klingai-reference-architecture

# Kling AI Reference Architecture ## Overview Production architecture for video generation platforms built on Kling AI. Covers API gateway, job queue, worker pool, storage, and monitoring layers. ## Architecture Diagram ``` User Request | [API Gateway / Load Balancer] | [Application Server] |--- validate prompt & estimate cost |--- enqueue job to Redis/SQS | [Job Queue (Redis / SQS / Pub/Sub)] | [Worker Pool (N workers)] |--- generate JWT token |--- POST https://api.klingai.com/v1/videos/text2video |--- receive task_id |--- register callback_url OR poll | [Webhook Receiver / Poller] |--- receive completion callback |--- download video from Kling CDN |--- upload to S3/GCS |--- update job status in DB |--- notify user | [Object Storage (S3 / GCS)] | [CDN (CloudFront / Cloud CDN)] | User views video ``` ## Component Details ### API Layer ```python from fastapi import FastAPI, HTTPException from pydantic import BaseModel app = FastAPI() class VideoRequest(BaseModel): prompt: str model: str = "kling-v2-master" duration: int = 5 mode: str = "standard" @app.post("/api/videos") async def create_video(req: VideoRequest): # 1. Validate if len(req.prompt) > 2500: raise HTTPException(400, "Prompt exceeds 2500 chars") # 2. Estimate cost credits = estimate_credits(req.duration, req.mode) if not budget_guard.check(credits): raise HTTPException(402, "Budg...