#Hnsw
Showing 29 of 29 repositories tagged #hnsw, ranked by stars
Milvus is a high-performance, cloud-native vector database built for scalable vector ANN search
Qdrant - High-performance, massive-scale Vector Database and Vector Search Engine for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
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.
A lightweight, lightning-fast, in-process vector database
The AI-native database built for LLM applications, providing incredibly fast hybrid search of dense vector, sparse vector, tensor (multi-vector), and full-text.
MinHash, LSH, LSH Forest, Weighted MinHash, HyperLogLog, HyperLogLog++, LSH Ensemble and HNSW
The AI-Native Search Database. Best for agent storage, it unifies vector, text, structured, and semi-structured data into a single engine. This all-in-one database makes agents smarter, easier to run, and more stable.
🚀 efficient approximate nearest neighbor search algorithm collections library written in Rust 🦀 .
🛰️ An approximate nearest-neighbor search library for Python and Java with a focus on ease of use, simplicity, and deployability.
RAG Time: A 5-week Learning Journey to Mastering RAG
PostgreSQL vector database extension for building AI applications
Nornicdb is a distributed low-latency, Graph+Vector, Temporal MVCC with all sub-ms HNSW search, graph traversal, and writes. Using Neo4j Bolt/Cypher and qdrant's gRPC means you can switch with no changes while adding intelligent features like schemas, managed embeddings, reranking+llm, GPU accel, Auto-TLP, Policy-based Memory Decay, and MCP server.
一款简单好用的 跨平台/多语言的 相似向量/相似词/相似句 高性能检索引擎。欢迎star & fork。Build together! Power another !
Visualize hnsw, faiss and other anns index
Fast, SQL powered, in-process vector search for any language with an SQLite driver
📚 从零开始的向量数据库原理与实践教程，在线阅读地址：https://easy-vecdb.datawhale.cc/
A resource-efficient C++ vector index engine built for low-RAM production workloads
Production-ready KV-backed HNSW implementation in Rust using LMDB
AI memory system combining vector search with temporal knowledge graph. Built-in cognitive engine for agents. Supports memory decay, contradiction detection, and MCP integration.
The local-first memory engine for AI agents. One offline Rust binary fuses vector + graph + columnar under SQL — remember / recall / why over the Model Context Protocol. why() reconnects a decision to its context across sessions, where pure vector recall (Mem0/Zep) goes blind. Runs on server, laptop, browser, edge. Zero cloud.
High-performance on-device vector search engine for Expo & React Native. Powered by C++ JSI and USearch (HNSW) for sub-millisecond similarity matching.
NeuronDB PostgreSQL extension: vector similarity search (HNSW, IVFFlat), embeddings, kNN, ML in SQL, and hybrid full-text + vector retrieval.
PostgreSQL-compatible SQL, graph, and vector database built from scratch in Rust.
Cognitive memory engine for AI agents — temporal decay, contradiction detection, autonomous consolidation, knowledge graph, ANN recall via HNSW. Embeddable Rust library with Python bindings; powers yantrikdb-server (HTTP gateway, MCP server, openraft cluster). AGPL.
TiDB AI SDK: Unified Multi-Modal Data Platform for AI Apps & Agents
Pixel-native visual RAG ported to Rust on the ruvector ANN substrate (HNSW + IVF-Flat) — screenshot/document retrieval over visual embeddings, a Rust port of PixelRAG, with a metaharness benchmark CLI: npx rupixel
Versioned, fast and scalable nearest neighbor search.
AI-Lake Format: self-contained Parquet + HNSW index — unifying tabular data, embeddings, and vector search in a single Iceberg-compatible file
Fast vector search in PostgreSQL