Represent, send, store and search multimodal data

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.

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

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

Framework for benchmarking fully-managed vector databases

Home Assistant LLM integration for local OpenAI-compatible services (llamacpp, vllm, etc)

This template demonstrates how to create a collaborative team of AI agents that work together to process, analyze, and generate insights from documents.

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”.

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

Weaviate Cluster WebApp is built to manage and interact with Weaviate Vector Database

Piazza-Updater automates updates to a Weaviate database with real-time vectorial data. By continuously searching the internet and integrating with Verba repositories, it enhances retrieval-augmented generation (RAG) capabilities, keeping your applications informed and responsive.

ChatIQ is a versatile Slack bot using GPT & Weaviate-powered long-term memory to accomplish various tasks.

This repository contains an example of how to use the Weaviate vector search engine's text2vec-openai module

Integrated LLM-based document and data Q&A with knowledge graph visualization

📃 A contracts clause summarization system using LLM and vector database

This project demonstrates how to parse emails, process them using OpenAI's GPT-3.5, and load the data into a Weaviate vector database for enhanced search capabilities. Utilizing few-shot prompts and parallel processing, it showcases the power of combining NLP techniques with vector search.

An LLM-powered self-studying app using retrieval-augmented generation prompting | Streamlit LLM Hackathon 2023