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name: server-skills description: Server-specific best practices for FastAPI, Celery, and Pydantic. Extends python-skills with framework-specific patterns. allowed-tools: Read, Grep, Glob, Bash user-invocable: false

Server Skills for LlamaFarm

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

Overview

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

Architecture Overview

Copyserver/
├── 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)

Copyfrom pydantic_settings import BaseSettings

class Settings(BaseSettings, env_file=".env"):
    HOST: str = "0.0.0.0"
    PORT: int = 8000
    LOG_LEVEL: str = "INFO"

settings = Settings()  # Module-level singleton

Structured Logging

Copyfrom 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

Copy# 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

Copyclass 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

1. 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

2. 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

3. Pydantic Models (Medium priority)

   • Use Pydantic v2 patterns (model_config, Field)
   • Proper validation with field constraints
   • Serialization with model_dump()

4. 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.