End-to-end Medical Chatbot using Llama2

🩺 Introduction

Welcome to the end-to-end medical chatbot project using Llama2! This project leverages the power of Meta's Llama2, LangChain, and Flask to create a highly knowledgeable and responsive medical assistant. The chatbot is designed to provide detailed answers to medical queries by integrating Mongo Atlas for vector search and Hugging Face models for inference.

🚀 How to Run?

Steps to Setup and Run the Medical Chatbot

1. Clone the Repository

Start by cloning the repository to your local machine and go to root folder.

repo https://github.com/Arjunhg/MediDoc.git
cd your-repository

2. Create a Conda Environment

Open the repository and create a Conda environment.

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

3. Install the Requirements

Install all the necessary dependencies from the requirements.txt file.

pip install -r requirements.txt

4. 🔧 Configure Environment Variables

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

MONGO_URI="your_mongo_uri"
DB_NAME="your_db_name"
COLLECTION_NAME="your_collection_name"
INDEX_NAME="your_index_name"
MODEL_PATH="your_model_path"

5. Download and Place the Model

Download the quantized Llama2 model and place it in the model directory.

# Download the Llama 2 Model:
llama-2-7b-chat.ggmlv3.q4_0.bin

# From the following link:
https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGML/tree/main

6.▶️ Running the Project

Finally, run the application:

python app.py

🛠 Tech Stack

Python: The core programming language used for the backend logic.
LangChain: Utilized for creating the language model chain.
Flask: A lightweight WSGI web application framework for Python.
Meta Llama2: The model used for generating responses.
Mongo Atlas Vector Search: Used for storing and searching vectorized documents.
Hugging Face: Provides pre-trained models for inference.

📜 License

This project is licensed under the MIT License - see the LICENSE file for details.