Local-GenAI-Search is a generative search engine based on Llama 3, langchain and qdrant that answers questions based on your local files

Official Java client for Qdrant

Building a Chain of Thought RAG Model with DSPy, Qdrant and Ollama

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

此存储库展示了用于检索增强生成（RAG）系统的各种先进技术。RAG 系统将信息检索与生成模型相结合，以提供准确且上下文丰富的响应。

All CPU efficient GPU-less Financial Analysis RAG Model with Qdrant, Langchain and GPT4All x Mistral-7B, run RAG without any GPU support!

QDrant Vector Database with Python Tutorials

About A simple RAG (Retrieval-Augmented Generation) app built with Streamlit. Upload PDFs, ask questions, and get context-aware answers using Qdrant and Hugging Face Transformers.

Explore how to perform Role Based Access Control in Qdrant Vector Datase

VoicePassport 🎤 is a proof-of-concept project exploring user authentication through voice recognition, blockchain-based security ⛓️, and vector databases 📊. It aims to experiment with innovative approaches to secure and efficient identity verification in an educational or research-oriented context.

A Streamlit web app for efficient management of Qdrant vector databases. Features include collection creation/deletion, point retrieval/search, and vector data upload, simplifying Qdrant operations through an intuitive interface.

Rust App on Qdrant - Vector database

A Neural Search Tool that helps you find relevant papers to read based on your interests

A Retrieval-Augmented Generation (RAG) project built with FastAPI, MongoDB, Qdrant, and JWT authentication—featuring secure document uploads, chunking, embeddings, and context-aware AI responses. Designed to be scalable, reliable, and production-ready.

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.

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.

RAG powered AI chatbot for Indian Language (Hindi) using LangChain, Ollama, Qdrant, and MLFlow

LlamaIndex&LangChine wiht LLM

Qdrant- vector database

We are going to showcase how to build a superhero character AI - where users can chat with their favourite superheroes.