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rag-qdrant-implementation

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AtharvaKulkarniIT / rag-qdrant-pipeline

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 langchain qdrant-client qdrant-rag qdrant-rag-eval rag-qdrant-implementation

Updated Oct 8, 2024
Jupyter Notebook

SAKET-SK / Full_Stack_AI_with_Python

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My notes from Full Stack AI with Python Course from Udemy : by Hitesh Choudhary and Piyush Garg

python git docker memory mcp python3 artificial-intelligence python-beginners ai-agents rag voice-ai llm langchain langgraph rag-qdrant-implementation full-stack-ai

Updated Feb 6, 2026
Python

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