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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A hands-on Python and Generative AI engineering repository covering LLMs, RAG, AI Agents, LangGraph, MCP, Voice AI, Agent Memory, and Docker. Practical implementations, code examples, and structured learning resources for building end-to-end AI applications.

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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