Add comprehensive test suites for prompt evaluation:
- test_prompt_accuracy.py: 365 lines testing prompt extraction accuracy
- test_prompt_quality_deep.py: 672 lines for deep quality analysis
- Refactor prompt.py to consolidate optimized variants (removed prompt_optimized.py)
- Apply ruff formatting and type hints across 30 files
- Update pyrightconfig.json for static type checking
- Modernize reproduce scripts and examples with improved type annotations
- Sync uv.lock dependencies
Format entire codebase with ruff and add type hints across all modules:
- Apply ruff formatting to all Python files (121 files, 17K insertions)
- Add type hints to function signatures throughout lightrag core and API
- Update test suite with improved type annotations and docstrings
- Add pyrightconfig.json for static type checking configuration
- Create prompt_optimized.py and test_extraction_prompt_ab.py test files
- Update ruff.toml and .gitignore for improved linting configuration
- Standardize code style across examples, reproduce scripts, and utilities
- Add enable_cot parameter to all LLM APIs
- Implement CoT for OpenAI with <think> tags
- Log warnings for unsupported providers
- Enable CoT in query operations
- Handle streaming and non-streaming CoT
Replace regex-based JSON extraction with json-repair for better handling of malformed LLM responses. Remove deprecated JSON parsing utilities and clean up keyword_extraction parameter across LLM providers.
- Remove locate_json_string_body_from_string() and convert_response_to_json()
- Use json-repair.loads() in extract_keywords_only() for robust parsing
- Clean up LLM interfaces and remove unused parameters
- Add json-repair dependency
