🧠 RAG_GENAI_Medical_Docbot or Chatbot

A smart medical chatbot webapp that leverages RAG (Retrieval-Augmented Generation) techniques to deliver accurate, context-aware responses using a custom knowledge base and advanced language models.

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🚀 Project Overview

This project is a smart medical chatbot web application built using the following technologies:

• Langchain for Retrieval-Augmented Generation (RAG) pipeline.
• Context-aware embeddings for building a vector database.
• Quantized Llama-2 Large Language Model (LLM) for efficient query answering.
• Web interface built with Python backend (Flask or FastAPI), HTML, CSS and served via a user-friendly UI.

The solution boosts response accuracy by up to 20% using context-based document retrieval before generation.

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📂 Project Structure

RAG_GENAI_DocBot/
├── data/                  # Contains datasets or documents used to build the knowledge base
├── model/                 # Pre-trained and quantized models (Llama-2 or others)
├── research/              # Notebooks or scripts for experiments and testing
├── src/                   # Source code for core logic (retrieval, embedding, RAG pipeline)
├── static/                # Static files (CSS, JS, images) for the frontend
├── templates/             # HTML templates for the web interface
├── .gitignore             # Files and folders to be ignored by Git
├── LICENSE                # License information
├── README.md              # Project overview and instructions
├── app.py                 # Main application (runs the web server)
├── requirements.txt       # Project dependencies
├── setup.py               # Setup script for packaging (optional for installation)
├── store_index.py         # Script to create/store vector embeddings and index
├── template.py            # Utility template Python file

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🛠️ Key Features

• Retrieval-Augmented Generation (RAG): Retrieves relevant documents from a custom knowledge base before generating responses.
• Context-aware Embeddings: Ensures that responses are accurate and specific to the user's query.
• Quantized Llama-2 LLM: Efficient, lightweight model serving for quick responses.
• Web Application Interface: User-friendly interface for entering queries and viewing answers.

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📌 Installation Instructions

Prerequisites

• Python 3.8+
• Git
• Virtual Environment (optional but recommended)

Clone the repository

git clone https://github.com/Aniketkumar121/RAG_GENAI_DocBot.git
cd RAG_GENAI_DocBot

Create virtual environment (optional but recommended)

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

Install dependencies

pip install -r requirements.txt

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📦 Running the Application

1️⃣ Build Vector Store (Optional if already generated)

python store_index.py

This will generate embeddings and store them in the vector database.

2️⃣ Run the web application

python app.py

This will start a local web server.
Open your browser and visit:

http://127.0.0.1:5000

to interact with the chatbot UI.

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🧠 How it works

1. User inputs a query via web interface.
2. Query is embedded and compared with pre-stored document embeddings in the vector database.
3. Top-k relevant documents are retrieved.
4. LLM (Llama-2) takes retrieved documents as context and generates an accurate response.
5. Response is displayed back to the user.

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📌 Technologies Used

• Langchain
• Pinecone VectorDB (vector database)
• Quantized Llama-2 LLM
• Flask (or FastAPI) for serving webapp
• HTML + CSS for frontend

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📈 Future Improvements

• Add user authentication.
• Track chat history.
• Improve vector store management.
• Deploy to cloud (AWS, GCP, Azure, etc.)

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