llama-farm

server-skills

810
45
# Install this skill:
npx skills add llama-farm/llamafarm --skill "server-skills"

Install specific skill from multi-skill repository

# Description

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

# SKILL.md


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

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

This skill extends the shared Python skills. See:

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 = 8000
    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

  1. FastAPI Routes (High priority)
  2. Proper async/sync function choice
  3. Response model defined with response_model=
  4. OpenAPI metadata (operation_id, tags, summary)
  5. HTTPException with proper status codes

  6. Celery Tasks (High priority)

  7. Use signatures for cross-service calls
  8. Implement proper timeout and polling
  9. Handle task failures gracefully
  10. Store group metadata for parallel tasks

  11. Pydantic Models (Medium priority)

  12. Use Pydantic v2 patterns (model_config, Field)
  13. Proper validation with field constraints
  14. Serialization with model_dump()

  15. Performance (Medium priority)

  16. Avoid blocking calls in async functions
  17. Use proper connection pooling for external services
  18. Implement caching where appropriate

See individual topic files for detailed checklists with grep patterns.

# Supported AI Coding Agents

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Learn more about the SKILL.md standard and how to use these skills with your preferred AI coding agent.