fastapi-microservice-patterns

Coding patterns for FastAPI microservices, which are small web services built with Python's FastAPI framework. They cover service startup and shutdown, dependency setup, health checks, routing, and cleanup.

In plain words
What is it for?
Use them to build FastAPI APIs, initialize service dependencies, organize routes, run health checks, and release resources when a service stops.
Why use it?
They give a consistent structure for services that connect to databases or machine-learning models, making startup, operation, and shutdown behavior easier to manage.

Cursor rule for Cursor

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add rules/rm2thaddeus/pixel_detective/fastapi-microservice-patterns
Clone the repo
git clone --depth 1 https://github.com/rm2thaddeus/Pixel_Detective

Made for: Cursor.

Per session 0 Nothing until a file matches its globs; then the whole rule loads.
When invoked 3,329 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00000 $0.03329
Opus 5 $0.00000 $0.01665
Sonnet 5 $0.00000 $0.00666
Haiku 4.5 $0.00000 $0.00333

Measured 2d ago against content hash b454534f9575, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

fastapi-microservice-patterns scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

backend/.cursor/rules/fastapi-microservice-patterns.mdc · 476 lines

How it starts

The opening of the file, as written. The whole thing — 476 lines — stays where its author put it; the contents beside it link to each section on GitHub.

FastAPI Microservice Development Patterns

🏗️ SERVICE ARCHITECTURE PATTERNS (From Vibe Coding Backend)

Based on proven patterns from ingestion_orchestration_fastapi_app and ml_inference_fastapi_app.

✅ MANDATORY SERVICE STRUCTURE:

1. Application Lifecycle Management
# ✅ ALWAYS use lifespan context manager for startup/shutdown
from contextlib import asynccontextmanager

@asynccontextmanager
async def lifespan(app: FastAPI):
    """Application lifespan manager for startup/shutdown."""
    # --- Startup ---
    logger.info("Starting up services...")
    
    # Initialize dependencies (database clients, ML models, etc.)
    app_state.qdrant_client = QdrantClient(host=qdrant_host, port=qdrant_port)
    
    # Perform health checks and capability probing
    _probe_safe_batch_size()  # Example from ML service
    
    logger.info("Startup complete.")
    yield
    
    # --- Shutdown ---
    logger.info("Shutting down services...")
    # Cleanup resources
    if torch.cuda.is_available():
        torch.cuda.empty_cache()
    logger.info("Shutdown complete.")

# ✅ Create app with lifespan
app = FastAPI(title="Your Service Name", lifespan=lifespan)
2. Router Organization Pattern
# ✅ ALWAYS organize endpoints in separate router modules
# Structure: /routers/{domain}.py

# backend/{service}/routers/collections.py
router = APIRouter(prefix="/api/v1/collections", tags=["collections"])

# backend/{service}/main.py
from .routers import search, images, collections, ingest

app.include_router(search.router)
app.include_router(images.router)
app.include_router(collections.router)
app.include_router(ingest.router)
3. Dependencies Module Pattern
# ✅ ALWAYS create centralized dependencies.py
# backend/{service}/dependencies.py

class AppState:
    """Centralized application state container."""
    def __init__(self):
        self.client: SomeClient | None = None
        self.config: Dict[str, Any] = {}

app_state = AppState()

def get_client() -> SomeClient:
    """Dependency function to get initialized client."""
    if app_state.client is None:
        raise RuntimeError("Client has not been initialized.")
    return app_state.client

Read the full file on GitHub · 476 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2d ago First seen · 476 lines · 0 tokens per session scan A b454534f9575

Subscribe to this mod's changes

fastapi-microservice-patterns is a cursor rule published in the GitHub repository rm2thaddeus/Pixel_Detective (21 stars, last pushed 6mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 3,329 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.