Configuration¶

Configure Pet Paradise Shop for your environment and requirements.

Environment Variables¶

All configuration is managed through environment variables in the .env file.

Azure OpenAI Configuration¶

# Required: Your Azure OpenAI API key
AZURE_OPENAI_API_KEY=your_azure_openai_api_key

# Required: Your Azure OpenAI endpoint
AZURE_OPENAI_ENDPOINT=https://your-resource.openai.azure.com/

# Required: Your deployment name (model)
AZURE_OPENAI_DEPLOYMENT=gpt-4

# Required: API version
AZURE_OPENAI_API_VERSION=2024-02-15-preview

Getting Azure OpenAI Credentials¶

Log in to Azure Portal
Navigate to your Azure OpenAI resource
Go to "Keys and Endpoint"
Copy your API key and endpoint
Note your deployment name from the "Deployments" section

MongoDB Configuration¶

# MongoDB connection URI
MONGODB_URI=mongodb://localhost:27017

# Database name
MONGODB_DATABASE=petshop

Connection String Examples¶

Local MongoDB:

MONGODB_URI=mongodb://localhost:27017

MongoDB with Authentication:

MONGODB_URI=mongodb://username:password@localhost:27017

MongoDB Atlas:

MONGODB_URI=mongodb+srv://username:password@cluster.mongodb.net/?retryWrites=true&w=majority

Docker MongoDB:

MONGODB_URI=mongodb://mongodb:27017

API Configuration¶

# Host to bind the API server
API_HOST=0.0.0.0

# Port for the API server
API_PORT=8000

# Base URL for API (used by tools)
API_BASE_URL=http://localhost:8000

Chainlit Configuration¶

Chainlit configuration is stored in .chainlit file.

Key Settings¶

[project]
# Enable/disable telemetry
enable_telemetry = false

# Session timeout (seconds)
session_timeout = 3600

[UI]
# App name displayed in UI
name = "Pet Paradise Shop"

# App description
description = "Pet shop ordering and support chat assistant"

# Collapse large content
default_collapse_content = true

Customization¶

To customize the UI theme, edit the .chainlit file:

[UI.theme.light]
background = "#FAFAFA"
paper = "#FFFFFF"

[UI.theme.light.primary]
main = "#F80061"
dark = "#980039"
light = "#FFE7EB"

Advanced Configuration¶

API Server Options¶

For production deployments, you can configure Uvicorn options:

# In api.py
uvicorn.run(
    app,
    host=host,
    port=port,
    workers=4,  # Number of worker processes
    log_level="info",
    access_log=True,
    ssl_keyfile="path/to/key.pem",  # For HTTPS
    ssl_certfile="path/to/cert.pem"
)

MongoDB Indexes¶

For better performance, create indexes:

# Add to database.py initialization
await db.db.pets.create_index([("type", 1)])
await db.db.pets.create_index([("price", 1)])
await db.db.pets.create_index([("available", 1)])
await db.db.orders.create_index([("created_at", -1)])

Tool Configuration¶

Customize tool behavior in tools.py:

# Change API base URL
API_BASE_URL = os.getenv("API_BASE_URL", "http://localhost:8000")

# Adjust HTTP timeout
async with httpx.AsyncClient(timeout=30.0) as client:
    # Tool implementation

Security Configuration¶

Production Recommendations¶

Use HTTPS: Configure SSL certificates for API
Restrict CORS: Limit allowed origins in api.py
Authentication: Add API key or OAuth
Rate Limiting: Implement rate limits
Secret Management: Use Azure Key Vault or similar

CORS Configuration¶

Edit api.py to restrict CORS:

app.add_middleware(
    CORSMiddleware,
    allow_origins=["https://yourdomain.com"],  # Specific origins
    allow_credentials=True,
    allow_methods=["GET", "POST"],  # Limit methods
    allow_headers=["*"],
)

Environment-Specific Configuration¶

Use different .env files for different environments:

# Development
cp .env.development .env

# Staging
cp .env.staging .env

# Production
cp .env.production .env

Docker Configuration¶

Docker Compose Environment¶

Create docker-compose.override.yml for local overrides:

version: '3.8'

services:
  api:
    environment:
      - DEBUG=true
      - LOG_LEVEL=debug

  chat:
    ports:
      - "8002:8001"  # Use different port

Build Arguments¶

Customize Docker builds:

# In Dockerfile.api
ARG PYTHON_VERSION=3.11
FROM python:${PYTHON_VERSION}-slim

Build with custom arguments:

docker build --build-arg PYTHON_VERSION=3.12 -f Dockerfile.api .

Logging Configuration¶

Application Logging¶

Configure logging in your application:

import logging

logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
    handlers=[
        logging.FileHandler('app.log'),
        logging.StreamHandler()
    ]
)

API Access Logs¶

Uvicorn provides access logging:

python api.py --log-config logging.yaml

Performance Tuning¶

Connection Pooling¶

MongoDB connection pool settings:

client = AsyncIOMotorClient(
    mongodb_uri,
    maxPoolSize=50,
    minPoolSize=10,
    serverSelectionTimeoutMS=5000
)

API Workers¶

Scale API with multiple workers:

uvicorn api:app --workers 4 --host 0.0.0.0 --port 8000

Caching¶

Add caching for frequently accessed data:

from functools import lru_cache

@lru_cache(maxsize=100)
async def get_pet_by_id(pet_id: str):
    # Implementation

Monitoring Configuration¶

Health Checks¶

The /health endpoint provides system status:

{
  "status": "healthy",
  "database": "connected"
}

Metrics¶

Consider adding Prometheus metrics:

from prometheus_fastapi_instrumentator import Instrumentator

Instrumentator().instrument(app).expose(app)

Next Steps¶

Start the system with Quick Start
Explore Architecture