server-skills

Development

Server-specific best practices for FastAPI, Celery, and Pydantic. Extends python-skills with framework-specific patterns.

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Server Skills for LlamaFarm

Framework-specific patterns and code review checklists for the LlamaFarm Server component.

Overview

Property	Value
Path	`server/`
Python	3.12+
Framework	FastAPI 0.116+
Task Queue	Celery 5.5+
Validation	Pydantic 2.x, pydantic-settings
Logging	structlog with FastAPIStructLogger

Links to Shared Skills

This skill extends the shared Python skills. See:

Python Patterns - Dataclasses, comprehensions, imports
Async Patterns - async/await, asyncio, concurrency
Typing Patterns - Type hints, generics, Pydantic
Testing Patterns - Pytest, fixtures, mocking
Error Handling - Exceptions, logging, context managers
Security Patterns - Path traversal, injection, secrets

Server-Specific Checklists

Topic	File	Key Points
FastAPI	fastapi.md	Routes, dependencies, middleware, exception handlers
Celery	celery.md	Task patterns, error handling, retries, signatures
Pydantic	pydantic.md	Pydantic v2 models, validation, serialization
Performance	performance.md	Async patterns, caching, connection pooling

Architecture Overview

server/
├── main.py                 # Uvicorn entry point, MCP mount
├── api/
│   ├── main.py             # FastAPI app factory, middleware setup
│   ├── errors.py           # Custom exceptions + exception handlers
│   ├── middleware/         # ASGI middleware (structlog, errors)
│   └── routers/            # API route modules
│       ├── projects/       # Project CRUD endpoints
│       ├── datasets/       # Dataset management
│       ├── rag/            # RAG query endpoints
│       └── ...
├── core/
│   ├── settings.py         # pydantic-settings configuration
│   ├── logging.py          # structlog setup, FastAPIStructLogger
│   └── celery/             # Celery app configuration
│       ├── celery.py       # Celery app instance
│       └── rag_client.py   # RAG task signatures and helpers
├── services/               # Business logic layer
│   ├── project_service.py  # Project CRUD operations
│   ├── dataset_service.py  # Dataset management
│   └── ...
├── agents/                 # AI agent implementations
└── tests/                  # Pytest test suite

Quick Reference

Settings Pattern (pydantic-settings)

from pydantic_settings import BaseSettings

class Settings(BaseSettings, env_file=".env"):
    HOST: str = "0.0.0.0"
    PORT: int = 14345
    LOG_LEVEL: str = "INFO"

settings = Settings()  # Module-level singleton

Structured Logging

from core.logging import FastAPIStructLogger

logger = FastAPIStructLogger(__name__)
logger.info("Operation completed", extra={"count": 10, "duration_ms": 150})
logger.bind(namespace=namespace, project=project_id)  # Add context

Custom Exceptions

# Define exception hierarchy
class NotFoundError(Exception): ...
class ProjectNotFoundError(NotFoundError):
    def __init__(self, namespace: str, project_id: str):
        self.namespace = namespace
        self.project_id = project_id
        super().__init__(f"Project {namespace}/{project_id} not found")

# Register handler in api/errors.py
async def _handle_project_not_found(request: Request, exc: Exception) -> Response:
    payload = ErrorResponse(error="ProjectNotFound", message=str(exc))
    return JSONResponse(status_code=404, content=payload.model_dump())

def register_exception_handlers(app: FastAPI) -> None:
    app.add_exception_handler(ProjectNotFoundError, _handle_project_not_found)

Service Layer Pattern

class ProjectService:
    @classmethod
    def get_project(cls, namespace: str, project_id: str) -> Project:
        project_dir = cls.get_project_dir(namespace, project_id)
        if not os.path.isdir(project_dir):
            raise ProjectNotFoundError(namespace, project_id)
        # ... load and validate

Review Checklist Summary

FastAPI Routes (High priority)
- Proper async/sync function choice
- Response model defined with response_model=
- OpenAPI metadata (operation_id, tags, summary)
- HTTPException with proper status codes
Celery Tasks (High priority)
- Use signatures for cross-service calls
- Implement proper timeout and polling
- Handle task failures gracefully
- Store group metadata for parallel tasks
Pydantic Models (Medium priority)
- Use Pydantic v2 patterns (model_config, Field)
- Proper validation with field constraints
- Serialization with model_dump()
Performance (Medium priority)
- Avoid blocking calls in async functions
- Use proper connection pooling for external services
- Implement caching where appropriate

See individual topic files for detailed checklists with grep patterns.