A cloud-native vector database, storage for next generation AI applications
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Updated
May 20, 2024 - Go
A cloud-native vector database, storage for next generation AI applications
Qdrant - High-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
A framework for the implementation of candidate generation/retrieval algorithms for recommender systems.
Unified embedding generation and search engine. Also available on cloud - cloud.marqo.ai
Weaviate is an open-source vector database that stores both objects and vectors, allowing for the combination of vector search with structured filtering with the fault tolerance and scalability of a cloud-native database.
PostgreSQL vector database extension for building AI applications
The AI-native database built for LLM applications, providing incredibly fast full-text and vector search
An embedded vector database designed to run on edge devices. Lightweight and fast with HNSW indexing algorithm.
Rcpp bindings for the approximate nearest neighbors library hnswlib
Vector Database implemented in Golang with support for full-text and vector search as well as fault tolerance via Raft.
An R package for blocking records for record linkage / data deduplication based on approximate nearest neighbours algorithms.
Fast and minimal header-only graph-based index for approximate nearest neighbor search (ANNS).
What if an HNSW index was just a file, and you could serve it from a CDN, and search it directly in the browser?
Genkit AI framework plugin for HNSW vector database. save data into vector store for Retrieval Augmented Generation (RAG) implementation in Generative AI
Simple and Efficient DiskANN implementation
🛰️ An approximate nearest-neighbor search library for Python and Java with a focus on ease of use, simplicity, and deployability.
Fast approximate nearest neighbor searching in Rust, based on HNSW index
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