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E
Vector DBFreemiumOpen Source
EPSILLA
A 10x faster, cheaper, and more accurate vector database
GPL-3.0
ABOUT
Traditional vector databases rely on HNSW indexing which suffers significant latency degradation at high precision targets above 95%. Epsilla solves this with a parallel graph traversal algorithm (SpeedANN) that delivers 10x faster vector search with over 99.9% precision, enabling cost-effective, production-scale similarity search for LLM-powered RAG applications where both speed and accuracy are critical. Its C++ core ensures minimal resource overhead.
INSTALL
docker pull epsilla/vectordb
docker run --pull=always -d -p 8888:8888 epsilla/vectordb
INTEGRATION GUIDE
1. Power retrieval-augmented generation (RAG) for LLM applications requiring high-speed, high-precision vector search
2. Run semantic similarity search across large-scale vector corpora with millions of embeddings
3. Build AI-powered conversational search on enterprise knowledge bases with real-time latency requirements
4. Construct agentic RAG and GraphRAG pipelines that need highly accurate document retrieval
5. Deploy a cost-effective vector search backend for embedding-based recommendation systems
TAGS
vector-databaseembeddingsragsimilarity-searchvector-searchgraph-traversal