Skincare AI Chatbot

A sophisticated AI-powered skincare consultant using LangChain LangGraph, FastAPI, and Pinecone. This chatbot provides personalized skincare advice, analyzes images, and suggests routines based on AI-driven assessments. It also supports voice-based conversations using Whisper for speech recognition and ElevenLabs for speech synthesis.

Features

• AI-Powered Skincare Assistant: Provides personalized skincare recommendations based on user input.
• Image-Based Skin Analysis: Uses AI to analyze skin conditions from uploaded images.
• Voice Conversation Support: Integrates Whisper for voice input processing and ElevenLabs for natural-sounding voice responses.
• Agentic Framework: Utilizes an agentic approach, chaining different tools together for enhanced interactions.
• Vector Database Integration: Utilizes Pinecone for efficient similarity search and knowledge retrieval.
• Web Search Capability: Integrates with Tavily for real-time skincare-related searches.
• FastAPI Backend: High-performance API with automatic OpenAPI documentation.
• Streamlit Frontend: User-friendly web interface for chatbot interaction.
• Comprehensive Logging: Detailed logs for monitoring and debugging.

Prerequisites

• Python 3.12+
• Poetry (for dependency management)
• API Keys:
  • GROQ_API_KEY
  • PINECONE_API_KEY
  • TAVILY_API_KEY
  • OPENAI_API_KEY
  • ELEVEN_LAB_API_KEY

Agentic Flow & Architecture

Installation

Local Development

1. Clone the repository:

   git clone https://github.com/Gershonbest/support-agent.git
   cd skincare-ai-chatbot

2. Install Poetry if you haven't already:

   pip install poetry

3. Install dependencies using Poetry:

   poetry install

4. Activate the virtual environment:

   • For older versions of Poetry:

     poetry shell

   • For the latest version of Poetry:

     eval $(poetry env activate)

5. Set up environment variables: Create a .env file in the root directory with the following variables:

   GROQ_API_KEY=your_groq_api_key
   PINECONE_API_KEY=your_pinecone_api_key
   TAVILY_API_KEY=your_tavily_api_key
   OPENAI_API_KEY=your_openai_api_key
   ELEVEN_LAB_API_KEY=your_eleven_lab_api_key
   

Running the Application

To start both the FastAPI backend and Streamlit frontend, simply run:

python main.py

This will automatically start the backend and frontend together.

API Endpoints

Health Check

GET /health

Returns the current health status of the service.

Chat Endpoint

POST /chat

Accepts chat messages and returns AI responses.

Request body:

{
    "message": "string",
    "thread_id": "optional[int]"
}

Development

Adding New Tools

1. Create a new tool function in src/agent_tools.py:

   @tool
   def new_tool(parameter: str):
       """Tool description"""
       # Implementation
       return result

2. Register the tool in src/agent.py.

Extending Vector Database

1. Add new methods to PineconeManagment class in src/vector_database/vector_db.py.
2. Update validators in src/validators/pinecone_validators.py if needed.

License

This project is licensed under the Apache-2.0 License.