End-to-end Medical Chatbot using Llama2

Introduction

This project aims to create an end-to-end medical chatbot using Meta's Llama2 model, LangChain, Flask, and Pinecone for vector storage. The chatbot is designed to provide users with medical information based on the input it receives.

Project Repository

You can find the project repository at the following link: Project Repo

Getting Started

Step 1: Clone the Repository

First, clone the repository to your local machine.

git clone https://github.com/your-repository-url
cd your-repository-folder

Step 2: Create a Conda Environment

Create and activate a new conda environment with Python 3.8.

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

Step 3: Install Requirements

Install the required Python packages from the requirements.txt file.

pip install -r requirements.txt

Step 4: Set Up Environment Variables

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

echo "PINECONE_API_KEY=xxxxxxxxxxxxxxxxxxxxxxxxxxxxx" >> .env
echo "PINECONE_API_ENV=xxxxxxxxxxxxxxxxxxxxxxxxxxxxx" >> .env

Step 5: Download the Quantized Model

Download the Llama 2 quantized model and place it in the model directory.

Download the Llama 2 Model:

• Model file: llama-2-7b-chat.ggmlv3.q4_0.bin
• Download link: Llama 2 Model on Hugging Face

Step 6: Store the Index

Run the following command to store the index using Pinecone.

python store_index.py

Step 7: Start the Application

Finally, run the following command to start the Flask application.

python app.py

Access the Application

Open your web browser and navigate to localhost to access the medical chatbot.

Tech Stack Used

• Python
• LangChain
• Flask
• Meta Llama2
• Pinecone

Contributing

Feel free to submit issues, fork the repository, and send pull requests. For major changes, please open an issue first to discuss what you would like to change.

License

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

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