pipecat/examples/context-summarization/context-summarization-dedicated-llm.py

#
# 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 <context_summary> 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="<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
    @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()