RAG-Ingest: A tool for converting PDFs to markdown and indexing them for enhanced Retrieval Augmented Generation (RAG) capabilities.
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.
UI Shadcn blocks collection for export and use vector database QDrant
This is the Proof Of Concept/Demo for the Final Year Project of Pranav Krishnakumar. It is a Meal Planner using Agentic RAG powered by Qwen2.5 Coder 32B, Qdrant Vector Database, Mem0 Chat Memory and Smolagents library for the AI Agent
Add a description, image, and links to the qdrant-rag topic page so that developers can more easily learn about it.
To associate your repository with the qdrant-rag topic, visit your repo's landing page and select "manage topics."