07725429b2
A UIWorker with a custom reply tool fans research out to three BaseWorker peers via start_user_job_group; their progress streams to the client as ui-task cards and the user can cancel a group mid-flight.
91 lines
3.3 KiB
Markdown
91 lines
3.3 KiB
Markdown
# async-tasks
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The UIWorker fans out long-running work to multiple peer workers in
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parallel, streams their progress to an in-flight panel on the page, and
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lets the user cancel mid-flight.
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## What it shows
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- The **`user_job_group` / `start_user_job_group`** API: dispatching
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parallel work to multiple peer workers and automatically forwarding
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every job lifecycle event to the client. The `reply` tool calls
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`start_user_job_group("wikipedia", "news", "scholar", payload=...,
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label=...)` and the `UIWorker` does the rest.
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- The four **`ui-task` envelopes** the worker forwards (`group_started`,
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`task_update`, `task_completed`, `group_completed`) and the
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client-side `RTVIEvent.UITask` event for consuming them. The client
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keeps a state map keyed by `task_id` and renders per-worker progress.
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- **Cancellation**: the in-flight card's Cancel button calls
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`client.cancelUITask(task_id, reason)`. The reserved `__cancel_task`
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event is translated by the `UIWorker` into `cancel_job_group(task_id)`
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on the registered group; cancelled workers report status `cancelled`.
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- **Background dispatch from a tool**: `start_user_job_group` returns
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immediately so the `reply` tool can speak its acknowledgement
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("Researching the Mariana Trench now") while the workers run — the
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main LLM is free to take follow-up turns.
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## What it adds vs. the prior demos
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The other examples use the request/response half of the bus protocol
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(main LLM → UIWorker → reply). This one adds the streaming job-group
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half: UIWorker → peer workers → progress events forwarded to the client.
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The architecture grows from "one delegate" to "one delegate plus a
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worker pool" — the peers are plain `BaseWorker`s launched on the runner.
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## Run
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Two terminals.
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**Terminal 1 — bot:**
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```bash
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cd examples/multi-worker/ui-worker/async-tasks
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uv run python bot.py
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```
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The bot starts on `http://localhost:7860`.
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**Terminal 2 — client:**
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```bash
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cd examples/multi-worker/ui-worker/async-tasks/client
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npm install # one-time
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npm run dev
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```
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Open `http://localhost:5173` and click **Connect**.
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## What to try
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The workers are simulated (canned summaries, randomized `asyncio.sleep`
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delays) so the demo focuses on the protocol, not the AI. Each research
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call takes a few seconds.
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- _"Research the Mariana Trench."_ — the worker spawns three peers,
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acknowledges in one short reply, and a card appears showing each
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peer's status as it progresses (searching → found N results →
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summarizing → completed).
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- _"Look up octopus cognition."_ — same flow; a second card stacks.
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- _"Research the moon, then research Mars."_ — two groups run
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concurrently.
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- _"How are you?"_ (no research) — quick reply, no job group.
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- **Click Cancel on an in-flight card** — the cancellation routes
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through, the peers' tasks raise `CancelledError`, and their responses
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come back as `cancelled`.
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## Requirements
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- `OPENAI_API_KEY`
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- `DEEPGRAM_API_KEY`
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- `CARTESIA_API_KEY`
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A `.env` in the example folder is the easiest way to set these (see
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`examples/multi-worker/env.example`).
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## What this example _doesn't_ show
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Real worker integrations (the peers are simulated), LLM-driven peers
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(these are pure data-fetch — a peer can itself be an `LLMWorker`),
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streaming chunks (`send_job_stream_data` for progressive output), or
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worker-to-worker fan-out (nested job groups).
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