#Weaviate
Showing 17 of 17 repositories tagged #weaviate, ranked by stars
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.
What if OpenAI Deep Research and Dify were one platform? OpenAgent — harness architecture for rapidly building vertical AI agents, with deep reasoning loops, visual workflows, RAG, and A2A delegation.
A modern desktop application for exploring, managing, and analyzing vector databases
一个99%由OpenAI ChatGPT开发的项目。A project that is 99% developed by OpenAI ChatGPT.
Production-ready RAG Framework (Python/FastAPI). 1-line config swaps: 6 Vector DBs (Weaviate, Pinecone, Qdrant, ChromaDB, pgvector, MongoDB), 5 LLMs (Gemini, OpenAI, Claude, Ollama, OpenRouter). OpenAI-compatible API. 2100+ tests.
Awesome Weaviate
AI-powered platform for OSINT intelligence analysis. Features archive discovery with hypothesis-driven investigation, GLiNER entity extraction, Mapbox geospatial visualization, network analysis, and document processing. Built with FastAPI, Next.js, Weaviate, and DSPy.
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.
Claude Skills for connecting Claude.ai to local Weaviate vector databases - manage collections, ingest data, and query with RAG
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.
An AI developer that writes LangChain Expression Language (LCEL).
This is a LlamaIndex project bootstrapped with create-llama to act as a full stack UI to accompany Retrieval-Augmented Generation (RAG) Bootstrap Application.
High-performance infrastructure libraries built in Rust, with seamless bindings for Python, Node.js, and WebAssembly.
Data pipelines and notebooks for RAG tuning using Fondant