{"id":30580599,"url":"https://github.com/45harry/end_to_end_medical_chatbot_using_llama2","last_synced_at":"2026-05-17T03:34:55.114Z","repository":{"id":310061337,"uuid":"1038532496","full_name":"45Harry/End_to_End_Medical_Chatbot_using_Llama2","owner":"45Harry","description":"Medical ChatBot Trained on The famous Gale Encyclopedia (1-5 vol) . 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It provides accurate medical information through a conversational web interface, leveraging advanced language models and vector search capabilities.\n\n## Features\n\n- **Advanced Medical Assistant:** \n  - Structured responses for medical queries\n  - Natural conversation handling\n  - Context-aware answers\n  - Source citations when available\n\n- **Multiple LLM Support:** \n  - Local Llama2 model integration\n  - Groq cloud API integration\n  - Flexible model switching\n\n- **Vector Search:** \n  - Pinecone vector database integration\n  - HuggingFace embeddings\n  - Efficient medical document retrieval\n\n- **Enhanced User Experience:**\n  - Clean Bootstrap-based chat interface\n  - Markdown-formatted responses\n  - Mobile-responsive design\n\n## Technical Architecture\n\n1. **Document Processing Pipeline:**\n   - PDF ingestion and chunking\n   - HuggingFace embedding generation\n   - Pinecone vector indexing\n\n2. **Query Processing:**\n   - User input analysis\n   - Context-based retrieval\n   - Structured response generation\n\n3. **Response Generation:**\n   - Custom prompt templates\n   - Source-backed answers\n   - Format-specific outputs\n\n## Project Structure\n\n```\nEnd_to_End_Medical_Chatbot_using_Llama2/\n├── app.py              # Main Flask application\n├── src/\n│   ├── helper.py       # Utility functions\n│   └── prompt.py       # Prompt templates\n├── templates/\n│   └── chat.html       # Web interface\n├── model/              # LLM model directory\n├── data/              # Medical PDF storage\n└── requirements.txt    # Dependencies\n```\n\n## Setup Instructions\n\n1. **Clone the repository:**\n   ```bash\n   git clone git@github.com:45Harry/End_to_End_Medical_Chatbot_using_Llama2.git\n   cd End_to_End_Medical_Chatbot_using_Llama2\n   ```\n\n2. **Install dependencies:**\n   ```bash\n   pip install -r requirements.txt\n   ```\n\n3. **Environment Setup:**\n   Create a `.env` file:\n   ```\n   PINECONE_API_KEY=your_pinecone_api_key\n   GROQ_API_KEY=your_groq_api_key\n   ```\n\n4. **Model Setup:**\n   - For Local Llama2:\n     Place `llama-2-7b-chat.ggmlv3.q4_0.bin` in `model/` directory\n   - For Groq:\n     Ensure valid API key in `.env`\n\n5. **Data Preparation:**\n   - Add medical PDFs to `data/` directory\n   - Run indexing script if needed\n\n6. **Launch Application:**\n   ```bash\n   python app.py\n   ```\n   Access at `http://localhost:5000`\n\n## Response Format\n\nThe chatbot provides structured responses for medical queries:\n\n- **Basic Definition:** Clear, concise explanation\n- **Key Characteristics:** Main features and details\n- **Types and Classifications:** Categories if applicable\n- **Clinical Significance:** Impact and implications\n\n## Environment Variables\n\nRequired environment variables:\n- `PINECONE_API_KEY`: For vector database access\n- `GROQ_API_KEY`: For cloud LLM access (if using Groq)\n\n## License\n\nThis project is licensed under the MIT License. See LICENSE file for details.\n\n## Acknowledgments\n\n- LangChain for the chain-of-thought framework\n- Pinecone for vector search capabilities\n- Meta for Llama2 model\n- Groq for cloud LLM services","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2F45harry%2Fend_to_end_medical_chatbot_using_llama2","html_url":"https://awesome.ecosyste.ms/projects/github.com%2F45harry%2Fend_to_end_medical_chatbot_using_llama2","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2F45harry%2Fend_to_end_medical_chatbot_using_llama2/lists"}