vearch/vearch

Health v2 · maintenance only

Adoption not part of the score

2321 stars · 365 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Vearch is a cloud-native distributed vector database for efficient similarity search of embedding vectors, supporting hybrid vector and scalar filtering with replication and elastic scaling. It provides RESTful APIs and SDKs in Python, Go, Java, and Rust, and integrates with AI frameworks like LangChain and LlamaIndex.

Use cases

• store and search embedding vectors for AI applications
• build a RAG knowledge base with a vector store backend
• hybrid search combining vector similarity with scalar filters
• index and search millions of images for visual search
• use a vector database as memory backend for LLM agents
• deploy a scalable self-hosted vector database on Kubernetes

When to choose

• you need distributed, horizontally scalable vector search with replication
• you want hybrid vector plus scalar filtering in one database
• you need LangChain or LlamaIndex integration for RAG
• you want a self-hosted cloud-native vector database with Helm deployment

When to avoid

• you only need a small in-process similarity search library without a server
• your stack requires a managed cloud vector database service
• you need full-text search as the primary workload rather than vector search

Facets

service · maturity active

vector-database search-engine database rag databases machine-learning artificial-intelligence cloud self-hosted python go jvm rust vector-search embeddings hybrid-search similarity-search distributed-database cloud-native ai-native document-retrieval langchain llamaindex search retrieval-augmented-generation docker kubernetes linux

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