From 69414e8a5ab8b9569c4e40b17cba7d186081fbdd Mon Sep 17 00:00:00 2001 From: filipi87 Date: Fri, 27 Feb 2026 18:42:23 -0300 Subject: [PATCH] Added example 54b-context-summarization-manual-openai.py demonstrating on-demand summarization triggered via a function call tool. --- ...54b-context-summarization-manual-openai.py | 179 ++++++++++++++++++ 1 file changed, 179 insertions(+) create mode 100644 examples/foundational/54b-context-summarization-manual-openai.py diff --git a/examples/foundational/54b-context-summarization-manual-openai.py b/examples/foundational/54b-context-summarization-manual-openai.py new file mode 100644 index 000000000..e8acf4bf1 --- /dev/null +++ b/examples/foundational/54b-context-summarization-manual-openai.py @@ -0,0 +1,179 @@ +# +# Copyright (c) 2024-2026, Daily +# +# SPDX-License-Identifier: BSD 2-Clause License +# + +"""Example demonstrating manual context summarization via a function call. + +This example shows how to trigger context summarization on demand rather than +automatically. The user can ask the bot to "summarize the conversation" and the +bot will call a function that pushes an LLMSummarizeContextFrame into the +pipeline, causing the LLM service to compress the conversation history. + +Unlike example 54, automatic summarization is NOT enabled here. Summarization +only happens when the user explicitly requests it through the function call. +""" + +import os + +from dotenv import load_dotenv +from loguru import logger + +from pipecat.adapters.schemas.function_schema import FunctionSchema +from pipecat.adapters.schemas.tools_schema import ToolsSchema +from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3 +from pipecat.audio.vad.silero import SileroVADAnalyzer +from pipecat.audio.vad.vad_analyzer import VADParams +from pipecat.frames.frames import LLMRunFrame, LLMSummarizeContextFrame +from pipecat.pipeline.pipeline import Pipeline +from pipecat.pipeline.runner import PipelineRunner +from pipecat.pipeline.task import PipelineParams, PipelineTask +from pipecat.processors.aggregators.llm_context import LLMContext +from pipecat.processors.aggregators.llm_response_universal import ( + LLMContextAggregatorPair, + LLMUserAggregatorParams, +) +from pipecat.runner.types import RunnerArguments +from pipecat.runner.utils import create_transport +from pipecat.services.cartesia.tts import CartesiaTTSService +from pipecat.services.deepgram.stt import DeepgramSTTService +from pipecat.services.llm_service import FunctionCallParams +from pipecat.services.openai.llm import OpenAILLMService +from pipecat.transports.base_transport import BaseTransport, TransportParams +from pipecat.transports.daily.transport import DailyParams +from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams +from pipecat.turns.user_stop import TurnAnalyzerUserTurnStopStrategy +from pipecat.turns.user_turn_strategies import UserTurnStrategies + +load_dotenv(override=True) + +# We use lambdas to defer transport parameter creation until the transport +# type is selected at runtime. +transport_params = { + "daily": lambda: DailyParams( + audio_in_enabled=True, + audio_out_enabled=True, + ), + "twilio": lambda: FastAPIWebsocketParams( + audio_in_enabled=True, + audio_out_enabled=True, + ), + "webrtc": lambda: TransportParams( + audio_in_enabled=True, + audio_out_enabled=True, + ), +} + + +async def summarize_conversation(params: FunctionCallParams): + """Trigger manual context summarization via a pipeline frame.""" + logger.info("Tool called: summarize_conversation") + await params.result_callback({"status": "summarization_requested"}) + await params.llm.queue_frame(LLMSummarizeContextFrame()) + + +async def run_bot(transport: BaseTransport, runner_args: RunnerArguments): + logger.info("Starting bot") + + stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY")) + + tts = CartesiaTTSService( + api_key=os.getenv("CARTESIA_API_KEY"), + voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady + ) + + llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY")) + + llm.register_function("summarize_conversation", summarize_conversation) + + summarize_function = FunctionSchema( + name="summarize_conversation", + description=( + "Summarize and compress the conversation history. " + "Call this when the user asks you to summarize the conversation " + "or when you want to free up context space." + ), + properties={}, + required=[], + ) + tools = ToolsSchema(standard_tools=[summarize_function]) + + messages = [ + { + "role": "system", + "content": ( + "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your " + "capabilities in a succinct way. Your output will be spoken aloud, so avoid " + "special characters that can't easily be spoken, such as emojis or bullet points. " + "Respond to what the user said in a creative and helpful way. " + "If the user asks you to summarize the conversation, call the " + "summarize_conversation function. After summarization, briefly acknowledge " + "that the conversation history has been compressed." + ), + }, + ] + + context = LLMContext(messages, tools=tools) + + # Automatic summarization is NOT enabled here (enable_auto_context_summarization + # defaults to False). The summarizer is still created internally so that + # LLMSummarizeContextFrame frames pushed via the function call are handled. + user_aggregator, assistant_aggregator = LLMContextAggregatorPair( + context, + user_params=LLMUserAggregatorParams( + user_turn_strategies=UserTurnStrategies( + stop=[TurnAnalyzerUserTurnStopStrategy(turn_analyzer=LocalSmartTurnAnalyzerV3())] + ), + vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)), + ), + ) + + pipeline = Pipeline( + [ + transport.input(), # Transport user input + stt, + user_aggregator, # User responses + llm, # LLM + tts, # TTS + transport.output(), # Transport bot output + assistant_aggregator, # Assistant spoken responses + ] + ) + + task = PipelineTask( + pipeline, + params=PipelineParams( + enable_metrics=True, + enable_usage_metrics=True, + ), + idle_timeout_secs=runner_args.pipeline_idle_timeout_secs, + ) + + @transport.event_handler("on_client_connected") + async def on_client_connected(transport, client): + logger.info("Client connected") + # Kick off the conversation. + messages.append({"role": "system", "content": "Please introduce yourself to the user."}) + await task.queue_frames([LLMRunFrame()]) + + @transport.event_handler("on_client_disconnected") + async def on_client_disconnected(transport, client): + logger.info("Client disconnected") + await task.cancel() + + runner = PipelineRunner(handle_sigint=runner_args.handle_sigint) + + await runner.run(task) + + +async def bot(runner_args: RunnerArguments): + """Main bot entry point compatible with Pipecat Cloud.""" + transport = await create_transport(runner_args, transport_params) + await run_bot(transport, runner_args) + + +if __name__ == "__main__": + from pipecat.runner.run import main + + main()