FAISS

FAISS is a high-performance similarity search library developed by Meta, designed for large-scale vector datasets. It supports efficient approximate nearest neighbor (ANN) search and is widely used in image retrieval, recommendation systems, and NLP applications. With GPU acceleration support, FAISS delivers significant performance improvements in indexing and querying. Its low memory usage and speed make it a core tool for building semantic search engines.

Official Website Github Website

Resource Introduction

Related Resources

Annoy

Annoy is a lightweight approximate nearest neighbor library developed by Spotify for fast vector retrieval in high-dimensional spaces. It uses a forest of binary trees to enable efficient indexing and searching. Annoy is easy to use, memory-efficient, and supports index persistence—making it ideal for recommendation systems and content matching applications.

Pinecone

Pinecone is a fully managed cloud-based vector database that provides scalable and highly available vector storage and search capabilities. Developers can easily index vectors from ML models and perform real-time similarity searches. Supporting multi-tenancy, dynamic scaling, and fine-grained filtering, Pinecone is ideal for production-grade semantic search, recommendation, and Q&A systems.

Weaviate

Weaviate is an open-source vector search engine supporting semantic search, knowledge graph construction, and hybrid search modes. It integrates with models like Hugging Face and OpenAI to automatically convert text, images, etc., into vectors for efficient retrieval. With RESTful and GraphQL APIs, Weaviate is suitable for intelligent search, recommendation systems, and semantic understanding

Milvus

Milvus is an open-source vector database focused on high-performance and accurate similarity search. Built on optimized algorithms like FAISS and ANNOY, it supports horizontal scaling, GPU acceleration, and distributed deployment. Milvus is widely used in image recognition, video analysis, and biometric identification—offering strong ecosystem compatibility and enterprise-grade stability.

Qdrant

Qdrant is a high-performance open-source vector search engine supporting similarity search and attribute-based filtering. It offers RESTful and gRPC APIs for easy integration into AI applications. Suitable for image retrieval, recommendations, and semantic search, Qdrant supports both local and cloud deployments—with strong flexibility and scalability.

Chroma

Chroma is a new open-source embedded vector database built for developers, emphasizing ease of use and integration. It supports local and embedded deployment, making it ideal for small projects or as the data layer for LLM applications. With a simple Python API, Chroma enables quick collection creation, vector insertion, and similarity search—ideal for semantic search and memory systems.