chore: PDD sync for #1086

.pdd/meta/llm_invoke_python.json

@@ -1,13 +1,13 @@
 {
-  "pdd_version": "0.0.241",
-  "timestamp": "2026-05-17T18:33:34.290918+00:00",
-  "command": "example",
-  "prompt_hash": "08f20f2e33886688d14103186bc55760a745b23e9bbe2626a0a7a6c4e1c1528c",
-  "code_hash": "42370b9d59b0caca264991021fb612f02bdae906c8043451288a64697af894ae",
+  "pdd_version": "0.0.244.dev7",
+  "timestamp": "2026-05-19T22:00:00.000000+00:00",
+  "command": "fix",
+  "prompt_hash": "8769b1adcb99a783edbfc75a0db123357c6a9959af78bba15ae2c80de00d802b",
+  "code_hash": "4c853854bca72a48109d0e6c6ac71c13869e37ff1bf2e5e17ad8b776500690ef",
   "example_hash": "48c5aece0ddd153f95ec8a53802d2173d4da12a920b398c5feebb307b9958417",
-  "test_hash": "630ae15410752b1bda2608d72ab104add66b1f9cbbd134c003183339b46353f9",
+  "test_hash": "b612ca94d95d2902db9219088726a76eee2bf636ead5a1495b2087f4c6cddf6b",
   "test_files": {
-    "test_llm_invoke.py": "630ae15410752b1bda2608d72ab104add66b1f9cbbd134c003183339b46353f9",
+    "test_llm_invoke.py": "b612ca94d95d2902db9219088726a76eee2bf636ead5a1495b2087f4c6cddf6b",
     "test_llm_invoke_csv_model_registration.py": "1583b5e076fe5227a11f3d1079034ab4a21bc6131a3433f37bd68e35a99ba49e",
     "test_llm_invoke_integration.py": "2eb4bd2565761a4148762c6fb73c887cb6e12ff962742e05f5c2d4d62b47aaf9",
     "test_llm_invoke_nested_schema.py": "c983a19874abacc3e0ea9d6ca2ec87495960d2dd96d4e93be4039ed1bc995b9b",
@@ -21,4 +21,4 @@
     "pdd/core/cloud.py": "0487c0b989996af144df3af1c818a6eeafe52fd209710e5a54eb43990c89ae97",
     "pdd/server/token_counter.py": "3391a7c708713e3d370bf9e981a837f1f7ade08af75d69f402301dbf65a79c9a"
   }
-}
+}

.pdd/meta/summarize_directory_python.json

@@ -1,17 +1,17 @@
 {
-  "pdd_version": "0.0.228",
-  "timestamp": "2026-05-06T03:28:21.684198+00:00",
-  "command": "regenerate-public",
-  "prompt_hash": "ea40b7699d4cf1ab98a7eed076aea7117a1c175ae16956afcfc7f06856837b17",
-  "code_hash": "a96e3b5ddb13717395fbc046cf0279486e1225d63deb51657f1ddb4eaf80c0af",
+  "pdd_version": "0.0.244.dev7",
+  "timestamp": "2026-05-19T22:00:00.000000+00:00",
+  "command": "fix",
+  "prompt_hash": "8aaf8f0055d1d10efb75b4219753f7a03c9a0094d730939b85e1952f7b8e004f",
+  "code_hash": "89663dc352f5a79a0bac618840d05bafbb900d47425d31e12b6229aac9797fa7",
   "example_hash": null,
-  "test_hash": "6198ba0d33bcb2d06b6a90106dd2ae7adfc112a1ca8de62c58b23b8f9c2502a9",
+  "test_hash": "16f0ccf2eaf2c2a1498528da7913ac22aca8cffcc26941bb34a961c1ced032fb",
   "test_files": {
-    "test_summarize_directory.py": "6198ba0d33bcb2d06b6a90106dd2ae7adfc112a1ca8de62c58b23b8f9c2502a9"
+    "test_summarize_directory.py": "16f0ccf2eaf2c2a1498528da7913ac22aca8cffcc26941bb34a961c1ced032fb"
   },
   "include_deps": {
-    "context/llm_invoke_example.py": "d749aa00a35c88c254c4aaa9e1280d85342d71214710e2f3f7a2bd9388a92168",
+    "context/llm_invoke_example.py": "48c5aece0ddd153f95ec8a53802d2173d4da12a920b398c5feebb307b9958417",
     "context/load_prompt_template_example.py": "a1cd6619182c6c951f5856dda4070e202875a5884bbfab9cc191d24de2f4951f",
-    "context/python_preamble.prompt": "57a3e51f529024ec0cb9658cd6ac61a7c8051ba0c8e887b31cf00b2e78a07d83"
+    "context/python_preamble.prompt": "0388ed131bf986f8752e1bc4c81e4da0460cfe2908ec8c60b1314edbab768254"
   }
-}
+}

README.md

@@ -804,8 +804,9 @@ The generated CSV file includes the following columns:
 - cost: The estimated cost of the operation in USD (e.g., 0.05 for 5 cents). This will be zero for local models or operations that do not use a LLM.
 - input_files: A list of input files involved in the operation
 - output_files: A list of output files generated or modified by the operation
+- attempted_models: A semicolon-delimited, chronological list of every model PDD attempted for this command, including the final successful model as well as any earlier models that failed and were abandoned (e.g., `vertex_ai/gemini-2.5-pro;deepseek/deepseek-chat`). For a normal single-model success this contains a single entry; empty only when no LLM attempt was recorded for the command.
 
-This comprehensive output allows for detailed tracking of not only the cost and type of operations but also the specific files involved in each PDD command execution.
+This comprehensive output allows for detailed tracking of not only the cost and type of operations but also the specific files involved in each PDD command execution, plus the full fallback chain when PDD switched models mid-run.
 
 ### Environment Variable
 

architecture.json

@@ -1138,8 +1138,8 @@
     }
   },
   {
-    "reason": "Tracks LLM usage costs across operations.",
-    "description": "Records token usage and calculates costs. Aggregates costs across workflow steps.",
+    "reason": "Tracks LLM usage costs across operations and records the attempted-model fallback chain.",
+    "description": "Click decorator that records timestamp, model, command, cost, input/output files, and the full attempted-model chain (semicolon-delimited) to the cost CSV. Accepts both legacy tuple and enriched-dict result shapes from decorated commands.",
     "dependencies": [],
     "priority": 33,
     "filename": "track_cost_python.prompt",
@@ -1155,15 +1155,15 @@
           {
             "name": "track_cost",
             "signature": "(func)",
-            "returns": "None",
+            "returns": "Callable",
             "sideEffects": [
               "None"
             ]
           },
           {
             "name": "extract_cost_and_model",
-            "signature": "(result: Any) -> Tuple[Any, str]",
-            "returns": "Tuple[Any, str]",
+            "signature": "(result: Any) -> Tuple[Any, str, List[str]]",
+            "returns": "Tuple[Any, str, List[str]]",
             "sideEffects": [
               "None"
             ]
@@ -2416,8 +2416,8 @@
           },
           {
             "name": "cloud_fix_errors",
-            "signature": "(unit_test: str, code: str, prompt: str, error: str, error_file: str, strength: float, temperature: float, verbose: bool = False, time: float = DEFAULT_TIME, code_file_ext: str = '.py') -> Tuple[bool, bool, str, str, str, float, str]",
-            "returns": "Tuple[bool, bool, str, str, str, float, str]",
+            "signature": "(unit_test: str, code: str, prompt: str, error: str, error_file: str, strength: float, temperature: float, verbose: bool = False, time: float = DEFAULT_TIME, code_file_ext: str = '.py', protect_tests: bool = False, failure_classification: str | None = None) -> Tuple[bool, bool, str, str, str, float, str, List[str]]",
+            "returns": "Tuple[bool, bool, str, str, str, float, str, List[str]]",
             "sideEffects": [
               "None"
             ]
@@ -2440,8 +2440,8 @@
           },
           {
             "name": "fix_error_loop",
-            "signature": "(unit_test_file: str, code_file: str, prompt_file: str, prompt: str, verification_program: str, strength: float, temperature: float, max_attempts: int, budget: float, error_log_file: str = 'error_log.txt', verbose: bool = False, time: float = DEFAULT_TIME, agentic_fallback: bool = True, use_cloud: bool = False)",
-            "returns": "None",
+            "signature": "(unit_test_file: str, code_file: str, prompt_file: str, prompt: str, verification_program: str, strength: float, temperature: float, max_attempts: int, budget: float, error_log_file: str = 'error_log.txt', verbose: bool = False, time: float = DEFAULT_TIME, agentic_fallback: bool = True, protect_tests: bool = False, use_cloud: bool = False, test_files: list[str] | None = None, failure_aware_retries: bool = True) -> Tuple[bool, str, str, int, float, str]",
+            "returns": "Tuple[bool, str, str, int, float, str]",
             "sideEffects": [
               "None"
             ]
@@ -8808,5 +8808,47 @@
       "x": 24600,
       "y": 800
     }
+  },
+  {
+    "reason": "Provides unified LLM invocation across all PDD operations with provider abstraction, retries, and context window validation.",
+    "description": "Implements the llm_invoke entry point with model selection, structured output handling, cloud/local routing, attempted-model tracking, batch mode, cost accumulation, and Pydantic schema validation.",
+    "dependencies": [
+      "path_resolution_python.prompt",
+      "server/token_counter_python.prompt"
+    ],
+    "priority": 236,
+    "filename": "llm_invoke_python.prompt",
+    "filepath": "pdd/llm_invoke.py",
+    "tags": [
+      "module",
+      "python"
+    ],
+    "interface": {
+      "type": "module",
+      "module": {
+        "functions": [
+          {
+            "name": "llm_invoke",
+            "signature": "(prompt, input_json, strength, temperature, verbose, output_pydantic, output_schema, time, use_batch_mode, messages, language, use_cloud)",
+            "returns": "Dict[str, Any]"
+          },
+          {
+            "name": "setup_file_logging",
+            "signature": "(log_file_path=None)",
+            "returns": "None"
+          },
+          {
+            "name": "set_verbose_logging",
+            "signature": "(verbose=False)",
+            "returns": "None"
+          },
+          {
+            "name": "set_quiet_logging",
+            "signature": "()",
+            "returns": "None"
+          }
+        ]
+      }
+    }
   }
 ]

context/cli_example.py

@@ -181,6 +181,9 @@ def example_cost_tracking_setup():
         - cost: Estimated cost in USD (e.g., 0.05 for 5 cents)
         - input_files: List of input files involved
         - output_files: List of output files generated/modified
+        - attempted_models: Semicolon-delimited model attempt chain. A
+          single-model success contains the successful model once; this field
+          is empty only when no LLM attempt was recorded.
 
     Args:
         None
@@ -355,4 +358,4 @@ def main():
 
 
 if __name__ == '__main__':
-    main()
+    main()

context/core/cli_example.py

@@ -181,6 +181,9 @@ def example_cost_tracking_setup():
         - cost: Estimated cost in USD (e.g., 0.05 for 5 cents)
         - input_files: List of input files involved
         - output_files: List of output files generated/modified
+        - attempted_models: Semicolon-delimited model attempt chain. A
+          single-model success contains the successful model once; this field
+          is empty only when no LLM attempt was recorded.
 
     Args:
         None