a172cf893d
Add 5 markdown documents that users can index to reproduce evaluation results. Changes: - Add sample_documents/ folder with 5 markdown files covering LightRAG features - Update sample_dataset.json with 3 improved, specific test questions - Shorten and correct evaluation README (removed outdated info about mock responses) - Add sample_documents reference with expected ~95% RAGAS score Test Results with sample documents: - Average RAGAS Score: 95.28% - Faithfulness: 100%, Answer Relevance: 96.67% - Context Recall: 88.89%, Context Precision: 95.56%
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LightRAG Vector Database Support
Supported Vector Databases
LightRAG supports multiple vector databases for flexible deployment options.
ChromaDB
ChromaDB is a vector database supported by LightRAG. ChromaDB provides simple deployment for development environments. The database offers efficient vector similarity search capabilities.
Neo4j
Neo4j is a graph database supported by LightRAG. Neo4j enables graph-based knowledge representation alongside vector search. The database combines relationship modeling with vector capabilities.
Milvus
Milvus is a vector database supported by LightRAG. Milvus provides high-performance vector search at scale. The database handles large-scale vector collections efficiently.
Qdrant
Qdrant is a vector database supported by LightRAG. Qdrant offers fast similarity search with filtering capabilities. The database provides production-ready vector search infrastructure.
MongoDB Atlas Vector Search
MongoDB Atlas Vector Search is supported by LightRAG. MongoDB Atlas combines document storage with vector search capabilities. The database enables unified data management for RAG applications.
Redis
Redis is supported by LightRAG for vector search operations. Redis provides in-memory vector search with low latency. The database offers fast retrieval for real-time applications.
Built-in Nano-VectorDB
LightRAG includes a built-in nano-vectordb for simple deployments. Nano-vectordb eliminates external database dependencies for small projects. The built-in database provides basic vector search functionality without additional setup.
Database Selection Benefits
The multiple database support enables developers to choose appropriate storage backends. LightRAG adapts to different deployment scenarios from development to production. Users can select databases based on scale, performance, and infrastructure requirements.