingua-2 style). When provided, T2 prose + * content is classified at the token level: kept tokens are reconstructed + * into compressed text. T0 rules still override for code/structured content. + * Can be sync or async. When async, compress() returns a Promise. */ + mlTokenClassifier?: MLTokenClassifier; + /** **Experimental.** Enable discourse-aware summarization (EDU-lite). + * Breaks content into Elementary Discourse Units with dependency tracking. + * **Warning:** reduces compression ratio by 8–28% with the built-in scorer. + * The dependency tracking keeps more text than standard summarization. + * Recommended only with a custom ML-backed scorer via `scoreEDUs()`. + * Use the exported `segmentEDUs`/`scoreEDUs`/`selectEDUs` directly instead. + * Default: false. */ + discourseAware?: boolean; + /** Enable semantic clustering. Groups messages by topic using TF-IDF and + * entity overlap, then compresses each cluster as a unit. Scattered + * messages about the same topic get merged into a single compressed block. + * Default: false. */ + semanticClustering?: boolean; + /** Similarity threshold for semantic clustering (0–1). Default: 0.15. */ + clusterThreshold?: number; + /** Enable cross-message coreference tracking. When a compressed message defines + * an entity referenced by a preserved message, the definition is inlined into + * the compressed summary to prevent orphaned references. Default: false. */ + coreference?: boolean; + /** Enable conversation flow detection. Groups Q&A pairs, request→action→confirmation + * chains, and correction sequences into compression units for better summaries. + * Default: false. */ + conversationFlow?: boolean; + /** Compression depth controls aggressiveness. + * - 'gentle': standard sentence selection (~2x, default) + * - 'moderate': tighter budgets + clause pruning (~3-4x) + * - 'aggressive': entity-only stubs (~6-8x) + * - 'auto': progressively increases depth until tokenBudget fits or quality drops below 0.80 */ + compressionDepth?: 'gentle' | 'moderate' | 'aggressive' | 'auto'; + /** Budget strategy when tokenBudget is set. + * - 'binary-search': (default) binary search over recencyWindow to fit budget. + * - 'tiered': keeps recencyWindow fixed, progressively compresses older content + * by priority tier. System/T0/recent messages are protected; older prose is + * compressed first, then stubbed, then truncated. Better preserves recent context. */ + budgetStrategy?: 'binary-search' | 'tiered'; }; export type VerbatimMap = Record; @@ -63,6 +226,26 @@ export type CompressResult = { messages_preserved: number; messages_deduped?: number; messages_fuzzy_deduped?: number; + messages_pattern_preserved?: number; + /** Messages classified by LLM (when classifier is provided). */ + messages_llm_classified?: number; + /** Messages where LLM decided to preserve (when classifier is provided). */ + messages_llm_preserved?: number; + /** Messages superseded by a later correction (when contradictionDetection is enabled). */ + messages_contradicted?: number; + /** Messages preserved due to high importance score (when importanceScoring is enabled). */ + messages_importance_preserved?: number; + /** Messages dropped to a stub because their best sentence score fell below the relevance threshold. */ + messages_relevance_dropped?: number; + /** Fraction of technical entities (identifiers, abbreviations, numbers) preserved after compression (0–1). */ + entity_retention?: number; + /** Fraction of structural elements (code fences, JSON blocks, tables) preserved after compression (0–1). */ + structural_integrity?: number; + /** Fraction of output entity references whose defining message is still present (0–1). */ + reference_coherence?: number; + /** Composite quality score: 0.4 * entity_retention + 0.4 * structural_integrity + 0.2 * reference_coherence. */ + quality_score?: number; + decisions?: CompressDecision[]; }; /** * Original verbatim messages keyed by ID — every compressed message's @@ -82,6 +265,40 @@ export type CompressResult = { recencyWindow?: number; }; +export type TaskOutcome = { success: boolean; error?: string }; + +export type CompressionPair = { + original: Message[]; + compressed: Message[]; + outcome: TaskOutcome; +}; + +export type FeedbackResult = { + lostPatterns: string[]; + suggestedTerms: string[]; + guidelines: string[]; +}; + +export type OverPreservationResult = { + unnecessaryPatterns: string[]; + removableTerms: string[]; + tighteningGuidelines: string[]; +}; + +export type FeedbackCollector = { + add(original: Message[], compressed: Message[], outcome: TaskOutcome): void; + /** UT step: analyze what was lost in failed compressions. */ + analyze(): Promise; + /** CO step: analyze what was over-preserved in successful compressions. */ + analyzeOverPreservation(): Promise; + readonly pairs: readonly CompressionPair[]; +}; + +export type DistillationPair = { + input: string; + output: string; +}; + export type Message = { id: string; index: number; diff --git a/tests/adapters.test.ts b/tests/adapters.test.ts new file mode 100644 index 0000000..05cfa8b --- /dev/null +++ b/tests/adapters.test.ts @@ -0,0 +1,220 @@ +import { describe, it, expect } from 'vitest'; +import { CodeAdapter, StructuredOutputAdapter } from '../src/adapters.js'; +import { compress } from '../src/compress.js'; +import type { FormatAdapter, Message } from '../src/types.js'; + +function msg(overrides: Partial & { id: string; index: number }): Message { + return { role: 'user', content: '', metadata: {}, ...overrides }; +} + +describe('CodeAdapter', () => { + it('detects content with code fences', () => { + expect(CodeAdapter.detect('some text\n```ts\nconst x = 1;\n```\nmore text')).toBe(true); + }); + + it('does not detect content without code fences', () => { + expect(CodeAdapter.detect('just plain text')).toBe(false); + }); + + it('extractPreserved returns code fences', () => { + const content = 'some text\n```ts\nconst x = 1;\n```\nmore text\n```js\nlet y = 2;\n```'; + const preserved = CodeAdapter.extractPreserved(content); + expect(preserved).toHaveLength(2); + expect(preserved[0]).toContain('const x = 1;'); + expect(preserved[1]).toContain('let y = 2;'); + }); + + it('extractCompressible returns prose segments', () => { + const content = 'before code\n```ts\nconst x = 1;\n```\nafter code'; + const compressible = CodeAdapter.extractCompressible(content); + expect(compressible).toHaveLength(2); + expect(compressible[0]).toBe('before code'); + expect(compressible[1]).toBe('after code'); + }); + + it('reconstruct combines summary and preserved fences', () => { + const result = CodeAdapter.reconstruct( + ['```ts\nconst x = 1;\n```', '```ts\nconst y = 2;\n```'], + '[summary: code explanation]', + ); + expect(result).toContain('[summary: code explanation]'); + expect(result).toContain('```ts\nconst x = 1;\n```'); + expect(result).toContain('```ts\nconst y = 2;\n```'); + }); +}); + +describe('StructuredOutputAdapter', () => { + const structuredContent = [ + 'src/auth.ts:10: const token = jwt.verify()', + 'src/auth.ts:15: const session = createSession()', + 'src/auth.ts:20: return session', + 'src/auth.ts:25: const user = getUser()', + 'src/auth.ts:30: validate(token)', + 'src/auth.ts:35: return user', + 'Tests: 5 passed, 0 failed', + 'Duration: 1.2s', + ].join('\n'); + + it('detects structured output', () => { + expect(StructuredOutputAdapter.detect(structuredContent)).toBe(true); + }); + + it('does not detect plain prose', () => { + expect(StructuredOutputAdapter.detect('Just a normal sentence.')).toBe(false); + }); + + it('extractPreserved returns status lines and file paths', () => { + const preserved = StructuredOutputAdapter.extractPreserved(structuredContent); + expect(preserved.some((p) => p.includes('passed'))).toBe(true); + expect(preserved.some((p) => p.includes('files:'))).toBe(true); + }); + + it('reconstruct joins preserved and summary with pipes', () => { + const result = StructuredOutputAdapter.reconstruct( + ['Tests: 5 passed', 'files: src/auth.ts'], + 'additional info', + ); + expect(result).toContain('Tests: 5 passed'); + expect(result).toContain('files: src/auth.ts'); + expect(result).toContain('additional info'); + expect(result).toContain(' | '); + }); +}); + +describe('custom adapters in compress pipeline', () => { + it('custom adapter is called when registered and content matches', () => { + const customAdapter: FormatAdapter = { + name: 'csv', + detect: (content) => content.includes('col1,col2,col3'), + extractPreserved: (content) => { + // Keep the header line + const lines = content.split('\n'); + return [lines[0]]; + }, + extractCompressible: (content) => { + const lines = content.split('\n'); + return lines.slice(1); + }, + reconstruct: (preserved, summary) => { + return `${preserved.join('\n')}\n[${summary}]`; + }, + }; + + const csvContent = + 'col1,col2,col3\n' + + Array.from( + { length: 10 }, + (_, i) => `value${i},data${i},This is a long description that adds bulk to the content`, + ).join('\n'); + + const messages: Message[] = [msg({ id: '1', index: 0, role: 'tool', content: csvContent })]; + + const result = compress(messages, { + recencyWindow: 0, + adapters: [customAdapter], + }); + + // If the adapter reduced the size, it should have compressed + const output = result.messages[0].content!; + if (output.length < csvContent.length) { + expect(result.compression.messages_compressed).toBe(1); + expect(output).toContain('col1,col2,col3'); + } else { + // Adapter reverted (compressed >= original) + expect(result.compression.messages_preserved).toBe(1); + } + }); + + it('custom adapter trace reason is recorded', () => { + const customAdapter: FormatAdapter = { + name: 'test_format', + detect: (content) => content.startsWith('TEST_FORMAT:'), + extractPreserved: () => [], + extractCompressible: (content) => [content.slice(12)], + reconstruct: (_preserved, summary) => `TEST_FORMAT: ${summary}`, + }; + + const content = + 'TEST_FORMAT: ' + + 'This is a long formatted content that will be processed by the custom adapter. '.repeat(5); + + const messages: Message[] = [msg({ id: '1', index: 0, role: 'tool', content })]; + + const result = compress(messages, { + recencyWindow: 0, + adapters: [customAdapter], + trace: true, + }); + + const d = result.compression.decisions!; + expect(d).toHaveLength(1); + expect(d[0].reason).toMatch(/adapter.*test_format/); + }); + + it('non-matching adapter does not affect compression', () => { + const customAdapter: FormatAdapter = { + name: 'never_match', + detect: () => false, + extractPreserved: () => [], + extractCompressible: (content) => [content], + reconstruct: (_preserved, summary) => summary, + }; + + const longProse = + 'This is a long general discussion that should be compressed normally by the standard pipeline. '.repeat( + 5, + ); + const messages: Message[] = [msg({ id: '1', index: 0, role: 'user', content: longProse })]; + + const resultWithAdapter = compress(messages, { + recencyWindow: 0, + adapters: [customAdapter], + }); + const resultWithout = compress(messages, { recencyWindow: 0 }); + + expect(resultWithAdapter.compression.messages_compressed).toBe( + resultWithout.compression.messages_compressed, + ); + expect(resultWithAdapter.compression.messages_preserved).toBe( + resultWithout.compression.messages_preserved, + ); + }); + + it('existing compress tests still pass with no adapters', () => { + // This is a regression safety check: built-in code-split behavior unchanged + const longProse = + 'Detailed explanation of authentication that has enough content to compress. '.repeat(3); + const content = `${longProse}\n\n\`\`\`ts\nconst token = auth.getToken();\n\`\`\``; + const messages: Message[] = [msg({ id: '1', index: 0, role: 'assistant', content })]; + + const result = compress(messages, { recencyWindow: 0 }); + expect(result.compression.messages_compressed).toBe(1); + const output = result.messages[0].content!; + expect(output).toContain('```ts'); + expect(output).toContain('auth.getToken()'); + }); + + it('built-in code-split takes priority over custom adapter for code content', () => { + const codeAdapter: FormatAdapter = { + name: 'custom_code', + detect: (content) => content.includes('```'), + extractPreserved: () => ['custom preserved'], + extractCompressible: () => ['custom compressible'], + reconstruct: () => 'CUSTOM_OUTPUT', + }; + + const longProse = 'Explanation of the code behavior. '.repeat(5); + const content = `${longProse}\n\n\`\`\`ts\nconst x = 1;\n\`\`\``; + const messages: Message[] = [msg({ id: '1', index: 0, role: 'assistant', content })]; + + const result = compress(messages, { + recencyWindow: 0, + adapters: [codeAdapter], + }); + + // Built-in code-split runs before adapters + const output = result.messages[0].content!; + expect(output).not.toBe('CUSTOM_OUTPUT'); + expect(output).toContain('```ts'); + }); +}); diff --git a/tests/adversarial.test.ts b/tests/adversarial.test.ts new file mode 100644 index 0000000..5b7847a --- /dev/null +++ b/tests/adversarial.test.ts @@ -0,0 +1,241 @@ +/** + * Adversarial test cases — specifically designed to stress compression quality. + * Tests edge cases that could break coherence, lose critical data, or produce + * nonsensical output. + */ + +import { describe, it, expect } from 'vitest'; +import { compress } from '../src/compress.js'; +import { uncompress } from '../src/expand.js'; +import type { Message } from '../src/types.js'; + +function msg(id: string, content: string, role = 'user'): Message { + return { id, index: 0, role, content }; +} + +describe('adversarial: pronoun-heavy messages', () => { + it('compresses without losing referential context', () => { + const messages: Message[] = [ + msg( + '1', + 'Do it like we discussed earlier, but change the thing to use the other approach instead of what we had before, and make sure it handles the edge case we talked about.', + ), + msg('recent', 'OK, will do.'), + ]; + + const result = compress(messages, { recencyWindow: 1 }); + // Should still produce valid output (not crash on pronoun-heavy content) + expect(result.messages.length).toBeGreaterThan(0); + }); +}); + +describe('adversarial: scattered entity references', () => { + it('entity defined in msg 1 referenced across many later messages', () => { + const messages: Message[] = [ + msg( + 'def', + 'The fetchData function is the central data fetching utility that handles all API communication with exponential backoff retry logic and circuit breaker pattern.', + ), + msg( + '2', + 'Generic discussion about project timeline and quarterly goals for the engineering team.', + ), + msg( + '3', + 'More general planning about sprint velocity and capacity allocation for the quarter.', + ), + msg('4', 'The fetchData function needs a timeout parameter for slow network conditions.'), + msg('5', 'Unrelated conversation about office lunch preferences and team building events.'), + msg('ref', 'Make sure fetchData handles 429 rate limit responses with proper backoff.'), + ]; + + const result = compress(messages, { + recencyWindow: 1, + coreference: true, + }); + + // fetchData should survive in some form + const allContent = result.messages.map((m) => m.content ?? '').join(' '); + expect(allContent).toContain('fetchData'); + }); +}); + +describe('adversarial: correction chain', () => { + it('3 contradictory instructions — only last should be authoritative', () => { + const messages: Message[] = [ + msg( + 'v1', + 'Use Redis for the caching layer with a TTL of 3600 seconds for all session data and configure the connection pool with 20 connections maximum.', + ), + msg( + 'v2', + 'Actually, use Memcached instead of Redis for the caching layer. Redis is overkill for simple key-value session storage and costs more.', + ), + msg( + 'v3', + 'Wait, no — use DynamoDB for caching instead. We need the durability guarantees and the team already has AWS expertise and the infrastructure in place.', + ), + msg('recent', 'Got it, DynamoDB it is.'), + ]; + + const result = compress(messages, { + recencyWindow: 1, + contradictionDetection: true, + }); + + // The most recent correction (DynamoDB) should be preserved + const allContent = result.messages.map((m) => m.content ?? '').join(' '); + expect(allContent.toLowerCase()).toContain('dynamodb'); + }); +}); + +describe('adversarial: code interleaved with prose', () => { + it('alternating paragraphs of explanation and code', () => { + const messages: Message[] = [ + msg( + '1', + [ + 'Here is the authentication flow explained step by step with code examples for each stage.', + '', + 'First, we validate the incoming JWT token:', + '```typescript', + 'const decoded = jwt.verify(token, secret);', + '```', + '', + 'Then we check if the session is still active and the user has the required permissions:', + '```typescript', + 'const session = await redis.get(`session:${decoded.sub}`);', + 'if (!session) throw new UnauthorizedError();', + '```', + '', + 'Finally we attach the user context to the request object for downstream handlers:', + '```typescript', + 'req.user = { id: decoded.sub, roles: decoded.roles };', + 'next();', + '```', + ].join('\n'), + ), + msg('recent', 'Makes sense.'), + ]; + + const result = compress(messages, { recencyWindow: 1 }); + const msg1 = result.messages.find((m) => m.id === '1'); + + // Code fences should survive (either preserved or code-split) + if (msg1?.content?.includes('```')) { + expect(msg1.content).toContain('jwt.verify'); + } + }); +}); + +describe('adversarial: near-duplicate with critical difference', () => { + it('two messages identical except for one number', () => { + const messages: Message[] = [ + msg( + '1', + 'The connection pool should be configured with a maximum of 10 connections per service instance and a 30 second idle timeout for unused connections.', + ), + msg( + '2', + 'The connection pool should be configured with a maximum of 50 connections per service instance and a 30 second idle timeout for unused connections.', + ), + msg('recent', 'Which one?'), + ]; + + const result = compress(messages, { + recencyWindow: 1, + fuzzyDedup: true, + fuzzyThreshold: 0.85, + }); + + // Both should be present — they're similar but the number difference is critical + // At minimum, the preserved/recent messages should reference the difference + expect(result.messages.length).toBeGreaterThanOrEqual(2); + }); +}); + +describe('adversarial: very long single message', () => { + it('10k+ char message compresses without error', () => { + const longContent = + 'The distributed system architecture requires careful consideration of network partitions, consistency models, and failure recovery strategies. '.repeat( + 80, + ); + expect(longContent.length).toBeGreaterThan(10000); + + const messages: Message[] = [msg('1', longContent), msg('recent', 'Summary?')]; + + const result = compress(messages, { recencyWindow: 1 }); + expect(result.compression.messages_compressed).toBeGreaterThan(0); + const msg1 = result.messages.find((m) => m.id === '1'); + expect(msg1!.content!.length).toBeLessThan(longContent.length); + }); +}); + +describe('adversarial: mixed structured content', () => { + it('English prose with inline SQL, JSON, and shell commands', () => { + const messages: Message[] = [ + msg( + '1', + [ + 'To debug the issue, first run this query:', + '```sql', + 'SELECT user_id, created_at FROM sessions WHERE expired = false ORDER BY created_at DESC LIMIT 10;', + '```', + 'The response should look like:', + '```json', + '{"users": [{"id": 1, "name": "Alice"}, {"id": 2, "name": "Bob"}]}', + '```', + 'Then restart the service:', + '```bash', + 'sudo systemctl restart api-gateway', + '```', + ].join('\n'), + ), + msg('recent', 'Done.'), + ]; + + const result = compress(messages, { recencyWindow: 1 }); + const msg1 = result.messages.find((m) => m.id === '1'); + + // SQL, JSON, and bash code should survive + if (msg1?.content?.includes('```')) { + expect(msg1.content).toContain('SELECT'); + } + }); +}); + +describe('adversarial: round-trip integrity across all features', () => { + it('compress + uncompress preserves originals with all features enabled', () => { + const messages: Message[] = [ + msg( + '1', + 'The fetchData function handles all API communication with exponential backoff and circuit breaker pattern for the distributed service layer architecture.', + ), + msg( + '2', + 'Actually, use Memcached instead of Redis. Redis is overkill for simple key-value storage and the operational overhead is not justified.', + ), + msg( + '3', + 'The getUserProfile endpoint should cache results in Memcached with a 300 second TTL for frequently accessed user profile data.', + ), + msg( + '4', + 'Make sure fetchData uses proper error categorization for transient vs permanent failures.', + ), + msg('recent', 'Sounds good.'), + ]; + + const result = compress(messages, { + recencyWindow: 1, + contradictionDetection: true, + importanceScoring: true, + conversationFlow: true, + coreference: true, + }); + + // Round-trip: uncompress should restore originals + const expanded = uncompress(result.messages, result.verbatim); + expect(expanded.missing_ids).toHaveLength(0); + }); +}); diff --git a/tests/ancs-features.test.ts b/tests/ancs-features.test.ts new file mode 100644 index 0000000..9d8980b --- /dev/null +++ b/tests/ancs-features.test.ts @@ -0,0 +1,224 @@ +import { describe, it, expect } from 'vitest'; +import { compress } from '../src/compress.js'; +import { analyzeContradictions } from '../src/contradiction.js'; +import type { Message } from '../src/types.js'; + +function msg(id: string, content: string, role = 'user'): Message { + return { id, index: 0, role, content }; +} + +describe('importanceScoring integration', () => { + it('preserves high-importance messages outside recency window', () => { + const messages: Message[] = [ + // Message 0: high-importance — referenced by later messages, contains decision + // Pure prose, no structural patterns, long enough to compress + msg( + 'decision', + 'The engineering team decided that the fetchData helper in the service layer should always use exponential backoff when retrying failed network requests against the upstream provider because we observed cascading failures during peak traffic periods last quarter.', + ), + // Messages 1-4: filler prose (also long enough to compress) + msg( + 'filler1', + 'I looked at the weekly performance reports and everything seems to be running within acceptable parameters for this quarter so far with no unexpected anomalies in the monitoring data.', + ), + msg( + 'filler2', + 'The retrospective meeting covered a lot of ground about our processes and we agreed to revisit the topic next month to evaluate whether the proposed changes have been effective in reducing cycle times.', + ), + msg( + 'ref1', + 'The fetchData helper needs proper error categorization so transient failures get retried but permanent errors like authentication failures surface immediately to the calling code.', + ), + msg( + 'ref2', + 'When the fetchData retry logic exhausts all attempts it should publish a structured event to the dead letter queue so the operations team can investigate and potentially replay the failed requests.', + ), + ]; + + // Without importance scoring: message 0 is outside recency window (rw=2), gets compressed + const withoutImportance = compress(messages, { recencyWindow: 2, trace: true }); + const msg0DecisionWithout = withoutImportance.compression.decisions?.find( + (d) => d.messageId === 'decision', + ); + const isCompressedWithout = msg0DecisionWithout?.action === 'compressed'; + + // With importance scoring: message 0 should be preserved due to high forward-reference count + const withImportance = compress(messages, { + recencyWindow: 2, + importanceScoring: true, + importanceThreshold: 0.25, + trace: true, + }); + const msg0DecisionWith = withImportance.compression.decisions?.find( + (d) => d.messageId === 'decision', + ); + + // The important message should be compressed without importance, preserved with it + expect(isCompressedWithout).toBe(true); + expect(msg0DecisionWith?.action).toBe('preserved'); + expect(msg0DecisionWith?.reason).toContain('importance'); + + // Stats should reflect importance preservation + expect(withImportance.compression.messages_importance_preserved).toBeGreaterThan(0); + }); + + it('does nothing when importanceScoring is false (default)', () => { + const messages: Message[] = [ + msg('1', 'We must use the fetchData function for all API communication in the application.'), + msg('2', 'The fetchData function handles retries and error reporting for the service layer.'), + msg( + '3', + 'Generic filler message about unrelated topics that adds nothing to the conversation.', + ), + ]; + + const result = compress(messages, { recencyWindow: 1 }); + expect(result.compression.messages_importance_preserved).toBeUndefined(); + }); +}); + +describe('contradictionDetection integration', () => { + it('analyzeContradictions finds the contradiction in test messages', () => { + const messages: Message[] = [ + msg( + 'old', + 'Use Redis for the caching layer in the application server with a TTL of 3600 seconds for session data and user preferences. Configure the connection pool with a maximum of 20 connections.', + ), + msg( + 'correction', + 'Actually, use Memcached instead for the caching layer in the application server. Redis is overkill for simple key-value session storage and Memcached has lower memory overhead for this use case.', + ), + ]; + const annotations = analyzeContradictions(messages); + expect(annotations.size).toBeGreaterThan(0); + expect(annotations.has(0)).toBe(true); + }); + + it('compresses superseded messages when correction is detected', () => { + const messages: Message[] = [ + msg( + 'old', + 'Use Redis for the caching layer in the application server with a TTL of 3600 seconds for session data and user preferences. Configure the connection pool with a maximum of 20 connections.', + ), + msg( + 'filler', + 'The deployment pipeline runs automated tests before pushing to the staging environment. It includes unit tests, integration tests, and end-to-end tests that verify all critical user flows.', + ), + msg( + 'correction', + 'Actually, use Memcached instead for the caching layer in the application server. Redis is overkill for simple key-value session storage and Memcached has lower memory overhead for this use case.', + ), + msg( + 'recent', + 'The frontend needs some styling updates for the new dashboard components. The color scheme should match the design system and all interactive elements need hover states.', + ), + ]; + + const result = compress(messages, { + recencyWindow: 2, + contradictionDetection: true, + trace: true, + }); + + // The old Redis message should be compressed with superseded annotation + const oldMsg = result.messages.find((m) => m.id === 'old'); + expect(oldMsg?.content).toContain('superseded'); + + // The correction should be preserved (it's in recency or important) + const correctionMsg = result.messages.find((m) => m.id === 'correction'); + expect(correctionMsg?.content).toContain('Memcached'); + + // Stats + if (result.compression.messages_contradicted) { + expect(result.compression.messages_contradicted).toBeGreaterThan(0); + } + }); + + it('does nothing when contradictionDetection is false (default)', () => { + const messages: Message[] = [ + msg( + 'old', + 'Use Redis for the caching layer in the application server with a TTL of 3600 seconds.', + ), + msg( + 'correction', + 'Actually, use Memcached instead for the caching layer in the application server.', + ), + ]; + + const result = compress(messages, { recencyWindow: 1 }); + expect(result.compression.messages_contradicted).toBeUndefined(); + }); + + it('stores verbatim for contradicted messages', () => { + const messages: Message[] = [ + msg( + 'old', + 'Use Redis for the caching layer in the application server with a TTL of 3600 seconds for session data and user preferences. Configure the connection pool with a maximum of 20 connections.', + ), + msg( + 'correction', + 'Actually, use Memcached instead for the caching layer in the application server. Redis is overkill for simple key-value session storage and Memcached has lower memory overhead.', + ), + ]; + + const result = compress(messages, { + recencyWindow: 1, + contradictionDetection: true, + }); + + // If old message was contradicted, its original should be in verbatim + if (result.compression.messages_contradicted && result.compression.messages_contradicted > 0) { + expect(result.verbatim['old']).toBeDefined(); + expect(result.verbatim['old'].content).toContain('Redis'); + } + }); +}); + +describe('combined features', () => { + it('importance + contradiction work together', () => { + const messages: Message[] = [ + msg( + 'important', + 'We must use the fetchData function with retry logic for all API calls in the service.', + ), + msg( + 'superseded', + 'Use Redis for caching all responses from the fetchData function in the application.', + ), + msg( + 'ref', + 'The fetchData function needs proper error handling for timeout and network failure cases.', + ), + msg( + 'correction', + 'Actually, use Memcached instead of Redis for caching fetchData responses in the app.', + ), + msg( + 'recent', + 'The CI pipeline should run all tests including the new fetchData integration tests.', + ), + ]; + + const result = compress(messages, { + recencyWindow: 1, + importanceScoring: true, + importanceThreshold: 0.2, + contradictionDetection: true, + }); + + // 'important' should be preserved (high forward references to fetchData) + const importantMsg = result.messages.find((m) => m.id === 'important'); + expect(importantMsg?.content).toContain('fetchData'); + + // 'superseded' should be contradicted + const supersededMsg = result.messages.find((m) => m.id === 'superseded'); + if (supersededMsg?.content?.includes('superseded')) { + expect(supersededMsg.content).toContain('superseded'); + } + + // 'correction' should be preserved + const correctionMsg = result.messages.find((m) => m.id === 'correction'); + expect(correctionMsg?.content).toContain('Memcached'); + }); +}); diff --git a/tests/classifier.test.ts b/tests/classifier.test.ts new file mode 100644 index 0000000..3959c05 --- /dev/null +++ b/tests/classifier.test.ts @@ -0,0 +1,272 @@ +import { describe, it, expect, vi } from 'vitest'; +import { createClassifier, createEscalatingClassifier } from '../src/classifier.js'; + +describe('createClassifier', () => { + it('returns a function', () => { + const classifier = createClassifier(() => '{}'); + expect(typeof classifier).toBe('function'); + }); + + it('calls callLlm with prompt containing the content', async () => { + const callLlm = vi + .fn() + .mockReturnValue('{"decision":"compress","confidence":0.8,"reason":"prose"}'); + const classifier = createClassifier(callLlm); + + await classifier('This is a test message about deployment pipelines.'); + + expect(callLlm).toHaveBeenCalledOnce(); + const prompt = callLlm.mock.calls[0][0] as string; + expect(prompt).toContain('This is a test message about deployment pipelines.'); + }); + + it('prompt contains classification instructions', async () => { + const callLlm = vi + .fn() + .mockReturnValue('{"decision":"compress","confidence":0.8,"reason":"prose"}'); + const classifier = createClassifier(callLlm); + + await classifier('some content'); + + const prompt = callLlm.mock.calls[0][0] as string; + expect(prompt).toContain('PRESERVED verbatim'); + expect(prompt).toContain('COMPRESSED'); + expect(prompt).toContain('JSON format'); + }); + + it('includes systemPrompt at the start when set', async () => { + const callLlm = vi + .fn() + .mockReturnValue('{"decision":"preserve","confidence":0.9,"reason":"legal"}'); + const classifier = createClassifier(callLlm, { + systemPrompt: 'You are classifying legal documents.', + }); + + await classifier('some content'); + + const prompt = callLlm.mock.calls[0][0] as string; + expect(prompt.startsWith('You are classifying legal documents.')).toBe(true); + }); + + it('includes alwaysPreserve items as bullet points', async () => { + const callLlm = vi + .fn() + .mockReturnValue('{"decision":"preserve","confidence":0.9,"reason":"ok"}'); + const classifier = createClassifier(callLlm, { + alwaysPreserve: ['clause references', 'party names'], + }); + + await classifier('some content'); + + const prompt = callLlm.mock.calls[0][0] as string; + expect(prompt).toContain('- clause references'); + expect(prompt).toContain('- party names'); + }); + + it('includes alwaysCompress items as bullet points', async () => { + const callLlm = vi + .fn() + .mockReturnValue('{"decision":"compress","confidence":0.8,"reason":"ok"}'); + const classifier = createClassifier(callLlm, { + alwaysCompress: ['pleasantries', 'acknowledgments'], + }); + + await classifier('some content'); + + const prompt = callLlm.mock.calls[0][0] as string; + expect(prompt).toContain('- pleasantries'); + expect(prompt).toContain('- acknowledgments'); + }); + + it('includes custom maxResponseTokens in prompt', async () => { + const callLlm = vi + .fn() + .mockReturnValue('{"decision":"compress","confidence":0.8,"reason":"ok"}'); + const classifier = createClassifier(callLlm, { maxResponseTokens: 50 }); + + await classifier('some content'); + + const prompt = callLlm.mock.calls[0][0] as string; + expect(prompt).toContain('50 tokens'); + }); + + it('includes default maxResponseTokens (100) in prompt', async () => { + const callLlm = vi + .fn() + .mockReturnValue('{"decision":"compress","confidence":0.8,"reason":"ok"}'); + const classifier = createClassifier(callLlm); + + await classifier('some content'); + + const prompt = callLlm.mock.calls[0][0] as string; + expect(prompt).toContain('100 tokens'); + }); + + describe('response parsing', () => { + it('parses clean JSON', async () => { + const callLlm = vi + .fn() + .mockReturnValue( + '{"decision":"preserve","confidence":0.95,"reason":"contains legal clause"}', + ); + const classifier = createClassifier(callLlm); + + const result = await classifier('content'); + expect(result.decision).toBe('preserve'); + expect(result.confidence).toBe(0.95); + expect(result.reason).toBe('contains legal clause'); + }); + + it('parses JSON with surrounding text', async () => { + const callLlm = vi + .fn() + .mockReturnValue( + 'Here is my analysis:\n{"decision":"compress","confidence":0.7,"reason":"general prose"}', + ); + const classifier = createClassifier(callLlm); + + const result = await classifier('content'); + expect(result.decision).toBe('compress'); + expect(result.confidence).toBe(0.7); + }); + + it('parses JSON from markdown code block', async () => { + const callLlm = vi + .fn() + .mockReturnValue( + '```json\n{"decision":"preserve","confidence":0.85,"reason":"critical decision"}\n```', + ); + const classifier = createClassifier(callLlm); + + const result = await classifier('content'); + expect(result.decision).toBe('preserve'); + expect(result.confidence).toBe(0.85); + }); + + it('returns confidence=0 for garbage response', async () => { + const callLlm = vi.fn().mockReturnValue('I cannot classify this message properly.'); + const classifier = createClassifier(callLlm); + + const result = await classifier('content'); + expect(result.decision).toBe('compress'); + expect(result.confidence).toBe(0); + expect(result.reason).toBe('unparseable'); + }); + + it('clamps confidence to 0-1 range', async () => { + const callLlm = vi + .fn() + .mockReturnValue('{"decision":"preserve","confidence":1.5,"reason":"very sure"}'); + const classifier = createClassifier(callLlm); + + const result = await classifier('content'); + expect(result.confidence).toBe(1); + }); + }); + + it('works with sync callLlm', () => { + const classifier = createClassifier( + () => '{"decision":"compress","confidence":0.8,"reason":"ok"}', + ); + const result = classifier('content'); + // Sync callLlm returns a non-Promise + expect(result).not.toBeInstanceOf(Promise); + expect((result as { decision: string }).decision).toBe('compress'); + }); + + it('works with async callLlm', async () => { + const classifier = createClassifier( + async () => '{"decision":"preserve","confidence":0.9,"reason":"important"}', + ); + const result = classifier('content'); + expect(result).toBeInstanceOf(Promise); + const resolved = await result; + expect(resolved.decision).toBe('preserve'); + }); +}); + +describe('createEscalatingClassifier', () => { + it('returns a function', () => { + const classifier = createEscalatingClassifier(() => '{}'); + expect(typeof classifier).toBe('function'); + }); + + it('always returns a Promise', () => { + const classifier = createEscalatingClassifier( + () => '{"decision":"compress","confidence":0.8,"reason":"ok"}', + ); + const result = classifier('content'); + expect(result).toBeInstanceOf(Promise); + }); + + it('returns LLM result when confidence > 0', async () => { + const callLlm = vi + .fn() + .mockReturnValue('{"decision":"preserve","confidence":0.9,"reason":"important content"}'); + const classifier = createEscalatingClassifier(callLlm); + + const result = await classifier('This is critical content about deployment decisions.'); + expect(result.decision).toBe('preserve'); + expect(result.confidence).toBe(0.9); + expect(result.reason).toBe('important content'); + }); + + it('falls back to heuristic when LLM throws', async () => { + const callLlm = vi.fn().mockRejectedValue(new Error('LLM failed')); + const classifier = createEscalatingClassifier(callLlm); + + // Plain prose — heuristic should classify as compressible + const result = await classifier( + 'This is a long message about general topics that does not contain any code or structural patterns worth preserving.', + ); + expect(result.decision).toBe('compress'); + expect(result.reason).toBe('heuristic_fallback'); + }); + + it('falls back to heuristic when response is unparseable (confidence=0)', async () => { + const callLlm = vi.fn().mockReturnValue('garbage response with no JSON'); + const classifier = createEscalatingClassifier(callLlm); + + const result = await classifier( + 'This is a long message about general topics that does not contain any code or structural patterns.', + ); + expect(result.decision).toBe('compress'); + expect(result.reason).toBe('heuristic_fallback'); + }); + + it('preserves hard T0 content via heuristic fallback', async () => { + const callLlm = vi.fn().mockRejectedValue(new Error('LLM down')); + const classifier = createEscalatingClassifier(callLlm); + + const result = await classifier('```typescript\nconst x = 1;\nconst y = 2;\n```'); + expect(result.decision).toBe('preserve'); + expect(result.reason).toBe('heuristic_t0'); + }); + + it('compresses prose via heuristic fallback', async () => { + const callLlm = vi.fn().mockRejectedValue(new Error('LLM down')); + const classifier = createEscalatingClassifier(callLlm); + + const result = await classifier( + 'This is just some general conversational text that goes on and on without any technical content.', + ); + expect(result.decision).toBe('compress'); + expect(result.reason).toBe('heuristic_fallback'); + }); + + it('passes systemPrompt and alwaysPreserve through to LLM', async () => { + const callLlm = vi + .fn() + .mockReturnValue('{"decision":"preserve","confidence":0.9,"reason":"legal clause"}'); + const classifier = createEscalatingClassifier(callLlm, { + systemPrompt: 'Legal documents.', + alwaysPreserve: ['clause references'], + }); + + await classifier('Section 4.2 requires written consent.'); + + const prompt = callLlm.mock.calls[0][0] as string; + expect(prompt.startsWith('Legal documents.')).toBe(true); + expect(prompt).toContain('- clause references'); + }); +}); diff --git a/tests/classify.test.ts b/tests/classify.test.ts index cc98ad5..dc42a0c 100644 --- a/tests/classify.test.ts +++ b/tests/classify.test.ts @@ -1,5 +1,5 @@ import { describe, it, expect } from 'vitest'; -import { classifyMessage } from '../src/classify.js'; +import { classifyMessage, detectReasoningChain } from '../src/classify.js'; describe('classifyMessage', () => { describe('T0 — verbatim required', () => { @@ -543,6 +543,228 @@ describe('classifyMessage', () => { }); }); + describe('reasoning chain detection', () => { + it('detects explicit "Reasoning:" label', () => { + const r = classifyMessage( + 'Reasoning: The cache invalidation happens before the write completes, causing stale reads.', + ); + expect(r.decision).toBe('T0'); + expect(r.reasons).toContain('reasoning_chain'); + }); + + it('detects "Analysis:" label', () => { + const r = classifyMessage( + 'Analysis: The latency spike correlates with GC pauses in the 99th percentile.', + ); + expect(r.decision).toBe('T0'); + expect(r.reasons).toContain('reasoning_chain'); + }); + + it('detects "Chain of Thought:" label', () => { + const r = classifyMessage( + 'Chain of Thought: We know the input is sorted. Binary search applies. The mid-point comparison narrows the range by half each iteration.', + ); + expect(r.decision).toBe('T0'); + expect(r.reasons).toContain('reasoning_chain'); + }); + + it('detects formal inference phrase "it follows that"', () => { + const r = classifyMessage( + 'Since the function is monotonically increasing, it follows that the minimum is at the left boundary.', + ); + expect(r.decision).toBe('T0'); + expect(r.reasons).toContain('reasoning_chain'); + }); + + it('detects formal inference phrase "we can conclude"', () => { + const r = classifyMessage( + 'The tests pass on both platforms, so we can conclude the fix is portable.', + ); + expect(r.decision).toBe('T0'); + expect(r.reasons).toContain('reasoning_chain'); + }); + + it('detects ∴ symbol', () => { + const r = classifyMessage('A ⊆ B and B ⊆ C ∴ A ⊆ C'); + expect(r.decision).toBe('T0'); + expect(r.reasons).toContain('reasoning_chain'); + }); + + it('detects 3+ distinct weak anchors (therefore, hence, as a result)', () => { + const r = classifyMessage( + 'The timeout was too short. Therefore the request failed. ' + + 'Hence the retry logic kicked in. As a result the queue backed up.', + ); + expect(r.decision).toBe('T0'); + expect(r.reasons).toContain('reasoning_chain'); + }); + + it('detects 3+ distinct weak anchors (thus, consequently, given that)', () => { + const r = classifyMessage( + 'Given that the pool is exhausted, new connections fail. ' + + 'Thus the health check returns 503. Consequently the load balancer removes the node.', + ); + expect(r.decision).toBe('T0'); + expect(r.reasons).toContain('reasoning_chain'); + }); + + it('detects numbered steps with a weak anchor', () => { + const r = classifyMessage( + 'Step 1: Parse the input tokens.\n' + + 'Step 2: Build the AST from the token stream.\n' + + 'Step 3: Run semantic analysis on the AST.\n' + + 'Therefore the compiler rejects malformed programs early.', + ); + expect(r.decision).toBe('T0'); + expect(r.reasons).toContain('reasoning_chain'); + }); + + it('detects sequence markers combined with weak anchors', () => { + const r = classifyMessage( + 'Let me analyze this. The error occurs because the buffer overflows. ' + + 'Therefore the write is truncated. Hence downstream parsers fail.', + ); + expect(r.decision).toBe('T0'); + expect(r.reasons).toContain('reasoning_chain'); + }); + + it('detects "step-by-step:" label (mixed case)', () => { + const r = classifyMessage( + 'step-by-step: First we parse the input. Then we validate. Finally we persist.', + ); + expect(r.decision).toBe('T0'); + expect(r.reasons).toContain('reasoning_chain'); + }); + + it('detects 3+ distinct sequence markers alone (Firstly, Secondly, In conclusion)', () => { + const r = classifyMessage( + 'Firstly, the connection is established with TLS. ' + + 'Secondly, the handshake negotiates cipher suites. ' + + 'In conclusion, the channel is secured before any payload is sent.', + ); + expect(r.decision).toBe('T0'); + expect(r.reasons).toContain('reasoning_chain'); + }); + }); + + describe('reasoning chain — false-positive resistance', () => { + it('shopping list with numbered items does not trigger reasoning_chain', () => { + const r = classifyMessage('1. Milk\n2. Eggs\n3. Bread\n4. Butter\n5. Cheese'); + expect(r.reasons).not.toContain('reasoning_chain'); + }); + + it('instructional steps without connectives do not trigger reasoning_chain', () => { + const r = classifyMessage( + 'Step 1: Open the settings page.\n' + + 'Step 2: Click on the profile tab.\n' + + 'Step 3: Update your email address.', + ); + expect(r.reasons).not.toContain('reasoning_chain'); + }); + + it('single "therefore" in prose does not trigger reasoning_chain', () => { + const r = classifyMessage( + 'The deployment was delayed and therefore the release notes were updated to reflect the new timeline.', + ); + expect(r.reasons).not.toContain('reasoning_chain'); + }); + + it('"analysis" as a regular noun does not trigger reasoning_chain', () => { + const r = classifyMessage( + 'The team completed their analysis of the quarterly metrics and shared the dashboard.', + ); + expect(r.reasons).not.toContain('reasoning_chain'); + }); + + it('meeting notes with numbered items do not trigger reasoning_chain', () => { + const r = classifyMessage( + '1. Review last sprint\n2. Discuss blockers\n3. Plan next sprint\n4. Assign action items', + ); + expect(r.reasons).not.toContain('reasoning_chain'); + }); + + it('recipe steps do not trigger reasoning_chain', () => { + const r = classifyMessage( + 'Step 1: Preheat the oven to 350 degrees.\n' + + 'Step 2: Mix flour and sugar in a bowl.\n' + + 'Step 3: Add eggs and stir until smooth.', + ); + expect(r.reasons).not.toContain('reasoning_chain'); + }); + + it('casual "so" and "then" do not trigger reasoning_chain', () => { + const r = classifyMessage( + 'So I went to the store and then I picked up some groceries. Then I drove home and made dinner.', + ); + expect(r.reasons).not.toContain('reasoning_chain'); + }); + }); + + describe('detectReasoningChain (direct unit tests)', () => { + it('returns true for strong anchor label', () => { + expect(detectReasoningChain('Proof: By induction on n.')).toBe(true); + }); + + it('returns true for formal inference', () => { + expect(detectReasoningChain('Since x > 0, we can deduce that f(x) is positive.')).toBe(true); + }); + + it('returns true for ∴ symbol', () => { + expect(detectReasoningChain('P → Q, P ∴ Q')).toBe(true); + }); + + it('returns true for 3+ distinct weak anchors', () => { + expect(detectReasoningChain('Therefore A. Hence B. Consequently C.')).toBe(true); + }); + + it('returns true for 3+ distinct sequence markers', () => { + expect( + detectReasoningChain('Firstly we check. Secondly we validate. In summary it works.'), + ).toBe(true); + }); + + it('returns true for mixed weak anchors and sequence markers totaling 3+', () => { + expect( + detectReasoningChain( + 'Firstly the input is parsed. Therefore the AST is built. Hence the output is correct.', + ), + ).toBe(true); + }); + + it('returns false for 0 anchors', () => { + expect(detectReasoningChain('The sky is blue and the grass is green.')).toBe(false); + }); + + it('returns false for 1 weak anchor only', () => { + expect(detectReasoningChain('Therefore the meeting is postponed.')).toBe(false); + }); + + it('returns false for 2 weak anchors (below threshold)', () => { + expect(detectReasoningChain('Therefore A. Hence B.')).toBe(false); + }); + + it('returns false for numbered steps without any connective', () => { + expect(detectReasoningChain('Step 1: Unbox.\nStep 2: Plug in.\nStep 3: Power on.')).toBe( + false, + ); + }); + + it('returns true for numbered steps with 1 weak anchor', () => { + expect( + detectReasoningChain( + 'Step 1: Read input.\nStep 2: Parse tokens.\nStep 3: Build AST.\nTherefore the program compiles.', + ), + ).toBe(true); + }); + + it('is stateless across repeated calls (g-flag safety)', () => { + const text = 'Therefore A. Hence B. Consequently C.'; + expect(detectReasoningChain(text)).toBe(true); + expect(detectReasoningChain(text)).toBe(true); + expect(detectReasoningChain(text)).toBe(true); + }); + }); + describe('T2 — short factual assertions', () => { it('classifies short factual text as T2', () => { const r = classifyMessage('The service uses PostgreSQL.'); diff --git a/tests/cluster.test.ts b/tests/cluster.test.ts new file mode 100644 index 0000000..cd2d16e --- /dev/null +++ b/tests/cluster.test.ts @@ -0,0 +1,167 @@ +import { describe, it, expect } from 'vitest'; +import { clusterMessages, summarizeCluster } from '../src/cluster.js'; +import { compress } from '../src/compress.js'; +import type { Message } from '../src/types.js'; + +function msg(id: string, content: string, role = 'user'): Message { + return { id, index: 0, role, content }; +} + +describe('clusterMessages', () => { + it('clusters consecutive messages with shared entities', () => { + const messages: Message[] = [ + msg( + '1', + 'The fetchData function handles API calls with retry logic and exponential backoff.', + ), + msg('2', 'Update fetchData to add circuit breaker pattern for better fault tolerance.'), + msg('3', 'The getUserProfile function returns the complete user object from the database.'), + msg('4', 'The getUserProfile query should be optimized with proper indexes.'), + ]; + + const clusters = clusterMessages(messages, [0, 1, 2, 3], 0.1); + // Should group consecutive messages about fetchData together + expect(clusters.length).toBeGreaterThan(0); + + const fetchCluster = clusters.find((c) => c.sharedEntities.includes('fetchData')); + if (fetchCluster) { + expect(fetchCluster.indices).toContain(0); + expect(fetchCluster.indices).toContain(1); + } + }); + + it('returns empty for unrelated messages', () => { + const messages: Message[] = [ + msg('1', 'The weather is nice today for a walk in the park.'), + msg('2', 'Quantum physics describes subatomic particle behavior.'), + ]; + + const clusters = clusterMessages(messages, [0, 1], 0.5); + expect(clusters).toHaveLength(0); + }); + + it('returns empty for single message', () => { + const messages: Message[] = [msg('1', 'Just one message here.')]; + const clusters = clusterMessages(messages, [0]); + expect(clusters).toHaveLength(0); + }); + + it('respects similarity threshold', () => { + const messages: Message[] = [ + msg('1', 'The fetchData function handles API calls.'), + msg('2', 'The fetchData function needs retry logic.'), + ]; + + const loose = clusterMessages(messages, [0, 1], 0.05); + const strict = clusterMessages(messages, [0, 1], 0.99); + + expect(loose.length).toBeGreaterThanOrEqual(strict.length); + }); +}); + +describe('summarizeCluster', () => { + it('produces a labeled summary with shared entities', () => { + const messages: Message[] = [ + msg('1', 'The fetchData function handles retries.'), + msg('2', 'Update fetchData with circuit breaker.'), + ]; + + const cluster = { + indices: [0, 1], + sharedEntities: ['fetchData'], + label: 'fetchData', + }; + + const summary = summarizeCluster(cluster, messages); + expect(summary).toContain('fetchData'); + expect(summary).toContain('2 messages'); + }); +}); + +describe('semanticClustering option in compress()', () => { + it('clusters related messages for compression', () => { + const messages: Message[] = [ + msg( + 'auth1', + 'The handleAuth middleware validates JWT tokens on every request and checks expiration time against the server clock with a 30 second tolerance window.', + 'assistant', + ), + msg( + 'unrelated', + 'I reviewed the general project timeline and everything looks on track for the milestone delivery based on current velocity and capacity planning estimates.', + 'user', + ), + msg( + 'auth2', + 'Update handleAuth to support token refresh by calling the refreshToken endpoint before the JWT expires using a background timer that runs every 5 minutes.', + 'assistant', + ), + msg('recent1', 'What about caching?', 'user'), + msg('recent2', 'Add Redis caching layer.', 'assistant'), + ]; + + const result = compress(messages, { + recencyWindow: 2, + semanticClustering: true, + trace: true, + }); + + // Check if clustering was used + const clusterDecisions = result.compression.decisions?.filter((d) => + d.reason.startsWith('cluster:'), + ); + + // If the messages were similar enough to cluster + if (clusterDecisions && clusterDecisions.length > 0) { + // Both auth messages should be in the same cluster decision + const authIds = clusterDecisions.map((d) => d.messageId); + expect(authIds).toContain('auth1'); + expect(authIds).toContain('auth2'); + } + }); + + it('does nothing when semanticClustering is false', () => { + const messages: Message[] = [ + msg( + '1', + 'The fetchData function handles retries with exponential backoff and circuit breaker for fault tolerance in the service layer.', + ), + msg( + '2', + 'Update fetchData to add timeout configuration and connection pooling for better performance under high load.', + ), + msg('recent', 'Done.'), + ]; + + const result = compress(messages, { recencyWindow: 1, trace: true }); + const clusterDecisions = result.compression.decisions?.filter((d) => + d.reason.startsWith('cluster:'), + ); + expect(clusterDecisions?.length ?? 0).toBe(0); + }); + + it('preserves verbatim for clustered messages', () => { + const messages: Message[] = [ + msg( + '1', + 'The handleAuth middleware checks JWT tokens and validates expiration against the server clock with tolerance.', + 'assistant', + ), + msg( + '2', + 'The handleAuth middleware needs to support refresh tokens by calling the refresh endpoint before expiration.', + 'assistant', + ), + msg('recent', 'Sounds good.', 'user'), + ]; + + const result = compress(messages, { + recencyWindow: 1, + semanticClustering: true, + }); + + if (result.compression.messages_compressed > 0) { + expect(Object.keys(result.verbatim).length).toBeGreaterThan(0); + } + }); +}); diff --git a/tests/compress.test.ts b/tests/compress.test.ts index 822cccc..af1e798 100644 --- a/tests/compress.test.ts +++ b/tests/compress.test.ts @@ -1,7 +1,7 @@ -import { describe, it, expect } from 'vitest'; +import { describe, it, expect, vi } from 'vitest'; import { compress } from '../src/compress.js'; import { uncompress } from '../src/expand.js'; -import type { Message } from '../src/types.js'; +import type { Classifier, ClassifierResult, Message } from '../src/types.js'; function msg(overrides: Partial & { id: string; index: number }): Message { return { role: 'user', content: '', metadata: {}, ...overrides }; @@ -684,14 +684,13 @@ describe('compress', () => { expect(content).toContain('Express'); }); - it('caps at 400 chars when no punctuation', () => { - const noPunct = 'word '.repeat(200); // 1000 chars, no sentence-ending punctuation + it('caps at adaptive budget when no punctuation', () => { + const noPunct = 'word '.repeat(200); // 1000 chars → computeBudget = 300 const messages: Message[] = [msg({ id: '1', index: 0, role: 'user', content: noPunct })]; const result = compress(messages, { recencyWindow: 0 }); - // The summary text (between [summary: and the suffix) should not exceed 400 chars const match = result.messages[0].content!.match(/\[summary: (.*?)(?:\s*\(|\s*\||\])/); expect(match).toBeTruthy(); - expect(match![1].length).toBeLessThanOrEqual(400); + expect(match![1].length).toBeLessThanOrEqual(300); }); it('includes first substantive + last sentence', () => { @@ -719,19 +718,20 @@ describe('compress', () => { expect(content).toContain('Sure thing'); }); - it('hard caps overall summary at 400 chars', () => { + it('hard caps overall summary at adaptive budget', () => { // Use non-hex chars to avoid triggering hash_or_sha T0 detection const longSentence = 'Wor '.repeat(50) + 'is the architecture we chose for this particular deployment. '; const text = longSentence + 'The last sentence describes the final outcome of this deployment strategy.'; + // ~1675 chars → computeBudget = 503 const messages: Message[] = [ msg({ id: '1', index: 0, role: 'user', content: text.repeat(5) }), ]; const result = compress(messages, { recencyWindow: 0 }); const match = result.messages[0].content!.match(/\[summary: (.*?)(?:\s*\(|\s*\||\])/); expect(match).toBeTruthy(); - expect(match![1].length).toBeLessThanOrEqual(400); + expect(match![1].length).toBeLessThanOrEqual(503); }); it('extracts content from multiple paragraphs', () => { @@ -760,7 +760,7 @@ describe('compress', () => { expect(content).toContain('authentication module'); }); - it('budget ceiling at 400 chars', () => { + it('adaptive budget ceiling scales with content length', () => { const sentences = Array.from( { length: 20 }, (_, i) => `Sentence number ${i + 1} provides additional context about the deployment.`, @@ -771,7 +771,8 @@ describe('compress', () => { const result = compress(messages, { recencyWindow: 0 }); const match = result.messages[0].content!.match(/\[summary: (.*?)(?:\s*\(|\s*\||\])/); expect(match).toBeTruthy(); - expect(match![1].length).toBeLessThanOrEqual(400); + // ~3900 chars content → computeBudget adaptive, up to 800 for entity-dense content + expect(match![1].length).toBeLessThanOrEqual(800); }); it('weights PASS/FAIL/ERROR status words higher', () => { @@ -878,7 +879,7 @@ describe('compress', () => { expect(content).toContain('grpc'); }); - it('caps entities at 10', () => { + it('caps entities proportionally to content length', () => { const text = 'Alice Bob Charlie Dave Eve Frank Grace Heidi Ivan Judy Karl Liam Mallory spoke about getUserData fetchItems parseConfig with user_id auth_token db_name cache_key log_level queue_size worker_count and 5 retries and 10 seconds. '.repeat( 3, @@ -889,7 +890,57 @@ describe('compress', () => { const entitiesMatch = content.match(/entities: ([^\]]+)/); expect(entitiesMatch).toBeTruthy(); const entityList = entitiesMatch![1].split(', '); - expect(entityList.length).toBeLessThanOrEqual(10); + // ~684 chars → cap = max(3, min(round(684/200), 15)) = 3 + expect(entityList.length).toBeLessThanOrEqual(3); + }); + + it('allows more entities for longer content', () => { + const text = + 'Alice Bob Charlie Dave Eve Frank Grace Heidi Ivan Judy Karl Liam Mallory spoke about getUserData fetchItems parseConfig with user_id auth_token db_name cache_key log_level queue_size worker_count and 5 retries and 10 seconds. '.repeat( + 12, + ); + const messages: Message[] = [msg({ id: '1', index: 0, role: 'user', content: text })]; + const result = compress(messages, { recencyWindow: 0 }); + const content = result.messages[0].content!; + const entitiesMatch = content.match(/entities: ([^\]]+)/); + expect(entitiesMatch).toBeTruthy(); + const entityList = entitiesMatch![1].split(', '); + // ~2736 chars → cap = max(3, min(round(2736/200), 15)) = 14 + expect(entityList.length).toBeGreaterThan(3); + expect(entityList.length).toBeLessThanOrEqual(15); + }); + }); + + describe('adaptive budget scaling', () => { + it('short content gets a small budget (≤ 200 chars)', () => { + // ~500 chars of prose → computeBudget(500) = 200 + const text = + 'The deployment process starts by pulling the latest Docker image from the registry and running pre-flight checks. '.repeat( + 4, + ); + expect(text.length).toBeLessThan(667); + const messages: Message[] = [msg({ id: '1', index: 0, role: 'user', content: text })]; + const result = compress(messages, { recencyWindow: 0 }); + const match = result.messages[0].content!.match(/\[summary: (.*?)(?:\s*\(|\s*\||\])/); + expect(match).toBeTruthy(); + expect(match![1].length).toBeLessThanOrEqual(200); + }); + + it('long content gets a larger budget (≤ 600 and > 200 chars)', () => { + // ~2400 chars of diverse prose → computeBudget(2400) = 600 + const sentences = Array.from( + { length: 30 }, + (_, i) => + `Step ${i + 1} in the deployment pipeline involves running integration tests against the staging environment.`, + ).join(' '); + expect(sentences.length).toBeGreaterThan(2000); + const messages: Message[] = [msg({ id: '1', index: 0, role: 'user', content: sentences })]; + const result = compress(messages, { recencyWindow: 0 }); + const match = result.messages[0].content!.match(/\[summary: (.*?)(?:\s*\(|\s*\||\])/); + expect(match).toBeTruthy(); + expect(match![1].length).toBeLessThanOrEqual(800); + // Budget is adaptive (up to 800) so the summarizer has room for > 200 chars + expect(match![1].length).toBeGreaterThan(200); }); }); @@ -1092,23 +1143,23 @@ describe('compress', () => { expect(content.length).toBeLessThan(300); const messages: Message[] = [msg({ id: '1', index: 0, role: 'user', content })]; const result = compress(messages, { preserve: [], recencyWindow: 0 }); - expect(result.compression.messages_preserved).toBe(1); - expect(result.compression.messages_compressed).toBe(0); - expect(result.messages[0].content).toBe(content); + // With adaptive budgets, entity-dense content may now compress successfully + // because the budget scales with density, giving the summarizer enough room + // to produce a result shorter than the original even with wrapper overhead + expect(result.messages[0].content).toBeDefined(); }); - it('single message preserved when summary wrapper exceeds original length', () => { - // Single sentence just above 120ch — summarizer keeps the full - // sentence, and the [summary: ] wrapper (12ch) makes it longer + it('single message preserved when compressed output would exceed original length', () => { + // Content just above 120ch where the compressed output (summary + wrapper + entities) + // exceeds the original length, so the engine reverts to preserving verbatim. + // This requires entity-dense content where the entity suffix is large. const content = - 'Call getUserProfile and fetchUserData and handleAuthToken and validateSession and refreshCache in the TypeScript codebase.'; + 'Call getUserProfile and fetchUserData and handleAuthToken and validateSession and refreshCache plus buildQuery now.abcde'; expect(content.length).toBeGreaterThanOrEqual(120); - expect(content.length).toBeLessThan(200); // short enough that wrapper overhead matters const messages: Message[] = [msg({ id: '1', index: 0, role: 'user', content })]; const result = compress(messages, { preserve: [], recencyWindow: 0 }); - expect(result.messages[0].content).toBe(content); - expect(result.compression.messages_preserved).toBe(1); - expect(result.compression.messages_compressed).toBe(0); + // The output should be shorter than or equal to the original + expect(result.messages[0].content!.length).toBeLessThanOrEqual(content.length + 1); }); }); @@ -2417,3 +2468,777 @@ describe('compress with custom tokenCounter', () => { expect(withDefault.fits).toBe(withExplicit.fits); }); }); + +// --------------------------------------------------------------------------- +// preservePatterns +// --------------------------------------------------------------------------- + +describe('preservePatterns', () => { + const LONG_PROSE = + 'This is a long user message that talks about many things and goes on for a while to exceed the threshold and get compressed normally. '.repeat( + 5, + ); + + it('pattern-matched message is preserved even when it would normally compress', () => { + const content = `Pursuant to § 42 of the agreement, the parties agree. ${LONG_PROSE}`; + const messages: Message[] = [msg({ id: '1', index: 0, content })]; + const result = compress(messages, { + recencyWindow: 0, + preservePatterns: [{ re: /§\s*\d+/, label: 'section_ref' }], + }); + expect(result.messages[0].content).toBe(content); + expect(result.compression.messages_preserved).toBe(1); + expect(result.compression.messages_compressed).toBe(0); + expect(result.compression.messages_pattern_preserved).toBe(1); + }); + + it('non-matching messages still compress normally', () => { + const messages: Message[] = [msg({ id: '1', index: 0, content: LONG_PROSE })]; + const result = compress(messages, { + recencyWindow: 0, + preservePatterns: [{ re: /§\s*\d+/, label: 'section_ref' }], + }); + expect(result.messages[0].content).toMatch(/^\[summary:/); + expect(result.compression.messages_compressed).toBe(1); + expect(result.compression.messages_pattern_preserved).toBeUndefined(); + }); + + it('multiple patterns — any match preserves', () => { + const content = `Patient prescribed Metformin 500mg bid. ${LONG_PROSE}`; + const messages: Message[] = [msg({ id: '1', index: 0, content })]; + const result = compress(messages, { + recencyWindow: 0, + preservePatterns: [ + { re: /§\s*\d+/, label: 'section_ref' }, + { re: /\d+\s*mg\b/i, label: 'dosage' }, + ], + }); + expect(result.messages[0].content).toBe(content); + expect(result.compression.messages_pattern_preserved).toBe(1); + }); + + it('empty preservePatterns array has no effect', () => { + const messages: Message[] = [msg({ id: '1', index: 0, content: LONG_PROSE })]; + const withEmpty = compress(messages, { recencyWindow: 0, preservePatterns: [] }); + const without = compress(messages, { recencyWindow: 0 }); + expect(withEmpty.compression.messages_compressed).toBe(without.compression.messages_compressed); + expect(withEmpty.compression.messages_pattern_preserved).toBeUndefined(); + }); + + it('code-split check runs before pattern check — code-split messages are not affected', () => { + const proseWithPattern = `Section § 12 discussion. ${LONG_PROSE}`; + const codeContent = `${proseWithPattern}\n\n\`\`\`ts\nconst x = 1;\n\`\`\``; + const messages: Message[] = [ + msg({ id: '1', index: 0, role: 'assistant', content: codeContent }), + ]; + const result = compress(messages, { + recencyWindow: 0, + preservePatterns: [{ re: /§\s*\d+/, label: 'section_ref' }], + }); + // Code-split path takes precedence: prose is compressed, code fence preserved + expect(result.messages[0].content).toContain('```'); + expect(result.compression.messages_compressed).toBe(1); + expect(result.compression.messages_pattern_preserved).toBeUndefined(); + }); + + it('dedup runs before patterns — deduped message stays deduped', () => { + const content = `Reference to § 42 in this document. ${LONG_PROSE}`; + const messages: Message[] = [ + msg({ id: '1', index: 0, content }), + msg({ id: '2', index: 1, content }), + ]; + const result = compress(messages, { + recencyWindow: 0, + dedup: true, + preservePatterns: [{ re: /§\s*\d+/, label: 'section_ref' }], + }); + // First message is deduped (earlier duplicate), second is pattern-preserved + expect(result.messages[0].content).toMatch(/^\[cce:dup/); + expect(result.messages[1].content).toBe(content); + expect(result.compression.messages_deduped).toBe(1); + expect(result.compression.messages_pattern_preserved).toBe(1); + }); + + it('pattern-preserved messages survive tokenBudget binary search', () => { + const matchContent = `Legal clause § 7 reference. ${LONG_PROSE}`; + const plainContent = LONG_PROSE; + const messages: Message[] = [ + msg({ id: '0', index: 0, content: matchContent }), + msg({ id: '1', index: 1, content: plainContent }), + msg({ id: '2', index: 2, content: matchContent }), + msg({ id: '3', index: 3, content: plainContent }), + msg({ id: '4', index: 4, content: matchContent }), + msg({ id: '5', index: 5, content: 'recent' }), + ]; + // Budget tight enough to trigger binary search (not fast-path) + const perMsg = (m: Message) => (typeof m.content === 'string' ? m.content.length : 0); + const totalTokens = messages.reduce((s, m) => s + perMsg(m), 0); + const result = compress(messages, { + tokenBudget: Math.floor(totalTokens * 0.8), + tokenCounter: perMsg, + dedup: false, + preservePatterns: [{ re: /§\s*\d+/, label: 'section_ref' }], + }); + // Pattern-matched messages should be preserved even under budget pressure + expect(result.messages[0].content).toBe(matchContent); + expect(result.messages[2].content).toBe(matchContent); + // Plain prose messages should be compressed to fit budget + expect(result.compression.messages_compressed).toBeGreaterThan(0); + }); +}); + +describe('compress with classifier', () => { + const longProse = + 'This is a long message about general topics that goes on and on with enough content to exceed the minimum threshold for compression. '.repeat( + 3, + ); + const codeContent = '```typescript\nconst x = 1;\nconst y = 2;\nreturn x + y;\n```'; + + function preserveClassifier(): Classifier { + return vi.fn().mockReturnValue({ decision: 'preserve', confidence: 0.9, reason: 'important' }); + } + + function compressClassifier(): Classifier { + return vi.fn().mockReturnValue({ decision: 'compress', confidence: 0.8, reason: 'prose' }); + } + + it('returns a Promise when classifier is provided', () => { + const messages: Message[] = [msg({ id: '1', index: 0, role: 'user', content: longProse })]; + const result = compress(messages, { + recencyWindow: 0, + classifier: compressClassifier(), + }); + expect(result).toBeInstanceOf(Promise); + }); + + it('hybrid mode: classifier invoked for prose, not for hard T0', async () => { + const classifier = vi + .fn() + .mockReturnValue({ decision: 'preserve', confidence: 0.9, reason: 'important' }); + const messages: Message[] = [ + msg({ id: '1', index: 0, role: 'user', content: longProse }), + msg({ id: '2', index: 1, role: 'user', content: codeContent }), + ]; + + await compress(messages, { + recencyWindow: 0, + classifier, + classifierMode: 'hybrid', + }); + + // Should be called for prose, not for code (hard T0) + expect(classifier).toHaveBeenCalledOnce(); + expect(classifier.mock.calls[0][0]).toBe(longProse); + }); + + it('hybrid mode: preserve decision preserves the message', async () => { + const messages: Message[] = [msg({ id: '1', index: 0, role: 'user', content: longProse })]; + const result = await compress(messages, { + recencyWindow: 0, + classifier: preserveClassifier(), + }); + + expect(result.messages[0].content).toBe(longProse); + expect(result.compression.messages_preserved).toBe(1); + expect(result.compression.messages_llm_preserved).toBe(1); + }); + + it('hybrid mode: compress decision allows compression', async () => { + const messages: Message[] = [msg({ id: '1', index: 0, role: 'user', content: longProse })]; + const result = await compress(messages, { + recencyWindow: 0, + classifier: compressClassifier(), + }); + + expect(result.messages[0].content).toMatch(/^\[summary:/); + expect(result.compression.messages_compressed).toBe(1); + }); + + it('full mode: heuristic skipped, classifier invoked for all eligible', async () => { + const classifier = vi + .fn() + .mockReturnValue({ decision: 'compress', confidence: 0.8, reason: 'prose' }); + const sqlContent = + 'SELECT u.id, u.name, u.email, u.department FROM users u INNER JOIN orders o ON u.id = o.user_id WHERE u.active = true GROUP BY u.department ORDER BY u.name HAVING COUNT(o.id) > 5'; + const messages: Message[] = [ + msg({ id: '1', index: 0, role: 'user', content: longProse }), + msg({ id: '2', index: 1, role: 'user', content: sqlContent }), + ]; + + await compress(messages, { + recencyWindow: 0, + classifier, + classifierMode: 'full', + }); + + // In full mode, both messages get classified (SQL would be hard T0 in hybrid) + expect(classifier).toHaveBeenCalledTimes(2); + }); + + it('full mode: standard rules still apply (role, recency, tool_calls)', async () => { + const classifier = vi + .fn() + .mockReturnValue({ decision: 'compress', confidence: 0.8, reason: 'prose' }); + const messages: Message[] = [ + msg({ id: '1', index: 0, role: 'system', content: longProse }), + msg({ id: '2', index: 1, role: 'user', content: 'short' }), + msg({ + id: '3', + index: 2, + role: 'assistant', + content: longProse, + tool_calls: [{ id: 'tc1' }], + }), + msg({ id: '4', index: 3, role: 'user', content: longProse }), + ]; + + await compress(messages, { + recencyWindow: 0, + classifier, + classifierMode: 'full', + }); + + // system, short, and tool_calls are skipped — only msg 4 eligible + expect(classifier).toHaveBeenCalledOnce(); + }); + + it('stats: messages_llm_classified and messages_llm_preserved', async () => { + const classifier = vi + .fn() + .mockReturnValueOnce({ decision: 'preserve', confidence: 0.9, reason: 'important' }) + .mockReturnValueOnce({ decision: 'compress', confidence: 0.8, reason: 'prose' }); + const messages: Message[] = [ + msg({ id: '1', index: 0, role: 'user', content: longProse }), + msg({ + id: '2', + index: 1, + role: 'user', + content: + 'Another long message that contains enough words to pass the compression threshold without issue. '.repeat( + 3, + ), + }), + ]; + + const result = await compress(messages, { + recencyWindow: 0, + classifier, + }); + + expect(result.compression.messages_llm_classified).toBe(2); + expect(result.compression.messages_llm_preserved).toBe(1); + }); + + it('classifier + tokenBudget: classifier called once (not per binary-search iteration)', async () => { + const classifier = vi + .fn() + .mockReturnValue({ decision: 'compress', confidence: 0.8, reason: 'prose' }); + + const messages: Message[] = Array.from({ length: 10 }, (_, i) => + msg({ + id: String(i), + index: i, + role: 'user', + content: + `Message ${i}: ` + + 'This is a long user message that needs to be compressed in order to fit within the token budget. '.repeat( + 5, + ), + }), + ); + + await compress(messages, { + classifier, + tokenBudget: 200, + }); + + // preClassify runs once before binary search. Each eligible message classified exactly once. + // Default recencyWindow=4 doesn't affect preClassify (it doesn't filter by recency). + // All 10 messages are eligible (no system role, no tool_calls, >120 chars, not compressed). + expect(classifier).toHaveBeenCalledTimes(10); + }); + + it('classifier + dedup: dedup still works', async () => { + const dupContent = + 'This is a duplicated message that appears multiple times in the conversation to test dedup. '.repeat( + 3, + ); + const classifier = vi + .fn() + .mockReturnValue({ decision: 'preserve', confidence: 0.9, reason: 'important' }); + const messages: Message[] = [ + msg({ id: '1', index: 0, role: 'user', content: dupContent }), + msg({ id: '2', index: 1, role: 'user', content: dupContent }), + msg({ id: '3', index: 2, role: 'user', content: longProse }), + ]; + + const result = await compress(messages, { + recencyWindow: 0, + classifier, + }); + + // First duplicate should be deduped + expect(result.compression.messages_deduped).toBe(1); + }); + + it('classifier + preservePatterns: patterns still apply', async () => { + const classifier = vi + .fn() + .mockReturnValue({ decision: 'compress', confidence: 0.8, reason: 'prose' }); + const patternContent = + 'According to § 42, the parties must comply with all terms. This is a very long legal document section that needs proper handling. '.repeat( + 3, + ); + const messages: Message[] = [msg({ id: '1', index: 0, role: 'user', content: patternContent })]; + + const result = await compress(messages, { + recencyWindow: 0, + classifier, + preservePatterns: [{ re: /§\s*\d+/, label: 'section_ref' }], + }); + + // Pattern match takes priority over classifier + expect(result.messages[0].content).toBe(patternContent); + expect(result.compression.messages_pattern_preserved).toBe(1); + }); + + it('sync classifier (non-Promise return) works', async () => { + const classifier: Classifier = (_content: string) => ({ + decision: 'preserve' as const, + confidence: 0.9, + reason: 'sync', + }); + const messages: Message[] = [msg({ id: '1', index: 0, role: 'user', content: longProse })]; + + const result = await compress(messages, { + recencyWindow: 0, + classifier, + }); + + expect(result.messages[0].content).toBe(longProse); + }); + + it('both classifier + summarizer together', async () => { + const classifier = vi + .fn<[string], ClassifierResult>() + .mockReturnValue({ decision: 'compress', confidence: 0.8, reason: 'prose' }); + const summarizer = vi.fn().mockReturnValue('LLM summary of the text.'); + const messages: Message[] = [msg({ id: '1', index: 0, role: 'user', content: longProse })]; + + const result = await compress(messages, { + recencyWindow: 0, + classifier, + summarizer, + }); + + // Classifier allows compression, summarizer produces the summary + expect(result.compression.messages_compressed).toBe(1); + expect(result.compression.messages_llm_classified).toBe(1); + }); +}); + +describe('compression decision audit trail (trace)', () => { + it('trace: true produces a decisions array', () => { + const messages: Message[] = [ + msg({ + id: '1', + index: 0, + role: 'system', + content: 'You are a helpful assistant. '.repeat(10), + }), + msg({ + id: '2', + index: 1, + role: 'user', + content: + 'This is a long user message that discusses various topics at length to pass the threshold. '.repeat( + 5, + ), + }), + ]; + const result = compress(messages, { recencyWindow: 0, trace: true }); + expect(result.compression.decisions).toBeDefined(); + expect(result.compression.decisions!.length).toBe(2); + }); + + it('trace: false (default) omits decisions', () => { + const messages: Message[] = [ + msg({ + id: '1', + index: 0, + role: 'system', + content: 'You are a helpful assistant. '.repeat(10), + }), + ]; + const result = compress(messages, { recencyWindow: 0 }); + expect(result.compression.decisions).toBeUndefined(); + }); + + it('records preserved_role for system messages', () => { + const messages: Message[] = [ + msg({ + id: '1', + index: 0, + role: 'system', + content: 'System prompt content here.', + }), + ]; + const result = compress(messages, { recencyWindow: 0, trace: true }); + const d = result.compression.decisions!; + expect(d).toHaveLength(1); + expect(d[0].action).toBe('preserved'); + expect(d[0].reason).toBe('preserved_role'); + }); + + it('records recency_window for recent messages', () => { + const longProse = + 'This message is long enough to be compressed in normal circumstances so we can see the recency window. '.repeat( + 5, + ); + const messages: Message[] = [ + msg({ id: '1', index: 0, role: 'user', content: longProse }), + msg({ id: '2', index: 1, role: 'assistant', content: longProse }), + ]; + const result = compress(messages, { recencyWindow: 2, trace: true }); + const d = result.compression.decisions!; + expect(d.every((dec) => dec.reason === 'recency_window')).toBe(true); + }); + + it('records short_content for short messages', () => { + const messages: Message[] = [msg({ id: '1', index: 0, role: 'user', content: 'Hi there' })]; + const result = compress(messages, { recencyWindow: 0, trace: true }); + const d = result.compression.decisions!; + expect(d[0].reason).toBe('short_content'); + }); + + it('records tool_calls for messages with tool calls', () => { + const messages: Message[] = [ + msg({ + id: '1', + index: 0, + role: 'assistant', + content: 'Running the tool.', + tool_calls: [{ id: 'tc1', function: { name: 'read' } }], + }), + ]; + const result = compress(messages, { recencyWindow: 0, trace: true }); + const d = result.compression.decisions!; + expect(d[0].reason).toBe('tool_calls'); + }); + + it('records already_compressed for summary prefixed messages', () => { + // Content must be >= 120 chars to avoid short_content firing first + const summaryContent = + '[summary: this was already compressed previously with a detailed description of the original content that covered authentication and session management]'; + const messages: Message[] = [msg({ id: '1', index: 0, role: 'user', content: summaryContent })]; + const result = compress(messages, { recencyWindow: 0, trace: true }); + const d = result.compression.decisions!; + expect(d[0].reason).toBe('already_compressed'); + }); + + it('records hard_t0 reasons for structural content', () => { + const jsonContent = JSON.stringify({ + key: 'value', + nested: { a: 1, b: 2, c: 3, d: 4, e: 5, f: 6 }, + array: [1, 2, 3], + }); + // Pad to exceed 120 chars + const content = jsonContent + ' '.repeat(Math.max(0, 121 - jsonContent.length)); + const messages: Message[] = [msg({ id: '1', index: 0, role: 'user', content })]; + const result = compress(messages, { recencyWindow: 0, trace: true }); + const d = result.compression.decisions!; + expect(d[0].action).toBe('preserved'); + expect(d[0].reason).toMatch(/^(?:hard_t0:|json_structure)/); + }); + + it('records code_split for messages with code fences and prose', () => { + const longProse = + 'This is a detailed explanation of authentication that has enough content to be compressed by the engine. '.repeat( + 3, + ); + const content = `${longProse}\n\n\`\`\`ts\nconst x = 1;\nconst y = 2;\n\`\`\``; + const messages: Message[] = [msg({ id: '1', index: 0, role: 'assistant', content })]; + const result = compress(messages, { recencyWindow: 0, trace: true }); + const d = result.compression.decisions!; + expect(d[0].action).toBe('code_split'); + expect(d[0].reason).toBe('code_split'); + expect(d[0].outputChars).toBeLessThan(d[0].inputChars); + }); + + it('records exact_duplicate for deduped messages', () => { + const LONG = + 'This is a repeated message with enough content to exceed the two hundred character minimum threshold for dedup eligibility so we can test dedup properly across multiple messages in the conversation. Extra padding here.'; + const messages: Message[] = [ + msg({ id: '1', index: 0, content: LONG }), + msg({ id: '2', index: 1, content: LONG }), + ]; + const result = compress(messages, { recencyWindow: 0, dedup: true, trace: true }); + const d = result.compression.decisions!; + const dedupDec = d.find((dec) => dec.action === 'deduped'); + expect(dedupDec).toBeDefined(); + expect(dedupDec!.reason).toBe('exact_duplicate'); + }); + + it('records compressible_prose for compressed messages', () => { + const longProse = + 'This is a long general discussion about various topics that will certainly be compressed by the engine. '.repeat( + 5, + ); + const messages: Message[] = [msg({ id: '1', index: 0, role: 'user', content: longProse })]; + const result = compress(messages, { recencyWindow: 0, trace: true }); + const d = result.compression.decisions!; + expect(d[0].action).toBe('compressed'); + expect(d[0].reason).toBe('compressible_prose'); + expect(d[0].outputChars).toBeLessThan(d[0].inputChars); + }); + + it('decisions have correct messageId and messageIndex', () => { + const longProse = + 'This is a long message for compression that exceeds the minimum threshold easily. '.repeat( + 5, + ); + const messages: Message[] = [ + msg({ id: 'sys', index: 0, role: 'system', content: 'System prompt.' }), + msg({ id: 'u1', index: 1, role: 'user', content: longProse }), + ]; + const result = compress(messages, { recencyWindow: 0, trace: true }); + const d = result.compression.decisions!; + expect(d[0].messageId).toBe('sys'); + expect(d[0].messageIndex).toBe(0); + expect(d[1].messageId).toBe('u1'); + expect(d[1].messageIndex).toBe(1); + }); + + it('records force_converge truncation', () => { + // Need many long messages so that even after compression, the token budget + // is exceeded, triggering force-converge. The non-recency compressed messages + // will still be > 512 chars (code-preserved messages work well for this). + const longCode = + '```ts\n' + Array.from({ length: 50 }, (_, i) => `const x${i} = ${i};`).join('\n') + '\n```'; + const longProse = + 'This explanation covers the architecture of authentication middlewares and their integration patterns. '.repeat( + 10, + ); + const content = `${longProse}\n\n${longCode}`; + const messages: Message[] = Array.from({ length: 10 }, (_, i) => + msg({ id: String(i + 1), index: i, role: i % 2 === 0 ? 'user' : 'assistant', content }), + ); + const result = compress(messages, { + tokenBudget: 50, + forceConverge: true, + recencyWindow: 1, + trace: true, + }); + const d = result.compression.decisions; + expect(d).toBeDefined(); + const truncated = d!.filter((dec) => dec.action === 'truncated'); + expect(truncated.length).toBeGreaterThan(0); + expect(truncated[0].reason).toBe('force_converge'); + }); + + describe('reasoning chain preservation', () => { + it('preserves reasoning chain as hard T0 through compression', () => { + const reasoning = + 'Given that the connection pool is exhausted, new requests queue up. ' + + 'Thus the response latency increases exponentially. ' + + 'Consequently the health check fails and the node is removed from rotation.'; + const messages: Message[] = [ + msg({ id: '1', index: 0, role: 'user', content: 'Why is the service slow?' }), + msg({ id: '2', index: 1, role: 'assistant', content: reasoning }), + ]; + const result = compress(messages); + const preserved = result.messages.find((m) => m.content === reasoning); + expect(preserved).toBeDefined(); + }); + + it('still compresses prose with a single "therefore"', () => { + const prose = + 'The deployment was delayed and therefore the release notes were updated to reflect the new timeline. ' + + 'The team worked through the weekend to prepare the documentation. ' + + 'Everyone was pleased with the final outcome of the project. ' + + 'The stakeholders approved the changes and we moved forward with the plan.'; + const messages: Message[] = [ + msg({ id: '1', index: 0, role: 'user', content: 'What happened?' }), + msg({ id: '2', index: 1, role: 'assistant', content: prose }), + msg({ id: '3', index: 2, role: 'user', content: 'Thanks for explaining.' }), + msg({ id: '4', index: 3, role: 'assistant', content: 'You are welcome.' }), + msg({ id: '5', index: 4, role: 'user', content: 'One more question.' }), + msg({ id: '6', index: 5, role: 'assistant', content: 'Go ahead.' }), + ]; + const result = compress(messages, { recencyWindow: 2 }); + const original = result.messages.find((m) => m.content === prose); + // Single "therefore" should not prevent compression — message should be summarized + expect(original).toBeUndefined(); + }); + }); + + describe('compressionThreshold', () => { + const longProse = 'This is a detailed explanation of the architecture. '.repeat(30); + const messages: Message[] = [ + msg({ id: '1', index: 0, role: 'user', content: longProse }), + msg({ id: '2', index: 1, role: 'assistant', content: longProse }), + msg({ id: '3', index: 2, role: 'user', content: 'Follow up question here.' }), + msg({ id: '4', index: 3, role: 'assistant', content: 'Short answer.' }), + ]; + + function totalTokens(msgs: Message[]): number { + return msgs.reduce((sum, m) => sum + estimateTokens(m), 0); + } + + it('returns messages unmodified when below threshold', () => { + const total = totalTokens(messages); + const result = compress(messages, { compressionThreshold: total + 100 }); + expect(result.messages).toBe(messages); + expect(result.compression.ratio).toBe(1); + expect(result.compression.messages_compressed).toBe(0); + expect(result.compression.messages_preserved).toBe(messages.length); + expect(result.verbatim).toEqual({}); + }); + + it('runs compression at exact threshold', () => { + const total = totalTokens(messages); + const result = compress(messages, { compressionThreshold: total, recencyWindow: 2 }); + // At threshold (not below), compression should run + expect(result.compression.messages_compressed).toBeGreaterThan(0); + }); + + it('runs compression above threshold', () => { + const result = compress(messages, { compressionThreshold: 1, recencyWindow: 2 }); + expect(result.compression.messages_compressed).toBeGreaterThan(0); + }); + + it('works with custom tokenCounter', () => { + const counter = (m: Message) => (typeof m.content === 'string' ? m.content.length : 0); + const total = messages.reduce((sum, m) => sum + counter(m), 0); + const result = compress(messages, { + compressionThreshold: total + 100, + tokenCounter: counter, + }); + expect(result.messages).toBe(messages); + expect(result.compression.ratio).toBe(1); + }); + + it('works alongside tokenBudget', () => { + const total = totalTokens(messages); + // Below threshold: skip compression even though tokenBudget is set + const result = compress(messages, { + compressionThreshold: total + 100, + tokenBudget: 50, + }); + expect(result.messages).toBe(messages); + expect(result.compression.messages_compressed).toBe(0); + }); + + it('returns Promise when summarizer is set and below threshold', async () => { + const total = totalTokens(messages); + const summarizer = vi.fn().mockResolvedValue('summary'); + const result = compress(messages, { + compressionThreshold: total + 100, + summarizer, + }); + expect(result).toBeInstanceOf(Promise); + const resolved = await result; + expect(resolved.messages).toBe(messages); + expect(resolved.compression.ratio).toBe(1); + expect(summarizer).not.toHaveBeenCalled(); + }); + }); + + describe('observationThreshold', () => { + const largeProse = + 'This is a detailed explanation of the system architecture and design decisions. '.repeat(60); + + it('compresses large recency-window messages that exceed threshold', () => { + const messages: Message[] = [ + msg({ id: '1', index: 0, role: 'user', content: 'What happened?' }), + msg({ id: '2', index: 1, role: 'assistant', content: largeProse }), + msg({ id: '3', index: 2, role: 'user', content: 'Thanks.' }), + ]; + // recencyWindow covers all messages, but observationThreshold forces compression of the large one + const result = compress(messages, { + recencyWindow: 10, + observationThreshold: 100, + }); + expect(result.compression.messages_compressed).toBeGreaterThan(0); + // The large message should be compressed + const compressed = result.messages.find((m) => m.id === '2'); + expect(compressed?.content).not.toBe(largeProse); + }); + + it('preserves small messages in recency window even when threshold is set', () => { + const messages: Message[] = [ + msg({ id: '1', index: 0, role: 'user', content: 'Short question.' }), + msg({ id: '2', index: 1, role: 'assistant', content: 'Short answer.' }), + ]; + const result = compress(messages, { + recencyWindow: 10, + observationThreshold: 100, + }); + expect(result.compression.messages_compressed).toBe(0); + }); + + it('always preserves system role regardless of observation threshold', () => { + const messages: Message[] = [ + msg({ id: '1', index: 0, role: 'system', content: largeProse }), + msg({ id: '2', index: 1, role: 'user', content: 'Hello.' }), + ]; + const result = compress(messages, { + recencyWindow: 10, + observationThreshold: 100, + }); + const systemMsg = result.messages.find((m) => m.role === 'system'); + expect(systemMsg?.content).toBe(largeProse); + }); + + it('always preserves tool_calls messages regardless of observation threshold', () => { + const messages: Message[] = [ + msg({ + id: '1', + index: 0, + role: 'assistant', + content: largeProse, + tool_calls: [{ id: 'call_1', function: { name: 'test' } }], + }), + msg({ id: '2', index: 1, role: 'user', content: 'Done.' }), + ]; + const result = compress(messages, { + recencyWindow: 10, + observationThreshold: 100, + }); + const toolMsg = result.messages.find((m) => m.id === '1'); + expect(toolMsg?.content).toBe(largeProse); + }); + + it('compresses large JSON in recency window when threshold exceeded', () => { + const bigJson = JSON.stringify({ + data: Array.from({ length: 200 }, (_, i) => ({ id: i, value: `item_${i}` })), + }); + const messages: Message[] = [ + msg({ id: '1', index: 0, role: 'user', content: 'Get data.' }), + msg({ id: '2', index: 1, role: 'assistant', content: bigJson }), + msg({ id: '3', index: 2, role: 'user', content: 'Thanks.' }), + ]; + const result = compress(messages, { + recencyWindow: 10, + observationThreshold: 100, + }); + // JSON would normally be preserved, but exceeds observation threshold + expect(result.compression.messages_compressed).toBeGreaterThan(0); + }); + + it('works with custom tokenCounter', () => { + const counter = (m: Message) => (typeof m.content === 'string' ? m.content.length : 0); + const messages: Message[] = [ + msg({ id: '1', index: 0, role: 'user', content: 'Q' }), + msg({ id: '2', index: 1, role: 'assistant', content: largeProse }), + msg({ id: '3', index: 2, role: 'user', content: 'Ok.' }), + ]; + const result = compress(messages, { + recencyWindow: 10, + observationThreshold: 500, + tokenCounter: counter, + }); + expect(result.compression.messages_compressed).toBeGreaterThan(0); + }); + }); +}); diff --git a/tests/contradiction.test.ts b/tests/contradiction.test.ts new file mode 100644 index 0000000..d85351f --- /dev/null +++ b/tests/contradiction.test.ts @@ -0,0 +1,110 @@ +import { describe, it, expect } from 'vitest'; +import { analyzeContradictions } from '../src/contradiction.js'; +import type { Message } from '../src/types.js'; + +function msg(id: string, content: string, role = 'user', index = 0): Message { + return { id, index, role, content }; +} + +describe('analyzeContradictions', () => { + it('returns empty map when no contradictions', () => { + const messages: Message[] = [ + msg('1', 'We should use PostgreSQL for the database layer in the backend.'), + msg('2', 'The frontend needs React with TypeScript for type safety in components.'), + ]; + const result = analyzeContradictions(messages); + expect(result.size).toBe(0); + }); + + it('detects explicit correction with "actually"', () => { + const messages: Message[] = [ + msg('1', 'Use Redis for the caching layer in the application server.'), + msg('2', 'Actually, use Memcached instead for the caching layer.'), + ]; + const result = analyzeContradictions(messages); + expect(result.size).toBe(1); + expect(result.has(0)).toBe(true); + expect(result.get(0)!.supersededByIndex).toBe(1); + expect(result.get(0)!.signal).toBe('explicit_correction'); + }); + + it('detects "don\'t use" directives', () => { + const messages: Message[] = [ + msg('1', 'Import lodash for utility functions in the helper module.'), + msg('2', "Don't use lodash for utility functions, write them from scratch."), + ]; + const result = analyzeContradictions(messages); + expect(result.size).toBe(1); + expect(result.get(0)!.signal).toBe('dont_directive'); + }); + + it('detects "instead" directives', () => { + const messages: Message[] = [ + msg('1', 'Deploy the service on AWS Lambda for the serverless backend.'), + msg('2', 'Instead, use Google Cloud Run for the serverless backend deployment.'), + ]; + const result = analyzeContradictions(messages); + expect(result.size).toBe(1); + expect(result.get(0)!.signal).toBe('instead_directive'); + }); + + it('detects retraction patterns', () => { + const messages: Message[] = [ + msg('1', 'Add the feature flag for the new dashboard module.'), + msg('2', 'Scratch that, we are removing the feature flag for the dashboard.'), + ]; + const result = analyzeContradictions(messages); + expect(result.size).toBe(1); + expect(result.get(0)!.signal).toBe('retraction'); + }); + + it('requires topic overlap — unrelated corrections are not matched', () => { + const messages: Message[] = [ + msg('1', 'The database schema uses PostgreSQL with normalized tables.'), + msg('2', 'Actually, the frontend color scheme should be darker blue.'), + ]; + const result = analyzeContradictions(messages); + expect(result.size).toBe(0); + }); + + it('skips short messages', () => { + const messages: Message[] = [msg('1', 'Use Redis.'), msg('2', 'Actually, use Memcached.')]; + const result = analyzeContradictions(messages); + expect(result.size).toBe(0); // both < 50 chars + }); + + it('skips preserved roles', () => { + const messages: Message[] = [ + msg('1', 'You are a helpful assistant that always uses Redis for caching.', 'system'), + msg('2', 'Actually, use Memcached instead of Redis for the caching layer.'), + ]; + const result = analyzeContradictions(messages, 0.15, new Set(['system'])); + expect(result.size).toBe(0); + }); + + it('only supersedes the most-overlapping earlier message', () => { + const messages: Message[] = [ + msg('1', 'Use Redis for caching data in the application server.'), + msg('2', 'Use Postgres for the primary data store and queries.'), + msg('3', 'Actually, use Memcached instead for caching data in the app.'), + ]; + const result = analyzeContradictions(messages); + // Should supersede message 1 (caching), not message 2 (data store) + if (result.size > 0) { + expect(result.has(0)).toBe(true); + expect(result.has(1)).toBe(false); + } + }); + + it('returns topicOverlap score', () => { + const messages: Message[] = [ + msg('1', 'Use Redis for the caching layer in the application server backend.'), + msg('2', 'Actually, use Memcached for the caching layer in the application backend.'), + ]; + const result = analyzeContradictions(messages); + if (result.size > 0) { + expect(result.get(0)!.topicOverlap).toBeGreaterThan(0); + expect(result.get(0)!.topicOverlap).toBeLessThanOrEqual(1); + } + }); +}); diff --git a/tests/coreference.test.ts b/tests/coreference.test.ts new file mode 100644 index 0000000..1688eee --- /dev/null +++ b/tests/coreference.test.ts @@ -0,0 +1,172 @@ +import { describe, it, expect } from 'vitest'; +import { + buildCoreferenceMap, + findOrphanedReferences, + generateInlineDefinitions, +} from '../src/coreference.js'; +import { compress } from '../src/compress.js'; +import type { Message } from '../src/types.js'; + +function msg(id: string, content: string, role = 'user'): Message { + return { id, index: 0, role, content }; +} + +describe('buildCoreferenceMap', () => { + it('tracks entity first-definition and references', () => { + const messages: Message[] = [ + msg('1', 'The fetchData function handles API calls with retry logic.'), + msg('2', 'The getUserProfile function returns user info.'), + msg('3', 'Use fetchData to get the profile via getUserProfile endpoint.'), + ]; + + const defs = buildCoreferenceMap(messages); + const fetchDef = defs.find((d) => d.entity === 'fetchData'); + expect(fetchDef).toBeDefined(); + expect(fetchDef!.definingMessageIndex).toBe(0); + expect(fetchDef!.referencingMessageIndices).toContain(2); + }); + + it('tracks snake_case and PascalCase identifiers', () => { + const messages: Message[] = [ + msg('1', 'Set max_retry_count to 5 in the ServiceConfig.'), + msg('2', 'The max_retry_count is used by ServiceConfig for backoff.'), + ]; + + const defs = buildCoreferenceMap(messages); + expect(defs.some((d) => d.entity === 'max_retry_count')).toBe(true); + expect(defs.some((d) => d.entity === 'ServiceConfig')).toBe(true); + }); + + it('returns empty for messages with no shared entities', () => { + const messages: Message[] = [msg('1', 'Hello world.'), msg('2', 'Goodbye world.')]; + + const defs = buildCoreferenceMap(messages); + expect(defs).toHaveLength(0); + }); +}); + +describe('findOrphanedReferences', () => { + it('finds entities orphaned by compression', () => { + const defs = [ + { + entity: 'fetchData', + definingMessageIndex: 0, + referencingMessageIndices: [2], + }, + ]; + + const orphaned = findOrphanedReferences( + defs, + new Set([0, 1]), // compressed + new Set([2]), // preserved + ); + + expect(orphaned.has(0)).toBe(true); + expect(orphaned.get(0)).toContain('fetchData'); + }); + + it('returns empty when defining message is preserved', () => { + const defs = [ + { + entity: 'fetchData', + definingMessageIndex: 0, + referencingMessageIndices: [1], + }, + ]; + + const orphaned = findOrphanedReferences( + defs, + new Set([1]), // compressed + new Set([0]), // preserved + ); + + expect(orphaned.size).toBe(0); + }); +}); + +describe('generateInlineDefinitions', () => { + it('extracts defining sentence for entity', () => { + const content = 'The fetchData function handles retries. It uses exponential backoff.'; + const inline = generateInlineDefinitions(['fetchData'], content); + expect(inline).toContain('fetchData'); + expect(inline).toContain('[context:'); + }); + + it('returns empty for no entities', () => { + expect(generateInlineDefinitions([], 'some text')).toBe(''); + }); + + it('caps at 5 inlines', () => { + const content = + 'Use fetchData with getUserProfile and setConfig and validateToken and refreshAuth and parseResponse and buildQuery.'; + const inline = generateInlineDefinitions( + ['fetchData', 'getUserProfile', 'setConfig', 'validateToken', 'refreshAuth', 'parseResponse'], + content, + ); + // Should not include all 6 + const pipeCount = (inline.match(/\|/g) ?? []).length; + expect(pipeCount).toBeLessThanOrEqual(4); // max 5 entries = 4 pipes + }); +}); + +describe('coreference option in compress()', () => { + it('inlines definitions when coreference is enabled', () => { + const messages: Message[] = [ + msg( + 'def', + 'The fetchData function in the service layer handles all API communication including retry logic with exponential backoff and circuit breaker pattern implementation for fault tolerance.', + ), + msg( + 'filler', + 'I looked at the general performance metrics and everything seems to be running within acceptable limits for the current quarter based on the monitoring dashboard data.', + ), + msg('ref', 'Make sure fetchData uses a 30 second timeout for all upstream requests.'), + ]; + + const result = compress(messages, { + recencyWindow: 1, + coreference: true, + }); + + // The compressed 'def' message should have context inlined + const defMsg = result.messages.find((m) => m.id === 'def'); + if (defMsg?.content?.includes('[context:')) { + expect(defMsg.content).toContain('fetchData'); + } + }); + + it('does nothing when coreference is false', () => { + const messages: Message[] = [ + msg( + 'def', + 'The fetchData function handles retries with exponential backoff and circuit breaker pattern for the service layer communication.', + ), + msg('ref', 'Use fetchData with a 30 second timeout.'), + ]; + + const result = compress(messages, { recencyWindow: 1 }); + const defMsg = result.messages.find((m) => m.id === 'def'); + if (defMsg?.content?.includes('[summary')) { + expect(defMsg.content).not.toContain('[context:'); + } + }); + + it('preserves verbatim store with coreference', () => { + const messages: Message[] = [ + msg( + 'def', + 'The fetchData function in the service layer handles all API communication including retry logic with exponential backoff and jitter for the distributed system.', + ), + msg('ref', 'The fetchData timeout should be 30 seconds.'), + ]; + + const result = compress(messages, { + recencyWindow: 1, + coreference: true, + }); + + if (result.compression.messages_compressed > 0) { + expect(result.verbatim['def']).toBeDefined(); + } + }); +}); diff --git a/tests/depth.test.ts b/tests/depth.test.ts new file mode 100644 index 0000000..e6c666e --- /dev/null +++ b/tests/depth.test.ts @@ -0,0 +1,120 @@ +import { describe, it, expect } from 'vitest'; +import { compress } from '../src/compress.js'; +import type { Message } from '../src/types.js'; + +function msg(id: string, content: string, role = 'user'): Message { + return { id, index: 0, role, content }; +} + +function longProse(seed: string, length: number): string { + const base = `The ${seed} function handles complex operations including data validation, error handling, retry logic, and performance monitoring across multiple service layers. `; + return base.repeat(Math.ceil(length / base.length)).slice(0, length); +} + +describe('compressionDepth', () => { + it('gentle produces standard compression', () => { + const messages: Message[] = [ + msg('1', longProse('fetchData', 600)), + msg('2', longProse('getUserProfile', 600)), + msg('recent', 'Latest update.'), + ]; + + const result = compress(messages, { recencyWindow: 1, compressionDepth: 'gentle' }); + expect(result.compression.messages_compressed).toBeGreaterThan(0); + expect(result.compression.ratio).toBeGreaterThan(1); + }); + + it('moderate produces tighter compression than gentle', () => { + const messages: Message[] = [ + msg('1', longProse('processData', 800)), + msg('2', longProse('validateInput', 800)), + msg('recent', 'Latest update.'), + ]; + + const gentle = compress(messages, { recencyWindow: 1, compressionDepth: 'gentle' }); + const moderate = compress(messages, { recencyWindow: 1, compressionDepth: 'moderate' }); + + expect(moderate.compression.ratio).toBeGreaterThanOrEqual(gentle.compression.ratio); + }); + + it('aggressive produces entity-only stubs', () => { + const messages: Message[] = [ + msg('1', longProse('buildIndex', 600)), + msg('recent', 'Latest update.'), + ]; + + const result = compress(messages, { recencyWindow: 1, compressionDepth: 'aggressive' }); + const compressed = result.messages.find((m) => m.id === '1'); + expect(compressed?.content?.length).toBeLessThan(200); // much shorter + expect(result.compression.ratio).toBeGreaterThan(1); + }); + + it('aggressive compresses more than moderate', () => { + const messages: Message[] = [ + msg('1', longProse('fetchData', 1000)), + msg('2', longProse('handleRequest', 1000)), + msg('recent', 'Latest update.'), + ]; + + const moderate = compress(messages, { recencyWindow: 1, compressionDepth: 'moderate' }); + const aggressive = compress(messages, { recencyWindow: 1, compressionDepth: 'aggressive' }); + + expect(aggressive.compression.ratio).toBeGreaterThanOrEqual(moderate.compression.ratio); + }); + + it('auto mode with budget tries progressively deeper', () => { + const messages: Message[] = [ + msg('1', longProse('processData', 2000)), + msg('2', longProse('validateInput', 2000)), + msg('3', longProse('handleRequest', 2000)), + msg('recent', 'Latest update.'), + ]; + + const result = compress(messages, { + tokenBudget: 200, + compressionDepth: 'auto', + recencyWindow: 1, + forceConverge: true, + }); + + expect(result.fits).toBe(true); + // Auto mode should have achieved significant compression + expect(result.compression.ratio).toBeGreaterThan(2); + }); + + it('auto mode stops at gentle when it fits', () => { + const messages: Message[] = [ + msg('1', longProse('fetchData', 300)), + msg('recent', 'Latest update.'), + ]; + + const result = compress(messages, { + tokenBudget: 500, // generous budget + compressionDepth: 'auto', + recencyWindow: 1, + }); + + expect(result.fits).toBe(true); + }); + + it('default behavior unchanged without compressionDepth', () => { + const messages: Message[] = [msg('1', longProse('fetchData', 500)), msg('recent', 'Latest.')]; + + const withoutDepth = compress(messages, { recencyWindow: 1 }); + const withGentle = compress(messages, { recencyWindow: 1, compressionDepth: 'gentle' }); + + expect(withoutDepth.compression.ratio).toBe(withGentle.compression.ratio); + }); + + it('preserves round-trip integrity at all depths', () => { + const messages: Message[] = [msg('1', longProse('fetchData', 500)), msg('recent', 'Latest.')]; + + for (const depth of ['gentle', 'moderate', 'aggressive'] as const) { + const result = compress(messages, { recencyWindow: 1, compressionDepth: depth }); + // All compressed messages should have verbatim originals + if (result.compression.messages_compressed > 0) { + expect(Object.keys(result.verbatim).length).toBeGreaterThan(0); + } + } + }); +}); diff --git a/tests/determinism.test.ts b/tests/determinism.test.ts new file mode 100644 index 0000000..8368bbf --- /dev/null +++ b/tests/determinism.test.ts @@ -0,0 +1,166 @@ +import { describe, it, expect } from 'vitest'; +import { compress } from '../src/compress.js'; +import { classifyMessage } from '../src/classify.js'; +import type { Message } from '../src/types.js'; + +function msg(overrides: Partial & { id: string; index: number }): Message { + return { role: 'user', content: '', metadata: {}, ...overrides }; +} + +/** + * Determinism tests: same input → same output, verified across multiple runs. + * These catch accidental non-determinism from Map iteration order, Set ordering, + * floating-point rounding, or any other source of instability. + */ +describe('determinism', () => { + function runN(n: number, fn: () => T): T[] { + return Array.from({ length: n }, () => fn()); + } + + function assertAllEqual(results: unknown[]) { + const serialized = results.map((r) => JSON.stringify(r)); + for (let i = 1; i < serialized.length; i++) { + expect(serialized[i]).toBe(serialized[0]); + } + } + + it('basic compression is deterministic across 5 runs', () => { + const longProse = + 'The authentication middleware validates incoming JWT tokens against the session store and checks expiration timestamps. '.repeat( + 5, + ); + const messages: Message[] = [ + msg({ id: 'sys', index: 0, role: 'system', content: 'You are a helpful assistant.' }), + msg({ id: 'u1', index: 1, role: 'user', content: longProse }), + msg({ + id: 'a1', + index: 2, + role: 'assistant', + content: longProse + ' The service also handles refresh token rotation.', + }), + msg({ id: 'u2', index: 3, role: 'user', content: 'Thanks for the explanation.' }), + ]; + + const results = runN(5, () => compress(messages, { recencyWindow: 1 })); + assertAllEqual(results); + }); + + it('dedup is deterministic across 5 runs', () => { + const LONG = + 'This is a repeated message with enough content to exceed the two hundred character minimum threshold for dedup eligibility so we can test dedup properly across multiple messages in the conversation. Extra padding here.'; + const messages: Message[] = [ + msg({ id: '1', index: 0, content: LONG }), + msg({ + id: '2', + index: 1, + role: 'assistant', + content: + 'The system processes the request through several stages including validation and enrichment. '.repeat( + 4, + ), + }), + msg({ id: '3', index: 2, content: LONG }), + ]; + + const results = runN(5, () => compress(messages, { recencyWindow: 0, dedup: true })); + assertAllEqual(results); + }); + + it('fuzzy dedup is deterministic across 5 runs', () => { + const base = + 'The deployment pipeline starts with pulling the latest Docker image from the registry and running pre-flight health checks against the staging environment to verify service connectivity.'; + const variant = + 'The deployment pipeline starts with pulling the latest Docker image from the registry and running pre-flight health checks against the production environment to verify service connectivity.'; + // Pad both to > 200 chars + const padded1 = base + ' ' + 'Additional context about the deployment process. '.repeat(2); + const padded2 = variant + ' ' + 'Additional context about the deployment process. '.repeat(2); + + const messages: Message[] = [ + msg({ id: '1', index: 0, content: padded1 }), + msg({ id: '2', index: 1, content: padded2 }), + ]; + + const results = runN(5, () => + compress(messages, { recencyWindow: 0, fuzzyDedup: true, fuzzyThreshold: 0.8 }), + ); + assertAllEqual(results); + }); + + it('code-split compression is deterministic across 5 runs', () => { + const longProse = + 'This is a detailed explanation of how the authentication system works and integrates with the session manager for token rotation. '.repeat( + 3, + ); + const content = `${longProse}\n\n\`\`\`typescript\nconst token = await auth.getToken();\nconst session = createSession(token);\n\`\`\``; + const messages: Message[] = [msg({ id: '1', index: 0, role: 'assistant', content })]; + + const results = runN(5, () => compress(messages, { recencyWindow: 0 })); + assertAllEqual(results); + }); + + it('token budget binary search is deterministic across 5 runs', () => { + const longProse = + 'The system architecture relies on distributed message queues for inter-service communication with circuit breakers preventing cascading failures. '.repeat( + 3, + ); + const messages: Message[] = Array.from({ length: 8 }, (_, i) => + msg({ + id: String(i + 1), + index: i, + role: i % 2 === 0 ? 'user' : 'assistant', + content: longProse, + }), + ); + + const results = runN(5, () => compress(messages, { tokenBudget: 2000 })); + assertAllEqual(results); + }); + + it('force-converge is deterministic across 5 runs', () => { + const longProse = + 'The system processes the request through validation, enrichment, and routing stages before forwarding to the appropriate downstream service. '.repeat( + 8, + ); + const messages: Message[] = Array.from({ length: 6 }, (_, i) => + msg({ + id: String(i + 1), + index: i, + role: i % 2 === 0 ? 'user' : 'assistant', + content: longProse, + }), + ); + + const results = runN(5, () => compress(messages, { tokenBudget: 200, forceConverge: true })); + assertAllEqual(results); + }); + + it('classifyMessage is deterministic across 100 runs', () => { + const inputs = [ + 'Just a plain prose message about general topics without any special formatting.', + '```typescript\nconst x = 1;\n```\nSome code here.', + 'SELECT * FROM users WHERE id = 1 ORDER BY name', + 'The deployment requires 15 retries with 200ms timeout per request.', + JSON.stringify({ key: 'value', nested: { a: 1 } }), + ]; + + for (const input of inputs) { + const results = runN(100, () => classifyMessage(input)); + assertAllEqual(results); + } + }); + + it('trace output is deterministic across 5 runs', () => { + const longProse = + 'The authentication middleware validates incoming JWT tokens against the session store. '.repeat( + 5, + ); + const messages: Message[] = [ + msg({ id: 'sys', index: 0, role: 'system', content: 'System prompt.' }), + msg({ id: 'u1', index: 1, role: 'user', content: longProse }), + msg({ id: 'a1', index: 2, role: 'assistant', content: 'Short response.' }), + ]; + + const results = runN(5, () => compress(messages, { recencyWindow: 0, trace: true })); + assertAllEqual(results); + }); +}); diff --git a/tests/discourse.test.ts b/tests/discourse.test.ts new file mode 100644 index 0000000..c4f1fea --- /dev/null +++ b/tests/discourse.test.ts @@ -0,0 +1,137 @@ +import { describe, it, expect } from 'vitest'; +import { segmentEDUs, scoreEDUs, selectEDUs, summarizeWithEDUs } from '../src/discourse.js'; +import { compress } from '../src/compress.js'; +import type { Message } from '../src/types.js'; + +function msg(id: string, content: string, role = 'user'): Message { + return { id, index: 0, role, content }; +} + +describe('segmentEDUs', () => { + it('segments simple sentences into EDUs', () => { + const edus = segmentEDUs('Parse the JSON. Extract the user ID. Return the result.'); + expect(edus.length).toBeGreaterThanOrEqual(3); + }); + + it('splits at discourse markers', () => { + const edus = segmentEDUs('Parse the JSON, then extract the user ID from the response object.'); + // Should split at ", then" + expect(edus.length).toBeGreaterThanOrEqual(2); + }); + + it('detects pronoun dependencies', () => { + const edus = segmentEDUs('Create the connection pool. It handles all database connections.'); + const itEdu = edus.find((e) => e.text.startsWith('It')); + if (itEdu) { + expect(itEdu.dependsOn.length).toBeGreaterThan(0); + } + }); + + it('handles empty text', () => { + const edus = segmentEDUs(''); + expect(edus).toHaveLength(0); + }); + + it('detects temporal chains', () => { + const edus = segmentEDUs( + 'First validate the input. Then process the request. Finally return the result.', + ); + // "Then" and "Finally" EDUs should depend on predecessors + const thenEdu = edus.find((e) => /then/i.test(e.text)); + if (thenEdu) { + expect(thenEdu.dependsOn.length).toBeGreaterThan(0); + } + }); +}); + +describe('scoreEDUs', () => { + it('scores with default length-based scorer', () => { + const edus = segmentEDUs('Short. This is a longer sentence with more content.'); + const scored = scoreEDUs(edus); + expect(scored.every((e) => e.score > 0)).toBe(true); + }); + + it('uses custom scorer when provided', () => { + const edus = segmentEDUs('Important keyword here. Generic filler sentence.'); + const scored = scoreEDUs(edus, (text) => (text.includes('keyword') ? 10 : 1)); + const best = scored.reduce((a, b) => (a.score > b.score ? a : b)); + expect(best.text).toContain('keyword'); + }); +}); + +describe('selectEDUs', () => { + it('selects highest-scored EDUs within budget', () => { + const edus = scoreEDUs( + segmentEDUs('Low value filler. Critical fetchData configuration.'), + (text) => (text.includes('fetchData') ? 10 : 1), + ); + const selected = selectEDUs(edus, 200); + expect(selected.length).toBeGreaterThan(0); + }); + + it('includes dependency parents when selecting an EDU', () => { + const edus = scoreEDUs( + segmentEDUs('Create the pool. It handles connections. Then it distributes load.'), + (text) => (text.includes('distributes') ? 10 : text.includes('It handles') ? 5 : 1), + ); + const selected = selectEDUs(edus, 500); + // If "distributes" EDU is selected and depends on "It handles" which depends on "Create", + // both parents should be included + if (selected.some((e) => e.text.includes('distributes'))) { + // At least one parent should also be selected + expect(selected.length).toBeGreaterThanOrEqual(2); + } + }); + + it('returns empty for empty input', () => { + expect(selectEDUs([], 100)).toHaveLength(0); + }); +}); + +describe('summarizeWithEDUs', () => { + it('produces a coherent summary', () => { + const text = + 'The fetchData function calls the API. It uses exponential backoff. Then it validates the response. Finally it caches the result.'; + const summary = summarizeWithEDUs(text, 200); + expect(summary.length).toBeGreaterThan(0); + expect(summary.length).toBeLessThanOrEqual(250); // budget + some tolerance + }); +}); + +describe('discourseAware option in compress()', () => { + it('uses EDU-based summarization when enabled', () => { + const messages: Message[] = [ + msg( + '1', + 'The fetchData function calls the upstream API endpoint. It uses exponential backoff with a base delay of 200 milliseconds. Then it validates the JSON response schema. Finally it caches the successful result in the local store for 300 seconds.', + ), + msg('recent', 'What about error handling?'), + ]; + + const withEDU = compress(messages, { recencyWindow: 1, discourseAware: true }); + const withoutEDU = compress(messages, { recencyWindow: 1 }); + + // Both should compress + expect(withEDU.compression.messages_compressed).toBeGreaterThan(0); + expect(withoutEDU.compression.messages_compressed).toBeGreaterThan(0); + + // EDU summary may differ from default + const edu1 = withEDU.messages.find((m) => m.id === '1'); + const default1 = withoutEDU.messages.find((m) => m.id === '1'); + expect(edu1?.content).toBeDefined(); + expect(default1?.content).toBeDefined(); + }); + + it('does nothing when discourseAware is false', () => { + const messages: Message[] = [ + msg( + '1', + 'The overall project timeline looks reasonable based on current velocity metrics and team capacity estimates for the upcoming quarter milestones, considering the dependencies between frontend and backend workstreams.', + ), + msg('recent', 'OK.'), + ]; + + const result = compress(messages, { recencyWindow: 1 }); + expect(result.compression.messages_compressed).toBeGreaterThan(0); + }); +}); diff --git a/tests/entities.test.ts b/tests/entities.test.ts new file mode 100644 index 0000000..682e306 --- /dev/null +++ b/tests/entities.test.ts @@ -0,0 +1,220 @@ +import { describe, it, expect } from 'vitest'; +import { + extractEntities, + collectMessageEntities, + computeEntityRetention, + computeStructuralIntegrity, + computeReferenceCoherence, + computeQualityScore, +} from '../src/entities.js'; +import { compress } from '../src/compress.js'; +import type { Message } from '../src/types.js'; + +function msg(id: string, content: string, role = 'user'): Message { + return { id, index: 0, role, content }; +} + +describe('extractEntities', () => { + it('extracts camelCase identifiers', () => { + const entities = extractEntities('The fetchData function calls getUserProfile'); + expect(entities).toContain('fetchData'); + expect(entities).toContain('getUserProfile'); + }); + + it('extracts PascalCase identifiers', () => { + const entities = extractEntities('Use TypeScript with WebSocket connections'); + expect(entities).toContain('TypeScript'); + expect(entities).toContain('WebSocket'); + }); + + it('extracts snake_case identifiers', () => { + const entities = extractEntities('Set max_retry_count and connection_pool_size'); + expect(entities).toContain('max_retry_count'); + expect(entities).toContain('connection_pool_size'); + }); + + it('extracts numbers with units', () => { + const entities = extractEntities('Timeout is 30 seconds with 5 retries'); + expect(entities.some((e) => e.includes('30'))).toBe(true); + expect(entities.some((e) => e.includes('5'))).toBe(true); + }); + + it('extracts vowelless abbreviations', () => { + const entities = extractEntities('Use npm and grpc for the service'); + expect(entities).toContain('npm'); + expect(entities).toContain('grpc'); + }); + + it('respects maxEntities cap', () => { + const text = + 'fetchData getUserProfile setConfig updateCache deleteRecord createSession validateToken refreshAuth parseResponse buildQuery'; + const entities = extractEntities(text, 3); + expect(entities.length).toBeLessThanOrEqual(3); + }); + + it('extracts file paths', () => { + const entities = extractEntities('Edit src/compress.ts and config.json files', 20); + expect(entities.some((e) => e.includes('compress.ts'))).toBe(true); + expect(entities.some((e) => e.includes('config.json'))).toBe(true); + }); + + it('extracts version numbers', () => { + const entities = extractEntities('Upgrade from v1.2.3 to 2.0.0'); + expect(entities.some((e) => e.includes('1.2.3'))).toBe(true); + expect(entities.some((e) => e.includes('2.0.0'))).toBe(true); + }); +}); + +describe('collectMessageEntities', () => { + it('collects entities across multiple messages', () => { + const messages = [ + msg('1', 'The fetchData function is critical'), + msg('2', 'We use getUserProfile in the auth flow'), + ]; + const entities = collectMessageEntities(messages); + expect(entities.has('fetchData')).toBe(true); + expect(entities.has('getUserProfile')).toBe(true); + }); + + it('skips empty messages', () => { + const messages = [msg('1', ''), msg('2', 'fetchData is used')]; + const entities = collectMessageEntities(messages); + expect(entities.has('fetchData')).toBe(true); + expect(entities.size).toBeGreaterThan(0); + }); +}); + +describe('computeEntityRetention', () => { + it('returns 1.0 when output preserves all entities', () => { + const input = [msg('1', 'Use fetchData with retryConfig')]; + const output = [msg('1', 'Use fetchData with retryConfig')]; + expect(computeEntityRetention(input, output)).toBe(1.0); + }); + + it('returns < 1.0 when entities are lost', () => { + const input = [msg('1', 'Use fetchData and getUserProfile and setConfig')]; + const output = [msg('1', '[summary: Use fetchData]')]; + const retention = computeEntityRetention(input, output); + expect(retention).toBeLessThan(1.0); + expect(retention).toBeGreaterThan(0); + }); + + it('returns 1.0 for empty input', () => { + const input = [msg('1', 'hello world')]; // no technical entities + const output = [msg('1', 'hi')]; + expect(computeEntityRetention(input, output)).toBe(1.0); + }); +}); + +describe('computeStructuralIntegrity', () => { + it('returns 1.0 when code fences are preserved', () => { + const content = 'Here is code:\n```js\nconsole.log("hi")\n```\nDone.'; + const input = [msg('1', content)]; + const output = [msg('1', content)]; + expect(computeStructuralIntegrity(input, output)).toBe(1.0); + }); + + it('returns 0.0 when all structural elements are removed', () => { + const input = [msg('1', '```js\nconsole.log("hi")\n```')]; + const output = [msg('1', '[summary: code was shown]')]; + expect(computeStructuralIntegrity(input, output)).toBe(0.0); + }); + + it('returns 1.0 when no structural elements exist', () => { + const input = [msg('1', 'Just plain prose here')]; + const output = [msg('1', 'Plain prose')]; + expect(computeStructuralIntegrity(input, output)).toBe(1.0); + }); +}); + +describe('computeReferenceCoherence', () => { + it('returns 1.0 when all defining messages are present', () => { + const input = [msg('1', 'Define fetchData here'), msg('2', 'Use fetchData later')]; + expect(computeReferenceCoherence(input, input)).toBe(1.0); + }); + + it('returns < 1.0 when a defining message is removed', () => { + const input = [ + msg('1', 'The fetchData function is defined in utils'), + msg('2', 'The fetchData function handles retries'), + ]; + const output = [msg('2', 'The fetchData function handles retries')]; + // fetchData defined in both, so msg 2 still has its own source — coherence should be 1.0 + expect(computeReferenceCoherence(input, output)).toBe(1.0); + }); +}); + +describe('computeQualityScore', () => { + it('returns all 1.0 for identical input/output', () => { + const messages = [msg('1', 'The fetchData function uses retryConfig')]; + const quality = computeQualityScore(messages, messages); + expect(quality.entity_retention).toBe(1.0); + expect(quality.structural_integrity).toBe(1.0); + expect(quality.reference_coherence).toBe(1.0); + expect(quality.quality_score).toBe(1.0); + }); + + it('quality_score is clamped to [0, 1]', () => { + const input = [msg('1', 'fetchData getUserProfile setConfig')]; + const output = [msg('1', '[summary: functions used]')]; + const quality = computeQualityScore(input, output); + expect(quality.quality_score).toBeGreaterThanOrEqual(0); + expect(quality.quality_score).toBeLessThanOrEqual(1.0); + }); +}); + +describe('quality metrics in compress()', () => { + it('includes quality metrics when compression occurs', () => { + const messages: Message[] = [ + msg( + '1', + 'The fetchData helper in the service layer should always use exponential backoff when retrying failed network requests against the upstream provider because we observed cascading failures during peak traffic periods.', + ), + msg( + '2', + 'The getUserProfile function needs to handle token expiration gracefully by triggering a silent refresh through the refreshAuth utility before the token actually expires to avoid interrupting the user experience.', + ), + msg('3', 'Sure, sounds good.'), + msg('4', 'What do you think?'), + ]; + + const result = compress(messages, { recencyWindow: 2 }); + + expect(result.compression.entity_retention).toBeDefined(); + expect(result.compression.structural_integrity).toBeDefined(); + expect(result.compression.reference_coherence).toBeDefined(); + expect(result.compression.quality_score).toBeDefined(); + expect(result.compression.entity_retention!).toBeGreaterThan(0); + expect(result.compression.quality_score!).toBeGreaterThan(0); + expect(result.compression.quality_score!).toBeLessThanOrEqual(1.0); + }); + + it('omits quality metrics when no compression occurs', () => { + const messages: Message[] = [msg('1', 'Short message'), msg('2', 'Another short one')]; + + const result = compress(messages, { recencyWindow: 10 }); + + expect(result.compression.entity_retention).toBeUndefined(); + expect(result.compression.quality_score).toBeUndefined(); + }); + + it('entity retention >= 0.5 for messages with known identifiers', () => { + const messages: Message[] = [ + msg( + '1', + 'The fetchData function calls getUserProfile which invokes validateToken and returns a refreshAuth promise with retryConfig options including maxRetries and connectionTimeout settings.', + ), + msg( + '2', + 'I looked at the general situation and everything seems to be running fine with no issues at all in the monitoring dashboard this week based on my observations.', + ), + msg('3', 'Latest message'), + msg('4', 'Current state'), + ]; + + const result = compress(messages, { recencyWindow: 2 }); + + // The summary should capture at least some of the entities from message 1 + expect(result.compression.entity_retention!).toBeGreaterThanOrEqual(0.3); + }); +}); diff --git a/tests/entropy.test.ts b/tests/entropy.test.ts new file mode 100644 index 0000000..c2b1791 --- /dev/null +++ b/tests/entropy.test.ts @@ -0,0 +1,181 @@ +import { describe, it, expect } from 'vitest'; +import { splitSentences, normalizeScores, combineScores } from '../src/entropy.js'; +import { compress } from '../src/compress.js'; +import type { Message } from '../src/types.js'; + +function msg(id: string, content: string, role = 'user'): Message { + return { id, index: 0, role, content }; +} + +describe('splitSentences', () => { + it('splits on sentence boundaries', () => { + const result = splitSentences('Hello world. How are you? Fine!'); + expect(result).toHaveLength(3); + }); + + it('handles single sentence', () => { + const result = splitSentences('Just one sentence'); + expect(result).toHaveLength(1); + }); + + it('handles empty text', () => { + const result = splitSentences(''); + expect(result).toHaveLength(0); + }); +}); + +describe('normalizeScores', () => { + it('normalizes to 0-1 range', () => { + const result = normalizeScores([2, 4, 6, 8, 10]); + expect(result[0]).toBe(0); + expect(result[4]).toBe(1); + expect(result[2]).toBeCloseTo(0.5); + }); + + it('handles all equal scores', () => { + const result = normalizeScores([5, 5, 5]); + expect(result).toEqual([0.5, 0.5, 0.5]); + }); + + it('handles empty array', () => { + expect(normalizeScores([])).toEqual([]); + }); +}); + +describe('combineScores', () => { + it('combines heuristic and entropy scores', () => { + const heuristic = [1, 5, 3]; + const entropy = [10, 2, 6]; + const combined = combineScores(heuristic, entropy); + expect(combined).toHaveLength(3); + // All should be between 0 and 1 + for (const s of combined) { + expect(s).toBeGreaterThanOrEqual(0); + expect(s).toBeLessThanOrEqual(1); + } + }); + + it('throws on mismatched lengths', () => { + expect(() => combineScores([1, 2], [1, 2, 3])).toThrow(); + }); + + it('respects entropy weight', () => { + const heuristic = [0, 10]; // normalized: [0, 1] + const entropy = [10, 0]; // normalized: [1, 0] + const combined = combineScores(heuristic, entropy, 1.0); // 100% entropy + // With full entropy weight, first should score higher + expect(combined[0]).toBeGreaterThan(combined[1]); + }); +}); + +describe('entropyScorer integration', () => { + it('uses sync entropy scorer in compress()', () => { + const messages: Message[] = [ + msg( + '1', + 'The fetchData function is critical for the service layer communication. Sure, that sounds good and we should proceed. The retry logic uses exponential backoff with jitter and circuit breaker pattern for fault tolerance.', + ), + msg('2', 'Latest update.'), + msg('3', 'Current state.'), + ]; + + // Mock scorer: give high scores to sentences with technical identifiers + const scorer = (sentences: string[]) => + sentences.map((s) => (s.includes('fetch') || s.includes('retry') ? 10 : 1)); + + const result = compress(messages, { + recencyWindow: 2, + entropyScorer: scorer, + entropyScorerMode: 'replace', + }); + + // Should still compress successfully + expect(result.compression.messages_compressed).toBeGreaterThan(0); + // The summary should favor the technical sentences + const msg1 = result.messages.find((m) => m.id === '1'); + expect(msg1?.content).toContain('summary'); + }); + + it('augment mode combines heuristic and entropy', () => { + const messages: Message[] = [ + msg( + '1', + 'The service returns 503 errors during peak traffic periods when load exceeds capacity thresholds. Sure, that sounds good and we should continue monitoring. The monitoring dashboard shows consistently high latency across multiple service endpoints.', + ), + msg('2', 'Latest update.'), + msg('3', 'Current state.'), + ]; + + // Mock scorer: boost the "503" sentence + const scorer = (sentences: string[]) => sentences.map((s) => (s.includes('503') ? 20 : 1)); + + const result = compress(messages, { + recencyWindow: 2, + entropyScorer: scorer, + entropyScorerMode: 'augment', + }); + + expect(result.compression.messages_compressed).toBeGreaterThan(0); + }); + + it('works with async entropy scorer', async () => { + const messages: Message[] = [ + msg( + '1', + 'The fetchData function handles retries and timeout logic for the service layer with exponential backoff and circuit breaker pattern implementation.', + ), + msg('2', 'Latest.'), + msg('3', 'Current.'), + ]; + + const asyncScorer = async (sentences: string[]) => + sentences.map((s) => (s.includes('fetch') ? 10 : 1)); + + // async scorer requires a summarizer to trigger async path + const result = await compress(messages, { + recencyWindow: 2, + entropyScorer: asyncScorer, + summarizer: (text) => text.slice(0, 100), // simple passthrough + }); + + expect(result.messages.length).toBeGreaterThan(0); + }); + + it('throws when async scorer used in sync mode', () => { + const messages: Message[] = [ + msg( + '1', + 'The fetchData function handles retries and timeout logic for the service layer with exponential backoff and circuit breaker pattern.', + ), + msg('2', 'Latest.'), + msg('3', 'Current.'), + ]; + + const asyncScorer = async (sentences: string[]) => + sentences.map((s) => (s.includes('fetch') ? 10 : 1)); + + expect(() => + compress(messages, { + recencyWindow: 2, + entropyScorer: asyncScorer, + }), + ).toThrow('Promise in sync mode'); + }); + + it('default behavior unchanged without entropy scorer', () => { + const messages: Message[] = [ + msg( + '1', + 'The fetchData helper function provides retry logic with exponential backoff for the distributed service layer across multiple availability zones.', + ), + msg('2', 'Latest.'), + msg('3', 'Current.'), + ]; + + const withoutEntropy = compress(messages, { recencyWindow: 2 }); + const withEntropy = compress(messages, { recencyWindow: 2 }); + + // Same result without scorer + expect(withoutEntropy.compression.ratio).toBe(withEntropy.compression.ratio); + }); +}); diff --git a/tests/feedback.test.ts b/tests/feedback.test.ts new file mode 100644 index 0000000..eaf5379 --- /dev/null +++ b/tests/feedback.test.ts @@ -0,0 +1,604 @@ +import { describe, it, expect, vi } from 'vitest'; +import { + createFeedbackCollector, + refineSummarizer, + tightenSummarizer, + refineSummarizerCandidates, + createDistillationPairs, + RECOMMENDED_HISTORY_THRESHOLD, + RECOMMENDED_OBSERVATION_THRESHOLD, +} from '../src/feedback.js'; +import type { + CompressResult, + CreateSummarizerOptions, + FeedbackResult, + Message, + OverPreservationResult, +} from '../src/types.js'; + +function msg(overrides: Partial & { id: string; index: number }): Message { + return { role: 'user', content: '', metadata: {}, ...overrides }; +} + +// --------------------------------------------------------------------------- +// createFeedbackCollector — UT step (analyze) +// --------------------------------------------------------------------------- + +describe('createFeedbackCollector', () => { + it('returns empty feedback when no pairs added', async () => { + const llm = vi.fn(); + const collector = createFeedbackCollector(llm); + const result = await collector.analyze(); + expect(result).toEqual({ lostPatterns: [], suggestedTerms: [], guidelines: [] }); + expect(llm).not.toHaveBeenCalled(); + }); + + it('returns empty feedback when all pairs succeeded', async () => { + const llm = vi.fn(); + const collector = createFeedbackCollector(llm); + const original = [msg({ id: '1', index: 0, content: 'hello world' })]; + const compressed = [msg({ id: '1', index: 0, content: '[summary: hello]' })]; + collector.add(original, compressed, { success: true }); + const result = await collector.analyze(); + expect(result).toEqual({ lostPatterns: [], suggestedTerms: [], guidelines: [] }); + expect(llm).not.toHaveBeenCalled(); + }); + + it('calls LLM with contrastive prompt when failed pairs exist', async () => { + const llm = vi.fn().mockResolvedValue( + JSON.stringify({ + lostPatterns: ['API endpoint URLs'], + suggestedTerms: ['fetchUser', 'POST /api/users'], + guidelines: ['Preserve all URL paths verbatim'], + }), + ); + const collector = createFeedbackCollector(llm); + const original = [msg({ id: '1', index: 0, content: 'Call POST /api/users to create' })]; + const compressed = [msg({ id: '1', index: 0, content: '[summary: API call]' })]; + collector.add(original, compressed, { success: false, error: 'Missing endpoint' }); + + const result = await collector.analyze(); + expect(llm).toHaveBeenCalledOnce(); + expect(result.lostPatterns).toEqual(['API endpoint URLs']); + expect(result.suggestedTerms).toEqual(['fetchUser', 'POST /api/users']); + expect(result.guidelines).toEqual(['Preserve all URL paths verbatim']); + + const prompt = llm.mock.calls[0][0] as string; + expect(prompt).toContain('POST /api/users'); + expect(prompt).toContain('[summary: API call]'); + expect(prompt).toContain('Missing endpoint'); + }); + + it('parses markdown-fenced JSON response', async () => { + const llm = vi.fn().mockResolvedValue( + '```json\n' + + JSON.stringify({ + lostPatterns: ['config keys'], + suggestedTerms: ['DB_HOST'], + guidelines: ['Keep env var names'], + }) + + '\n```', + ); + const collector = createFeedbackCollector(llm); + collector.add( + [msg({ id: '1', index: 0, content: 'Set DB_HOST=localhost' })], + [msg({ id: '1', index: 0, content: '[summary: config]' })], + { success: false }, + ); + const result = await collector.analyze(); + expect(result.lostPatterns).toEqual(['config keys']); + expect(result.suggestedTerms).toEqual(['DB_HOST']); + }); + + it('throws on malformed JSON', async () => { + const llm = vi.fn().mockResolvedValue('not json at all'); + const collector = createFeedbackCollector(llm); + collector.add( + [msg({ id: '1', index: 0, content: 'test' })], + [msg({ id: '1', index: 0, content: '[summary]' })], + { success: false }, + ); + await expect(collector.analyze()).rejects.toThrow(); + }); + + it('reflects added pairs via .pairs', () => { + const collector = createFeedbackCollector(vi.fn()); + expect(collector.pairs).toHaveLength(0); + const original = [msg({ id: '1', index: 0, content: 'a' })]; + const compressed = [msg({ id: '1', index: 0, content: 'b' })]; + collector.add(original, compressed, { success: true }); + collector.add(original, compressed, { success: false }); + expect(collector.pairs).toHaveLength(2); + expect(collector.pairs[0].outcome.success).toBe(true); + expect(collector.pairs[1].outcome.success).toBe(false); + }); +}); + +// --------------------------------------------------------------------------- +// createFeedbackCollector — CO step (analyzeOverPreservation) +// --------------------------------------------------------------------------- + +describe('createFeedbackCollector — analyzeOverPreservation', () => { + it('returns empty result when no pairs added', async () => { + const llm = vi.fn(); + const collector = createFeedbackCollector(llm); + const result = await collector.analyzeOverPreservation(); + expect(result).toEqual({ + unnecessaryPatterns: [], + removableTerms: [], + tighteningGuidelines: [], + }); + expect(llm).not.toHaveBeenCalled(); + }); + + it('returns empty result when no successful pairs', async () => { + const llm = vi.fn(); + const collector = createFeedbackCollector(llm); + collector.add( + [msg({ id: '1', index: 0, content: 'test' })], + [msg({ id: '1', index: 0, content: '[summary]' })], + { success: false }, + ); + const result = await collector.analyzeOverPreservation(); + expect(result).toEqual({ + unnecessaryPatterns: [], + removableTerms: [], + tighteningGuidelines: [], + }); + expect(llm).not.toHaveBeenCalled(); + }); + + it('calls LLM with over-preservation prompt for successful pairs', async () => { + const llm = vi.fn().mockResolvedValue( + JSON.stringify({ + unnecessaryPatterns: ['verbose error descriptions'], + removableTerms: ['DEBUG_MODE'], + tighteningGuidelines: ['Omit debug-level details from summaries'], + }), + ); + const collector = createFeedbackCollector(llm); + collector.add( + [ + msg({ + id: '1', + index: 0, + content: 'DEBUG_MODE=true, error: connection timeout at 10.0.0.1', + }), + ], + [msg({ id: '1', index: 0, content: '[summary: debug config and connection error]' })], + { success: true }, + ); + + const result = await collector.analyzeOverPreservation(); + expect(llm).toHaveBeenCalledOnce(); + expect(result.unnecessaryPatterns).toEqual(['verbose error descriptions']); + expect(result.removableTerms).toEqual(['DEBUG_MODE']); + expect(result.tighteningGuidelines).toEqual(['Omit debug-level details from summaries']); + + const prompt = llm.mock.calls[0][0] as string; + expect(prompt).toContain('compression efficiency'); + expect(prompt).toContain('DEBUG_MODE'); + }); + + it('handles markdown-fenced JSON in CO response', async () => { + const llm = vi.fn().mockResolvedValue( + '```json\n' + + JSON.stringify({ + unnecessaryPatterns: ['timestamps'], + removableTerms: [], + tighteningGuidelines: ['Skip timestamps'], + }) + + '\n```', + ); + const collector = createFeedbackCollector(llm); + collector.add( + [msg({ id: '1', index: 0, content: 'data' })], + [msg({ id: '1', index: 0, content: 'compressed' })], + { success: true }, + ); + const result = await collector.analyzeOverPreservation(); + expect(result.unnecessaryPatterns).toEqual(['timestamps']); + }); + + it('throws on malformed CO JSON', async () => { + const llm = vi.fn().mockResolvedValue('invalid'); + const collector = createFeedbackCollector(llm); + collector.add( + [msg({ id: '1', index: 0, content: 'test' })], + [msg({ id: '1', index: 0, content: 'c' })], + { success: true }, + ); + await expect(collector.analyzeOverPreservation()).rejects.toThrow(); + }); +}); + +// --------------------------------------------------------------------------- +// refineSummarizer (UT) +// --------------------------------------------------------------------------- + +describe('refineSummarizer', () => { + it('merges suggestedTerms into preserveTerms without duplicates', () => { + const opts: CreateSummarizerOptions = { preserveTerms: ['foo', 'bar'] }; + const feedback: FeedbackResult = { + lostPatterns: [], + suggestedTerms: ['bar', 'baz'], + guidelines: [], + }; + const result = refineSummarizer(opts, feedback); + expect(result.preserveTerms).toEqual(['foo', 'bar', 'baz']); + }); + + it('creates preserveTerms when none existed', () => { + const opts: CreateSummarizerOptions = {}; + const feedback: FeedbackResult = { + lostPatterns: [], + suggestedTerms: ['fetchUser'], + guidelines: [], + }; + const result = refineSummarizer(opts, feedback); + expect(result.preserveTerms).toEqual(['fetchUser']); + }); + + it('appends guidelines to existing systemPrompt', () => { + const opts: CreateSummarizerOptions = { systemPrompt: 'You summarize code.' }; + const feedback: FeedbackResult = { + lostPatterns: [], + suggestedTerms: [], + guidelines: ['Keep URLs', 'Keep error codes'], + }; + const result = refineSummarizer(opts, feedback); + expect(result.systemPrompt).toBe('You summarize code.\n\n- Keep URLs\n- Keep error codes'); + }); + + it('creates systemPrompt from guidelines when none existed', () => { + const opts: CreateSummarizerOptions = {}; + const feedback: FeedbackResult = { + lostPatterns: [], + suggestedTerms: [], + guidelines: ['Preserve all identifiers'], + }; + const result = refineSummarizer(opts, feedback); + expect(result.systemPrompt).toBe('- Preserve all identifiers'); + }); + + it('returns unchanged options on empty feedback', () => { + const opts: CreateSummarizerOptions = { + maxResponseTokens: 500, + mode: 'aggressive', + systemPrompt: 'existing', + preserveTerms: ['x'], + }; + const feedback: FeedbackResult = { lostPatterns: [], suggestedTerms: [], guidelines: [] }; + const result = refineSummarizer(opts, feedback); + expect(result).toEqual(opts); + expect(result).not.toBe(opts); + }); + + it('preserves maxResponseTokens and mode passthrough', () => { + const opts: CreateSummarizerOptions = { maxResponseTokens: 500, mode: 'aggressive' }; + const feedback: FeedbackResult = { + lostPatterns: [], + suggestedTerms: ['term'], + guidelines: ['rule'], + }; + const result = refineSummarizer(opts, feedback); + expect(result.maxResponseTokens).toBe(500); + expect(result.mode).toBe('aggressive'); + }); +}); + +// --------------------------------------------------------------------------- +// tightenSummarizer (CO) +// --------------------------------------------------------------------------- + +describe('tightenSummarizer', () => { + it('removes terms listed in removableTerms', () => { + const opts: CreateSummarizerOptions = { preserveTerms: ['foo', 'bar', 'baz'] }; + const feedback: OverPreservationResult = { + unnecessaryPatterns: [], + removableTerms: ['bar'], + tighteningGuidelines: [], + }; + const result = tightenSummarizer(opts, feedback); + expect(result.preserveTerms).toEqual(['foo', 'baz']); + }); + + it('appends tighteningGuidelines to systemPrompt', () => { + const opts: CreateSummarizerOptions = { systemPrompt: 'Base prompt.' }; + const feedback: OverPreservationResult = { + unnecessaryPatterns: [], + removableTerms: [], + tighteningGuidelines: ['Be more concise', 'Skip debug info'], + }; + const result = tightenSummarizer(opts, feedback); + expect(result.systemPrompt).toBe('Base prompt.\n\n- Be more concise\n- Skip debug info'); + }); + + it('creates systemPrompt from tighteningGuidelines when none existed', () => { + const opts: CreateSummarizerOptions = {}; + const feedback: OverPreservationResult = { + unnecessaryPatterns: [], + removableTerms: [], + tighteningGuidelines: ['Remove timestamps'], + }; + const result = tightenSummarizer(opts, feedback); + expect(result.systemPrompt).toBe('- Remove timestamps'); + }); + + it('returns unchanged options on empty feedback', () => { + const opts: CreateSummarizerOptions = { + maxResponseTokens: 300, + preserveTerms: ['x'], + systemPrompt: 'existing', + }; + const feedback: OverPreservationResult = { + unnecessaryPatterns: [], + removableTerms: [], + tighteningGuidelines: [], + }; + const result = tightenSummarizer(opts, feedback); + expect(result).toEqual(opts); + expect(result).not.toBe(opts); + }); + + it('preserves maxResponseTokens and mode', () => { + const opts: CreateSummarizerOptions = { maxResponseTokens: 500, mode: 'aggressive' }; + const feedback: OverPreservationResult = { + unnecessaryPatterns: [], + removableTerms: ['x'], + tighteningGuidelines: [], + }; + const result = tightenSummarizer(opts, feedback); + expect(result.maxResponseTokens).toBe(500); + expect(result.mode).toBe('aggressive'); + }); +}); + +// --------------------------------------------------------------------------- +// refineSummarizerCandidates +// --------------------------------------------------------------------------- + +describe('refineSummarizerCandidates', () => { + it('generates N candidate options from LLM response', async () => { + const llm = vi.fn().mockResolvedValue( + JSON.stringify([ + { preserveTerms: ['apiKey'], guidelines: ['Keep auth tokens'] }, + { preserveTerms: ['endpoint'], guidelines: ['Keep URLs'] }, + { preserveTerms: ['userId', 'apiKey'], guidelines: ['Keep all identifiers'] }, + ]), + ); + const opts: CreateSummarizerOptions = { preserveTerms: ['base'] }; + const feedback: FeedbackResult = { + lostPatterns: ['auth info'], + suggestedTerms: ['apiKey'], + guidelines: ['Keep tokens'], + }; + + const candidates = await refineSummarizerCandidates(llm, opts, feedback, 3); + expect(candidates).toHaveLength(3); + expect(llm).toHaveBeenCalledOnce(); + + // Each candidate should merge new terms with existing + expect(candidates[0].preserveTerms).toEqual(['base', 'apiKey']); + expect(candidates[1].preserveTerms).toEqual(['base', 'endpoint']); + expect(candidates[2].preserveTerms).toEqual(['base', 'userId', 'apiKey']); + }); + + it('deduplicates terms against existing preserveTerms', async () => { + const llm = vi + .fn() + .mockResolvedValue(JSON.stringify([{ preserveTerms: ['existing', 'new'], guidelines: [] }])); + const opts: CreateSummarizerOptions = { preserveTerms: ['existing'] }; + const feedback: FeedbackResult = { lostPatterns: [], suggestedTerms: [], guidelines: [] }; + + const candidates = await refineSummarizerCandidates(llm, opts, feedback, 1); + expect(candidates[0].preserveTerms).toEqual(['existing', 'new']); + }); + + it('appends candidate guidelines to existing systemPrompt', async () => { + const llm = vi + .fn() + .mockResolvedValue(JSON.stringify([{ preserveTerms: [], guidelines: ['New rule'] }])); + const opts: CreateSummarizerOptions = { systemPrompt: 'Base.' }; + const feedback: FeedbackResult = { lostPatterns: [], suggestedTerms: [], guidelines: [] }; + + const candidates = await refineSummarizerCandidates(llm, opts, feedback, 1); + expect(candidates[0].systemPrompt).toBe('Base.\n\n- New rule'); + }); + + it('handles markdown-fenced JSON', async () => { + const llm = vi + .fn() + .mockResolvedValue( + '```json\n' + JSON.stringify([{ preserveTerms: ['a'], guidelines: ['b'] }]) + '\n```', + ); + const opts: CreateSummarizerOptions = {}; + const feedback: FeedbackResult = { lostPatterns: [], suggestedTerms: [], guidelines: [] }; + + const candidates = await refineSummarizerCandidates(llm, opts, feedback, 1); + expect(candidates).toHaveLength(1); + expect(candidates[0].preserveTerms).toEqual(['a']); + }); + + it('returns fewer candidates when LLM provides fewer than requested', async () => { + const llm = vi + .fn() + .mockResolvedValue(JSON.stringify([{ preserveTerms: ['only'], guidelines: ['one'] }])); + const opts: CreateSummarizerOptions = {}; + const feedback: FeedbackResult = { lostPatterns: [], suggestedTerms: [], guidelines: [] }; + + const candidates = await refineSummarizerCandidates(llm, opts, feedback, 5); + expect(candidates).toHaveLength(1); + expect(candidates[0].preserveTerms).toEqual(['only']); + }); + + it('throws on non-array JSON', async () => { + const llm = vi.fn().mockResolvedValue('{"not": "array"}'); + const opts: CreateSummarizerOptions = {}; + const feedback: FeedbackResult = { lostPatterns: [], suggestedTerms: [], guidelines: [] }; + + await expect(refineSummarizerCandidates(llm, opts, feedback)).rejects.toThrow(); + }); + + it('defaults to 5 candidates', async () => { + const llm = vi.fn().mockResolvedValue( + JSON.stringify( + Array.from({ length: 5 }, (_, i) => ({ + preserveTerms: [`term_${i}`], + guidelines: [`rule_${i}`], + })), + ), + ); + const opts: CreateSummarizerOptions = {}; + const feedback: FeedbackResult = { lostPatterns: [], suggestedTerms: [], guidelines: [] }; + + const candidates = await refineSummarizerCandidates(llm, opts, feedback); + expect(candidates).toHaveLength(5); + + // Verify the prompt asked for 5 + const prompt = llm.mock.calls[0][0] as string; + expect(prompt).toContain('5'); + }); +}); + +// --------------------------------------------------------------------------- +// createDistillationPairs +// --------------------------------------------------------------------------- + +describe('createDistillationPairs', () => { + it('extracts pairs from compressed messages with verbatim originals', () => { + const result: CompressResult = { + messages: [ + msg({ + id: '1', + index: 0, + content: '[summary: discussed API design]', + metadata: { _cce_original: { ids: ['orig_1'], summary_id: 'sum_1', version: 0 } }, + }), + msg({ id: '2', index: 1, content: 'preserved message' }), + ], + compression: { + original_version: 0, + ratio: 2, + token_ratio: 2, + messages_compressed: 1, + messages_preserved: 1, + }, + verbatim: { + orig_1: msg({ + id: 'orig_1', + index: 0, + content: 'We discussed the API design at length including REST vs GraphQL tradeoffs.', + }), + }, + }; + + const pairs = createDistillationPairs(result); + expect(pairs).toHaveLength(1); + expect(pairs[0].input).toContain('REST vs GraphQL'); + expect(pairs[0].output).toBe('[summary: discussed API design]'); + }); + + it('handles merged messages (multiple source IDs)', () => { + const result: CompressResult = { + messages: [ + msg({ + id: 'merged', + index: 0, + content: '[summary: two discussions merged]', + metadata: { + _cce_original: { ids: ['a', 'b'], summary_id: 'sum_m', version: 0 }, + }, + }), + ], + compression: { + original_version: 0, + ratio: 2, + token_ratio: 2, + messages_compressed: 2, + messages_preserved: 0, + }, + verbatim: { + a: msg({ id: 'a', index: 0, content: 'First discussion topic.' }), + b: msg({ id: 'b', index: 1, content: 'Second discussion topic.' }), + }, + }; + + const pairs = createDistillationPairs(result); + expect(pairs).toHaveLength(1); + expect(pairs[0].input).toContain('First discussion'); + expect(pairs[0].input).toContain('Second discussion'); + }); + + it('skips messages without _cce_original metadata', () => { + const result: CompressResult = { + messages: [msg({ id: '1', index: 0, content: 'just a regular message' })], + compression: { + original_version: 0, + ratio: 1, + token_ratio: 1, + messages_compressed: 0, + messages_preserved: 1, + }, + verbatim: {}, + }; + + const pairs = createDistillationPairs(result); + expect(pairs).toHaveLength(0); + }); + + it('skips when verbatim entry is missing', () => { + const result: CompressResult = { + messages: [ + msg({ + id: '1', + index: 0, + content: '[summary: lost]', + metadata: { _cce_original: { ids: ['gone'], summary_id: 'sum', version: 0 } }, + }), + ], + compression: { + original_version: 0, + ratio: 2, + token_ratio: 2, + messages_compressed: 1, + messages_preserved: 0, + }, + verbatim: {}, + }; + + const pairs = createDistillationPairs(result); + expect(pairs).toHaveLength(0); + }); + + it('returns empty array for no-op compression', () => { + const result: CompressResult = { + messages: [msg({ id: '1', index: 0, content: 'hello' })], + compression: { + original_version: 0, + ratio: 1, + token_ratio: 1, + messages_compressed: 0, + messages_preserved: 1, + }, + verbatim: {}, + }; + + const pairs = createDistillationPairs(result); + expect(pairs).toHaveLength(0); + }); +}); + +// --------------------------------------------------------------------------- +// Recommended thresholds +// --------------------------------------------------------------------------- + +describe('recommended thresholds', () => { + it('exports RECOMMENDED_HISTORY_THRESHOLD as 4096', () => { + expect(RECOMMENDED_HISTORY_THRESHOLD).toBe(4096); + }); + + it('exports RECOMMENDED_OBSERVATION_THRESHOLD as 1024', () => { + expect(RECOMMENDED_OBSERVATION_THRESHOLD).toBe(1024); + }); +}); diff --git a/tests/flow.test.ts b/tests/flow.test.ts new file mode 100644 index 0000000..033bf9f --- /dev/null +++ b/tests/flow.test.ts @@ -0,0 +1,225 @@ +import { describe, it, expect } from 'vitest'; +import { detectFlowChains, summarizeChain } from '../src/flow.js'; +import { compress } from '../src/compress.js'; +import type { Message } from '../src/types.js'; + +function msg(id: string, content: string, role = 'user'): Message { + return { id, index: 0, role, content }; +} + +describe('detectFlowChains', () => { + it('detects Q&A pairs', () => { + const messages: Message[] = [ + msg( + 'q', + 'How does the fetchData function handle retries when the upstream service is unavailable?', + 'user', + ), + msg( + 'a', + 'The fetchData function uses exponential backoff with a base delay of 200ms and a maximum of 5 retries. It also implements a circuit breaker pattern.', + 'assistant', + ), + msg('recent', 'Thanks!', 'user'), + ]; + + const chains = detectFlowChains(messages, 2, new Set(['system'])); + expect(chains.length).toBe(1); + expect(chains[0].type).toBe('qa'); + expect(chains[0].indices).toContain(0); + expect(chains[0].indices).toContain(1); + }); + + it('detects request → action chains', () => { + const messages: Message[] = [ + msg('req', 'Can you add logging to the authentication middleware for debugging?', 'user'), + msg( + 'action', + "Done! I've added structured logging to the auth middleware. Each request now logs the token validation step and any errors.", + 'assistant', + ), + msg('conf', 'Perfect, thanks!', 'user'), + msg('recent', 'Now lets work on the API.', 'user'), + ]; + + const chains = detectFlowChains(messages, 3, new Set(['system'])); + expect(chains.length).toBe(1); + expect(chains[0].type).toBe('request_action'); + expect(chains[0].indices).toContain(0); + expect(chains[0].indices).toContain(1); + // Confirmation should be included + expect(chains[0].indices).toContain(2); + }); + + it('detects correction chains', () => { + const messages: Message[] = [ + msg( + 'original', + 'Use Redis for the caching layer with a 3600 second TTL for all session data.', + 'user', + ), + msg( + 'correction', + 'Actually, use Memcached instead. Redis is overkill for simple key-value session storage.', + 'user', + ), + msg('recent', 'Got it.', 'assistant'), + ]; + + const chains = detectFlowChains(messages, 2, new Set(['system'])); + expect(chains.length).toBe(1); + expect(chains[0].type).toBe('correction'); + }); + + it('skips system messages', () => { + const messages: Message[] = [ + msg('sys', 'You are a helpful assistant.', 'system'), + msg('q', 'How does authentication work in this app?', 'user'), + msg('recent', 'It uses JWT tokens.', 'assistant'), + ]; + + const chains = detectFlowChains(messages, 2, new Set(['system'])); + // System message should not be part of any chain + for (const chain of chains) { + expect(chain.indices).not.toContain(0); + } + }); + + it('returns empty for messages all in recency window', () => { + const messages: Message[] = [ + msg('1', 'How does it work?', 'user'), + msg('2', 'It uses JWT tokens.', 'assistant'), + ]; + + const chains = detectFlowChains(messages, 0, new Set(['system'])); + expect(chains).toHaveLength(0); + }); +}); + +describe('summarizeChain', () => { + it('produces Q&A summary', () => { + const messages: Message[] = [ + msg('q', 'How does the fetchData function handle retries?', 'user'), + msg('a', 'It uses exponential backoff with 5 retries.', 'assistant'), + ]; + + const chain = { indices: [0, 1], type: 'qa' as const, label: 'test' }; + const summary = summarizeChain(chain, messages); + expect(summary).toContain('Q:'); + expect(summary).toContain('A:'); + }); + + it('produces request→action summary', () => { + const messages: Message[] = [ + msg('req', 'Can you add logging to the auth middleware?', 'user'), + msg('action', 'Done! Added structured logging.', 'assistant'), + msg('conf', 'Perfect!', 'user'), + ]; + + const chain = { indices: [0, 1, 2], type: 'request_action' as const, label: 'test' }; + const summary = summarizeChain(chain, messages); + expect(summary).toContain('Request:'); + expect(summary).toContain('confirmed'); + }); + + it('produces correction summary', () => { + const messages: Message[] = [ + msg('old', 'Use Redis for caching.', 'user'), + msg('fix', 'Actually, use Memcached instead.', 'user'), + ]; + + const chain = { indices: [0, 1], type: 'correction' as const, label: 'test' }; + const summary = summarizeChain(chain, messages); + expect(summary).toContain('Correction:'); + expect(summary).toContain('Memcached'); + }); +}); + +describe('conversationFlow option in compress()', () => { + it('compresses Q&A pairs as units', () => { + const messages: Message[] = [ + msg( + 'q', + 'How does the fetchData function handle retries when the upstream service is down and returning 503 errors consistently across all endpoints in the distributed system?', + 'user', + ), + msg( + 'a', + 'The fetchData function uses exponential backoff with a base delay of 200 milliseconds and a maximum of 5 retries before giving up and throwing a ServiceUnavailable error to the calling service layer code.', + 'assistant', + ), + msg( + 'filler', + 'I also looked at the general monitoring data and everything seems to be running within acceptable parameters for this quarter without any unexpected issues in the system.', + 'assistant', + ), + msg('recent1', 'What about caching?', 'user'), + msg('recent2', 'We can add Redis caching.', 'assistant'), + ]; + + const withFlow = compress(messages, { + recencyWindow: 2, + conversationFlow: true, + trace: true, + }); + + // Q&A should be compressed as a unit + const flowDecisions = withFlow.compression.decisions?.filter((d) => + d.reason.startsWith('flow:'), + ); + expect(flowDecisions?.length).toBeGreaterThan(0); + + // The compressed Q&A should mention both question and answer + const qaMsg = withFlow.messages.find( + (m) => typeof m.content === 'string' && m.content.includes('Q:'), + ); + expect(qaMsg).toBeDefined(); + }); + + it('does nothing when conversationFlow is false', () => { + const messages: Message[] = [ + msg( + 'q', + 'How does the fetchData function handle retries when upstream returns 503 errors and the circuit breaker is open?', + 'user', + ), + msg( + 'a', + 'It uses exponential backoff with a maximum of 5 retries and 200ms base delay before throwing ServiceUnavailable.', + 'assistant', + ), + msg('recent', 'Got it.', 'user'), + ]; + + const result = compress(messages, { recencyWindow: 1, trace: true }); + const flowDecisions = result.compression.decisions?.filter((d) => d.reason.startsWith('flow:')); + expect(flowDecisions?.length ?? 0).toBe(0); + }); + + it('preserves verbatim store for flow-compressed messages', () => { + const messages: Message[] = [ + msg( + 'q', + 'How does the fetchData function handle retries when the upstream service returns 503 errors during peak traffic?', + 'user', + ), + msg( + 'a', + 'The fetchData function uses exponential backoff with a base delay of 200 milliseconds. After 5 retries it throws a ServiceUnavailable error.', + 'assistant', + ), + msg('recent', 'Thanks, that helps.', 'user'), + ]; + + const result = compress(messages, { + recencyWindow: 1, + conversationFlow: true, + }); + + // Both original messages should be in verbatim + if (result.compression.messages_compressed > 0) { + expect(result.verbatim['q']).toBeDefined(); + expect(result.verbatim['a']).toBeDefined(); + } + }); +}); diff --git a/tests/importance.test.ts b/tests/importance.test.ts new file mode 100644 index 0000000..cadaf63 --- /dev/null +++ b/tests/importance.test.ts @@ -0,0 +1,105 @@ +import { describe, it, expect } from 'vitest'; +import { + computeImportance, + scoreContentSignals, + DEFAULT_IMPORTANCE_THRESHOLD, +} from '../src/importance.js'; +import type { Message } from '../src/types.js'; + +function msg(id: string, content: string, role = 'user'): Message { + return { id, index: 0, role, content }; +} + +describe('scoreContentSignals', () => { + it('returns 0 for plain prose', () => { + expect(scoreContentSignals('The weather is nice today.')).toBe(0); + }); + + it('scores decision content', () => { + const score = scoreContentSignals('We must use PostgreSQL for the database.'); + expect(score).toBeGreaterThan(0); + }); + + it('scores correction content highest', () => { + const correctionScore = scoreContentSignals('Actually, use Redis instead of Memcached.'); + const decisionScore = scoreContentSignals('We should use Redis for caching.'); + expect(correctionScore).toBeGreaterThan(decisionScore); + }); + + it('scores constraint content', () => { + const score = scoreContentSignals('There is a hard deadline for this feature.'); + expect(score).toBeGreaterThan(0); + }); + + it('caps at 0.40', () => { + // Message with all signals + const score = scoreContentSignals( + 'Actually, we must use PostgreSQL. This is a hard requirement and a blocker for the deadline.', + ); + expect(score).toBeLessThanOrEqual(0.4); + }); +}); + +describe('computeImportance', () => { + it('returns empty map for empty messages', () => { + const scores = computeImportance([]); + expect(scores.size).toBe(0); + }); + + it('gives higher score to messages referenced by later messages', () => { + const messages: Message[] = [ + msg('1', 'We should use the fetchData function to get results from the API.'), + msg('2', 'The fetchData function needs error handling for timeout cases.'), + msg('3', 'Also add retry logic to fetchData for network failures.'), + msg('4', 'The weather looks nice today and I had a great lunch.'), + ]; + + const scores = computeImportance(messages); + + // Message 1 mentions fetchData which is referenced by messages 2 and 3 + const score1 = scores.get(0)!; + const score4 = scores.get(3)!; + expect(score1).toBeGreaterThan(score4); + }); + + it('gives recency bonus to later messages', () => { + const messages: Message[] = [ + msg('1', 'Some generic content about nothing in particular here.'), + msg('2', 'Another generic message about different unrelated topics.'), + ]; + + const scores = computeImportance(messages); + // Message 2 (index 1) should have higher recency than message 1 (index 0) + expect(scores.get(1)!).toBeGreaterThan(scores.get(0)!); + }); + + it('boosts messages with decision/correction content', () => { + const messages: Message[] = [ + msg('1', 'The sky is blue and the grass is green today.'), + msg('2', 'We must always validate user input before processing.'), + ]; + + const scores = computeImportance(messages); + expect(scores.get(1)!).toBeGreaterThan(scores.get(0)!); + }); + + it('all scores are in 0–1 range', () => { + const messages: Message[] = [ + msg('1', 'Actually, we must use the fetchData function. This is a hard requirement.'), + msg('2', 'The fetchData function handles all API calls.'), + msg('3', 'Make sure fetchData has retry logic.'), + ]; + + const scores = computeImportance(messages); + for (const score of scores.values()) { + expect(score).toBeGreaterThanOrEqual(0); + expect(score).toBeLessThanOrEqual(1); + } + }); +}); + +describe('DEFAULT_IMPORTANCE_THRESHOLD', () => { + it('is 0.65', () => { + expect(DEFAULT_IMPORTANCE_THRESHOLD).toBe(0.65); + }); +}); diff --git a/tests/ml-classifier.test.ts b/tests/ml-classifier.test.ts new file mode 100644 index 0000000..e35399a --- /dev/null +++ b/tests/ml-classifier.test.ts @@ -0,0 +1,164 @@ +import { describe, it, expect } from 'vitest'; +import { + compressWithTokenClassifierSync, + compressWithTokenClassifier, + whitespaceTokenize, + createMockTokenClassifier, +} from '../src/ml-classifier.js'; +import { compress } from '../src/compress.js'; +import type { Message } from '../src/types.js'; + +function msg(id: string, content: string, role = 'user'): Message { + return { id, index: 0, role, content }; +} + +describe('whitespaceTokenize', () => { + it('splits text on whitespace', () => { + expect(whitespaceTokenize('hello world foo')).toEqual(['hello', 'world', 'foo']); + }); + + it('handles multiple spaces', () => { + expect(whitespaceTokenize('a b c')).toEqual(['a', 'b', 'c']); + }); + + it('returns empty for empty string', () => { + expect(whitespaceTokenize('')).toEqual([]); + }); +}); + +describe('createMockTokenClassifier', () => { + it('keeps tokens matching patterns', () => { + const classifier = createMockTokenClassifier([/fetch/i, /retr/i]); + const result = classifier('The fetchData function handles retries gracefully.'); + const kept = result.filter((t) => t.keep); + expect(kept.some((t) => t.token.includes('fetch'))).toBe(true); + expect(kept.some((t) => t.token.includes('retries'))).toBe(true); + }); + + it('marks non-matching tokens as remove', () => { + const classifier = createMockTokenClassifier([/^fetch$/]); + const result = classifier('The fetchData function'); + const removed = result.filter((t) => !t.keep); + expect(removed.length).toBeGreaterThan(0); + }); +}); + +describe('compressWithTokenClassifierSync', () => { + it('produces shorter output', () => { + const classifier = createMockTokenClassifier([ + /fetch/i, + /retry/i, + /backoff/i, + /function/i, + /handles/i, + ]); + const text = + 'The fetchData function handles retries with exponential backoff for all API calls in the service layer.'; + const result = compressWithTokenClassifierSync(text, classifier); + expect(result.length).toBeLessThan(text.length); + expect(result).toContain('fetchData'); + }); + + it('falls back when compressed is longer', () => { + // Classifier that keeps everything — compression won't help + const classifier = createMockTokenClassifier([/.*/]); + const text = 'Short text.'; + const result = compressWithTokenClassifierSync(text, classifier); + expect(result.length).toBeGreaterThan(0); + }); + + it('throws on async classifier in sync mode', () => { + const asyncClassifier = async (content: string) => + whitespaceTokenize(content).map((t) => ({ token: t, keep: true, confidence: 0.9 })); + + expect(() => compressWithTokenClassifierSync('test text', asyncClassifier)).toThrow( + 'Promise in sync mode', + ); + }); +}); + +describe('compressWithTokenClassifier (async)', () => { + it('works with async classifier', async () => { + const classifier = async (content: string) => + whitespaceTokenize(content).map((t) => ({ + token: t, + keep: /fetch|retry|function/i.test(t), + confidence: 0.9, + })); + + const result = await compressWithTokenClassifier( + 'The fetchData function handles retries gracefully in the service layer.', + classifier, + ); + expect(result).toContain('fetchData'); + expect(result).toContain('function'); + }); +}); + +describe('mlTokenClassifier option in compress()', () => { + it('uses token classifier for prose compression', () => { + const classifier = createMockTokenClassifier([ + /fetch/i, + /retry/i, + /backoff/i, + /function/i, + /exponential/i, + /service/i, + ]); + + const messages: Message[] = [ + msg( + '1', + 'The fetchData function in the service layer handles all API communication with exponential backoff retry logic and circuit breaker pattern for fault tolerance across distributed services.', + ), + msg('recent', 'What about timeouts?'), + ]; + + const result = compress(messages, { + recencyWindow: 1, + mlTokenClassifier: classifier, + }); + + expect(result.compression.messages_compressed).toBeGreaterThan(0); + const msg1 = result.messages.find((m) => m.id === '1'); + // Should contain key tokens + expect(msg1?.content).toContain('fetch'); + }); + + it('preserves code fences even with ML classifier', () => { + const classifier = createMockTokenClassifier([/fetch/i]); + + const messages: Message[] = [ + msg( + '1', + 'Use fetchData like this:\n\n```typescript\nconst data = await fetchData(url);\n```\n\nThe fetchData function handles retries automatically with exponential backoff for all requests.', + ), + msg('recent', 'Got it.'), + ]; + + const result = compress(messages, { + recencyWindow: 1, + mlTokenClassifier: classifier, + }); + + // Code fence should survive (code-split preserves fences) + const msg1 = result.messages.find((m) => m.id === '1'); + if (msg1?.content?.includes('```')) { + expect(msg1.content).toContain('fetchData'); + } + }); + + it('default behavior unchanged without ML classifier', () => { + const messages: Message[] = [ + msg( + '1', + 'The fetchData function handles retries with exponential backoff for the distributed service layer communication.', + ), + msg('recent', 'OK.'), + ]; + + const withML = compress(messages, { recencyWindow: 1 }); + const withoutML = compress(messages, { recencyWindow: 1 }); + expect(withML.compression.ratio).toBe(withoutML.compression.ratio); + }); +}); diff --git a/tests/relevance.test.ts b/tests/relevance.test.ts new file mode 100644 index 0000000..c41f21b --- /dev/null +++ b/tests/relevance.test.ts @@ -0,0 +1,150 @@ +import { describe, it, expect } from 'vitest'; +import { compress, bestSentenceScore } from '../src/index.js'; +import type { Message } from '../src/types.js'; + +function msg(id: string, content: string, role = 'user'): Message { + return { id, index: 0, role, content }; +} + +describe('bestSentenceScore', () => { + it('scores technical content higher than filler', () => { + const technical = bestSentenceScore( + 'The fetchData function uses exponential backoff with 5 retries.', + ); + const filler = bestSentenceScore( + 'Sure, that sounds good and I think we should probably do that.', + ); + expect(technical).toBeGreaterThan(filler); + }); + + it('returns the best sentence score from multi-sentence text', () => { + const score = bestSentenceScore('Well, okay. The fetchData function is critical. Sure.'); + // Should return the score of the best sentence (the one with fetchData) + expect(score).toBeGreaterThan(0); + }); + + it('handles single-sentence text', () => { + const score = bestSentenceScore('Hello world'); + expect(typeof score).toBe('number'); + }); +}); + +describe('relevanceThreshold option', () => { + it('drops low-relevance messages to stubs when threshold is set', () => { + const messages: Message[] = [ + msg( + 'filler1', + 'I think that sounds like a reasonable approach and we should probably go ahead with it since it seems like the right thing to do at this point in the project.', + ), + msg( + 'filler2', + 'Yeah I agree with everything you said and I think we are on the right track with this approach and should continue moving forward with the current plan.', + ), + msg('recent1', 'The fetchData function needs retry logic.'), + msg('recent2', 'Add exponential backoff to the service layer.'), + ]; + + const result = compress(messages, { + recencyWindow: 2, + relevanceThreshold: 5, // moderate threshold — filler scores below this + trace: true, + }); + + // Filler messages should be dropped to a stub + const filler1Out = result.messages.find((m) => m.id === 'filler1'); + expect(filler1Out?.content).toContain('omitted'); + + // Stats should reflect the drop + expect(result.compression.messages_relevance_dropped).toBeGreaterThan(0); + }); + + it('keeps high-relevance messages as normal summaries', () => { + const messages: Message[] = [ + msg( + 'technical', + 'The fetchData helper should use exponential backoff with a maximum of 5 retries and a base delay of 200ms. The connectionPool should be configured with maxConnections set to 20 and idleTimeout of 30 seconds.', + ), + msg('recent', 'Latest update.'), + msg('recent2', 'Current state.'), + ]; + + const result = compress(messages, { + recencyWindow: 2, + relevanceThreshold: 2, // low threshold — technical content scores above this + trace: true, + }); + + // Technical message should NOT be dropped to a stub + const techOut = result.messages.find((m) => m.id === 'technical'); + expect(techOut?.content).not.toContain('omitted'); + expect(result.compression.messages_relevance_dropped ?? 0).toBe(0); + }); + + it('does nothing when relevanceThreshold is not set', () => { + const messages: Message[] = [ + msg( + 'filler', + 'I think that sounds reasonable and we should go ahead with the current plan since everything looks good so far from my perspective.', + ), + msg('recent', 'Latest.'), + msg('recent2', 'Current.'), + ]; + + const result = compress(messages, { recencyWindow: 2 }); + expect(result.compression.messages_relevance_dropped).toBeUndefined(); + }); + + it('groups consecutive dropped messages into a single stub', () => { + const messages: Message[] = [ + msg( + 'filler1', + 'Sure, that makes sense and I agree we should continue with the current approach without any major changes to the plan going forward for the rest of the project.', + ), + msg( + 'filler2', + 'Okay great, I think everything is looking good and we can proceed as discussed earlier in our conversation about the project timeline and milestones ahead.', + ), + msg( + 'filler3', + 'Right, sounds good to me and I have nothing else to add at this point so we can move forward with confidence in our current direction and approach.', + ), + msg('recent1', 'Add retry logic.'), + msg('recent2', 'Fix the timeout.'), + ]; + + const result = compress(messages, { + recencyWindow: 2, + relevanceThreshold: 5, + }); + + // All 3 filler messages should be in one group stub + const stubs = result.messages.filter((m) => m.content?.includes('omitted')); + expect(stubs.length).toBe(1); + expect(stubs[0].content).toContain('3 messages'); + }); + + it('preserves verbatim store for dropped messages (round-trip)', () => { + const messages: Message[] = [ + msg( + 'filler', + 'I think everything looks good and we should proceed with the current plan as discussed in our previous conversation about the project status.', + ), + msg('recent', 'Continue with the plan.'), + msg('recent2', 'Confirmed.'), + ]; + + const result = compress(messages, { + recencyWindow: 2, + relevanceThreshold: 5, + }); + + // Original content should be in verbatim store + if ( + result.compression.messages_relevance_dropped && + result.compression.messages_relevance_dropped > 0 + ) { + expect(result.verbatim['filler']).toBeDefined(); + expect(result.verbatim['filler'].content).toContain('everything looks good'); + } + }); +}); diff --git a/tests/retention.test.ts b/tests/retention.test.ts new file mode 100644 index 0000000..d2deaa8 --- /dev/null +++ b/tests/retention.test.ts @@ -0,0 +1,145 @@ +import { describe, it, expect } from 'vitest'; +import { + extractKeywords, + extractEntities, + extractStructural, + analyzeRetention, +} from '../bench/baseline.js'; + +describe('retention analysis', () => { + describe('extractKeywords', () => { + it('catches camelCase identifiers', () => { + const keywords = extractKeywords('The getUserProfile function calls createSession.'); + expect(keywords).toContain('getUserProfile'); + expect(keywords).toContain('createSession'); + }); + + it('catches PascalCase identifiers', () => { + const keywords = extractKeywords('Use the WebSocket and TypeScript classes.'); + expect(keywords).toContain('WebSocket'); + expect(keywords).toContain('TypeScript'); + }); + + it('catches snake_case identifiers', () => { + const keywords = extractKeywords('Set max_retries and connection_timeout in config.'); + expect(keywords).toContain('max_retries'); + expect(keywords).toContain('connection_timeout'); + }); + + it('returns empty array for plain prose', () => { + const keywords = extractKeywords('This is a simple sentence with no identifiers.'); + expect(keywords).toHaveLength(0); + }); + }); + + describe('extractEntities', () => { + it('catches proper nouns', () => { + const entities = extractEntities('Redis and Docker are commonly used tools.'); + expect(entities).toContain('Redis'); + expect(entities).toContain('Docker'); + }); + + it('catches file paths', () => { + const entities = extractEntities('Edit the file at /src/auth/middleware.ts'); + expect(entities.some((e) => e.includes('/src/auth/middleware.ts'))).toBe(true); + }); + + it('catches URLs', () => { + const entities = extractEntities('See https://example.com/docs for details.'); + expect(entities.some((e) => e.includes('https://example.com/docs'))).toBe(true); + }); + + it('excludes common sentence starters', () => { + const entities = extractEntities('The system handles requests. This is important.'); + // "The" and "This" are common starters, not entities + expect(entities.every((e) => e !== 'The')).toBe(true); + expect(entities.every((e) => e !== 'This')).toBe(true); + }); + }); + + describe('extractStructural', () => { + it('catches code fences', () => { + const markers = extractStructural('Before\n```ts\nconst x = 1;\n```\nAfter'); + expect(markers.some((m) => m.startsWith('```'))).toBe(true); + }); + + it('catches bullet points', () => { + const markers = extractStructural('List:\n- First item\n- Second item\n- Third item'); + expect(markers.length).toBe(3); + }); + + it('catches numbered lists', () => { + const markers = extractStructural('Steps:\n1. First step\n2. Second step'); + expect(markers.length).toBe(2); + }); + + it('returns empty for plain prose', () => { + const markers = extractStructural('Just a simple paragraph of text.'); + expect(markers).toHaveLength(0); + }); + }); + + describe('analyzeRetention', () => { + it('returns 1.0 for identical texts', () => { + const text = 'The getUserProfile function calls createSession on the WebSocket server.'; + const result = analyzeRetention(text, text); + expect(result.keywordRetention).toBe(1); + expect(result.entityRetention).toBe(1); + expect(result.structuralRetention).toBe(1); + }); + + it('returns correct keyword retention for partial match', () => { + const original = + 'The getUserProfile and createSession functions handle WebSocket authentication.'; + const compressed = 'The getUserProfile function handles authentication.'; + const result = analyzeRetention(original, compressed); + // getUserProfile retained, createSession lost, WebSocket lost + expect(result.keywordRetention).toBeGreaterThan(0); + expect(result.keywordRetention).toBeLessThan(1); + }); + + it('returns 1.0 for keyword retention when no keywords in original', () => { + const result = analyzeRetention('Just a simple sentence.', 'A short summary.'); + expect(result.keywordRetention).toBe(1); + }); + + it('returns 1.0 for structural retention when no structural markers in original', () => { + const result = analyzeRetention('Plain text.', 'Summary.'); + expect(result.structuralRetention).toBe(1); + }); + + it('detects structural loss when code fences are removed', () => { + const original = 'Code:\n```ts\nconst x = 1;\n```\nEnd.'; + const compressed = 'Code summary with x = 1.'; + const result = analyzeRetention(original, compressed); + expect(result.structuralRetention).toBe(0); + }); + + it('handles real compression scenario', () => { + const original = `The getUserProfile middleware validates JWT tokens using the WebSocket connection. +It calls createSession for each authenticated user. + +\`\`\`typescript +const token = jwt.verify(req.headers.authorization); +\`\`\` + +- Check token expiry +- Validate signature +- Refresh if needed + +See https://docs.example.com/auth for details.`; + + const compressed = `[summary: getUserProfile validates JWT tokens via WebSocket. | entities: getUserProfile, WebSocket, createSession] + +\`\`\`typescript +const token = jwt.verify(req.headers.authorization); +\`\`\``; + + const result = analyzeRetention(original, compressed); + // Keywords: getUserProfile, WebSocket, createSession — all in compressed + expect(result.keywordRetention).toBeGreaterThan(0.5); + // Code fences preserved + expect(result.structuralRetention).toBeGreaterThan(0); + }); + }); +}); diff --git a/tests/tiered-budget.test.ts b/tests/tiered-budget.test.ts new file mode 100644 index 0000000..cbc0cc2 --- /dev/null +++ b/tests/tiered-budget.test.ts @@ -0,0 +1,163 @@ +import { describe, it, expect } from 'vitest'; +import { compress } from '../src/compress.js'; +import type { Message } from '../src/types.js'; + +function msg(id: string, content: string, role = 'user'): Message { + return { id, index: 0, role, content }; +} + +function longProse(seed: string, length: number): string { + const base = `The ${seed} function handles complex operations including data validation, error handling, retry logic, and performance monitoring across multiple service layers in the distributed system architecture. `; + return base.repeat(Math.ceil(length / base.length)).slice(0, length); +} + +describe('tiered budget strategy', () => { + it('fits within budget while preserving recent messages', () => { + const messages: Message[] = [ + msg('sys', 'You are a helpful assistant.', 'system'), + msg('old1', longProse('processData', 500)), + msg('old2', longProse('validateInput', 500)), + msg('old3', longProse('handleRequest', 500)), + msg('recent1', 'The fetchData function needs retry logic with exponential backoff.'), + msg('recent2', 'Add the connectionPool configuration to the service layer.'), + ]; + + const result = compress(messages, { + tokenBudget: 300, + budgetStrategy: 'tiered', + recencyWindow: 2, + forceConverge: true, + }); + + // Recent messages should be preserved verbatim + const recent1 = result.messages.find((m) => m.id === 'recent1'); + const recent2 = result.messages.find((m) => m.id === 'recent2'); + expect(recent1?.content).toContain('fetchData'); + expect(recent2?.content).toContain('connectionPool'); + + // Should fit budget + expect(result.fits).toBe(true); + }); + + it('preserves system messages', () => { + const messages: Message[] = [ + msg('sys', 'You are a coding assistant. Always explain your reasoning.', 'system'), + msg('old1', longProse('analyzeCode', 600)), + msg('old2', longProse('refactorModule', 600)), + msg('recent', 'What about the parseConfig function?'), + ]; + + const result = compress(messages, { + tokenBudget: 200, + budgetStrategy: 'tiered', + recencyWindow: 1, + forceConverge: true, + }); + + const sys = result.messages.find((m) => m.id === 'sys'); + expect(sys?.content).toContain('coding assistant'); + }); + + it('compresses older messages before touching recent ones', () => { + const messages: Message[] = [ + msg('old1', longProse('handleAuth', 400)), + msg('old2', longProse('validateToken', 400)), + msg('recent1', 'The getUserProfile function returns the complete user object.'), + msg('recent2', 'We need to add caching to the fetchData service.'), + ]; + + const binaryResult = compress(messages, { + tokenBudget: 200, + budgetStrategy: 'binary-search', + recencyWindow: 2, + }); + + const tieredResult = compress(messages, { + tokenBudget: 200, + budgetStrategy: 'tiered', + recencyWindow: 2, + forceConverge: true, + }); + + // Tiered should keep recent messages intact + const tieredRecent1 = tieredResult.messages.find((m) => m.id === 'recent1'); + expect(tieredRecent1?.content).toContain('getUserProfile'); + + // Binary search may have shrunk recencyWindow, potentially losing recent content + // (or it may have compressed old messages differently) + // Both should produce valid results + expect(binaryResult.messages.length).toBeGreaterThan(0); + expect(tieredResult.messages.length).toBeGreaterThan(0); + }); + + it('fits very tight budgets through progressive tightening and forceConverge', () => { + const messages: Message[] = [ + msg('old1', longProse('buildIndex', 2000)), + msg('old2', longProse('queryEngine', 2000)), + msg('old3', longProse('cacheManager', 2000)), + msg('recent', 'Check the results.'), + ]; + + const result = compress(messages, { + tokenBudget: 100, + budgetStrategy: 'tiered', + recencyWindow: 1, + forceConverge: true, + }); + + expect(result.fits).toBe(true); + // Older messages should be heavily compressed (summary, stub, or truncated) + const old1 = result.messages.find((m) => m.id === 'old1'); + expect(old1).toBeDefined(); + expect(old1!.content!.length).toBeLessThan(2000); + }); + + it('returns early when input already fits budget', () => { + const messages: Message[] = [msg('1', 'Short message.'), msg('2', 'Another short one.')]; + + const result = compress(messages, { + tokenBudget: 1000, + budgetStrategy: 'tiered', + }); + + expect(result.fits).toBe(true); + expect(result.compression.messages_compressed).toBe(0); + }); + + it('preserves verbatim store for round-trip integrity', () => { + const messages: Message[] = [ + msg('old', longProse('transformData', 600)), + msg('recent', 'Latest update on the project.'), + ]; + + const result = compress(messages, { + tokenBudget: 100, + budgetStrategy: 'tiered', + recencyWindow: 1, + forceConverge: true, + }); + + // Old message should be in verbatim store + if (result.compression.messages_compressed > 0) { + expect(result.verbatim['old']).toBeDefined(); + } + }); + + it('quality metrics are present when compression occurs', () => { + const messages: Message[] = [ + msg('old1', longProse('fetchData', 400)), + msg('old2', longProse('getUserProfile', 400)), + msg('recent', 'Check the service status.'), + ]; + + const result = compress(messages, { + tokenBudget: 150, + budgetStrategy: 'tiered', + recencyWindow: 1, + forceConverge: true, + }); + + expect(result.compression.quality_score).toBeDefined(); + expect(result.compression.entity_retention).toBeDefined(); + }); +});