52 lines
1.7 KiB
Python
52 lines
1.7 KiB
Python
#
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# Copyright (c) 2024–2025, Daily
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#
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# SPDX-License-Identifier: BSD 2-Clause License
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#
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"""User response aggregation for text frames.
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This module provides an aggregator that collects user responses and outputs
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them as TextFrame objects, useful for capturing and processing user input
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in conversational pipelines.
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"""
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from pipecat.frames.frames import TextFrame
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from pipecat.processors.aggregators.llm_response import LLMUserContextAggregator
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from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
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class UserResponseAggregator(LLMUserContextAggregator):
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"""Aggregates user responses into TextFrame objects.
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This aggregator extends LLMUserContextAggregator to specifically handle
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user input by collecting text responses and outputting them as TextFrame
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objects when the aggregation is complete.
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"""
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def __init__(self, **kwargs):
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"""Initialize the user response aggregator.
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Args:
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**kwargs: Additional arguments passed to parent LLMUserContextAggregator.
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"""
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super().__init__(context=OpenAILLMContext(), **kwargs)
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async def push_aggregation(self):
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"""Push the aggregated user response as a TextFrame.
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Creates a TextFrame from the current aggregation if it contains content,
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resets the aggregation state, and pushes the frame downstream.
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"""
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if len(self._aggregation) > 0:
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frame = TextFrame(self._aggregation.strip())
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# Reset the aggregation. Reset it before pushing it down, otherwise
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# if the tasks gets cancelled we won't be able to clear things up.
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self._aggregation = ""
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await self.push_frame(frame)
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# Reset our accumulator state.
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await self.reset()
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