💡Medicure RAG Chatbot🤖

An AI chatbot implementing RAG technique with Meta-Llama2-7b Large Language Model, along with langchain and pinecone vector databases. Resource Used 📖 : The Gale Encyclopedia of Medicine

Screenshots

Technologies Used

1. Streamlit- WebApp UI
2. Pinecone - Vector Database
3. Langchain and sentence-transformers - RetrieveQAChain and Embedding Model
4. Meta Llama-2-7b-chat quantized Model - Large Language Model(LLM) from Hugging Face Hub

Solution Approach

Pinecone vector db stores the text_chunks embeddings generated from the Book Pdf. LangChain is used for building the LLMChain with promptTemplate to perform the similarity search from pinecone and then fine-grain the output with LLM.

Running Web App Locally

To run web app locally, follow these steps:

1.Clone the Repo:

git clone https://github.com/4darsh-Dev/medicure-rag-chatbot.git 

2. Configure poetry:

     pip install poetry
     poetry init
     poetry shell
     

3. Install Requirements:

     poetry install
     

4. Run the Streamlit App:

   poetry streamlit run app.py

5. Access Your App: After running the command, Streamlit will start a local web server and provide a URL where you can access your app. Typically, it will be something like http://localhost:8501. Open this URL in your web browser.

6. Stop the Streamlit Server: To stop the Streamlit server, go back to the terminal or command prompt where it's running and press Ctrl + C to terminate the server.

Hi, I'm Adarsh! 👋

🔗 Links

Feedback

If you have any feedback, please reach out to us at [email protected]