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

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

Weaviate Memory & CPU Estimator: Resource planning tool based on official Weaviate documentation.

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.

Learning journey with Vector Databases for GenAI RAG applications, exploring Chroma, Pinecone, and Weaviate for efficient retrieval, indexing, and querying in AI-driven systems.

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.

Personal knowledge management

a Retrieval-Augmented Generation (RAG) architecture using OpenAI, FastAPI, and Streamlit, with vector storage support via Weaviate, Azure Cosmos DB, or Elasticsearch.

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.

weaviate local gui