Vector Database
16.0k 2026-04-13

weaviate/weaviate

An open-source, cloud-native vector database enabling semantic search at scale by combining vector similarity with structured filtering and integrated AI models.

Core Features

Stores both objects and vectors for hybrid search
Supports automatic vectorization with integrated AI models (OpenAI, HuggingFace, etc.)
Enables Retrieval-Augmented Generation (RAG) and reranking
Cloud-native architecture with fault tolerance, scalability, multi-tenancy, and RBAC
Client libraries for Python, JavaScript/TypeScript

Quick Start

docker compose up -d

Detailed Introduction

Weaviate is a powerful open-source, cloud-native vector database designed for semantic search at scale. It uniquely stores both objects and their corresponding vector embeddings, allowing users to perform complex queries that combine vector similarity search with traditional structured filtering. With built-in support for automatic vectorization via popular AI models and capabilities for RAG and reranking, Weaviate serves as a foundational component for AI-driven applications like chatbots, recommendation engines, and intelligent search systems, offering robust scalability and enterprise-grade features.

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