LightRAG

gmakstutis/LightRAG

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Author	SHA1	Message	Date
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	5cdb4b0ef2	fix: Apply ruff formatting and rename test_dataset to sample_dataset Lint Fixes (ruff): - Sort imports alphabetically (I001) - Add blank line after import traceback (E302) - Add trailing comma to dict literals (COM812) - Reformat writer.writerow for readability (E501) Rename test_dataset.json → sample_dataset.json: - Avoids .gitignore pattern conflict (test_* is ignored) - More descriptive name - it's a sample/template, not actual test data - Updated all references in eval_rag_quality.py and README.md Resolves lint-and-format CI check failure. Addresses reviewer feedback about test dataset naming.	2025-11-02 10:36:03 +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

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

5cdb4b0ef2

fix: Apply ruff formatting and rename test_dataset to sample_dataset

**Lint Fixes (ruff)**:
- Sort imports alphabetically (I001)
- Add blank line after import traceback (E302)
- Add trailing comma to dict literals (COM812)
- Reformat writer.writerow for readability (E501)

**Rename test_dataset.json → sample_dataset.json**:
- Avoids .gitignore pattern conflict (test_* is ignored)
- More descriptive name - it's a sample/template, not actual test data
- Updated all references in eval_rag_quality.py and README.md

Resolves lint-and-format CI check failure.
Addresses reviewer feedback about test dataset naming.

2025-11-02 10:36:03 +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