# # Copyright (c) 2024-2026, Daily # # SPDX-License-Identifier: BSD 2-Clause License # """Example demonstrating advanced context summarization configuration. This example shows how to customize context summarization with: - A dedicated cheap/fast LLM for generating summaries (Gemini Flash) - A custom summary message template (XML tags) - A custom summarization prompt - A summarization timeout - The on_summary_applied event for observability """ import asyncio 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.vad.silero import SileroVADAnalyzer from pipecat.frames.frames import LLMRunFrame 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_context_summarizer import SummaryAppliedEvent from pipecat.processors.aggregators.llm_response_universal import ( LLMAssistantAggregatorParams, 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.google.llm import GoogleLLMService 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.utils.context.llm_context_summarization import ( LLMAutoContextSummarizationConfig, LLMContextSummaryConfig, ) 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, ), } # Custom summarization prompt tailored to the application CUSTOM_SUMMARIZATION_PROMPT = """Summarize this conversation, preserving: - Key decisions and agreements - Important facts and user preferences - Any pending action items or unresolved questions Be concise. Use clear, factual statements grouped by topic. Omit greetings, small talk, and resolved tangents.""" # Tool functions for the LLM async def get_current_weather(params: FunctionCallParams): """Get the current weather.""" logger.info("Tool called: get_current_weather") await asyncio.sleep(1) # Simulate some processing await params.result_callback({"conditions": "nice", "temperature": "75"}) 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"), settings=CartesiaTTSService.Settings( voice="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady ), ) system_prompt = """You are a helpful LLM in a voice 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. You have access to tools to get the current weather - use them when relevant. When you see a block, it contains a compressed summary of earlier conversation. Use it as reference but don't mention it to the user. """ # Primary LLM for conversation (could be any provider) llm = OpenAILLMService( api_key=os.getenv("OPENAI_API_KEY"), settings=OpenAILLMService.Settings( system_instruction=system_prompt, ), ) # Dedicated cheap/fast LLM for summarization only summarization_llm = GoogleLLMService( api_key=os.getenv("GOOGLE_API_KEY"), settings=GoogleLLMService.Settings( model="gemini-2.5-flash", ), ) # Register tool functions llm.register_function("get_current_weather", get_current_weather) weather_function = FunctionSchema( name="get_current_weather", description="Get the current weather", properties={ "location": { "type": "string", "description": "The city and state, e.g. San Francisco, CA", }, "format": { "type": "string", "enum": ["celsius", "fahrenheit"], "description": "The temperature unit to use. Infer this from the user's location.", }, }, required=["location", "format"], ) tools = ToolsSchema(standard_tools=[weather_function]) context = LLMContext(tools=tools) # Create aggregators with custom summarization user_aggregator, assistant_aggregator = LLMContextAggregatorPair( context, user_params=LLMUserAggregatorParams( vad_analyzer=SileroVADAnalyzer(), ), assistant_params=LLMAssistantAggregatorParams( enable_auto_context_summarization=True, auto_context_summarization_config=LLMAutoContextSummarizationConfig( # Trigger thresholds (low values to demonstrate quickly) max_context_tokens=1000, max_unsummarized_messages=10, summary_config=LLMContextSummaryConfig( # Summary generation target_context_tokens=800, min_messages_after_summary=2, summarization_prompt=CUSTOM_SUMMARIZATION_PROMPT, # Custom summary format - wrap in XML tags so the system # prompt can identify summaries vs. live conversation summary_message_template="\n{summary}\n", # Use a dedicated cheap LLM for summarization instead of # the primary conversation model llm=summarization_llm, # Cancel summarization if it takes longer than 60 seconds summarization_timeout=60.0, ), ), ), ) # Listen for summarization events @assistant_aggregator.event_handler("on_summary_applied") async def on_summary_applied(aggregator, summarizer, event: SummaryAppliedEvent): logger.info( f"Context summarized: {event.original_message_count} messages -> " f"{event.new_message_count} messages " f"({event.summarized_message_count} summarized, " f"{event.preserved_message_count} preserved)" ) 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. context.add_message( {"role": "developer", "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()