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🩺 Medical Chatbot Assistant using LLaMA 2, Hugging Face, and Pinecone

Welcome to the Medical Chatbot Assistant project! This repository contains a powerful and efficient medical chatbot built using the LLaMA 2 model, Hugging Face embeddings, and Pinecone vector database. The chatbot is designed to assist users with medical inquiries, providing reliable and accurate responses.

End.to.end.Medical.Chatbot.Implementation.mp4

Medical.Chatbot.with.llama.3.mp4

🚀 Features

• LLaMA 2 Model Integration: Powered by Meta's LLaMA 2 model, offering state-of-the-art conversational AI.
• Hugging Face Embeddings: Utilizes Hugging Face's embeddings for precise and context-aware responses.
• Pinecone Vector Database: Efficiently stores and retrieves embeddings, ensuring quick and relevant answers.
• Scalable: Easily scale the system to handle a growing number of users and queries.
• Customizable: Adapt the chatbot for various medical specializations or integrate it with other healthcare systems.

🛠️ Installation

1. Clone the repository:

   git clone https://github.com/muhammadadilnaeem/Medical-Chatbot-Assistant-Using-Llama2-and-HuggingFace-Embeddings-and-Pinecone-Vector-db.git
   cd Medical-Chatbot-Assistant-Using-Llama2-and-HuggingFace-Embeddings-and-Pinecone-Vector-db

2. Install dependencies:

   pip install -r requirements.txt

3. Set up environment variables:

   Create a .env file in the root directory and add your API keys and configuration settings:

   HUGGINGFACE_API_KEY=your_huggingface_api_key
   PINECONE_API_KEY=your_pinecone_api_key

4. Run the application:

   python app.py

📚 Usage

• Ask Medical Questions: The chatbot is trained to understand and respond to a wide range of medical queries. Simply type your question, and the bot will provide an accurate response.
• Customize the Knowledge Base: You can add or modify the medical data the chatbot uses by updating the embeddings stored in Pinecone.

🧠 How It Works

1. User Query: The user inputs a medical question.
2. Embeddings: The question is converted into embeddings using Hugging Face models.
3. Pinecone Retrieval: The embeddings are matched against a database of medical knowledge stored in Pinecone.
4. Response Generation: The LLaMA 2 model generates a response based on the retrieved information.

🤖 Future Enhancements

• Multi-language Support: Extend the chatbot to support multiple languages.
• Voice Interface: Integrate with speech-to-text and text-to-speech for a more interactive experience.
• Integration with EHR Systems: Connect the chatbot to Electronic Health Records (EHR) for personalized advice.

📄 License

This project is licensed under the MIT License. See the LICENSE file for more details.

📧 Contact

For any questions or inquiries, please reach out to me at madilnaeem0@gmail.com.