Move dedicated LLM summarization into LLMContextSummarizer
The dedicated LLM logic lived in LLMAssistantAggregator, creating two code paths and requiring the aggregator to call a private LLMService method. Move it into the summarizer which already owns the config and summarization lifecycle, keeping the aggregator handler as a single-line upstream push.
This commit is contained in:
Mark Backman
@@ -654,85 +654,79 @@ class TestSummaryGenerationExceptions(unittest.IsolatedAsyncioTestCase):
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class TestDedicatedLLMSummarization(unittest.IsolatedAsyncioTestCase):
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"""Tests for dedicated LLM summarization in LLMAssistantAggregator."""
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"""Tests for dedicated LLM summarization in LLMContextSummarizer."""
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def _create_context_and_frame(self):
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"""Create a context with enough messages and a matching request frame."""
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context = LLMContext()
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context.add_message({"role": "user", "content": "Message 1"})
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context.add_message({"role": "assistant", "content": "Response 1"})
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context.add_message({"role": "user", "content": "Message 2"})
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frame = LLMContextSummaryRequestFrame(
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request_id="dedicated_test",
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context=context,
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min_messages_to_keep=1,
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target_context_tokens=1000,
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summarization_prompt="Summarize this",
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summarization_timeout=5.0,
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)
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return context, frame
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async def test_dedicated_llm_success(self):
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"""Test that dedicated LLM generates summary and feeds result to summarizer."""
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from pipecat.processors.aggregators.llm_context_summarizer import LLMContextSummarizer
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from pipecat.processors.aggregators.llm_response_universal import (
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LLMAssistantAggregator,
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LLMAssistantAggregatorParams,
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)
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async def asyncSetUp(self):
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from pipecat.utils.asyncio.task_manager import TaskManager, TaskManagerParams
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context, frame = self._create_context_and_frame()
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self.task_manager = TaskManager()
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self.task_manager.setup(TaskManagerParams(loop=asyncio.get_running_loop()))
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# Create a mock dedicated LLM
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dedicated_llm = LLMService()
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dedicated_llm._generate_summary = AsyncMock(return_value=("Dedicated summary", 1))
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def _create_context_and_config(self, dedicated_llm):
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"""Create a context with enough messages and a config with a dedicated LLM."""
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context = LLMContext()
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for i in range(10):
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context.add_message(
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{"role": "user", "content": f"Test message {i} that adds tokens to context."}
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)
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config = LLMContextSummarizationConfig(
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max_context_tokens=50,
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max_context_tokens=50, # Very low to trigger easily
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llm=dedicated_llm,
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summarization_timeout=5.0,
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)
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params = LLMAssistantAggregatorParams(
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enable_context_summarization=True,
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context_summarization_config=config,
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)
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aggregator = LLMAssistantAggregator(context, params=params)
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return context, config
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# Mock summarizer.process_frame to capture the result
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result_frames = []
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original_process = aggregator._summarizer.process_frame
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async def test_dedicated_llm_success(self):
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"""Test that dedicated LLM generates summary and applies result."""
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from pipecat.processors.aggregators.llm_context_summarizer import LLMContextSummarizer
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async def capture_process(frame):
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result_frames.append(frame)
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await original_process(frame)
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dedicated_llm = LLMService()
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dedicated_llm._generate_summary = AsyncMock(return_value=("Dedicated summary", 5))
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aggregator._summarizer.process_frame = capture_process
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context, config = self._create_context_and_config(dedicated_llm)
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original_message_count = len(context.messages)
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summarizer = LLMContextSummarizer(context=context, config=config)
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await summarizer.setup(self.task_manager)
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# Call the method directly
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await aggregator._generate_summary_with_dedicated_llm(dedicated_llm, frame)
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# Track whether on_request_summarization event fires (it should NOT)
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event_fired = False
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@summarizer.event_handler("on_request_summarization")
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async def on_request_summarization(summarizer, frame):
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nonlocal event_fired
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event_fired = True
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# Trigger summarization via LLM response start
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from pipecat.frames.frames import LLMFullResponseStartFrame
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await summarizer.process_frame(LLMFullResponseStartFrame())
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# Wait for the background task to complete
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await asyncio.sleep(0.1)
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# The event should NOT have fired (dedicated LLM handles it internally)
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self.assertFalse(event_fired)
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# Verify the dedicated LLM was called
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dedicated_llm._generate_summary.assert_called_once_with(frame)
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dedicated_llm._generate_summary.assert_called_once()
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# Verify result was fed to the summarizer
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self.assertEqual(len(result_frames), 1)
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result = result_frames[0]
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self.assertIsInstance(result, LLMContextSummaryResultFrame)
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self.assertEqual(result.request_id, "dedicated_test")
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self.assertEqual(result.summary, "Dedicated summary")
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self.assertEqual(result.last_summarized_index, 1)
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self.assertIsNone(result.error)
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# Verify summary was applied to context (message count should decrease)
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self.assertLess(len(context.messages), original_message_count)
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# Verify summary message is present
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summary_messages = [
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msg for msg in context.messages if "Conversation summary:" in msg.get("content", "")
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]
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self.assertEqual(len(summary_messages), 1)
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self.assertIn("Dedicated summary", summary_messages[0]["content"])
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await summarizer.cleanup()
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async def test_dedicated_llm_timeout(self):
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"""Test that dedicated LLM timeout produces error result."""
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from pipecat.processors.aggregators.llm_response_universal import (
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LLMAssistantAggregator,
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LLMAssistantAggregatorParams,
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)
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"""Test that dedicated LLM timeout produces error and clears state."""
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from pipecat.processors.aggregators.llm_context_summarizer import LLMContextSummarizer
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context, _ = self._create_context_and_frame()
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# Create a mock dedicated LLM that hangs
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dedicated_llm = LLMService()
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async def slow_summary(frame):
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@@ -741,161 +735,116 @@ class TestDedicatedLLMSummarization(unittest.IsolatedAsyncioTestCase):
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dedicated_llm._generate_summary = slow_summary
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config = LLMContextSummarizationConfig(
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max_context_tokens=50,
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llm=dedicated_llm,
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)
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params = LLMAssistantAggregatorParams(
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enable_context_summarization=True,
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context_summarization_config=config,
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)
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aggregator = LLMAssistantAggregator(context, params=params)
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context, config = self._create_context_and_config(dedicated_llm)
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config.summarization_timeout = 0.1 # Very short timeout
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summarizer = LLMContextSummarizer(context=context, config=config)
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await summarizer.setup(self.task_manager)
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# Mock summarizer.process_frame to capture the result
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result_frames = []
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original_message_count = len(context.messages)
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async def capture_process(frame):
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result_frames.append(frame)
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# Trigger summarization
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from pipecat.frames.frames import LLMFullResponseStartFrame
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aggregator._summarizer.process_frame = capture_process
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await summarizer.process_frame(LLMFullResponseStartFrame())
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# Create frame with very short timeout
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frame = LLMContextSummaryRequestFrame(
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request_id="timeout_test",
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context=context,
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min_messages_to_keep=1,
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target_context_tokens=1000,
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summarization_prompt="Summarize this",
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summarization_timeout=0.1,
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)
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# Wait for the background task to complete (timeout + some buffer)
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await asyncio.sleep(0.3)
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await aggregator._generate_summary_with_dedicated_llm(dedicated_llm, frame)
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# Context should be unchanged (timeout = error = no summary applied)
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self.assertEqual(len(context.messages), original_message_count)
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# Verify error result was fed to summarizer
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self.assertEqual(len(result_frames), 1)
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result = result_frames[0]
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self.assertIsInstance(result, LLMContextSummaryResultFrame)
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self.assertEqual(result.request_id, "timeout_test")
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self.assertEqual(result.summary, "")
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self.assertEqual(result.last_summarized_index, -1)
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self.assertIn("timed out", result.error)
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# Summarization state should be cleared so new requests can be made
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self.assertFalse(summarizer._summarization_in_progress)
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await summarizer.cleanup()
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async def test_dedicated_llm_exception(self):
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"""Test that dedicated LLM exceptions produce error result."""
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from pipecat.processors.aggregators.llm_response_universal import (
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LLMAssistantAggregator,
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LLMAssistantAggregatorParams,
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)
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"""Test that dedicated LLM exceptions produce error and clear state."""
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from pipecat.processors.aggregators.llm_context_summarizer import LLMContextSummarizer
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context, frame = self._create_context_and_frame()
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# Create a mock dedicated LLM that raises
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dedicated_llm = LLMService()
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dedicated_llm._generate_summary = AsyncMock(
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side_effect=RuntimeError("LLM connection failed")
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)
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config = LLMContextSummarizationConfig(
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max_context_tokens=50,
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llm=dedicated_llm,
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)
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params = LLMAssistantAggregatorParams(
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enable_context_summarization=True,
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context_summarization_config=config,
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)
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aggregator = LLMAssistantAggregator(context, params=params)
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aggregator.push_error = AsyncMock()
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context, config = self._create_context_and_config(dedicated_llm)
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summarizer = LLMContextSummarizer(context=context, config=config)
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await summarizer.setup(self.task_manager)
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# Mock summarizer.process_frame to capture the result
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result_frames = []
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original_message_count = len(context.messages)
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async def capture_process(frame):
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result_frames.append(frame)
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# Trigger summarization
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from pipecat.frames.frames import LLMFullResponseStartFrame
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aggregator._summarizer.process_frame = capture_process
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await summarizer.process_frame(LLMFullResponseStartFrame())
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await aggregator._generate_summary_with_dedicated_llm(dedicated_llm, frame)
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# Wait for the background task to complete
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await asyncio.sleep(0.1)
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# Verify error result was fed to summarizer
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self.assertEqual(len(result_frames), 1)
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result = result_frames[0]
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self.assertIsInstance(result, LLMContextSummaryResultFrame)
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self.assertEqual(result.request_id, "dedicated_test")
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self.assertEqual(result.summary, "")
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self.assertEqual(result.last_summarized_index, -1)
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self.assertIn("LLM connection failed", result.error)
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# Context should be unchanged (exception = error = no summary applied)
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self.assertEqual(len(context.messages), original_message_count)
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# push_error should have been called
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aggregator.push_error.assert_called_once()
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# Summarization state should be cleared
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self.assertFalse(summarizer._summarization_in_progress)
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async def test_on_request_summarization_routes_to_dedicated_llm(self):
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"""Test that _on_request_summarization routes to dedicated LLM when configured."""
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from pipecat.processors.aggregators.llm_response_universal import (
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LLMAssistantAggregator,
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LLMAssistantAggregatorParams,
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)
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await summarizer.cleanup()
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context, frame = self._create_context_and_frame()
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async def test_dedicated_llm_does_not_emit_event(self):
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"""Test that summarizer does NOT emit on_request_summarization when dedicated LLM is set."""
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from pipecat.processors.aggregators.llm_context_summarizer import LLMContextSummarizer
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dedicated_llm = LLMService()
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dedicated_llm._generate_summary = AsyncMock(return_value=("Summary", 1))
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config = LLMContextSummarizationConfig(
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max_context_tokens=50,
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llm=dedicated_llm,
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)
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params = LLMAssistantAggregatorParams(
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enable_context_summarization=True,
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context_summarization_config=config,
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)
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aggregator = LLMAssistantAggregator(context, params=params)
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aggregator.push_frame = AsyncMock()
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context, config = self._create_context_and_config(dedicated_llm)
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summarizer = LLMContextSummarizer(context=context, config=config)
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await summarizer.setup(self.task_manager)
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# Track what coroutine is passed to create_task
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created_coros = []
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original_create_task = aggregator.create_task
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event_fired = False
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def mock_create_task(coro, *args, **kwargs):
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created_coros.append(coro)
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# Actually run the coroutine to avoid "never awaited" warning
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task = asyncio.ensure_future(coro)
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return task
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@summarizer.event_handler("on_request_summarization")
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async def on_request_summarization(summarizer, frame):
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nonlocal event_fired
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event_fired = True
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aggregator.create_task = mock_create_task
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from pipecat.frames.frames import LLMFullResponseStartFrame
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await aggregator._on_request_summarization(aggregator._summarizer, frame)
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await summarizer.process_frame(LLMFullResponseStartFrame())
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await asyncio.sleep(0.1)
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# Should NOT push frame upstream
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aggregator.push_frame.assert_not_called()
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self.assertFalse(event_fired)
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# Should have created a task for the dedicated LLM
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self.assertEqual(len(created_coros), 1)
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await summarizer.cleanup()
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# Wait for the task to complete
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await asyncio.sleep(0.05)
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async def test_no_dedicated_llm_emits_event(self):
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"""Test that summarizer emits on_request_summarization when no dedicated LLM."""
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from pipecat.processors.aggregators.llm_context_summarizer import LLMContextSummarizer
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async def test_on_request_summarization_pushes_upstream_without_dedicated_llm(self):
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"""Test that _on_request_summarization pushes upstream when no dedicated LLM."""
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from pipecat.processors.aggregators.llm_response_universal import (
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LLMAssistantAggregator,
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LLMAssistantAggregatorParams,
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)
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from pipecat.processors.frame_processor import FrameDirection
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context, frame = self._create_context_and_frame()
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context = LLMContext()
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for i in range(10):
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context.add_message(
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{"role": "user", "content": f"Test message {i} that adds tokens to context."}
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)
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config = LLMContextSummarizationConfig(max_context_tokens=50)
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params = LLMAssistantAggregatorParams(
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enable_context_summarization=True,
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context_summarization_config=config,
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)
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aggregator = LLMAssistantAggregator(context, params=params)
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aggregator.push_frame = AsyncMock()
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summarizer = LLMContextSummarizer(context=context, config=config)
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await summarizer.setup(self.task_manager)
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await aggregator._on_request_summarization(aggregator._summarizer, frame)
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request_frame = None
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# Should push frame upstream
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aggregator.push_frame.assert_called_once_with(frame, FrameDirection.UPSTREAM)
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@summarizer.event_handler("on_request_summarization")
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async def on_request_summarization(summarizer, frame):
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nonlocal request_frame
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request_frame = frame
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from pipecat.frames.frames import LLMFullResponseStartFrame
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await summarizer.process_frame(LLMFullResponseStartFrame())
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self.assertIsNotNone(request_frame)
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self.assertIsInstance(request_frame, LLMContextSummaryRequestFrame)
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await summarizer.cleanup()
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class TestLLMSpecificMessageHandling(unittest.TestCase):
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