A multi-modal vector database that supports upserts and vector queries using unified SQL (MySQL-Compatible) on structured and unstructured data, while meeting the requirements of high concurrency and ultra-low latency.
embedding-search 开源项目推荐(20 个)
embedding-search 话题在 GitHub 上没有填写简介,这里按 Star 数汇总最相关的热门仓库。
热门项目 全部 20
GitHub 共 20 个仓库,已全部展示VQLite - Simple and Lightweight Vector Search Engine based on Google ScaNN
Github Issues + AI 代码审查的相似性检测
Magna is an AI-powered embedding similarity search tool for searching within large documents.
👀 Semantic search over PDF, Markdown, and source code; chunks and indexes files into AI embeddings in a vector database for meaning-based search.
A multi-modal vector database that supports upserts and vector queries using unified SQL (MySQL-Compatible) on structured and unstructured data, while meeting the requirements of high concurrency and ultra-low latency.
Vector Search Engine for the next generation of AI applications
用于搜索和筛选 Web 数据的 Lightfeed SDK
High-performance distributed vector database & embedding search engine in Rust, Python, TypeScript, and Go with SIMD HNSW indexing.
Embedding Search sample with RediSearch
Image Search
该项目在 GitHub 上没有填写描述
Solution for static embedding search
The project demonstrates the fundamental concept behind semantic search, where similarity is based on meaning rather than exact keyword matches.
A production-grade benchmarking suite that evaluates vector databases (Qdrant, Milvus, Weaviate, ChromaDB, Pinecone, SQLite, TopK) for music semantic search applications. Features automated performance testing, statistical analysis across 15-20 iterations, real-time web UI for database comparison, and comprehensive reporting with production.
Context-aware protein sequence retrieval and refinement system. Features embedding-based similarity search, biological context refinement, and structured LLM extraction.
A full-stack Retrieval-Augmented Generation (RAG) chatbot application with multi-language support
Embedding Search sample with Voyager
Production-style RAG API built with FastAPI, PostgreSQL and pgvector, featuring PDF ingestion, semantic retrieval, grounded answers, citations and evaluation.
轻量级向量数据库召回引擎。C++17 实现,自研 HNSW / IVF / BM25,单二进制部署,HTTP API 即开即用。
关于这个话题
- 清单取自 GitHub 话题页并按 Star 数排序,每次展示前 20 个项目;原始页面见 https://github.com/topics/embedding-search。
- 该话题在 GitHub 上共有 20 个仓库, 本站收录的是其中最热门的 20 个,不是全量列表。
- Star / Fork 每日自动刷新一次,实时数据以 GitHub 为准。
- 当前 20 个项目累计 1923 Star,覆盖 7 种主要语言,数据更新于 2026-11-08。
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