#Similarity-search
Showing 46 of 46 repositories tagged #similarity-search, ranked by stars
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
Simple, Elastic-quality search for Postgres
Semantic cache for LLMs. Fully integrated with LangChain and llama_index.
🦛 CHONK docs with Chonkie ✨ — The lightweight ingestion library for fast, efficient and robust RAG pipelines
A cross-platform video structuring (video analysis) framework. If you find it helpful, please give it a star: ) 跨平台的视频结构化（视频分析）框架，觉得有帮助的请给个星星 : )
🧠 AI-powered enterprise search engine 🔎
🚀 efficient approximate nearest neighbor search algorithm collections library written in Rust 🦀 .
JVector: the most advanced embedded vector search engine
Vald. A Highly Scalable Distributed Vector Search Engine
Collections of vector search related libraries, service and research papers
A @ClickHouse fork that supports high-performance vector search and full-text search.
ArcadeDB Multi-Model Database, one DBMS that supports SQL, Cypher, Gremlin, HTTP/JSON, MongoDB and Redis. ArcadeDB is a conceptual fork of OrientDB, the first Multi-Model DBMS. ArcadeDB supports Vector Embeddings.
Similarities: a toolkit for similarity calculation and semantic search. 相似度计算、匹配搜索工具包，支持亿级数据文搜文、文搜图、图搜图，python3开发，开箱即用。
cuVS - a library for vector search and clustering on the GPU
Blazing fast framework for fine-tuning similarity learning models
An Open-Source Package for Deep Learning to Hash (DeepHash)
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 !
Search and organise images and videos offline with on-device AI.
Go to: https://github.com/alexklibisz/elastiknn
TorchDR - PyTorch Dimensionality Reduction
Fast similarity search using DuckDB
Swfit library for fuzzy search. No dependencies lib.
Emacs package that helps org-mode users (re)discover similar documents
An LLM GUI application; enables you to interact with your files, offering dynamic parameters that can modify response behavior during runtime.
Examples of my tutorial on how to use Neo4j for empowering AI RAG systems
Reliable and Efficient Semantic Prompt Caching with vCache
Semantic search in Unity!
The ChatGPT Long Term Memory package is a powerful tool designed to empower your projects with the ability to handle a large number of simultaneous users and external sources.
Faiss-based library for efficient similarity search
High-performance on-device vector search engine for Expo & React Native. Powered by C++ JSI and USearch (HNSW) for sub-millisecond similarity matching.
Kubernetes operator for Qdrant
NeuronDB PostgreSQL extension: vector similarity search (HNSW, IVFFlat), embeddings, kNN, ML in SQL, and hybrid full-text + vector retrieval.
Rust client library for ChromaDB
Embedded-first vector database that grows into a self-hosted service and lightweight cluster.
这是为希望学习FAISS向量数据库的同学准备的全面入门指导，帮助你快速建立相关概念，更好地阅读官方文档。
TiDB AI SDK: Unified Multi-Modal Data Platform for AI Apps & Agents
Scalable Qdrant vector database cluster with Docker Compose, monitoring, and comprehensive documentation for high-performance similarity search applications.
This repository contains hybrid-rag a LLMOPS python package
A vector quantization library for Rust :crab: with Python bindings 🐍
An application that enable the users to upload PDF files and ask questions regarding their content using Retrieval Augmented Generation (RAG)
This project demonstrates how to build a semantic search + RAG pipeline using .NET
Edge Vector search engine with Vulkan GPU acceleration, hierarchical indexing (HRM2), and native LangChain integration. Gaussian splat-based architecture for similarity search on resource-constrained devices.
Automatic trailer generation using AI