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This repository showcases a simple yet powerful chatbot built using the LangChain framework in a Jupyter Notebook environment. The chatbot leverages modular LangChain components for conversational AI, making it flexible and easy to integrate with various backends or memory stores.

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Intelligent Document Question Answering System Using Retrieval Augmented Generation RAG

This repository showcases a simple yet powerful chatbot built using the LangChain framework in a Jupyter Notebook environment. The chatbot leverages modular LangChain components for conversational AI, making it flexible and easy to integrate with various backends or memory stores.

πŸš€ Features

  • βœ… Built with LangChain, a leading framework for building LLM applications
  • 🧱 Uses LangChain's modular components: text_splitters, core, community, chroma
  • πŸ“¦ Easy setup with a requirements.txt
  • πŸ“š Interactive development using Jupyter Notebook

πŸ› οΈ Tech Stack

  • Python 3.10.16
  • LangChain Modules
    • langchain
    • langchain_core
    • langchain_community
    • langchain_chroma
    • langchain_text_splitters

πŸ§ͺ How to Run

  1. Clone the repo:
    git clone https://github.com/devpatel0005/Intelligent-Document-Question-Answering-System-Using-Retrieval-Augmented-Generation-RAG-.git
    cd chatbot-langchain
  2. Install dependencies:
  pip install -r requirements.txt

πŸ“Œ Use Cases

Prototyping intelligent assistants

Building retrieval-based QA systems

Exploring LangChain component integration

πŸ™Œ Acknowledgments:

  • Thanks to the LangChain community for creating an awesome framework to build LLM applications.

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This repository showcases a simple yet powerful chatbot built using the LangChain framework in a Jupyter Notebook environment. The chatbot leverages modular LangChain components for conversational AI, making it flexible and easy to integrate with various backends or memory stores.

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