FastAPI (Python)

Install

pip install njiraai

Middleware

from fastapi import FastAPI
from pydantic import BaseModel

from njiraai import NjiraAI
from njiraai.middleware import create_middleware

app = FastAPI()

njira = NjiraAI(
    api_key="your-api-key",
    project_id="your-project-id",
    mode="active",
)

# Add middleware
app.add_middleware(create_middleware(njira))


class ChatRequest(BaseModel):
    message: str


@app.post("/chat")
async def chat(request: ChatRequest):
    # Enforce before processing
    decision = await njira.enforce_pre(
        input_data=request.message,
        metadata={"endpoint": "/chat"},
    )

    if decision["verdict"] == "block":
        return {
            "error": "Blocked",
            "reasons": decision["reasons"],
            "traceId": decision["traceId"],
        }

    # Your LLM logic here
    response = f"Echo: {request.message}"

    return {"response": response, "traceId": decision["traceId"]}

What the middleware does

• Creates/propagates request_id from x-request-id header
• Sets up per-request trace context via contextvars
• Adds x-njira-trace-id and x-njira-request-id response headers
• Flushes traces after the response

Custom user/tags extraction

def get_user(request):
    return request.headers.get("x-user-id")

def get_tags(request):
    return {"tenant": request.headers.get("x-tenant", "")}

app.add_middleware(create_middleware(njira, get_user=get_user, get_tags=get_tags))