LightRAG

gmakstutis/LightRAG

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Author	SHA1	Message	Date
clssck	69358d830d	test(lightrag,examples,api): comprehensive ruff formatting and type hints 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	2025-12-05 15:17:06 +01:00
anouarbm	36694eb9f2	fix(evaluation): Move import-time validation to runtime and improve documentation Changes: - Move sys.exit() calls from module level to __init__() method - Raise proper exceptions (ImportError, ValueError, EnvironmentError) instead of sys.exit() - Add lazy import for RAGEvaluator in __init__.py using __getattr__ - Update README to clarify sample_dataset.json contains generic test data (not personal) - Fix README to reflect actual output format (JSON + CSV, not HTML) - Improve documentation for custom test case creation Addresses code review feedback about import-time validation and module exports.	2025-11-03 05:56:38 +01:00
anouarbm	1ad0bf82f9	feat: add RAGAS evaluation framework for RAG quality assessment This contribution adds a comprehensive evaluation system using the RAGAS framework to assess LightRAG's retrieval and generation quality. Features: - RAGEvaluator class with four key metrics: * Faithfulness: Answer accuracy vs context * Answer Relevance: Query-response alignment * Context Recall: Retrieval completeness * Context Precision: Retrieved context quality - HTTP API integration for live system testing - JSON and CSV report generation - Configurable test datasets - Complete documentation with examples - Sample test dataset included Changes: - Added lightrag/evaluation/eval_rag_quality.py (RAGAS evaluator implementation) - Added lightrag/evaluation/README.md (comprehensive documentation) - Added lightrag/evaluation/__init__.py (package initialization) - Updated pyproject.toml with optional 'evaluation' dependencies - Updated .gitignore to exclude evaluation results directory Installation: pip install lightrag-hku[evaluation] Dependencies: - ragas>=0.3.7 - datasets>=4.3.0 - httpx>=0.28.1 - pytest>=8.4.2 - pytest-asyncio>=1.2.0	2025-11-01 21:36:39 +01:00

Author

SHA1

Message

Date

clssck

69358d830d

test(lightrag,examples,api): comprehensive ruff formatting and type hints

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

2025-12-05 15:17:06 +01:00

anouarbm

36694eb9f2

fix(evaluation): Move import-time validation to runtime and improve documentation

Changes:
- Move sys.exit() calls from module level to __init__() method
- Raise proper exceptions (ImportError, ValueError, EnvironmentError) instead of sys.exit()
- Add lazy import for RAGEvaluator in __init__.py using __getattr__
- Update README to clarify sample_dataset.json contains generic test data (not personal)
- Fix README to reflect actual output format (JSON + CSV, not HTML)
- Improve documentation for custom test case creation

Addresses code review feedback about import-time validation and module exports.

2025-11-03 05:56:38 +01:00

anouarbm

1ad0bf82f9

feat: add RAGAS evaluation framework for RAG quality assessment

This contribution adds a comprehensive evaluation system using the RAGAS
framework to assess LightRAG's retrieval and generation quality.

Features:
- RAGEvaluator class with four key metrics:
  * Faithfulness: Answer accuracy vs context
  * Answer Relevance: Query-response alignment
  * Context Recall: Retrieval completeness
  * Context Precision: Retrieved context quality
- HTTP API integration for live system testing
- JSON and CSV report generation
- Configurable test datasets
- Complete documentation with examples
- Sample test dataset included

Changes:
- Added lightrag/evaluation/eval_rag_quality.py (RAGAS evaluator implementation)
- Added lightrag/evaluation/README.md (comprehensive documentation)
- Added lightrag/evaluation/__init__.py (package initialization)
- Updated pyproject.toml with optional 'evaluation' dependencies
- Updated .gitignore to exclude evaluation results directory

Installation:
pip install lightrag-hku[evaluation]

Dependencies:
- ragas>=0.3.7
- datasets>=4.3.0
- httpx>=0.28.1
- pytest>=8.4.2
- pytest-asyncio>=1.2.0

2025-11-01 21:36:39 +01:00

3 commits