Qdrant - High-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
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Updated
Sep 12, 2024 - Rust
Qdrant - High-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
Approximate Nearest Neighbors in C++/Python optimized for memory usage and loading/saving to disk
Open-source vector similarity search for Postgres
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.
Postgres with GPUs for ML/AI apps.
A distributed approximate nearest neighborhood search (ANN) library which provides a high quality vector index build, search and distributed online serving toolkits for large scale vector search scenario.
Developer-friendly, serverless vector database for AI applications. Easily add long-term memory to your LLM apps!
🚀 efficient approximate nearest neighbor search algorithm collections library written in Rust 🦀 .
The AI-native database built for LLM applications, providing incredibly fast hybrid search of dense vector, sparse vector, tensor (multi-vector), and full-text
Fast Open-Source Search & Clustering engine × for Vectors & 🔜 Strings × in C++, C, Python, JavaScript, Rust, Java, Objective-C, Swift, C#, GoLang, and Wolfram 🔍
Vald. A Highly Scalable Distributed Vector Search Engine
Nearest Neighbor Search with Neighborhood Graph and Tree for High-dimensional Data
A Python nearest neighbor descent for approximate nearest neighbors
PostgreSQL vector database extension for building AI applications
Some useful tips for faiss
TOROS N2 - lightweight approximate Nearest Neighbor library which runs fast even with large datasets
PECOS - Prediction for Enormous and Correlated Spaces
An easy to use Neural Search Engine. Index latent vectors along with JSON metadata and do efficient k-NN search.
Framework for evaluating ANNS algorithms on billion scale datasets.
Pure python implementation of product quantization for nearest neighbor search
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