Use this file to discover all available pages before exploring further.
What this builds. The async twin of background-run: build and deploy a pipeline with await-able methods, then start it in the background and await the final result.
You’ll end up with. A task_id you can log immediately, then the awaited result dict once the background run finishes.
import asynciofrom vectorshift.pipeline import PipelinePIPELINE_NAME = "bg_run_async_example"async def main(): try: pipeline = await Pipeline.afetch(name=PIPELINE_NAME) print(f"Pipeline fetched: id={pipeline.id}, branch_id={pipeline.branch_id}") except Exception as e: print(f"Error fetching pipeline: {e}") pipeline = await Pipeline.anew(name=PIPELINE_NAME) print(f"Pipeline created: id={pipeline.id}, branch_id={pipeline.branch_id}") inp = pipeline.add(name="input_0", id="input_0").input(input_type="string") llm = pipeline.add(name="llm", id="llm").llm( provider="openai", model="gpt-4o", prompt=inp.text ) pipeline.add(name="output_0", id="output_0").output( output_type="string", value=llm.response ) await pipeline.asave(deploy=True) # Start in background handler = await pipeline.astart( inputs={"input_0": "Tell me a fun fact about oceans."} ) print(f"Task ID: {handler.task_id}") # Check status status = await handler.arun_status() print(f"Status: {status['status']}") # Wait for completion result = await handler.aresult(poll_interval=2.0, timeout=60.0) print(f"Result: {result}")asyncio.run(main())