Python Skills for LlamaFarm

Shared Python best practices and code review checklists for all Python components in the LlamaFarm monorepo.

Applicable Components

Component	Path	Python	Key Dependencies
Server	server/	3.12+	FastAPI, Celery, Pydantic, structlog
RAG	rag/	3.11+	LlamaIndex, ChromaDB, Celery
Universal Runtime	runtimes/universal/	3.11+	PyTorch, transformers, FastAPI
Config	config/	3.11+	Pydantic, JSONSchema
Common	common/	3.10+	HuggingFace Hub

Quick Reference

Topic	File	Key Points
Patterns	patterns.md	Dataclasses, Pydantic, comprehensions, imports
Async	async.md	async/await, asyncio, concurrent execution
Typing	typing.md	Type hints, generics, protocols, Pydantic
Testing	testing.md	Pytest fixtures, mocking, async tests
Errors	error-handling.md	Custom exceptions, logging, context managers
Security	security.md	Path traversal, injection, secrets, deserialization

Code Style

LlamaFarm uses ruff with shared configuration in ruff.toml:

line-length = 88
target-version = "py311"
select = ["E", "F", "I", "B", "UP", "SIM"]

Key rules:

• E, F: Core pyflakes and pycodestyle
• I: Import sorting (isort)
• B: Bugbear (common pitfalls)
• UP: Upgrade syntax to modern Python
• SIM: Simplify code patterns

Architecture Patterns

Settings with pydantic-settings

from pydantic_settings import BaseSettings

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

settings = Settings()  # Singleton at module level

Structured Logging with structlog

from core.logging import FastAPIStructLogger  # Server
from core.logging import RAGStructLogger      # RAG
from core.logging import UniversalRuntimeLogger  # Runtime

logger = FastAPIStructLogger(__name__)
logger.info("Operation completed", extra={"count": 10, "duration_ms": 150})

Abstract Base Classes for Extensibility

from abc import ABC, abstractmethod

class Component(ABC):
    def __init__(self, name: str, config: dict[str, Any] | None = None):
        self.name = name or self.__class__.__name__
        self.config = config or {}

    @abstractmethod
    def process(self, documents: list[Document]) -> ProcessingResult:
        pass

Dataclasses for Internal Data

from dataclasses import dataclass, field

@dataclass
class Document:
    content: str
    metadata: dict[str, Any] = field(default_factory=dict)
    id: str = field(default_factory=lambda: str(uuid.uuid4()))

Pydantic Models for API Boundaries

from pydantic import BaseModel, Field, ConfigDict

class EmbeddingRequest(BaseModel):
    model: str
    input: str | list[str]
    encoding_format: Literal["float", "base64"] | None = "float"

    model_config = ConfigDict(str_strip_whitespace=True)

Directory Structure

Each Python component follows this structure:

component/
├── pyproject.toml     # UV-managed dependencies
├── core/              # Core functionality
│   ├── __init__.py
│   ├── settings.py    # Pydantic Settings
│   └── logging.py     # structlog setup
├── services/          # Business logic (server)
├── models/            # ML models (runtime)
├── tasks/             # Celery tasks (rag)
├── utils/             # Utility functions
└── tests/
    ├── conftest.py    # Shared fixtures
    └── test_*.py

Review Checklist Summary

When reviewing Python code in LlamaFarm:

1. Patterns (Medium priority)

   • Modern Python syntax (3.10+ type hints)
   • Dataclass vs Pydantic used appropriately
   • No mutable default arguments

2. Async (High priority)

   • No blocking calls in async functions
   • Proper asyncio.Lock usage
   • Cancellation handled correctly

3. Typing (Medium priority)

   • Complete return type hints
   • Generic types parameterized
   • Pydantic v2 patterns

4. Testing (Medium priority)

   • Fixtures properly scoped
   • Async tests use pytest-asyncio
   • Mocks cleaned up

5. Errors (High priority)

   • Custom exceptions with context
   • Structured logging with extra dict
   • Proper exception chaining

6. Security (Critical priority)

   • Path traversal prevention
   • Input sanitization
   • Safe deserialization

See individual topic files for detailed checklists with grep patterns.