fastapi-dependency-injection

Architecture rules for FastAPI applications that require dependency injection. Dependency injection supplies shared clients and models to request handlers through provider functions.

In plain words
What is it for?
Sharing the Qdrant database client and machine-learning model through centralized application state and dependency providers in FastAPI routes.
Why use it?
They help prevent circular imports, where modules depend on each other and Python cannot load them cleanly.

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-dependency-injection
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 1,519 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 $0.01519
Opus 5 $0.00000 $0.00759
Sonnet 5 $0.00000 $0.00304
Haiku 4.5 $0.00000 $0.00152

Measured 2d ago against content hash fe95237184d3, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

fastapi-dependency-injection 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-dependency-injection.mdc · 219 lines

How it starts

The opening of the file, as written. The whole thing — 219 lines — stays where its author put it; the contents beside it link to each section on GitHub.

FastAPI Dependency Injection & Architecture Rules

🚨 CRITICAL: Prevent Circular Import Dependencies

Sprint 10 suffered from circular import issues between main.py and router files. ALWAYS use dependency injection.

✅ MANDATORY PATTERN: Dependency Injection

1. Dependencies Module Setup
# backend/dependencies.py
from qdrant_client import QdrantClient
from sentence_transformers import SentenceTransformer

class AppState:
    """Centralized application state container."""
    def __init__(self):
        self.qdrant_client: QdrantClient | None = None
        self.ml_model: SentenceTransformer | None = None
        self.active_collection: str | None = None

# Global instance - safe because it's explicitly typed
app_state = AppState()

# Dependency provider functions
def get_qdrant_client() -> QdrantClient:
    """Dependency function to get the initialized Qdrant client."""
    if app_state.qdrant_client is None:
        raise RuntimeError("Qdrant client has not been initialized.")
    return app_state.qdrant_client

def get_ml_model() -> SentenceTransformer:
    """Dependency function to get the initialized ML model."""
    if app_state.ml_model is None:
        raise RuntimeError("ML model has not been initialized.")
    return app_state.ml_model

def get_active_collection() -> str:
    """Dependency function to get the currently active collection name."""
    if app_state.active_collection is None:
        return "default_collection"  # Or raise HTTPException
    return app_state.active_collection
2. Main App Initialization
# backend/main.py
from contextlib import asynccontextmanager
from fastapi import FastAPI
from .dependencies import app_state
from .routers import search, images, collections

@asynccontextmanager
async def lifespan(app: FastAPI):
    """Application lifespan manager for startup/shutdown."""
    # --- Startup ---
    print("INFO:     Starting up services...")
    
    # Initialize services into app_state
    app_state.qdrant_client = QdrantClient(host="localhost", port=6333)
    app_state.ml_model = SentenceTransformer("clip-ViT-B-32")
    app_state.active_collection = "default_collection"
    
    print("INFO:     Startup complete.")
    yield
    
    # --- Shutdown ---
    print("INFO:     Shutting down services...")

# Create app with lifespan
app = FastAPI(lifespan=lifespan)

# Include routers - NO circular imports
app.include_router(search.router)
app.include_router(images.router)
app.include_router(collections.router)

Read the full file on GitHub · 219 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 · 219 lines · 0 tokens per session scan A fe95237184d3

Subscribe to this mod's changes

fastapi-dependency-injection 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 1,519 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.