Server Skills for LlamaFarm Skill

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.

Agent Skills: Server Skills for LlamaFarm

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