🧠 AI-Medico-Chatbot

AI-Medico-Chatbot is an intelligent conversational assistant tailored for the medical domain. It leverages Retrieval-Augmented Generation (RAG) with Pinecone and a large language model (LLM) from Groq to provide accurate and contextual responses based on domain-specific documents.

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🩺 Features

• ChatGPT-style frontend with typing animation
• Uses LangChain, Groq, and Pinecone
• Integrates HuggingFace Sentence Transformers for embeddings
• Retrieval-Augmented Generation (RAG) pipeline
• Chat history rendering with Flask + Jinja
• Real-time search from vector index
• Easy to customize for any knowledge base

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🔧 Technologies Used

Component	Technology
Frontend	HTML, CSS, JavaScript (ChatGPT-style UI)
Backend	Flask, Python 3.13
Vector DB	Pinecone
Embeddings	HuggingFace Sentence Transformers
LLM	Groq (e.g., Gemma-2B / Gemma-7B)
RAG	LangChain with Retrieval + Prompt Template

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🚀 Getting Started

1. Clone the repository

git clone https://github.com/yourusername/AI-Medical-Assistant.git
cd AI-Medical-Assistant

2. Create virtual Environment

python -m venv venv
source venv/bin/activate    # On Windows: venv\Scripts\activate

3. Install the dependencies

pip install -r requirements.txt

4. Setup .env file

PINECONE_API = 'YOUR PINECONE API'
PINECONE_HOST = 'YOUR PINCONE HOST'
PINECONE_ENV = 'YOUR PINCONE ENV'
GROQ_API = 'YOUR GROQ API'

5. Store the Vector in Pinecone Vector DB

python store_index.py

6. Run the app

python app.py

📌 TODOs / Future Enhancements Add document upload feature

Enable WebSocket for real-time streaming

Support Markdown rendering (for code, tables, etc.)

Avatar customization

Use LangSmith for tracing and debugging