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:

Patterns (Medium priority)

• Modern Python syntax (3.10+ type hints)

• Dataclass vs Pydantic used appropriately

• No mutable default arguments

Async (High priority)

• No blocking calls in async functions

• Proper asyncio.Lock usage

• Cancellation handled correctly

Typing (Medium priority)

• Complete return type hints

• Generic types parameterized

• Pydantic v2 patterns

Testing (Medium priority)

• Fixtures properly scoped

• Async tests use pytest-asyncio

• Mocks cleaned up

Errors (High priority)

• Custom exceptions with context

• Structured logging with extra dict

• Proper exception chaining

Security (Critical priority)

• Path traversal prevention

• Input sanitization

• Safe deserialization

See individual topic files for detailed checklists with grep patterns.