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Ready-to-run cloud templates for building real-time RAG, AI pipelines, and enterprise search applications that synchronize with various live data sources.
Awesome Infra for AI › Vector Databases & Retrieval Infrastructure
tensorchord/pgvecto.rs
pgvecto.rs is a PostgreSQL extension designed to enhance the database's capabilities with robust vector similarity search functionalities. Written in Rust and based on pgrx, it offers a performant and scalable solution for integrating vector search into existing PostgreSQL deployments. This extension stands out from the standard pgvector by supporting higher vector dimensions (up to 65535), providing a more complete filtering mechanism through its VBASE method, and introducing additional data types like binary vectors, FP16, and INT8. It also leverages dynamic SIMD instruction dispatching for optimal performance based on the host machine's capabilities. A key differentiator is its approach to indexing, managing storage and memory separately from PostgreSQL's native engine, which allows for specialized optimizations suited for vector data. While a new implementation, VectorChord, is now recommended by the developers for better stability and performance, pgvecto.rs remains a significant open-source contribution for those seeking to implement advanced vector search directly within a PostgreSQL environment, crucial for retrieval-augmented generation (RAG) and other AI/ML applications requiring efficient similarity search.
https://github.com/tensorchord/pgvecto.rs
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