Add example 54c showing custom context summarization

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