Added example 54b-context-summarization-manual-openai.py demonstrating on-demand summarization triggered via a function call tool.

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()