https://github.com/4darsh-dev/medicure-rag-chatbot

An AI chatbot implementing RAG technique with Meta-Llama2-7b Large Language Model, along with langchain and pinecone vector databases.
https://github.com/4darsh-dev/medicure-rag-chatbot

chatbotai huggingface-transformers langchain llama2-7b medical-chatbot pineconedb python retrieval-augmented-generation transformers vector-database

Last synced: about 1 month ago
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An AI chatbot implementing RAG technique with Meta-Llama2-7b Large Language Model, along with langchain and pinecone vector databases.

• Host: GitHub
• URL: https://github.com/4darsh-dev/medicure-rag-chatbot
• Owner: 4darsh-Dev
• License: mit
• Created: 2024-07-04T19:30:41.000Z (10 months ago)
• Default Branch: main
• Last Pushed: 2024-07-25T08:44:40.000Z (9 months ago)
• Last Synced: 2025-03-10T06:53:13.252Z (about 2 months ago)
• Topics: chatbotai, huggingface-transformers, langchain, llama2-7b, medical-chatbot, pineconedb, python, retrieval-augmented-generation, transformers, vector-database
• Language: Python
• Homepage: https://huggingface.co/spaces/4darsh-Dev/medicure
• Size: 99.6 KB
• Stars: 2
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • Funding: .github/funding.yml
  • License: LICENSE

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README

        
# 💡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**:

  ```bash

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

  ```

2. **Configure poetry**:

    ```bash

      pip install poetry

      poetry init

      poetry shell

      

    ```

3. **Install Requirements**: 

    ```bash

      poetry install

      

    ```

4. **Run the Streamlit App**:

    ```bash

    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

[![portfolio](https://img.shields.io/badge/my_portfolio-000?style=for-the-badge&logo=ko-fi&logoColor=white)](https://adarshmaurya.onionreads.com/)

[![linkedin](https://img.shields.io/badge/linkedin-0A66C2?style=for-the-badge&logo=linkedin&logoColor=white)](https://www.linkedin.com/in/adarsh-maurya-dev/)

## Feedback

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