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

Giga Parsers 🦾😎

A unified interface for vector databases

Conduit connector for Weaviate

Website for the Weaviate vector database

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

Official Weaviate TypeScript Client

An LLM microservice for the learning platform Artemis

Intelligent Document Search for the Staatsarchiv Zurich.

Weaviate connector for Apache Spark

AI-powered Q&A chatbot using Qdrant and Ollama for retrieval-based responses about magnesium-binding protein structures. Integrates Flask for web interface.

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

Ruby wrapper for the Weaviate vector search database API

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.

Framework for benchmarking fully-managed vector databases

YouTube Subtitles Search: A Go-based web app that fetches and displays YouTube video transcripts. Built for learning Go and experimenting with AI/ML concepts. Features include transcript retrieval, display with timecodes, and a simple UI. Future plans include vector search and audio-to-text functionality.

This LLM enabled chatbot that helps answer customer questions for Paderno kitchen products on Canadian Tire Corporation website.