A cloud-native vector database, storage for next generation AI applications
-
Updated
Jul 26, 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/
The AI-native database built for LLM applications, providing incredibly fast hybrid search of dense vector, sparse vector, tensor (multi-vector), and full-text
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.
Unified embedding generation and search engine. Also available on cloud - cloud.marqo.ai
Fast and minimal header-only graph-based index for approximate nearest neighbor search (ANNS). https://blaisemuhirwa.github.io/flatnav-experimental
Vector Database implemented in Golang with support for full-text and vector search as well as fault tolerance via Raft.
An embedded vector database designed to run on edge devices. Lightweight and fast with HNSW indexing algorithm.
PostgreSQL vector database extension for building AI applications
🛰️ 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
A specialized implementation of the Hierarchical Navigable Small World (HNSW) data structure adapted for efficient nearest neighbor lookup of approximate matching hashes
Client Side Vector Database
A fast and tunable vector search extension for SQLite
This sample shows how to build vector similarity search on Azure Cosmos DB for PostgreSQL using the pgvector extension and the multi-modal embeddings APIs of Azure AI Vision.
Graph-based Approximate Nearest Neighbor Search
Experimental HNSW Index implementation.
Comparison of IVFFlat and HNSW Algorithms
MSVBASE is a system that efficiently supports complex queries of both approximate similarity search and relational operators. It integrates high-dimensional vector indices into PostgreSQL, a relational database to facilitate complex approximate similarity queries.
MinHash, LSH, LSH Forest, Weighted MinHash, HyperLogLog, HyperLogLog++, LSH Ensemble and HNSW
Add a description, image, and links to the hnsw topic page so that developers can more easily learn about it.
To associate your repository with the hnsw topic, visit your repo's landing page and select "manage topics."