/Medical-Chatbot-Assistant-Using-Llama2-and-HuggingFace-Embeddings-and-Pinecone-Vector-db

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.

Primary LanguageJupyter NotebookMIT LicenseMIT

🩺 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

🚀 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.