diff --git a/examples/foundational/54-context-summarization-openai.py b/examples/foundational/54-context-summarization-openai.py index 652a3af13..45f27854f 100644 --- a/examples/foundational/54-context-summarization-openai.py +++ b/examples/foundational/54-context-summarization-openai.py @@ -20,14 +20,13 @@ 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 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, @@ -42,8 +41,6 @@ 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 from pipecat.utils.context.llm_context_summarization import LLMContextSummarizationConfig load_dotenv(override=True) @@ -120,10 +117,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments): 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)), + vad_analyzer=SileroVADAnalyzer(), ), assistant_params=LLMAssistantAggregatorParams( enable_context_summarization=True, @@ -138,6 +132,19 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments): ), ) + # Listen for summarization events + summarizer = assistant_aggregator._summarizer + if summarizer: + + @summarizer.event_handler("on_summary_applied") + async def on_summary_applied(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 diff --git a/examples/foundational/54a-context-summarization-google.py b/examples/foundational/54a-context-summarization-google.py index a7fe4ba5e..2ce29e959 100644 --- a/examples/foundational/54a-context-summarization-google.py +++ b/examples/foundational/54a-context-summarization-google.py @@ -20,14 +20,13 @@ 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 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, @@ -42,8 +41,6 @@ from pipecat.services.llm_service import FunctionCallParams 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 from pipecat.utils.context.llm_context_summarization import LLMContextSummarizationConfig load_dotenv(override=True) @@ -120,10 +117,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments): 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)), + vad_analyzer=SileroVADAnalyzer(), ), assistant_params=LLMAssistantAggregatorParams( enable_context_summarization=True, @@ -138,6 +132,19 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments): ), ) + # Listen for summarization events + summarizer = assistant_aggregator._summarizer + if summarizer: + + @summarizer.event_handler("on_summary_applied") + async def on_summary_applied(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 diff --git a/examples/foundational/54c-context-summarization-dedicated-llm.py b/examples/foundational/54c-context-summarization-dedicated-llm.py new file mode 100644 index 000000000..3b2195e80 --- /dev/null +++ b/examples/foundational/54c-context-summarization-dedicated-llm.py @@ -0,0 +1,231 @@ +# +# 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 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 LLMContextSummarizationConfig + +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"), + voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady + ) + + # Primary LLM for conversation (could be any provider) + llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY")) + + # Dedicated cheap/fast LLM for summarization only + summarization_llm = GoogleLLMService( + api_key=os.getenv("GOOGLE_API_KEY"), + 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]) + + 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. 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.\n\n" + "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." + ), + }, + ] + + context = LLMContext(messages, tools=tools) + + # Create aggregators with custom summarization + user_aggregator, assistant_aggregator = LLMContextAggregatorPair( + context, + user_params=LLMUserAggregatorParams( + vad_analyzer=SileroVADAnalyzer(), + ), + assistant_params=LLMAssistantAggregatorParams( + enable_context_summarization=True, + context_summarization_config=LLMContextSummarizationConfig( + # Trigger thresholds (low values to demonstrate quickly) + max_context_tokens=1000, + max_unsummarized_messages=10, + # 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 + summarizer = assistant_aggregator._summarizer + if summarizer: + + @summarizer.event_handler("on_summary_applied") + async def on_summary_applied(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. + 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()