FastAPI-deep-research.md

Deep Research FastAPI is a template/project scaffold designed to help developers quickly build and deploy production-ready FastAPI applications, especially for research, data science, or AI-related projects. It is often used as a starting point for building APIs with features like authentication, database integration, and async support out of the box.

Here’s a breakdown of what the Deep Research FastAPI config (typically found in config.py or similar files) can do, based on common implementations:

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1. Core Configuration

These settings define the foundational behavior of your FastAPI app:

• App Metadata: Title, description, version, and contact info for API documentation (OpenAPI/Swagger).
• Debug Mode: Enable/disable debug logs and auto-reload during development.
• Environment: Switch between development, staging, and production modes.
• CORS (Cross-Origin Resource Sharing): Configure allowed origins, methods, and headers for web clients.

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2. Security & Authentication

• JWT (JSON Web Tokens): Settings for token expiration, secret keys, and algorithms.
• OAuth2: Configuration for OAuth2 flows (e.g., Google, GitHub).
• API Keys: Support for API key-based authentication.
• Rate Limiting: Limit requests per user/IP to prevent abuse.

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3. Database & ORM

• Database URL: Connection strings for PostgreSQL, MySQL, SQLite, etc.
• SQLAlchemy/Alembic: ORM and migration tool settings.
• Async Database Support: Configuration for async database drivers (e.g., asyncpg for PostgreSQL).
• Session Management: Database session lifecycle and connection pooling.

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4. Logging

• Log Level: Set verbosity (e.g., DEBUG, INFO, WARNING).
• Log Format: JSON or plain text, with custom fields.
• Log Handlers: File, console, or external services (e.g., Sentry, ELK).

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5. API Features

• Pagination: Default page size, max limits.
• File Uploads: Allowed file types, size limits, and storage paths.
• Background Tasks: Celery, RQ, or FastAPI’s built-in background tasks.
• Webhooks: Outgoing webhook URLs and retries.

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6. External Services

• Cloud Storage: AWS S3, Google Cloud Storage, or Azure Blob config.
• Email: SMTP settings for sending emails (e.g., password resets).
• Third-Party APIs: Keys for services like Stripe, SendGrid, or OpenAI.

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7. Performance & Scaling

• Worker Count: Number of Gunicorn/Uvicorn workers.
• Timeouts: Request/response timeouts.
• Caching: Redis or Memcached settings for caching responses.

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8. Testing

• Test Database URL: Separate DB for testing.
• Mock Services: Toggle for mocking external APIs during tests.

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9. Deployment

• Docker: Containerization settings.
• Kubernetes: Helm charts or K8s manifests.
• Serverless: Configuration for AWS Lambda, Google Cloud Functions, etc.

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10. Custom Features

• Feature Flags: Enable/disable experimental features.
• Custom Middleware: Add middleware for request/response processing.

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Example Config Snippet

# config.py
class Settings:
    # Core
    APP_NAME = "Deep Research API"
    DEBUG = True
    ENVIRONMENT = "development"

    # Database
    DATABASE_URL = "postgresql+asyncpg://user:pass@localhost:5432/db"

    # Security
    SECRET_KEY = "your-secret-key"
    JWT_ALGORITHM = "HS256"
    ACCESS_TOKEN_EXPIRE_MINUTES = 30

    # CORS
    CORS_ORIGINS = ["http://localhost:3000"]

    # Logging
    LOG_LEVEL = "DEBUG"

    # External
    AWS_S3_BUCKET = "my-bucket"
    OPENAI_API_KEY = "sk-..."

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How to Use

1. Environment Variables: Most configs are loaded from .env files or environment variables (e.g., using pydantic.BaseSettings).
2. Validation: Use Pydantic to validate config values at startup.
3. Overrides: Allow runtime overrides for testing or local development.

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Where to Find the Full Config?

• Check the Deep Research FastAPI GitHub repo (if public).
• Look for config.py, settings.py, or .env.example in the project root.

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