AI Quotation Agent

A WebSocket-powered AI agent that provides accurate and competitive pricing quotes based on data from a Notion pricing database. This agent leverages OpenAI's GPT-4o model and integrates with external APIs to deliver real-time pricing information.

🚀 Features

• Real-time WebSocket Communication: Interactive chat interface with instant responses
• Notion Integration: Fetches pricing data from Notion database
• Currency Conversion: Supports multi-currency quotes using live exchange rates
• Web Search Capabilities: Can research market rates and industry standards
• Session Management: Maintains conversation context across multiple interactions
• API Security: Protected endpoints with API key authentication

🏗️ Architecture

The system consists of two main components:

1. WebSocket Agent (main.py): Core AI agent with FastAPI WebSocket server
2. Streamlit Frontend (frontend.py): Optional web interface for testing

WebSocket Agent

The agent is built using the openai-agents library and provides:

• Intelligent Pricing: Analyzes client requirements and suggests appropriate rates
• Tool Integration: Uses function tools for external data fetching
• Conversation Memory: Maintains context throughout the chat session
• Error Handling: Robust error management and user feedback

🛠️ Setup

Prerequisites

• Python 3.13+
• OpenAI API key
• Notion API access (optional)
• Exchange rate API key (optional)

Installation

1. Clone the repository:

git clone <repository-url>
cd quotation-agent

1. Install dependencies:

pip install -r requirements.txt

1. Configure environment variables in .env:

OPENAI_API_KEY=your_openai_api_key
API_KEY=your_api_security_key
NOTION_API_KEY=your_notion_api_key
NOTION_PAGE_ID=your_notion_page_id
EXCHANGE_RATE_API_KEY=your_exchange_rate_api_key

1. Run the WebSocket server:

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

🔌 WebSocket API

Connection

Connect to the WebSocket endpoint:

ws://localhost:8000/ws/chat

Message Format

Send messages as JSON with the following structure:

{
  "session_id": "unique-session-id",
  "message": "I need a quote for my project requirements",
  "user_id": "user123",
  "timestamp": "2024-01-01T10:00:00Z",
  "message_type": "chat"
}

Response Format

Receive responses in the following format:

{
  "session_id": "unique-session-id",
  "message": "Based on your requirements...",
  "sender": "agent",
  "timestamp": "2024-01-01T10:00:01Z",
  "message_type": "response",
  "success": true,
  "error": null
}

Special Commands

• Send "/clear" or set message_type: "clear" to reset conversation history

🛠️ Available Tools

The agent has access to several function tools:

1. Notion Pricing Data

• Function: get_pricing_from_notion
• Purpose: Fetches current pricing information from Notion database
• Integration: Uses Notion API to retrieve structured pricing data

2. Currency Conversion

• Function: currency_conversion
• Purpose: Converts prices between different currencies
• API: Uses exchangerate-api.com for live exchange rates

3. Web Search

• Tool: WebSearchTool()
• Purpose: Researches market rates and industry standards
• Usage: Helps provide competitive pricing context

📋 REST API Endpoints

Health Check

GET /

Returns API status (requires API key authentication)

Clear Chat History

POST /clear-chat

Clears the global conversation history

🐳 Docker Deployment

The project includes a Dockerfile for containerized deployment:

# Build the image
docker build -t quotation-agent .

# Run the container
docker run -p 8000:8000 --env-file .env quotation-agent

💡 Usage Examples

Basic Quote Request

{
  "session_id": "session123",
  "message": "I need a quote for my project requirements",
  "message_type": "chat"
}

Currency-Specific Quote

{
  "session_id": "session123",
  "message": "What would this cost in USD?",
  "message_type": "chat"
}

Follow-up Questions

{
  "session_id": "session123",
  "message": "Can you break down the pricing for each deliverable?",
  "message_type": "chat"
}

🧪 Testing with Frontend

An optional Streamlit frontend is provided for testing:

streamlit run frontend.py

Access the web interface at http://localhost:8501

📊 Pricing Data Structure

The agent works with pricing data from pricing.txt as its source of information. This document contains structured pricing information that the agent uses to generate accurate quotes.

🔧 Configuration

Key configuration options in main.py:

• Model: GPT-4o for optimal reasoning
• Session Management: UUID-based session tracking
• Error Handling: Comprehensive exception management
• API Security: Header-based authentication

🤝 Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Test with the WebSocket API
5. Submit a pull request

📝 License

This project is licensed under the MIT License.

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Built with ❤️ by Tom Shaw