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​.

Java version of LangChain

Represent, send, store and search multimodal data

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

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

Weaviate vector database – examples

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

Full Spectrum AI

Awesome Weaviate

Website for the Weaviate vector database

Official Weaviate TypeScript Client

Framework for benchmarking fully-managed vector databases

Ruby wrapper for the Weaviate vector search database API

🎩 Magic in Pocket / 🪄 口袋里的“魔法”.

Designed for offline use, this RAG application template is based on Andrej Baranovskij's tutorials. It offers a starting point for building your own local RAG pipeline, independent of online APIs and cloud-based LLM services like OpenAI.

Async bulk data ingestion and querying in various document, graph and vector databases via their Python clients