feat(llm): add top_p, top_k, temperature sampling parameters

[ScarletCarpet]

Brief:
Add three commonly-used LLM sampling parameters (top_p, top_k, temperature) with support from both CLI flags and provider config. This improves the stability of LLM outputs but requires considering the trade-offs.

Test:

• test on deepseekv4 with temp=0.0 and top_p=0.8 and review the same code it got 80% similarity of output.
• there is no param field on http request to LLM without customlize temp/topp/topk

Change Log:

• Add fields to ChatRequest, ClientConfig, and both OpenAI/Anthropic clients
• Add ApplyDefaults() to fill unset fields from ClientConfig
• Add provider config fields for built-in and custom providers
• Add CLI flags --top-p, --top-k, --temperature for review and scan commands
• Add config set support for providers..top_p, top_k, temperature
• Propagate params through agent.Args → llmloop.Deps → ChatRequest
• Update all ChatRequest call sites (plan, main loop, compression, dedup, summary, relocation, llm test)
• Update README.md and README.zh-CN.md with new configuration items and CLI flags
• Add unit tests for ApplyDefaults, build params, resolver, config set, and flag parsing

Discussion:

• Suggest deprecating the LLM config, as it duplicates the provider config
• One way to set the temperature without this commit is via extra_body, but it is less elegant
• Suggest deprecating other languages' READMEs, keeping only English and Chinese to reduce the maintenance workload

Description

Type of Change

• Bug fix (non-breaking change that fixes an issue)
• New feature (non-breaking change that adds functionality)
• Breaking change (fix or feature that would cause existing functionality to change)
• Refactoring (no functional changes)
• Documentation update
• CI / Build / Tooling

How Has This Been Tested?

• make test passes locally
• Manual testing (describe below)

Checklist

• My code follows the project's coding style (go fmt, go vet)
• I have performed a self-review of my code
• I have added tests that prove my fix is effective or my feature works
• New and existing unit tests pass locally with my changes
• I have updated the documentation accordingly (if applicable)
• I have signed the CLA

Related Issues

[github-actions]

🔍 OpenCodeReview found 10 issue(s) in this PR.

• ✅ 9 posted as inline comment(s)
• 📝 1 posted as summary

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📊 Posting Statistics:

• ✅ Successfully posted: 9 comment(s)
• ❌ Failed to post: 1 comment(s)

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⚠️ Inline comments shown in summary

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📄 cmd/opencodereview/scan_cmd.go (L95-L98)

⚠️ GitHub could not post this as an inline comment: Unprocessable Entity: "Line could not be resolved"

Missing input validation: There is no validation for negative values of temperature, top-p, or top-k. While LLM provider APIs will reject invalid values, adding early validation here (similar to the existing checks for --max-tools, --max-git-procs, etc.) would provide better user experience with clearer error messages. For example, temperature and top-p should typically be non-negative, and top-k should be positive when set.