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.
Aquiles-RAG 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.
SEAS - A Smart Enrollment Advisory System for CTU
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.
Archive and search Product Hunt data locally with Qdrant + MCP (GraphQL, embeddings, vector search).
Implementation of the GraphRAG system based on QDrantDB + Neo4j DB on a clean Bun + TypeScript architecture
Demonstration on how to implement storage and search for JSON structured data
WinUI 3 application to take notes backed by Ollama and Qdrant.
MeowMuse is an ai-powered assistant to help manage your cat's health
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