A document based RAG application
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Updated
Mar 28, 2025 - Rust
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.
Website lịch sử Việt Nam tích hợp ChatBot AI sử dụng VectorDb và GraphDB
Kho lưu trữ tài liệu lịch sử của học sinh, sử dụng chatbotAI để trả lời câu hỏi
SEAS - A Smart Enrollment Advisory System for CTU, built with RAG, async FastAPI, async SQLAlchemy, and async Qdrant.
Retrieval-Augmented Generation. A production-shaped reference stack for grounded chat over your own documents — FastAPI + LangChain agents, Qdrant vector search, and a Next.js 15 frontend with a deterministic calculator tool wired in.
Your Knowledge Partner for Academic Discovery
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.
A hybrid search system for financial intelligence combining BM25, dense, and sparse vectors with Qdrant to improve retrieval across both exact keyword and semantic queries.
Demonstration on how to implement storage and search for JSON structured data
CP server exposing Qdrant vector DB store and GPU-accelerated embedding pipeline as tools for Claude Code, Claude.ai, and OpenClaw.
Full-stack developer portfolio styled as a lab plate — Angular 22 + FastAPI, with Bunsen, a RAG-grounded AI assistant streamed over WebSockets.
A 10-day beginner LangChain bootcamp that runs entirely on free AI APIs. Ten notebooks and ten slide decks, from your first model call to a RAG assistant that cites real 10-K filings. No paid account, no GPU.
🚀 Qdrant Vector DB Cluster with Docker Compose – Lightning-fast AI similarity search at scale!
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