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64 lines
2.0 KiB
Python
64 lines
2.0 KiB
Python
# Copyright (c) Microsoft. All rights reserved.
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"""Basic streaming pipeline using the functional workflow API.
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Stream workflow events in real time with run(stream=True).
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"""
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import asyncio
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from agent_framework import workflow
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# Plain async functions — no decorators needed for simple helpers.
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async def fetch_data(url: str) -> dict[str, str | int]:
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"""Simulate fetching data from a URL."""
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return {"url": url, "content": f"Data from {url}", "status": 200}
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async def transform_data(data: dict[str, str | int]) -> str:
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"""Transform raw data into a summary string."""
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return f"[{data['status']}] {data['content']}"
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async def validate_result(summary: str) -> bool:
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"""Validate the transformed result."""
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return len(summary) > 0 and "[200]" in summary
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# @workflow enables .run(stream=True), which returns a ResponseStream
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# you can iterate over with `async for`. Without @workflow, you'd just
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# have a normal async function with no streaming capability.
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@workflow
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async def data_pipeline(url: str) -> str:
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"""A simple sequential data pipeline."""
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raw = await fetch_data(url)
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summary = await transform_data(raw)
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is_valid = await validate_result(summary)
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return f"{summary} (valid={is_valid})"
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async def main():
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# run(stream=True) returns a ResponseStream that yields events as they
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# are produced. The raw stream includes lifecycle events (started, status)
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# alongside application events — filter by event.type to find what you need.
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stream = data_pipeline.run("https://example.com/api/data", stream=True)
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async for event in stream:
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if event.type == "output":
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print(f"Output: {event.data}")
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# After iteration, get_final_response() returns the WorkflowRunResult
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result = await stream.get_final_response()
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print(f"Final state: {result.get_final_state()}")
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"""
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Expected output:
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Output: [200] Data from https://example.com/api/data (valid=True)
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Final state: WorkflowRunState.IDLE
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"""
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if __name__ == "__main__":
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asyncio.run(main())
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