HappyAI Chat

A sophisticated customer support assistant powered by LangChain LangGraph, FastAPI, and Pinecone. This project implements a conversational AI system that can provide information about HappyAI's services, expertise, and projects while maintaining context through vector database integration.

Features

• Intelligent Chat Interface: Conversational AI assistant specialized in HappyAI customer support
• Vector Database Integration: Utilizes Pinecone for efficient similarity search and document retrieval
• Web Search Capability: Integrates with Tavily for real-time web search functionality
• FastAPI Backend: High-performance API with automatic OpenAPI documentation
• Docker Support: Containerized deployment for consistency across environments
• Comprehensive Logging: Detailed logging system for monitoring and debugging

Prerequisites

• Python 3.12+
• Docker (for containerized deployment)
• API Keys:
  • OpenAI API key
  • Pinecone API key
  • Tavily API key (for web search functionality)

Installation

Local Development

1. Clone the repository:

   git clone https://github.com/okoliechykwuka/happyai-chat.git
   cd happyai-chat

2. Create and activate a virtual environment:

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

3. Install dependencies:

   pip install -r requirements.txt

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

   OPENAI_API_KEY=your_openai_api_key
   PINECONE_API_KEY=your_pinecone_api_key
   TAVILY_API_KEY=your_tavily_api_key
   

Docker Deployment

1. Pull the Docker image:

   docker pull chukypedro15/happyai-chat

2. Run the container:

   docker run -d -p 8000:8000 \
     -e OPENAI_API_KEY=your_openai_api_key \
     -e PINECONE_API_KEY=your_pinecone_api_key \
     -e TAVILY_API_KEY=your_tavily_api_key \
     chukypedro15/happyai-chat

Project Structure

happyai-chat/
├── data/
│   └── faq.json
├── src/
│   ├── utils/
│   │   ├── __init__.py
│   │   └── env_setup.py
│   ├── validators/
│   │   ├── agent_validators.py
│   │   └── pinecone_validators.py
│   ├── vector_database/
│   │   ├── utils.py
│   │   └── vector_db.py
│   ├── agent_tools.py
│   ├── agent.py
│   └── models.py
├── Dockerfile
├── main.py
├── requirements.txt
└── README.md

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]"
}

Usage

Starting the Server

1. Local development:

   uvicorn main:app --reload --host 0.0.0.0 --port 8000

2. Docker:

   docker run -p 8000:8000 yourdockerhub/happyai-chat

Making Requests

import requests

url = "http://localhost:8000/chat"
payload = {
    "message": "What services does HappyAI offer?",
    "thread_id": 42
}
response = requests.post(url, json=payload)
print(response.json())

Configuration

The application can be configured through environment variables:

• OPENAI_API_KEY: Your OpenAI API key
• PINECONE_API_KEY: Your Pinecone API key
• TAVILY_API_KEY: Your Tavily API key

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.