A lightweight Streamlit web app built for developers who need a quick, visual way to manage and understand their Weaviate cluster. It's designed for development, staging, and testing environments (not for production-scale use).
This app helps you estimate storage requirements for Weaviate vector database based on your data characteristics. You can either calculate estimates from basic parameters or extrapolate from existing measurements.
weaviate performace lab
WeaviateDB Scripts & Snippets For Python Client
A state-of-the-art Retrieval-Augmented Generation (RAG) system that transforms document processing and knowledge retrieval through hierarchical organization, advanced embedding techniques, and intelligent conversation management. This project combines cutting-edge AI technologies to create a sophisticated document intelligence platform.
Weaviate Memory & CPU Estimator: Resource planning tool based on official Weaviate documentation.
Learning journey with Vector Databases for GenAI RAG applications, exploring Chroma, Pinecone, and Weaviate for efficient retrieval, indexing, and querying in AI-driven systems.
A clean, context-managed Python wrapper for simplifying multi-tenant data isolation in Weaviate.
Leo’s Search is an open-source AI-powered multimodal search engine built with Python, Flask, and Weaviate-client. It enables intelligent, context-aware search across text, images, and more. Fast, flexible, and fully customizable, it brings modern semantic search to developers and organizations.
MediProc-AI is a high-performance, multi-agent medical intelligence system designed to unify siloed clinical data. It transforms raw medical documents (images/PDFs) into actionable clinical insights using a "Triple-Database Hybrid RAG" architecture.
a Retrieval-Augmented Generation (RAG) architecture using OpenAI, FastAPI, and Streamlit, with vector storage support via Weaviate, Azure Cosmos DB, or Elasticsearch.
Personal knowledge management
Desktop admin console for Weaviate vector database clusters.
weaviate local gui
How to use AI in SWE ?
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