GENERAL ASSISTANT WITH MEDICAL EXPERTISE - AI POWERED CHATBOT

Overview

This repository contains an AI Assistant powered by Large Language Model, designed to provide accurate and contextually relevant responses to medical queries. Built using Flask, Cohere’s Language Model, and Pinecone for vector storage, this chatbot can answer a wide range of health-related questions and offer context-aware follow-ups. The chatbot remembers previous queries to offer more personalized responses.

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Key Features

• AI-Powered Responses: Uses Cohere’s Language Model to generate medical answers.
• Context-Aware Conversations: The chatbot can maintain the context of the conversation, enabling more accurate follow-up answers.
• Efficient Document Retrieval: Integrated with Pinecone, a vector database, for fast and scalable document search.
• Flask Web Application: A user-friendly web interface powered by Flask for seamless interaction.
• Easy Setup: Simple installation steps and easy API key configuration for deployment.

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Technologies Used

• Flask: Web framework for building the backend.
• Cohere: Language model for generating answers.
• Pinecone: Vector database for document retrieval and storage.
• HTML/CSS/Bootstrap: Frontend for the chat interface.
• Python: Programming language for implementing AI logic and web server.

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Pre-requisites

To get started with the project, you’ll need the following:

• Python 3.7+ (preferably in a virtual environment)
• Cohere API Key: For generating AI responses.
• Pinecone API Key: For vector search and document retrieval.
• Flask: To run the web application.

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Setting Up the Project

Follow the steps below to set up and run the chatbot on your local machine:

1. Clone the Repository

Start by cloning this repository to your local machine:

git clone https://github.com/Bevinaa/Medical-Chatbot-Application
cd End-to-end-Medical-Chatbot-Generative-AI

2. Set Up a Virtual Environment (Recommended)

You can set up a virtual environment to isolate dependencies for this project: "venv" is used here as environment name

python -m venv venv

Activate the virtual environment:

• Windows:

  venv\Scripts\activate

• Mac/Linux:

  source venv/bin/activate

3. Install Dependencies

Install the required dependencies using pip:

pip install -r requirements.txt

4. Create API Keys

You will need two API keys: Cohere API Key for the language model and Pinecone API Key for vector storage. Here's how to get them:

Cohere API Key:

1. Go to Cohere API and create an account if you haven’t already.
2. Once logged in, navigate to the API Keys section and generate a new API key.
3. Copy the API key for use in the project.

Pinecone API Key:

1. Visit Pinecone and create an account.
2. After logging in, navigate to API Keys from your dashboard.
3. Generate an API key and copy it.

5. Configure API Keys

Create a .env file in the root directory of the project and add the following lines, replacing the placeholders with your actual API keys:

COHERE_API_KEY = your_cohere_api_key
PINECONE_API_KEY = your_pinecone_api_key

6. Running the Flask Application

Once you've set up the environment and configured the API keys, you can run the Flask app with the following command:

python app.py

The chatbot should now be running at http://127.0.0.1:5000/. Open this URL in your browser to interact with the chatbot.

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How to Use the Chatbot

1. Visit the main page (/).
2. You’ll see a user interface where you can type your health-related question.
3. After submitting your question, the chatbot will generate a response based on the medical documents it retrieves from Pinecone.

You can ask follow-up questions, and the chatbot will maintain the context of your conversation to provide more relevant answers.

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Project Structure

• app.py: Main Python script that runs the Flask application.
• chat.html: HTML template for the chatbot user interface.
• requirements.txt: List of Python dependencies for the project.
• src/: Contains helper functions and code for document embedding and retrieval.
• .env: Configuration file to store sensitive API keys.

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Contributions

If you would like to contribute to this project, feel free to fork the repository, make changes, and submit a pull request.

Make sure to follow the standard guidelines for Python projects and ensure that all dependencies are correctly listed in the requirements.txt file.

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Contact

For any questions, issues, or support, feel free to reach out:

Author: Bevina
Email: bevina2110@gmail.com

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Output

Below are some screenshots showing the chatbot interface and how it responds to user queries.

1. Chat Interface

This is the main chat interface where users can ask health-related questions.

2. Context-Aware Follow-up

An example showing the chatbot’s ability to maintain conversation context across multiple questions.

3. Sample Response

An example of a response generated by the chatbot based on a user query.

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