Refactor: Centralize keyword_extraction parameter handling in OpenAI LLM implementations#2401
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• Move response format to core function • Remove duplicate format assignments • Standardize keyword extraction flow • Clean up redundant parameter handling • Improve Azure OpenAI compatibility
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| # Handle keyword extraction mode | ||
| if keyword_extraction: | ||
| kwargs["response_format"] = GPTKeywordExtractionFormat |
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When keyword_extraction is true this new block sets response_format, causing the request to go through beta.chat.completions.parse, but the non-streaming path still assumes response.choices[0].message.content is a string and immediately probes it with "\u" in content. Structured parse responses often set content=None and populate message.parsed instead, so keyword extraction calls will now raise a TypeError before returning any result. Consider handling message.parsed or null content before performing string operations.
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Added support for structured output (JSON mode) from the OpenAI API in `openai.py` and `azure_openai.py`. When `response_format` is used to request structured data, the new logic checks for the `message.parsed` attribute. If it exists, it's serialized into a JSON string as the final content. If not, the code falls back to the existing `message.content` handling, ensuring backward compatibility.
- Remove beta prefix from completions.parse - Update OpenAI dependency to >=2.0.0 - Fix whitespace formatting - Update all requirement files - Clean up pyproject.toml dependencies
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Refactor: Centralize keyword_extraction parameter handling in OpenAI LLM implementations
Summary
Refactored the
keyword_extractionparameter handling in OpenAI and Azure OpenAI LLM implementations to follow the DRY (Don't Repeat Yourself) principle. All keyword extraction logic is now centralized in the base*_complete_if_cachefunctions, eliminating code duplication across wrapper functions.Enhance keyword extraction compatibility to handle cases where the LLM cannot reliably generate JSON output.
Changes
lightrag/llm/openai.pyopenai_complete_if_cache: Added keyword extraction handling that setsresponse_formattoGPTKeywordExtractionFormatwhenkeyword_extraction=Trueopenai_complete()- removed inconsistent"json"format handlinggpt_4o_complete()- removed duplicate format settinggpt_4o_mini_complete()- removed duplicate format settinglightrag/llm/azure_openai.pyGPTKeywordExtractionFormatfromlightrag.typesazure_openai_complete_if_cache:keyword_extraction: bool = FalseparameterGPTKeywordExtractionFormatkwargs.pop("keyword_extraction", None)azure_openai_complete: Now properly passeskeyword_extractionparameter to the base functionBenefits
✅ Single source of truth: All keyword extraction logic centralized in base functions
✅ Consistency: Both implementations use the same
GPTKeywordExtractionFormat✅ Maintainability: Future changes only need to be made in one location per file
✅ Code quality: Eliminates code duplication and improves readability
Testing
Breaking Changes
None. This is a pure refactoring that maintains full backward compatibility.