Medical-Chatbot

A medical chatbot that answers user queries by retrieving relevant information from a collection of medical documents using LangChain, Pinecone, and Meta's Llama2 model.

Table of Contents

• Introduction
• Methodology
• Tech Stack
• Installation
• Usage
• Website Screenshots

Methodology

The medical chatbot project employs a multi-step approach to deliver accurate and relevant responses to user queries:

1. Data Ingestion and Processing

   • The project begins by ingesting and processing a collection of medical PDFs.
   • Text is extracted from these PDFs and split into manageable chunks.

2. Embedding Generation

   • The extracted text chunks are embedded using HuggingFace's pre-trained model to capture their semantic meanings.

3. Storage in Pinecone

   • The generated embeddings are stored in Pinecone for efficient and scalable vector search.

4. Response Generation

   • For generating responses, the chatbot utilizes Meta's Llama2 model.
   • The model queries Pinecone to retrieve the most relevant document snippets based on user input.

5. Integration with Flask

   • The entire system is integrated into a Flask web application.
   • This provides an interactive interface for real-time communication with users.

This methodology ensures that the chatbot can process complex medical documents and deliver accurate, contextually relevant responses to user queries.

Tech Stack

• Python
• LangChain
• Flask
• Meta Llama2
• Pinecone

Installation

Steps

1. Clone the repository

   https://github.com/shikharrajat/Medical-Chatbot.git
   cd Medical-Chatbot

2. Create a conda environment

   conda create -n env python=3.8 -y
   conda activate env

3. Install the requirements

   pip install -r requirements.txt

4. Create a .env file

   Create a .env file in the root directory and add your Pinecone credentials:

   PINECONE_API_KEY="your_pinecone_api_key"
   

5. Download the quantized Llama2 model

   Download the Llama2 model (llama-2-7b-chat.ggmlv3.q4_0.bin) from the HuggingFace link and place it in the model directory.

Usage

1. Run the indexing script

   python store_index.py

2. Start the Flask application

   python app.py

3. Open your browser and go to

   http://localhost:8080

Website Screenshots