A document based RAG application

Scalable Qdrant vector database cluster with Docker Compose, monitoring, and comprehensive documentation for high-performance similarity search applications.

Is a high-performance Augmented Recovery-Generation (RAG) solution based on Redis, Qdrant or PostgreSQL. It offers a high-level interface using FastAPI REST APIs

RAG-Ingest: A tool for converting PDFs to markdown and indexing them for enhanced Retrieval Augmented Generation (RAG) capabilities.

Local-first TypeScript MCP server for Qdrant with client isolation, LM Studio integration, and scalable document workflows.

A microservices-based RAG platform that supports multi-format document parsing, semantic search, and conversational AI, powered by Google AI and Qdrant.

Implementation of the GraphRAG system based on QDrantDB + Neo4j DB on a clean Bun + TypeScript architecture

MedSage is a multimodal healthcare assistant that combines LLMs, vector search, and real-time reasoning to deliver fast, reliable medical insights. It supports symptom analysis, medical document Q&A, universal file RAG, multilingual interactions, and emergency SOS with live location.

SEAS - A Smart Enrollment Advisory System for CTU, built with RAG, async FastAPI, async SQLAlchemy, and async Qdrant.

This is a RAG (Retrieval-Augmented Generation) model that leverages Qdrant as a vector store and Google Gemini for intelligent document retrieval and context-aware response generation. It efficiently processes PDF documents to provide detailed answers to user queries based on the extracted context.

CP server exposing Qdrant vector DB store and GPU-accelerated embedding pipeline as tools for Claude Code, Claude.ai, and OpenClaw.

Demonstration on how to implement storage and search for JSON structured data

🚀 Qdrant Vector DB Cluster with Docker Compose – Lightning-fast AI similarity search at scale!

Archive and search Product Hunt data locally with Qdrant + MCP (GraphQL, embeddings, vector search).

Refactored RAG demo showcasing clean architecture, dependency injection, and separation of concerns for maintainable AI applications.

WinUI 3 application to take notes backed by Ollama and Qdrant.

demo of qdrant vector database

AI-powered voice health assistant using Vapi + Qdrant RAG + Groq LLM

simple data ingestion pipeline and batch processing for RAG on kubernetes

🚀 Qdrant Data Migration: Source → Target Server