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Add example 54c showing custom context summarization

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This commit is contained in:
Mark Backman
2026-02-26 21:45:22 -05:00
parent be8ea818c8
commit 712305c5b1
3 changed files with 261 additions and 16 deletions
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23
examples/foundational/54-context-summarization-openai.py
View File

@@ -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

23
examples/foundational/54a-context-summarization-google.py
View File

@@ -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

231
examples/foundational/54c-context-summarization-dedicated-llm.py Normal file
View File

@@ -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 <context_summary> 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="<context_summary>\n{summary}\n</context_summary>",
# 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()