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Vector DBFreeOpen Source
INFINITY
AI-native database for hybrid search in LLM applications
Apache-2.0
ABOUT
RAG applications need to search across multiple representation types — dense vector embeddings for semantic similarity, sparse vectors for keyword matching, and full-text for exact term search — but most databases only support one search paradigm well. Infinity combines all four search modalities (dense, sparse, tensor, full-text) in a single engine with high-speed ingestion and sub-millisecond query latencies, eliminating the need to maintain separate systems for different search strategies in LLM applications.
INTEGRATION GUIDE
1. Power a RAG pipeline that combines dense semantic search with BM25 keyword matching for higher retrieval accuracy
2. Store and query multi-vector embeddings for sophisticated document understanding and cross-modal retrieval
3. Build a hybrid search backend for AI applications requiring both semantic and exact-match capabilities in one database
4. Ingest and index millions of document chunks at high throughput for real-time AI application queries
5. Replace separate vector and full-text search systems with a single unified database for LLM application backends
TAGS
vector-databasehybrid-searchfull-text-searchembeddingdense-vectorsparse-vectortensorai-native