f826da9ac9
A ReplyToolMixin UIWorker that grounds in the user's text selection (the <selection> block in the snapshot) and points back via select_text — both directions of deictic reference.
292 lines
10 KiB
Python
292 lines
10 KiB
Python
#
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# Copyright (c) 2026, Daily
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#
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# SPDX-License-Identifier: BSD 2-Clause License
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#
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"""Deixis — the UIWorker grounds in what the user just selected.
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The page renders an article. The user selects a paragraph (or any span
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of text) and asks "explain this", "rephrase that", "where does it talk
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about RNA editing?", and so on. The client captures
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``window.getSelection()`` and emits a ``<selection ref="...">selected
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text</selection>`` block in the snapshot. The UIWorker reads it as a
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deictic reference: "this paragraph" resolves to the selected element.
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Two directions:
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- **Read**: user selects text → ``<selection>`` block in ``<ui_state>``
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→ the worker grounds its answer in the selected content.
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- **Write**: the worker says "this paragraph" → ``select_text=ref`` puts
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the page's text selection on that element → the user sees what the
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worker is referring to.
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Same skeleton as ``pointing``. ``DeixisWorker`` composes
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``ReplyToolMixin``: the ``reply(answer, scroll_to, highlight,
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select_text)`` bundle covers both pointing-style and reading-style apps.
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Architecture::
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Main worker (PipelineWorker, owns transport + RTVI):
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transport.in → STT → user_agg → LLM → TTS → transport.out → assistant_agg
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└── answer_about_screen(query) tool
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└── params.pipeline_worker.job("ui", name="respond", payload={query})
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DeixisWorker (ReplyToolMixin + UIWorker):
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└── inherited: reply(answer, scroll_to, highlight, select_text)
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Run::
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uv run python bot.py
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Then open the client at ``http://localhost:5173`` (see ``README.md``).
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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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"""
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import os
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from dotenv import load_dotenv
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from loguru import logger
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from pipecat.adapters.schemas.tools_schema import ToolsSchema
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from pipecat.audio.vad.silero import SileroVADAnalyzer
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from pipecat.frames.frames import LLMRunFrame
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from pipecat.pipeline.job_context import JobError
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from pipecat.pipeline.pipeline import Pipeline
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from pipecat.pipeline.runner import PipelineRunner
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from pipecat.pipeline.worker import PipelineParams, PipelineWorker
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from pipecat.processors.aggregators.llm_context import LLMContext
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from pipecat.processors.aggregators.llm_response_universal import (
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LLMContextAggregatorPair,
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LLMUserAggregatorParams,
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)
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from pipecat.runner.types import RunnerArguments
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from pipecat.runner.utils import create_transport
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from pipecat.services.cartesia.tts import CartesiaTTSService
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from pipecat.services.deepgram.stt import DeepgramSTTService
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from pipecat.services.llm_service import FunctionCallParams
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from pipecat.services.openai.llm import OpenAILLMService
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from pipecat.transports.base_transport import BaseTransport, TransportParams
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from pipecat.transports.daily.transport import DailyParams
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from pipecat.workers.ui import ReplyToolMixin, UIWorker
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load_dotenv(override=True)
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MAIN_NAME = "main"
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transport_params = {
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"daily": lambda: DailyParams(audio_in_enabled=True, audio_out_enabled=True),
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"webrtc": lambda: TransportParams(audio_in_enabled=True, audio_out_enabled=True),
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}
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VOICE_PROMPT = """\
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You are the voice layer of a screen-aware reading assistant. A \
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separate UI layer sees the page (and the user's selection) and \
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writes the spoken reply.
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For every user utterance about the article, call \
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``answer_about_screen`` with the user's request verbatim. The tool's \
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response is the spoken reply, already TTS-ready.
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Only respond directly for pure pleasantries (greetings, thanks, \
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goodbyes). Keep direct replies to one short spoken sentence."""
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# The UI wire-format guide (UI_STATE_PROMPT_GUIDE) is appended to the LLM's
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# system instruction automatically by UIWorker, so this prompt only needs the
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# app-specific behavior.
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UI_PROMPT = """\
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You help the user read and understand an article. The current \
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``<ui_state>`` block is in your context, and may contain a \
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``<selection>`` block when the user has highlighted text.
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## Tool: reply
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Every turn calls ``reply`` exactly once. One tool call per turn, no \
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chaining.
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``reply(answer, scroll_to=None, highlight=None, select_text=None)``:
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- ``answer`` (REQUIRED): the spoken reply, plain language, two short \
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sentences max. No markdown, no symbols, no quoting long passages.
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- ``scroll_to`` (OPTIONAL): a snapshot ref. Set when the paragraph \
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you want to point at is tagged ``[offscreen]``.
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- ``highlight`` (OPTIONAL): a list of snapshot refs to flash briefly. \
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Use for short emphasis: "look at this fact". Don't use it for a \
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whole paragraph; ``select_text`` is better for that.
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- ``select_text`` (OPTIONAL): a single snapshot ref. Sets the page's \
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text selection to that element. Use this when you say "this \
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paragraph" or "the section that talks about X" so the user sees \
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exactly what you're referring to.
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## Reading the user's selection
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If ``<ui_state>`` contains a ``<selection ref="...">selected \
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text</selection>`` block, the user has highlighted something. Treat \
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that selection as the deictic referent for words like "this", \
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"that", "this paragraph", "what I selected". Ground your answer in \
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the selected content, not the article as a whole.
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When answering about the user's selection, do NOT also call \
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``select_text`` — they already selected it; pointing back at the \
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same span is redundant.
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## Decision rules
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- User has a selection AND asks something deictic ("explain this", \
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"rephrase that", "what does this mean") → ground in the selection. \
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Just ``answer``; no visual fields.
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- User asks "where does it say X?" or "show me the part about X" → \
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find the matching paragraph, ``answer`` briefly, set \
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``select_text=ref`` to point at it, and ``scroll_to=ref`` if it's \
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``[offscreen]``.
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- User asks a content question without selection → ``answer`` with \
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the relevant fact. Optionally set ``select_text=ref`` if the \
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answer is sourced from one specific paragraph.
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## Examples
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(refs are illustrative; use the actual refs from the current \
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``<ui_state>``)
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- User selects the third paragraph, asks "explain this" → \
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``reply(answer="The skin acts as its own light sensor. Even though \
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octopuses are colorblind, their skin can detect light directly, \
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which is how they match colors so accurately.")``
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- "Where does it talk about RNA editing?" (paragraph e15, offscreen) \
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→ ``reply(answer="Here, in the paragraph about RNA editing.", \
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scroll_to="e15", select_text="e15")``
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- "How many neurons does an octopus have?" (no selection) → \
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``reply(answer="About five hundred million, with two thirds of \
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them in the arms.", select_text="e7")``
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- "Hi, what's this article about?" (no selection) → \
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``reply(answer="It's a short essay on octopus cognition. Select any \
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paragraph and I'll explain it.")``"""
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class DeixisWorker(ReplyToolMixin, UIWorker):
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"""UIWorker that grounds in the user's selection and points back via select_text.
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Composes ``ReplyToolMixin``, which exposes a single
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``reply(answer, scroll_to=None, highlight=None, select_text=None, ...)``
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LLM tool. The same bundle pointing apps use also covers
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reading-style apps: ``select_text`` is for "this paragraph" / "the
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section about X" (durable text selection), while ``highlight``
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flashes briefly for short emphasis.
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"""
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def __init__(self):
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llm = OpenAILLMService(
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api_key=os.environ["OPENAI_API_KEY"],
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settings=OpenAILLMService.Settings(system_instruction=UI_PROMPT),
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)
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super().__init__("ui", llm=llm)
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async def answer_about_screen(params: FunctionCallParams, query: str):
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"""Ask the screen-aware UI worker to answer about the article / selection.
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Args:
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query (str): The user's request, passed verbatim.
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"""
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logger.info(f"answer_about_screen('{query}')")
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try:
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async with params.pipeline_worker.job(
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"ui", name="respond", payload={"query": query}, timeout=10
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) as t:
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pass
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except JobError as e:
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logger.warning(f"ui job failed: {e}")
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await params.result_callback("Something went wrong on my side.")
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return
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speak = (t.response or {}).get("speak")
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await params.result_callback(speak or "I'm not sure how to answer that.")
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async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
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logger.info("Starting deixis bot")
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runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
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stt = DeepgramSTTService(api_key=os.environ["DEEPGRAM_API_KEY"])
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tts = CartesiaTTSService(
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api_key=os.environ["CARTESIA_API_KEY"],
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settings=CartesiaTTSService.Settings(
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voice=os.getenv("CARTESIA_VOICE_ID", "71a7ad14-091c-4e8e-a314-022ece01c121"),
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),
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)
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llm = OpenAILLMService(
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api_key=os.environ["OPENAI_API_KEY"],
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settings=OpenAILLMService.Settings(system_instruction=VOICE_PROMPT),
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)
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llm.register_direct_function(answer_about_screen, cancel_on_interruption=False, timeout_secs=30)
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context = LLMContext(tools=ToolsSchema(standard_tools=[answer_about_screen]))
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aggregators = LLMContextAggregatorPair(
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context,
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user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
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)
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pipeline = Pipeline(
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[
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transport.input(),
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stt,
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aggregators.user(),
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llm,
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tts,
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transport.output(),
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aggregators.assistant(),
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]
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)
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worker = PipelineWorker(
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pipeline,
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name=MAIN_NAME,
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params=PipelineParams(enable_metrics=True, enable_usage_metrics=True),
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idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
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)
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@transport.event_handler("on_client_connected")
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async def on_client_connected(transport, client):
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logger.info("Client connected")
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context.add_message(
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{
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"role": "developer",
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"content": (
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"Greet the user briefly. Tell them they can select a "
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"paragraph and ask you to explain or rephrase it. One "
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"short sentence."
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),
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}
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)
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await worker.queue_frame(LLMRunFrame())
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@transport.event_handler("on_client_disconnected")
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async def on_client_disconnected(transport, client):
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logger.info("Client disconnected")
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await runner.cancel()
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await runner.launch_worker(DeixisWorker())
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await runner.launch_worker(worker)
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await runner.run()
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async def bot(runner_args: RunnerArguments):
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"""Main bot entry point compatible with Pipecat Cloud."""
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transport = await create_transport(runner_args, transport_params)
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await run_bot(transport, runner_args)
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if __name__ == "__main__":
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from pipecat.runner.run import main
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main()
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