Add extensive test suites for API routes and utilities:
- Implement test_search_routes.py (406 lines) for search endpoint validation
- Implement test_upload_routes.py (724 lines) for document upload workflows
- Implement test_s3_client.py (618 lines) for S3 storage operations
- Implement test_citation_utils.py (352 lines) for citation extraction
- Implement test_chunking.py (216 lines) for text chunking validation
Add S3 storage client implementation:
- Create lightrag/storage/s3_client.py with S3 operations
- Add storage module initialization with exports
- Integrate S3 client with document upload handling
Enhance API routes and core functionality:
- Add search_routes.py with full-text and graph search endpoints
- Add upload_routes.py with multipart document upload support
- Update operate.py with bulk operations and health checks
- Enhance postgres_impl.py with bulk upsert and parameterized queries
- Update lightrag_server.py to register new API routes
- Improve utils.py with citation and formatting utilities
Update dependencies and configuration:
- Add S3 and test dependencies to pyproject.toml
- Update docker-compose.test.yml for testing environment
- Sync uv.lock with new dependencies
Apply code quality improvements across all modified files:
- Add type hints to function signatures
- Update imports and router initialization
- Fix logging and error handling
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 KaTeX extensions (mhchem for chemistry, copy-tex for copying)
- Add CASCADE to AGE extension for PostgreSQL
- Remove future dependency, replace passlib with bcrypt
- Fix Jina embedding configuration and provider defaults
- Update gunicorn help text and bump API version to 0258
- Documentation and README updates
- Update import from PyPDF2 to pypdf
- Change dependency to pypdf>=6.1.0
- Update all requirements files
- Remove PyPDF2 from lock file
- Use modern pypdf library
This contribution adds optional Langfuse support for LLM observability and tracing.
Langfuse provides a drop-in replacement for the OpenAI client that automatically
tracks all LLM interactions without requiring code changes.
Features:
- Optional Langfuse integration with graceful fallback
- Automatic LLM request/response tracing
- Token usage tracking
- Latency metrics
- Error tracking
- Zero code changes required for existing functionality
Implementation:
- Modified lightrag/llm/openai.py to conditionally use Langfuse's AsyncOpenAI
- Falls back to standard OpenAI client if Langfuse is not installed
- Logs observability status on import
Configuration:
To enable Langfuse tracing, install the observability extras and set environment variables:
```bash
pip install lightrag-hku[observability]
export LANGFUSE_PUBLIC_KEY="your_public_key"
export LANGFUSE_SECRET_KEY="your_secret_key"
export LANGFUSE_HOST="https://cloud.langfuse.com" # or your self-hosted instance
```
If Langfuse is not installed or environment variables are not set, LightRAG
will use the standard OpenAI client without any functionality changes.
Changes:
- Modified lightrag/llm/openai.py (added optional Langfuse import)
- Updated pyproject.toml with optional 'observability' dependencies
Dependencies (optional):
- langfuse>=3.8.1
• Add constraints-offline.txt for exact versions
• Set upper bounds in pyproject.toml
• Combine pip installs in Dockerfile
• Update requirements with version bounds
• Prevent dependency conflicts
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
- Add nano-vectordb and networkx to pyproject.toml dependencies
- Replace dynamic imports with direct imports for 4 default storage implementations
- Improve startup performance while maintaining backward compatibility
