da9070ecf7
Remove deprecated storage backends and Kubernetes deployment configuration: - Delete unused storage implementations: FAISS, JSON, Memgraph, Milvus, MongoDB, Nano Vector DB, Neo4j, NetworkX, Qdrant, Redis - Remove Kubernetes deployment manifests and installation scripts - Delete legacy examples for deprecated backends - Consolidate to PostgreSQL-only storage backend Streamline dependencies and add new capabilities: - Remove deprecated code documentation and migration guides - Add full-text search caching layer with FTS cache module - Implement metrics collection and monitoring pipeline - Add explain and metrics API routes - Simplify configuration with PostgreSQL-focused setup Update documentation and configuration: - Rewrite README to focus on supported features - Update environment and configuration examples - Remove Kubernetes-specific documentation - Add new utility scripts for PDF uploads and pipeline monitoring |
||
|---|---|---|
| .. | ||
| 01_lightrag_overview.md | ||
| 02_rag_architecture.md | ||
| 03_lightrag_improvements.md | ||
| 05_evaluation_and_deployment.md | ||
| README.md | ||
Sample Documents for Evaluation
These markdown files correspond to test questions in ../sample_dataset.json.
Usage
- Index documents into LightRAG (via WebUI, API, or Python)
- Run evaluation:
python lightrag/evaluation/eval_rag_quality.py - Expected results: ~91-100% RAGAS score per question
Files
01_lightrag_overview.md- LightRAG framework and hallucination problem02_rag_architecture.md- RAG system components03_lightrag_improvements.md- LightRAG vs traditional RAG04_supported_databases.md- Vector database support05_evaluation_and_deployment.md- Metrics and deployment
Note
Documents use clear entity-relationship patterns for LightRAG's default entity extraction prompts. For better results with your data, customize lightrag/prompt.py.