Commit graph

5 commits

Author SHA1 Message Date
anouarbm
026bca00d9 fix: Use actual retrieved contexts for RAGAS evaluation
**Critical Fix: Contexts vs Ground Truth**
- RAGAS metrics now evaluate actual retrieval performance
- Previously: Used ground_truth as contexts (always perfect scores)
- Now: Uses retrieved documents from LightRAG API (real evaluation)

**Changes to generate_rag_response (lines 100-156)**:
- Remove unused 'context' parameter
- Change return type: Dict[str, str] → Dict[str, Any]
- Extract contexts as list of strings from references[].text
- Return 'contexts' key instead of 'context' (JSON dump)
- Add response.raise_for_status() for better error handling
- Add httpx.HTTPStatusError exception handler

**Changes to evaluate_responses (lines 180-191)**:
- Line 183: Extract retrieved_contexts from rag_response
- Line 190: Use [retrieved_contexts] instead of [[ground_truth]]
- Now correctly evaluates: retrieval quality, not ground_truth quality

**Impact on RAGAS Metrics**:
- Context Precision: Now ranks actual retrieved docs by relevance
- Context Recall: Compares ground_truth against actual retrieval
- Faithfulness: Verifies answer based on actual retrieved contexts
- Answer Relevance: Unchanged (question-answer relevance)

Fixes incorrect evaluation methodology. Based on RAGAS documentation:
- contexts = retrieved documents from RAG system
- ground_truth = reference answer for context_recall metric

References:
- https://docs.ragas.io/en/stable/concepts/components/eval_dataset/
- https://docs.ragas.io/en/stable/concepts/metrics/
2025-11-02 16:16:00 +01:00
anouarbm
b12b693a81 fixed ruff format of csv path 2025-11-02 11:46:22 +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
aa916f28d2 docs: add generic test_dataset.json for evaluation examples
Test cases with generic examples about:
- LightRAG framework features and capabilities
- RAG system architecture and components
- Vector database support (ChromaDB, Neo4j, Milvus, etc.)
- LLM provider integrations (OpenAI, Anthropic, Ollama, etc.)
- RAG evaluation metrics explanation
- Deployment options (Docker, FastAPI, direct integration)
- Knowledge graph-based retrieval concepts

Changes:
- Added generic test_dataset.json with 8 LightRAG-focused test cases
- File added with git add -f to override test_* pattern

This provides realistic, reusable examples for users testing their
LightRAG deployments and helps demonstrate the evaluation framework.
2025-11-01 22:27:26 +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