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​.

Represent, send, store and search multimodal data

This repository contains various advanced techniques for Retrieval-Augmented Generation (RAG) systems.

The universal tool suite for vector database management. Manage Pinecone, Chroma, Qdrant, Weaviate and more vector databases with ease.

Weaviate vector database – examples

AI Agent Development Platform - Supports multiple models (OpenAI/DeepSeek/Wenxin/Tongyi), knowledge base management, workflow automation, and enterprise-grade security. Built with Flask + Vue3 + LangChain, featuring one-click Docker deployment.

一个99%由OpenAI ChatGPT开发的项目。A project that is 99% developed by OpenAI ChatGPT.

A python native client for easy interaction with a Weaviate instance.

Knowledge work automation with AI agents

Telegram LLM bot backed by OpenAI, Whisper, Beam, LLaMA, Weaviate, MinIO and MongoDB

Build super simple end-to-end data & ETL pipelines for your vector databases and Generative AI applications

Official Weaviate TypeScript Client

Website for the Weaviate vector database

Awesome Weaviate

Full Spectrum AI

Framework for benchmarking fully-managed vector databases

Weaviate Web UI

Ruby wrapper for the Weaviate vector search database API

Designed for offline use, this RAG application template offers a starting point for building your own local RAG pipeline, independent of online APIs and cloud-based LLM services like OpenAI.

🎩 Magic in Pocket / 🪄 口袋里的“魔法”.