🤖 Vendor Assignment & Communication AI Agent

This project is a proof-of-concept (POC) for building an intelligent backend AI agent that performs vendor selection and personalized communication drafting for customer service tasks (e.g., booking a cab, finding a plumber, etc.). It's powered by a combination of FastAPI for API services and Streamlit for a simple and interactive frontend.

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📌 Problem Statement

Customer tasks like hotel booking or urgent repairs are handled by backend agents, who in turn coordinate with vendors. This AI agent helps:

1. 🧠 Select the most suitable vendor based on task requirements.
2. ✉️ Draft a personalized message to the vendor in their preferred communication style.

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⚙️ Tech Stack

• Python 3.9+
• FastAPI – backend API for vendor selection and message generation
• Streamlit – frontend interface to simulate task entry and vendor interaction
• Uvicorn – ASGI server for FastAPI
• Docker & Docker Compose – for containerized deployment
• Google's Generative LLM (Gemini) – for task interpretation and message generation

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🚀 Getting Started

1. Clone the repository

git clone https://github.com/your-username/vendor-ai-agent.git
cd vendor-ai-agent

2. Set Up Environment Variables

Create a .env file in the root directory and add the following

OPENAI_API_KEY=your_openai_api_key

3. Docker Setup

1. Build and Run the Containers

docker-compose up --build

This will:

• Build the FastAPI backend container
• Build the Streamlit frontend container
• Load the environment variables from .env

2. Access the Application

FastAPI (API docs): http://0.0.0.0:8000

Streamlit (UI): http://0.0.0.0:8501

Manual (Non-Docker) Development Setup

1. Install dependencies

python -m venv venv
source venv/bin/activate

Install the required packages

pip install -r requirements.txt

2. Run Backend Server (FastAPI)

Start the FastAPI server

uvicorn backend.app.main:app --reload

3. Run the Frontend (Streamlit)

In a new terminal, navigate to the project directory and run:

streamlit run frontend.app.frontend.py

Mock Data Format

Vendor Sample

{
    "vendor_id": "V001",
    "name": "Rapid Plumbers",
    "category_expertise": ["repairs", "plumbing"],
    "response_time_hours": 1,
    "preferred_communication_style": "casual",
    "past_reliability_score": 4.8,
    "constraints": ["Only serves downtown area", "Requires 2hr lead time for non-urgent tasks"]
}

Task Sample

{
    "task_id": "T101",
    "task_description": "I need a plumber urgently for a leaking tap in the kitchen.",
    "category": "plumbing",
    "urgency": "high",
    "special_requirements": "Need someone who can bring spare parts for standard faucet types."
}