\n\n

Full Issue Text

\n\n\n\n```markdown\n## Why\nPR #4301 addressed issue #4300 but verification identified concerns (verdict: **CONCERNS**). This follow-up addresses the remaining gaps with improved task structure to ensure robust error handling, complete provider abstraction, and comprehensive test coverage.\n\n## Tasks\n- [ ] Enhance the retry logic in the configuration patch processing function to retry up to `self.retries` times upon a parse failure. Add automated tests to simulate repeated parse failures and verify error logging.\n- [ ] Implement the `LLMProviderConfig` dataclass to encapsulate provider configuration parameters.\n- [ ] Develop the `create_llm()` factory function for dynamic provider instantiation and add unit and integration tests.\n- [ ] Update the `pyproject.toml` file to include all necessary provider dependencies with explicit version constraints.\n- [ ] Create or update the documentation file to include setup instructions, configuration examples, and troubleshooting guidance.\n- [ ] Develop an example usage script demonstrating configuration and error handling for LLM providers.\n- [ ] Expand evaluation test cases to include specific scenarios and integrate them into the eval harness.\n- [ ] Review and update the unknown key validation logic to use the latest authoritative schema and handle dynamic keys.\n\n## Acceptance Criteria\n- [ ] The retry logic in `config_patch.py` attempts to reprocess a configuration patch up to `self.retries` times upon a parse failure, logging each error encountered.\n- [ ] The `LLMProviderConfig` dataclass encapsulates provider configuration parameters and supports at least 2 LLM providers.\n- [ ] The `create_llm()` factory function dynamically instantiates LLM providers and passes all unit and integration tests.\n- [ ] The `pyproject.toml` file includes all necessary provider dependencies with explicit version constraints.\n- [ ] The `examples/nl_config_demo.py` script demonstrates configuration and error handling for multiple LLM providers.\n- [ ] The documentation in `docs/llm_providers.md` includes setup instructions, configuration examples, and troubleshooting guidance for the LLM provider abstraction layer.\n- [ ] The evaluation harness in `tools/eval_config_patch.py` includes at least 10 test cases, covering specified scenarios.\n- [ ] The unknown key validator in `validation.py` detects keys not defined in the authoritative schema and flags them with `needs_review=True`, handling dynamic array indices and wildcard keys.\n\n## Implementation Notes\n- Focus on concurrent development of provider abstraction, integration tests, dependency updates, example usage, and documentation to ensure alignment with acceptance criteria.\n- Ensure comprehensive test coverage by integrating evaluation and demonstration components together.\n\n

Background (previous attempt context)

\n\n- The previous implementation focused solely on validation/retry logic and neglected the complete multi-provider abstraction (e.g., missing `LLMProviderConfig` and `create_llm`).\n- Partial implementation of evaluation test cases and the absence of the example script resulted in key acceptance criteria being unmet.\n\n

\n```\n\n\n\n

\n\n—\nPR created automatically to engage Codex.\n\n\n> **Source:** Issue #4308\n\n","base","head","base_sha","10f60428048553a2caf3ba18be5313271f763ca7","head_sha","5dd63e88e43077fd8889dc2563e316a2e06441e7","head_repo_id","head_repo_full_name","merge_commit_sha","f3b89e4e73fb6310b7a62715efdc28ac58c2c8de","comments","diff","user",{"_273":274,"_161":274,"_324":-5,"_277":278,"_53":275},"commits",14,"changed_files",11,"additions",457,"deletions",52,"email","events",[],[],"messages",[],{"_104":724,"_108":105,"_375":725},{"_104":359,"_360":361},{"_104":333,"_334":335,"_336":337,"_338":339,"_340":-5},"pr","pr_title","Log config patch parse retry failures","pr_message","### Motivation\n\n- Improve observability when the LLM-driven config patch parser fails to decode or validate responses during retry attempts.\n- Ensure each parse failure is recorded so operators can triage recurring LLM parse errors.\n- Align behavior with the acceptance criteria that retry attempts should log each error encountered.\n\n### Description\n\n- Add `logger.warning` calls in `ConfigPatchChain.run` to log parse failures for `json.JSONDecodeError` and `ValidationError` cases that indicate JSON parsing errors, including attempt counts and formatted error via `_format_retry_error` in `src/trend_analysis/llm/chain.py`.\n- Add a unit test `test_config_patch_chain_logs_parse_errors_for_each_retry` that uses `caplog` to assert that parse failures are logged for every retry in `tests/test_config_patch_chain.py`.\n- Minor test import reordering to satisfy linters and formatting adjustments applied by `black`.\n\n### Testing\n\n- Ran `black` and formatting hooks which completed successfully.\n- Ran `ruff` lint checks which passed after import reordering.\n- Ran `mypy` type checks (core + app) which passed during pre-commit.","output_diff",{"_104":338,"_341":342,"_139":140,"_343":-5,"_313":344,"_345":346,"_347":348,"_349":350,"_351":352,"_353":354,"_355":-5},"pre_apply_patch","repo_id","/workspace/Trend_Model_Project","preapply_patch_base_commit_sha","diff --git a/src/trend_analysis/llm/chain.py b/src/trend_analysis/llm/chain.py\nindex d9ddfbe308f4db7bf1516f3f0c77909bacc4da7f..2e56751578bd52175a548518e209a93ac00f7e69 100644\n--- a/src/trend_analysis/llm/chain.py\n+++ b/src/trend_analysis/llm/chain.py\n@@ -137,61 +137,75 @@ class ConfigPatchChain:\n allowed_schema=schema_text,\n instruction=instruction,\n system_prompt=system_prompt,\n safety_rules=safety_rules,\n )\n last_error: Exception | None = None\n for attempt in range(self.retries + 1):\n if attempt == 0:\n response_text = self._invoke_llm(prompt_text)\n else:\n response_text = self._invoke_llm(\n build_retry_prompt(\n current_config=config_text,\n allowed_schema=schema_text,\n instruction=instruction,\n error_message=_format_retry_error(last_error),\n system_prompt=system_prompt,\n safety_rules=safety_rules,\n )\n )\n try:\n patch = self._parse_patch(response_text)\n break\n except json.JSONDecodeError as exc:\n last_error = exc\n+ logger.warning(\n+ \"ConfigPatch parse attempt %s/%s failed: %s\",\n+ attempt + 1,\n+ self.retries + 1,\n+ _format_retry_error(exc),\n+ )\n if attempt >= self.retries:\n raise ValueError(\n \"Failed to parse ConfigPatch after \"\n f\"{self.retries + 1} attempts: {_format_retry_error(exc)}\"\n ) from exc\n except ValidationError as exc:\n # Check if this is a JSON parsing error or a schema validation error\n- is_json_error = any(err.get(\"type\") == \"json_invalid\" for err in exc.errors())\n+ is_json_error = any(\n+ err.get(\"type\") == \"json_invalid\" for err in exc.errors()\n+ )\n if is_json_error:\n # Wrap JSON parsing errors in ValueError for retry exhaustion\n last_error = exc\n+ logger.warning(\n+ \"ConfigPatch parse attempt %s/%s failed: %s\",\n+ attempt + 1,\n+ self.retries + 1,\n+ _format_retry_error(exc),\n+ )\n if attempt >= self.retries:\n raise ValueError(\n \"Failed to parse ConfigPatch after \"\n f\"{self.retries + 1} attempts: {_format_retry_error(exc)}\"\n ) from exc\n else:\n # Let schema validation errors (like extra_forbidden) propagate\n if attempt >= self.retries:\n raise\n last_error = exc\n else:\n raise ValueError(\"Failed to parse ConfigPatch: unknown error\")\n \n schema = self._schema_for_validation(allowed_schema, instruction)\n unknown_keys = validate_patch_keys(patch.operations, schema)\n if unknown_keys:\n patch.needs_review = True\n for entry in unknown_keys:\n if entry.suggestion:\n logger.warning(\n \"Unknown config key '%s'. Did you mean '%s'?\",\n entry.path,\n entry.suggestion,\n )\n else:\ndiff --git a/tests/test_config_patch_chain.py b/tests/test_config_patch_chain.py\nindex 4f2a774bfd499ae5d43c28cbc1d69ad26b06d1d9..d7d5423d84c3a3ddae2ab5697bc6475557eb5857 100644\n--- a/tests/test_config_patch_chain.py\n+++ b/tests/test_config_patch_chain.py\n@@ -1,43 +1,43 @@\n \"\"\"Tests for ConfigPatchChain.\"\"\"\n \n from __future__ import annotations\n \n import json\n-\n-import pytest\n-\n-pytest.importorskip(\"langchain_core\")\n+import logging\n \n import jsonschema\n+import pytest\n from langchain_core.runnables import RunnableLambda\n from pydantic import ValidationError\n \n from trend_analysis.config.patch import ConfigPatch\n from trend_analysis.llm.chain import ConfigPatchChain\n from trend_analysis.llm.prompts import build_config_patch_prompt\n \n+pytest.importorskip(\"langchain_core\")\n+\n \n def test_config_patch_chain_run_parses_patch() -> None:\n captured: dict[str, str] = {}\n \n def _respond(prompt_value, **_kwargs) -> str:\n messages = prompt_value.to_messages()\n captured[\"prompt\"] = messages[0].content\n payload = {\n \"operations\": [\n {\n \"op\": \"set\",\n \"path\": \"portfolio.max_weight\",\n \"value\": 0.3,\n \"rationale\": \"Align with instruction\",\n }\n ],\n \"risk_flags\": [],\n \"summary\": \"Update max_weight\",\n }\n return json.dumps(payload)\n \n llm = RunnableLambda(_respond)\n chain = ConfigPatchChain(\n llm=llm,\n prompt_builder=build_config_patch_prompt,\n@@ -108,51 +108,54 @@ def test_config_patch_chain_batch_schema_conformance() -> None:\n assert valid_count / total_cases >= 0.95\n \n \n def test_config_patch_chain_flags_unknown_keys() -> None:\n def _respond(_prompt_value, **_kwargs) -> str:\n payload = {\n \"operations\": [\n {\n \"op\": \"set\",\n \"path\": \"analysis.turbo_mode\",\n \"value\": True,\n }\n ],\n \"risk_flags\": [],\n \"summary\": \"Enable turbo mode.\",\n }\n return json.dumps(payload)\n \n llm = RunnableLambda(_respond)\n chain = ConfigPatchChain(\n llm=llm,\n prompt_builder=build_config_patch_prompt,\n schema={\n \"type\": \"object\",\n \"properties\": {\n- \"analysis\": {\"type\": \"object\", \"properties\": {\"top_n\": {\"type\": \"integer\"}}}\n+ \"analysis\": {\n+ \"type\": \"object\",\n+ \"properties\": {\"top_n\": {\"type\": \"integer\"}},\n+ }\n },\n },\n )\n \n patch = chain.run(\n current_config={\"analysis\": {\"top_n\": 8}},\n instruction=\"Enable turbo mode.\",\n )\n \n assert patch.needs_review is True\n \n \n def test_config_patch_chain_unknown_keys_raise() -> None:\n def _respond(prompt_value, **_kwargs) -> str:\n _ = prompt_value.to_messages()\n payload = {\n \"operations\": [],\n \"risk_flags\": [],\n \"summary\": \"No changes\",\n \"extra_field\": \"unexpected\",\n }\n return json.dumps(payload)\n \n llm = RunnableLambda(_respond)\n chain = ConfigPatchChain(\n@@ -238,25 +241,52 @@ def test_config_patch_chain_retries_on_parse_error() -> None:\n assert \"PREVIOUS ERROR\" in prompts[1]\n assert \"ValidationError\" in prompts[1]\n \n \n def test_config_patch_chain_retry_exhausted_raises_error() -> None:\n def _respond(_prompt_value, **_kwargs) -> str:\n return \"not-json\"\n \n llm = RunnableLambda(_respond)\n chain = ConfigPatchChain(\n llm=llm,\n prompt_builder=build_config_patch_prompt,\n schema={\"type\": \"object\"},\n retries=1,\n )\n \n with pytest.raises(ValueError) as excinfo:\n chain.run(\n current_config={\"portfolio\": {\"max_weight\": 0.2}},\n instruction=\"Set max_weight to 0.25.\",\n )\n \n message = str(excinfo.value)\n assert \"Failed to parse ConfigPatch after 2 attempts\" in message\n assert \"ValidationError\" in message\n+\n+\n+def test_config_patch_chain_logs_parse_errors_for_each_retry(caplog) -> None:\n+ def _respond(_prompt_value, **_kwargs) -> str:\n+ return \"not-json\"\n+\n+ llm = RunnableLambda(_respond)\n+ chain = ConfigPatchChain(\n+ llm=llm,\n+ prompt_builder=build_config_patch_prompt,\n+ schema={\"type\": \"object\"},\n+ retries=2,\n+ )\n+\n+ with caplog.at_level(logging.WARNING):\n+ with pytest.raises(ValueError):\n+ chain.run(\n+ current_config={\"portfolio\": {\"max_weight\": 0.2}},\n+ instruction=\"Set max_weight to 0.25.\",\n+ )\n+\n+ attempts = [\n+ record\n+ for record in caplog.records\n+ if \"ConfigPatch parse attempt\" in record.message\n+ ]\n+ assert len(attempts) == 3\n","external_storage_diff",{"_356":357,"_358":-5},"files_modified",2,"lines_added",50,"lines_removed",6,"commit_message","Add parse error logging for config patch retries","additional_parent_commit_shas","file_id","file_000000000c60722fa106528772246782","ttl","partial_repo_snapshot","files",[362,363],{"_364":532,"_366":-5,"_367":533,"_369":-5},{"_364":365,"_366":-5,"_367":368,"_369":-5},"path","tests/test_config_patch_chain.py","contents","line_range_contents",[370],"missing_reason",{"_371":372,"_373":374,"_375":376,"_377":70},"line_range_start",1,"line_range_end",292,"content",[378,183,379,183,380,381,183,382,383,384,385,183,386,387,388,183,389,183,183,390,391,183,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,183,408,409,410,411,412,413,183,414,415,416,413,183,417,418,419,183,183,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,183,441,183,392,442,443,183,408,409,410,411,412,413,183,444,445,446,424,447,448,449,450,451,452,453,454,455,456,457,183,458,183,183,459,460,395,396,397,398,461,462,402,403,404,463,406,407,183,408,409,410,411,464,465,466,467,468,469,402,470,471,413,183,414,472,473,413,183,474,183,183,475,392,442,395,476,404,477,478,406,407,183,408,409,410,411,412,413,183,479,480,448,481,450,183,183,482,392,442,395,476,404,483,406,407,183,408,409,410,411,412,413,183,414,415,484,413,183,485,486,183,183,487,488,489,490,491,492,397,493,494,495,496,497,498,499,500,501,402,502,503,413,183,392,393,504,505,183,408,409,410,411,412,506,413,183,414,415,507,413,183,418,508,509,510,183,183,511,460,512,183,408,409,410,411,412,506,413,183,513,480,448,514,450,183,515,516,517,183,183,518,460,512,183,408,409,410,411,412,519,413,183,520,521,522,523,524,525,183,526,527,528,529,530,531],"contains_end_of_file","\"\"\"Tests for ConfigPatchChain.\"\"\"","from __future__ import annotations","import json","import logging","import jsonschema","import pytest","from langchain_core.runnables import RunnableLambda","from pydantic import ValidationError","from trend_analysis.config.patch import ConfigPatch","from trend_analysis.llm.chain import ConfigPatchChain","from trend_analysis.llm.prompts import build_config_patch_prompt","pytest.importorskip(\"langchain_core\")","def test_config_patch_chain_run_parses_patch() -> None:"," captured: dict[str, str] = {}"," def _respond(prompt_value, **_kwargs) -> str:"," messages = prompt_value.to_messages()"," captured[\"prompt\"] = messages[0].content"," payload = {"," \"operations\": ["," {"," \"op\": \"set\","," \"path\": \"portfolio.max_weight\","," \"value\": 0.3,"," \"rationale\": \"Align with instruction\","," }"," ],"," \"risk_flags\": [],"," \"summary\": \"Update max_weight\","," }"," return json.dumps(payload)"," llm = RunnableLambda(_respond)"," chain = ConfigPatchChain("," llm=llm,"," prompt_builder=build_config_patch_prompt,"," schema={\"type\": \"object\"},"," )"," patch = chain.run("," current_config={\"portfolio\": {\"max_weight\": 0.2}},"," instruction=\"Set max_weight to 0.3.\","," assert patch.operations[0].path == \"portfolio.max_weight\""," assert patch.summary == \"Update max_weight\""," assert \"USER INSTRUCTION\" in captured[\"prompt\"]","def test_config_patch_chain_batch_schema_conformance() -> None:"," total_cases = 100"," valid_cases = 96"," responses: list[str] = []"," for idx in range(total_cases):"," if idx < valid_cases:"," payload = {"," \"operations\": ["," {"," \"op\": \"set\","," \"path\": \"portfolio.max_weight\","," \"value\": round(0.2 + idx * 0.001, 3),"," \"rationale\": \"Align with instruction\","," }"," ],"," \"risk_flags\": [],"," \"summary\": f\"Update max_weight {idx}\","," }"," responses.append(json.dumps(payload))"," else:"," responses.append(json.dumps({\"operations\": [], \"risk_flags\": []}))"," response_iter = iter(responses)"," _ = prompt_value.to_messages()"," return next(response_iter)"," schema = ConfigPatch.json_schema()"," validator = jsonschema.Draft202012Validator(schema)"," valid_count = 0"," prompt = chain.build_prompt("," current_config={\"portfolio\": {\"max_weight\": 0.2}},"," instruction=f\"Set max_weight to {0.2 + idx * 0.001:.3f}.\","," )"," response_text = chain._invoke_llm(prompt)"," try:"," payload = json.loads(response_text)"," except json.JSONDecodeError:"," continue"," if validator.is_valid(payload):"," valid_count += 1"," assert valid_count / total_cases >= 0.95","def test_config_patch_chain_flags_unknown_keys() -> None:"," def _respond(_prompt_value, **_kwargs) -> str:"," \"path\": \"analysis.turbo_mode\","," \"value\": True,"," \"summary\": \"Enable turbo mode.\","," schema={"," \"type\": \"object\","," \"properties\": {"," \"analysis\": {"," \"type\": \"object\","," \"properties\": {\"top_n\": {\"type\": \"integer\"}},"," },"," },"," current_config={\"analysis\": {\"top_n\": 8}},"," instruction=\"Enable turbo mode.\","," assert patch.needs_review is True","def test_config_patch_chain_unknown_keys_raise() -> None:"," \"operations\": [],"," \"summary\": \"No changes\","," \"extra_field\": \"unexpected\","," with pytest.raises(ValidationError):"," chain.run("," instruction=\"Do nothing.\",","def test_config_patch_chain_omits_unknown_key_instruction() -> None:"," \"summary\": \"Unknown key portfolio.unknown_setting; no changes applied.\","," instruction=\"Set portfolio.unknown_setting to 0.5.\","," assert patch.operations == []"," assert \"unknown key\" in patch.summary.lower()","def test_config_patch_chain_retries_on_parse_error() -> None:"," prompts: list[str] = []"," responses = iter("," ["," \"not-json\","," json.dumps("," \"operations\": ["," {"," \"op\": \"set\","," \"path\": \"portfolio.max_weight\","," \"value\": 0.25,"," }"," ],"," \"risk_flags\": [],"," \"summary\": \"Update max_weight\","," ),"," ]"," prompts.append(messages[0].content)"," return next(responses)"," retries=1,"," instruction=\"Set max_weight to 0.25.\","," assert len(prompts) == 2"," assert \"PREVIOUS ERROR\" in prompts[1]"," assert \"ValidationError\" in prompts[1]","def test_config_patch_chain_retry_exhausted_raises_error() -> None:"," return \"not-json\""," with pytest.raises(ValueError) as excinfo:"," instruction=\"Set max_weight to 0.25.\","," message = str(excinfo.value)"," assert \"Failed to parse ConfigPatch after 2 attempts\" in message"," assert \"ValidationError\" in message","def test_config_patch_chain_logs_parse_errors_for_each_retry(caplog) -> None:"," retries=2,"," with caplog.at_level(logging.WARNING):"," with pytest.raises(ValueError):"," chain.run("," current_config={\"portfolio\": {\"max_weight\": 0.2}},"," instruction=\"Set max_weight to 0.25.\","," )"," attempts = ["," record"," for record in caplog.records"," if \"ConfigPatch parse attempt\" in record.message"," ]"," assert len(attempts) == 3","src/trend_analysis/llm/chain.py",[534],{"_371":372,"_373":535,"_375":536,"_377":70},281,[537,183,379,183,380,381,538,539,540,183,385,183,386,541,542,543,183,544,183,545,183,183,546,547,548,183,549,550,551,552,553,554,555,183,556,557,558,559,560,561,562,563,564,565,566,567,568,183,569,570,571,572,573,574,575,576,450,183,556,577,558,559,560,561,562,578,564,565,566,567,579,183,580,581,582,583,450,584,569,570,571,572,585,586,575,576,450,183,587,588,559,589,590,591,592,593,594,595,183,596,597,598,599,450,600,601,450,602,603,604,605,606,607,450,183,608,588,559,589,590,591,592,593,609,610,183,596,597,598,599,450,600,601,450,611,603,604,605,606,607,450,612,613,614,615,616,617,618,619,620,621,622,623,624,625,626,627,628,629,630,631,632,633,634,635,636,626,637,638,639,640,641,642,643,644,645,626,646,647,648,649,650,651,652,653,625,654,655,656,657,658,659,660,654,661,648,439,662,183,663,664,665,666,667,668,649,669,670,671,625,659,672,673,183,674,675,183,676,677,678,679,183,680,681,682,683,684,685,686,687,452,688,689,682,183,690,691,183,692,693,183,694,588,695,590,696,697,627,698,699,700,701,183,702,703,704,183,183,705,706,707,708,709,710,711,183,183,712,713,714,715,183,183,716,717,718,719,720,721,722,723],"\"\"\"LangChain wrapper for ConfigPatch generation.\"\"\"","import os","from dataclasses import dataclass","from typing import Any, Callable, Iterable","from trend_analysis.llm.prompts import build_retry_prompt, format_config_for_prompt","from trend_analysis.llm.schema import load_compact_schema, select_schema_sections","from trend_analysis.llm.validation import validate_patch_keys","PromptBuilder = Callable[..., str]","logger = logging.getLogger(__name__)","@dataclass(slots=True)","class ConfigPatchChain:"," \"\"\"Container for the ConfigPatch LangChain pipeline.\"\"\""," llm: Any"," prompt_builder: PromptBuilder"," schema: dict[str, Any] | None"," temperature: float = 0.0"," model: str | None = None"," max_tokens: int | None = None"," retries: int = 1"," @classmethod"," def from_defaults("," cls,"," *,"," llm: Any,"," schema: dict[str, Any] | None = None,"," prompt_builder: PromptBuilder,"," temperature: float = 0.0,"," model: str | None = None,"," max_tokens: int | None = None,"," retries: int = 1,"," ) -> \"ConfigPatchChain\":"," \"\"\"Build a chain with standard prompt builder + schema.\"\"\""," return cls("," llm=llm,"," prompt_builder=prompt_builder,"," schema=schema,"," temperature=temperature,"," model=model,"," max_tokens=max_tokens,"," retries=retries,"," def from_env("," temperature: float | None = None,"," \"\"\"Build a chain using environment overrides for model/temperature.\"\"\""," env_temperature = ("," temperature"," if temperature is not None"," else _read_env_float(\"TREND_LLM_TEMPERATURE\", default=0.0)"," env_model = model if model is not None else os.environ.get(\"TREND_LLM_MODEL\")"," temperature=env_temperature,"," model=env_model,"," def build_prompt("," self,"," current_config: str | dict[str, Any],"," instruction: str,"," allowed_schema: str | None = None,"," system_prompt: str | None = None,"," safety_rules: Iterable[str] | None = None,"," ) -> str:"," \"\"\"Render the ConfigPatch prompt text.\"\"\""," config_text = ("," current_config"," if isinstance(current_config, str)"," else format_config_for_prompt(current_config)"," schema_text = allowed_schema or self._serialize_schema("," self._select_schema(instruction=instruction)"," return self.prompt_builder("," current_config=config_text,"," allowed_schema=schema_text,"," instruction=instruction,"," system_prompt=system_prompt,"," safety_rules=safety_rules,"," def run("," ) -> ConfigPatch:"," \"\"\"Invoke the LLM and parse the ConfigPatch response.\"\"\""," prompt_text = self.prompt_builder("," last_error: Exception | None = None"," for attempt in range(self.retries + 1):"," if attempt == 0:"," response_text = self._invoke_llm(prompt_text)"," else:"," response_text = self._invoke_llm("," build_retry_prompt("," current_config=config_text,"," allowed_schema=schema_text,"," instruction=instruction,"," error_message=_format_retry_error(last_error),"," system_prompt=system_prompt,"," safety_rules=safety_rules,"," )"," )"," try:"," patch = self._parse_patch(response_text)"," break"," except json.JSONDecodeError as exc:"," last_error = exc"," logger.warning("," \"ConfigPatch parse attempt %s/%s failed: %s\","," attempt + 1,"," self.retries + 1,"," _format_retry_error(exc),"," if attempt >= self.retries:"," raise ValueError("," \"Failed to parse ConfigPatch after \""," f\"{self.retries + 1} attempts: {_format_retry_error(exc)}\""," ) from exc"," except ValidationError as exc:"," # Check if this is a JSON parsing error or a schema validation error"," is_json_error = any("," err.get(\"type\") == \"json_invalid\" for err in exc.errors()"," if is_json_error:"," # Wrap JSON parsing errors in ValueError for retry exhaustion"," last_error = exc"," logger.warning("," \"ConfigPatch parse attempt %s/%s failed: %s\","," attempt + 1,"," self.retries + 1,"," _format_retry_error(exc),"," if attempt >= self.retries:"," raise ValueError("," \"Failed to parse ConfigPatch after \""," f\"{self.retries + 1} attempts: {_format_retry_error(exc)}\""," ) from exc"," else:"," # Let schema validation errors (like extra_forbidden) propagate"," raise"," raise ValueError(\"Failed to parse ConfigPatch: unknown error\")"," schema = self._schema_for_validation(allowed_schema, instruction)"," unknown_keys = validate_patch_keys(patch.operations, schema)"," if unknown_keys:"," patch.needs_review = True"," for entry in unknown_keys:"," if entry.suggestion:"," \"Unknown config key '%s'. Did you mean '%s'?\","," entry.path,"," entry.suggestion,"," logger.warning(\"Unknown config key '%s'.\", entry.path)"," return patch"," def _invoke_llm(self, prompt_text: str) -> str:"," from langchain_core.prompts import ChatPromptTemplate"," template = ChatPromptTemplate.from_messages([(\"system\", \"{prompt}\")])"," chain = template | self._bind_llm()"," response = chain.invoke({\"prompt\": prompt_text})"," return getattr(response, \"content\", None) or str(response)"," def _bind_llm(self) -> Any:"," if not hasattr(self.llm, \"bind\"):"," return self.llm"," params: dict[str, Any] = {\"temperature\": self.temperature}"," if self.model is not None:"," params[\"model\"] = self.model"," if self.max_tokens is not None:"," params[\"max_tokens\"] = self.max_tokens"," return self.llm.bind(**params)"," except TypeError:"," def _serialize_schema(self, schema: dict[str, Any]) -> str:"," return json.dumps(schema, indent=2, ensure_ascii=True)"," def _parse_patch(self, response_text: str) -> ConfigPatch:"," return ConfigPatch.model_validate_json(_strip_code_fence(response_text))"," def _schema_for_validation("," allowed_schema: str | None,"," ) -> dict[str, Any] | None:"," if allowed_schema:"," return json.loads(allowed_schema) # type: ignore[no-any-return]"," except json.JSONDecodeError:"," return None"," return self._select_schema(instruction=instruction)"," def _select_schema(self, *, instruction: str) -> dict[str, Any]:"," schema = self.schema or load_compact_schema()"," return select_schema_sections(schema, instruction)","def _strip_code_fence(text: str) -> str:"," stripped = text.strip()"," if stripped.startswith(\"```\") and stripped.endswith(\"```\"):"," lines = stripped.splitlines()"," if len(lines) >= 3:"," return \"\\n\".join(lines[1:-1]).strip()"," return stripped","def _format_retry_error(error: Exception | None) -> str:"," if error is None:"," return \"Unknown parse error.\""," return f\"{error.__class__.__name__}: {error}\"","def _read_env_float(name: str, *, default: float) -> float:"," value = os.environ.get(name)"," if value is None or value == \"\":"," return default"," try:"," return float(value)"," except ValueError as exc:"," raise ValueError(f\"{name} must be a float, got {value!r}.\") from exc","message",[726,727,728,729,730],{"_731":57,"_57":738},{"_731":733,"_364":532,"_371":736,"_373":737},{"_731":57,"_57":735},{"_731":733,"_364":365,"_371":734,"_373":374},{"_731":57,"_57":732},"content_type","\n\n**Testing**\n* ✅ `black`\n* ✅ `ruff`\n* ✅ `mypy`","repo_file_citation",200,"\n* Added a regression test to verify parse failures are logged on every retry attempt.",127,213,"**Summary**\n* Added logging for each ConfigPatch parse failure attempt to make retry errors visible in the LLM chain.",{"_104":314,"_53":96,"_108":314,"_741":742,"_743":744,"_745":746,"_143":-5,"_130":747},[95],"input_items",[767,768,769,770,771,772,773,774,775],"external_post_config",{"_748":749,"_246":180,"_750":291,"_257":-5,"_341":240,"_751":18,"_752":-5,"_753":-5,"_754":755,"_756":757,"_758":-5,"_759":-5,"_760":761,"_762":18,"_763":70,"_764":65,"_765":18,"_766":-5},"user_model_slug","slug",1767973750.593335,"installation_id",71148695,"pr_number","enable_internal_github_app_routing","gitlab_service_account_project_id","review_trigger","comment_id",3729467024,"comment_type","issue","pr_write_guard_token","pending_pr_reaction_id","pending_comment_reaction_id",322967045,"comment_if_no_findings","comment_on_error","share_url","is_parallel_review","review_summary_id",{"_104":724,"_108":314,"_375":791},{"_298":299,"_300":786,"_787":788,"_789":140,"_310":-5,"_104":790},{"_104":776,"_300":785,"_364":-5,"_778":-5,"_779":-5},{"_104":776,"_300":784,"_364":-5,"_778":-5,"_779":-5},{"_104":776,"_300":783,"_364":-5,"_778":-5,"_779":-5},{"_104":776,"_300":782,"_364":-5,"_778":-5,"_779":-5},{"_104":776,"_300":781,"_364":-5,"_778":-5,"_779":-5},{"_104":776,"_300":780,"_364":-5,"_778":-5,"_779":-5},{"_104":776,"_300":777,"_364":-5,"_778":-5,"_779":-5},"pull_request_comment","**Summary**\n* Added scope, tasks, and acceptance criteria checklists for issue #4308 keepalive tracking in the Codex bootstrap file. [agents/codex-4308.mdL1-L24](https://github.com/stranske/Trend_Model_Project/blob/25972eea0ffc722b3730694cb888bb6f662b2006/agents/codex-4308.md#L1-L24)\n\n**Testing**\n* ⚠️ Not run (not requested).\n\n [View task →](https://chatgpt.com/s/cd_69610eecc35c8191a7bb3a8bae6a76cf)","side","line","@codex start\n\nReuse the scope, acceptance criteria, and task list from the source issue and publish them here with `- [ ]` checklists so keepalive keeps watching.","\n## 🤖 Keepalive Loop Status\n\n**PR #4309** | Agent: **Codex** | Iteration 0/5\n\n### Current State\n| Metric | Value |\n|--------|-------|\n| Iteration progress | [----------] 0/5 |\n| Action | run (agent-run-skipped) |\n| Gate | success |\n| Tasks | 0/16 complete |\n| Keepalive | ✅ enabled |\n| Autofix | ❌ disabled |\n\n### Last Codex Run\n| Result | Value |\n|--------|-------|\n| Status | ⏭️ Skipped |\n| Reason | agent-run-skipped |\n\n### 🔍 Failure Classification\n| Error type | infrastructure |\n| Error category | transient |\n| Suggested recovery | Capture logs and context; retry once and escalate if the issue persists. |\n\n","\nStatus | ✅ no new diagnostics\nHistory points | 0\nTimestamp | 2026-01-09 14:03:30 UTC\nReport artifact | `autofix-report-pr-4309`\nRemaining | 0\nNew | 0\nNo additional artifacts\n","## Pull request overview\n\nThis is a bootstrap PR that creates a placeholder file to enable Codex agent engagement on issue #4308. The PR adds a single markdown file following the established repository pattern for automated agent bootstrapping.\n\n**Key Changes:**\n- Adds `agents/codex-4308.md` with an HTML comment to bootstrap Codex for issue #4308\n\n\n\n","PR created. Comment `@codex start` to request the plan. Tell Codex to reuse the scope, acceptance criteria, and task list from the source issue and publish them here with `- [ ]` checklists so keepalive keeps watching. After Codex replies, follow the instructions posted on the source issue to begin execution.","### Issue #4308: [Follow-up] Enhance the retry logic in the configuration patch (PR #4301)\n\n\n## Automated Status Summary\n#### Scope\nPR #4301 addressed issue #4300 but verification identified concerns (verdict: **CONCERNS**). This follow-up addresses the remaining gaps with improved task structure to ensure robust error handling, complete provider abstraction, and comprehensive test coverage.\n\n#### Tasks\n- [ ] Enhance the retry logic in the configuration patch processing function to retry up to `self.retries` times upon a parse failure. Add automated tests to simulate repeated parse failures and verify error logging.\n- [ ] Implement the `LLMProviderConfig` dataclass to encapsulate provider configuration parameters.\n- [ ] Develop the `create_llm()` factory function for dynamic provider instantiation and add unit and integration tests.\n- [ ] Update the `pyproject.toml` file to include all necessary provider dependencies with explicit version constraints.\n- [ ] Create or update the documentation file to include setup instructions, configuration examples, and troubleshooting guidance.\n- [ ] Develop an example usage script demonstrating configuration and error handling for LLM providers.\n- [ ] Expand evaluation test cases to include specific scenarios and integrate them into the eval harness.\n- [ ] Review and update the unknown key validation logic to use the latest authoritative schema and handle dynamic keys.\n\n#### Acceptance Criteria\n- [ ] The retry logic in `config_patch.py` attempts to reprocess a configuration patch up to `self.retries` times upon a parse failure, logging each error encountered.\n- [ ] The `LLMProviderConfig` dataclass encapsulates provider configuration parameters and supports at least 2 LLM providers.\n- [ ] The `create_llm()` factory function dynamically instantiates LLM providers and passes all unit and integration tests.\n- [ ] The `pyproject.toml` file includes all necessary provider dependencies with explicit version constraints.\n- [ ] The `examples/nl_config_demo.py` script demonstrates configuration and error handling for multiple LLM providers.\n- [ ] The documentation in `docs/llm_providers.md` includes setup instructions, configuration examples, and troubleshooting guidance for the LLM provider abstraction layer.\n- [ ] The evaluation harness in `tools/eval_config_patch.py` includes at least 10 test cases, covering specified scenarios.\n- [ ] The unknown key validator in `validation.py` detects keys not defined in the authoritative schema and flags them with `needs_review=True`, handling dynamic array indices and wildcard keys.\n\n\n

Full Issue Text

\n\n```markdown\n## Why\nPR #4301 addressed issue #4300 but verification identified concerns (verdict: **CONCERNS**). This follow-up addresses the remaining gaps with improved task structure to ensure robust error handling, complete provider abstraction, and comprehensive test coverage.\n\n## Tasks\n- [ ] Enhance the retry logic in the configuration patch processing function to retry up to `self.retries` times upon a parse failure. Add automated tests to simulate repeated parse failures and verify error logging.\n- [ ] Implement the `LLMProviderConfig` dataclass to encapsulate provider configuration parameters.\n- [ ] Develop the `create_llm()` factory function for dynamic provider instantiation and add unit and integration tests.\n- [ ] Update the `pyproject.toml` file to include all necessary provider dependencies with explicit version constraints.\n- [ ] Create or update the documentation file to include setup instructions, configuration examples, and troubleshooting guidance.\n- [ ] Develop an example usage script demonstrating configuration and error handling for LLM providers.\n- [ ] Expand evaluation test cases to include specific scenarios and integrate them into the eval harness.\n- [ ] Review and update the unknown key validation logic to use the latest authoritative schema and handle dynamic keys.\n\n## Acceptance Criteria\n- [ ] The retry logic in `config_patch.py` attempts to reprocess a configuration patch up to `self.retries` times upon a parse failure, logging each error encountered.\n- [ ] The `LLMProviderConfig` dataclass encapsulates provider configuration parameters and supports at least 2 LLM providers.\n- [ ] The `create_llm()` factory function dynamically instantiates LLM providers and passes all unit and integration tests.\n- [ ] The `pyproject.toml` file includes all necessary provider dependencies with explicit version constraints.\n- [ ] The `examples/nl_config_demo.py` script demonstrates configuration and error handling for multiple LLM providers.\n- [ ] The documentation in `docs/llm_providers.md` includes setup instructions, configuration examples, and troubleshooting guidance for the LLM provider abstraction layer.\n- [ ] The evaluation harness in `tools/eval_config_patch.py` includes at least 10 test cases, covering specified scenarios.\n- [ ] The unknown key validator in `validation.py` detects keys not defined in the authoritative schema and flags them with `needs_review=True`, handling dynamic array indices and wildcard keys.\n\n## Implementation Notes\n- Focus on concurrent development of provider abstraction, integration tests, dependency updates, example usage, and documentation to ensure alignment with acceptance criteria.\n- Ensure comprehensive test coverage by integrating evaluation and demonstration components together.\n\n

Background (previous attempt context)

\n```\n\n

","\n## Automated Status Summary\n#### Scope\nPR #4301 addressed issue #4300 but verification identified concerns (verdict: **CONCERNS**). This follow-up addresses the remaining gaps with improved task structure to ensure robust error handling, complete provider abstraction, and comprehensive test coverage.\n\n\n## Context for Agent\n\n### Related Issues/PRs\n- [#4301](https://github.com/stranske/Trend_Model_Project/issues/4301)\n- [#4300](https://github.com/stranske/Trend_Model_Project/issues/4300)\n\n\n#### Tasks\n- [ ] Enhance the retry logic in the configuration patch processing function to retry up to `self.retries` times upon a parse failure. Add automated tests to simulate repeated parse failures and verify error logging.\n- [ ] Implement the `LLMProviderConfig` dataclass to encapsulate provider configuration parameters.\n- [ ] Develop the `create_llm()` factory function for dynamic provider instantiation and add unit and integration tests.\n- [ ] Update the `pyproject.toml` file to include all necessary provider dependencies with explicit version constraints.\n- [ ] Create or update the documentation file to include setup instructions, configuration examples, and troubleshooting guidance.\n- [ ] Develop an example usage script demonstrating configuration and error handling for LLM providers.\n- [ ] Expand evaluation test cases to include specific scenarios and integrate them into the eval harness.\n- [ ] Review and update the unknown key validation logic to use the latest authoritative schema and handle dynamic keys.\n\n#### Acceptance criteria\n- [ ] The retry logic in `config_patch.py` attempts to reprocess a configuration patch up to `self.retries` times upon a parse failure, logging each error encountered.\n- [ ] The `LLMProviderConfig` dataclass encapsulates provider configuration parameters and supports at least 2 LLM providers.\n- [ ] The `create_llm()` factory function dynamically instantiates LLM providers and passes all unit and integration tests.\n- [ ] The `pyproject.toml` file includes all necessary provider dependencies with explicit version constraints.\n- [ ] The `examples/nl_config_demo.py` script demonstrates configuration and error handling for multiple LLM providers.\n- [ ] The documentation in `docs/llm_providers.md` includes setup instructions, configuration examples, and troubleshooting guidance for the LLM provider abstraction layer.\n- [ ] The evaluation harness in `tools/eval_config_patch.py` includes at least 10 test cases, covering specified scenarios.\n- [ ] The unknown key validator in `validation.py` detects keys not defined in the authoritative schema and flags them with `needs_review=True`, handling dynamic array indices and wildcard keys.\n\n\n\n

\n\n

Full Issue Text

Background (previous attempt context)

\n```\n\n\n\n

\n\n—\nPR created automatically to engage Codex.\n\n\n> **Source:** Issue #4308\n\n","base_ref","f651d94d481a8042ca7f637cdb2f383216da884f","head_ref","pull_request_info",[792],{"_731":57,"_57":793},"@codex\n\nPlease work on the first unchecked task from the task list and commit your 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