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.

🚀 Features

• ✅ Built with LangChain, a leading framework for building LLM applications
• 🧱 Uses LangChain's modular components: text_splitters, core, community, chroma
• 📦 Easy setup with a requirements.txt
• 📚 Interactive development using Jupyter Notebook

🛠️ Tech Stack

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

🧪 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

📌 Use Cases

Prototyping intelligent assistants

Building retrieval-based QA systems

Exploring LangChain component integration

🙌 Acknowledgments:

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