Add tests for context summarization improvements
Cover summary message role, template, on_summary_applied event, summarization timeout, and dedicated LLM routing/error handling.
This commit is contained in:
Mark Backman
@@ -6,10 +6,11 @@
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"""Tests for context summarization feature."""
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import asyncio
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import unittest
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from unittest.mock import AsyncMock, MagicMock, patch
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from unittest.mock import AsyncMock
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from pipecat.frames.frames import LLMContextSummaryRequestFrame
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from pipecat.frames.frames import LLMContextSummaryRequestFrame, LLMContextSummaryResultFrame
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from pipecat.processors.aggregators.llm_context import LLMContext, LLMSpecificMessage
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from pipecat.services.llm_service import LLMService
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from pipecat.utils.context.llm_context_summarization import (
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@@ -601,6 +602,301 @@ class TestSummaryGenerationExceptions(unittest.IsolatedAsyncioTestCase):
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self.assertGreater(last_index, -1)
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self.assertEqual(last_index, 1) # Should be the index of the last summarized message
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async def test_generate_summary_task_timeout(self):
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"""Test that _generate_summary_task handles timeout correctly."""
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llm_service = LLMService()
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# Mock _generate_summary to hang
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async def slow_summary(frame):
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await asyncio.sleep(10)
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return ("summary", 1)
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llm_service._generate_summary = slow_summary
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broadcast_calls = []
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async def mock_broadcast(frame_class, **kwargs):
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broadcast_calls.append((frame_class, kwargs))
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llm_service.broadcast_frame = mock_broadcast
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llm_service.push_error = AsyncMock()
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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="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, # Very short timeout
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)
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await llm_service._generate_summary_task(frame)
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# Should have broadcast an error result
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self.assertEqual(len(broadcast_calls), 1)
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_, kwargs = broadcast_calls[0]
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self.assertEqual(kwargs["request_id"], "timeout_test")
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self.assertEqual(kwargs["summary"], "")
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self.assertEqual(kwargs["last_summarized_index"], -1)
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# error is None for timeout path (push_error is called instead)
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self.assertIsNone(kwargs["error"])
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# push_error should have been called with timeout message
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llm_service.push_error.assert_called_once()
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call_args = llm_service.push_error.call_args
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error_msg = call_args.kwargs.get("error_msg") or call_args.args[0]
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self.assertIn("timed out", error_msg)
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class TestDedicatedLLMSummarization(unittest.IsolatedAsyncioTestCase):
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"""Tests for dedicated LLM summarization in LLMAssistantAggregator."""
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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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from pipecat.utils.asyncio.task_manager import TaskManager, TaskManagerParams
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context, frame = self._create_context_and_frame()
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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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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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# 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 capture_process(frame):
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result_frames.append(frame)
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await original_process(frame)
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aggregator._summarizer.process_frame = capture_process
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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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# Verify the dedicated LLM was called
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dedicated_llm._generate_summary.assert_called_once_with(frame)
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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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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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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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await asyncio.sleep(10)
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return ("summary", 1)
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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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# Mock summarizer.process_frame to capture the result
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result_frames = []
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async def capture_process(frame):
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result_frames.append(frame)
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aggregator._summarizer.process_frame = capture_process
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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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await aggregator._generate_summary_with_dedicated_llm(dedicated_llm, frame)
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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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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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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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# Mock summarizer.process_frame to capture the result
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result_frames = []
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async def capture_process(frame):
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result_frames.append(frame)
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aggregator._summarizer.process_frame = capture_process
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await aggregator._generate_summary_with_dedicated_llm(dedicated_llm, frame)
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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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# push_error should have been called
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aggregator.push_error.assert_called_once()
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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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context, frame = self._create_context_and_frame()
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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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# 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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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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aggregator.create_task = mock_create_task
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await aggregator._on_request_summarization(aggregator._summarizer, frame)
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# Should NOT push frame upstream
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aggregator.push_frame.assert_not_called()
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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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# Wait for the task to complete
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await asyncio.sleep(0.05)
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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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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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await aggregator._on_request_summarization(aggregator._summarizer, frame)
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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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class TestLLMSpecificMessageHandling(unittest.TestCase):
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"""Tests that LLMSpecificMessage objects are correctly skipped in summarization."""
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@@ -14,7 +14,10 @@ from pipecat.frames.frames import (
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LLMFullResponseStartFrame,
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)
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from pipecat.processors.aggregators.llm_context import LLMContext
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from pipecat.processors.aggregators.llm_context_summarizer import LLMContextSummarizer
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from pipecat.processors.aggregators.llm_context_summarizer import (
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LLMContextSummarizer,
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SummaryAppliedEvent,
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)
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from pipecat.utils.asyncio.task_manager import TaskManager, TaskManagerParams
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from pipecat.utils.context.llm_context_summarization import LLMContextSummarizationConfig
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@@ -291,6 +294,252 @@ class TestLLMContextSummarizer(unittest.IsolatedAsyncioTestCase):
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await summarizer.cleanup()
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async def test_summary_message_role_is_user(self):
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"""Test that the summary message uses the user role."""
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config = LLMContextSummarizationConfig(max_context_tokens=50, min_messages_after_summary=2)
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summarizer = LLMContextSummarizer(context=self.context, config=config)
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await summarizer.setup(self.task_manager)
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# Add messages and trigger summarization
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for i in range(10):
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self.context.add_message({"role": "user", "content": "Test message."})
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request_frame = None
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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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await summarizer.process_frame(LLMFullResponseStartFrame())
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self.assertIsNotNone(request_frame)
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# Simulate receiving a summary result
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summary_result = LLMContextSummaryResultFrame(
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request_id=request_frame.request_id,
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summary="This is a test summary.",
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last_summarized_index=5,
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)
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await summarizer.process_frame(summary_result)
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# Find the summary message and verify its role is "user"
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summary_msg = next(
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(msg for msg in self.context.messages if "summary" in msg.get("content", "").lower()),
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None,
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)
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self.assertIsNotNone(summary_msg)
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self.assertEqual(summary_msg["role"], "user")
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await summarizer.cleanup()
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async def test_summary_message_default_template(self):
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"""Test that the default summary_message_template is used."""
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config = LLMContextSummarizationConfig(max_context_tokens=50, min_messages_after_summary=2)
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summarizer = LLMContextSummarizer(context=self.context, config=config)
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await summarizer.setup(self.task_manager)
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for i in range(10):
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self.context.add_message({"role": "user", "content": "Test message."})
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request_frame = None
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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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await summarizer.process_frame(LLMFullResponseStartFrame())
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summary_result = LLMContextSummaryResultFrame(
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request_id=request_frame.request_id,
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summary="Key facts from conversation.",
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last_summarized_index=5,
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)
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await summarizer.process_frame(summary_result)
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# Default template wraps with "Conversation summary: {summary}"
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summary_msg = next(
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(
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msg
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for msg in self.context.messages
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if "Conversation summary:" in msg.get("content", "")
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),
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None,
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)
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self.assertIsNotNone(summary_msg)
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self.assertEqual(
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summary_msg["content"], "Conversation summary: Key facts from conversation."
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)
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await summarizer.cleanup()
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async def test_summary_message_custom_template(self):
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"""Test that a custom summary_message_template is applied."""
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config = LLMContextSummarizationConfig(
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max_context_tokens=50,
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min_messages_after_summary=2,
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summary_message_template="<context_summary>\n{summary}\n</context_summary>",
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)
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summarizer = LLMContextSummarizer(context=self.context, config=config)
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await summarizer.setup(self.task_manager)
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for i in range(10):
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self.context.add_message({"role": "user", "content": "Test message."})
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request_frame = None
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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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await summarizer.process_frame(LLMFullResponseStartFrame())
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summary_result = LLMContextSummaryResultFrame(
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request_id=request_frame.request_id,
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summary="Key facts from conversation.",
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last_summarized_index=5,
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)
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await summarizer.process_frame(summary_result)
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# Custom template wraps with XML tags
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summary_msg = next(
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(msg for msg in self.context.messages if "<context_summary>" in msg.get("content", "")),
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None,
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)
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self.assertIsNotNone(summary_msg)
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self.assertEqual(
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summary_msg["content"],
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"<context_summary>\nKey facts from conversation.\n</context_summary>",
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)
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await summarizer.cleanup()
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async def test_on_summary_applied_event(self):
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"""Test that on_summary_applied event fires with correct data."""
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config = LLMContextSummarizationConfig(max_context_tokens=50, min_messages_after_summary=2)
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summarizer = LLMContextSummarizer(context=self.context, config=config)
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await summarizer.setup(self.task_manager)
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# Add messages (1 system + 10 user = 11 total)
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for i in range(10):
|
||||
self.context.add_message({"role": "user", "content": "Test message."})
|
||||
|
||||
request_frame = None
|
||||
applied_event = None
|
||||
|
||||
@summarizer.event_handler("on_request_summarization")
|
||||
async def on_request_summarization(summarizer, frame):
|
||||
nonlocal request_frame
|
||||
request_frame = frame
|
||||
|
||||
@summarizer.event_handler("on_summary_applied")
|
||||
async def on_summary_applied(summarizer, event):
|
||||
nonlocal applied_event
|
||||
applied_event = event
|
||||
|
||||
original_count = len(self.context.messages) # 11
|
||||
await summarizer.process_frame(LLMFullResponseStartFrame())
|
||||
|
||||
# Summarize up to index 7 (system=0, user1..user7), keep last 3 (user8, user9, user10)
|
||||
summary_result = LLMContextSummaryResultFrame(
|
||||
request_id=request_frame.request_id,
|
||||
summary="Test summary.",
|
||||
last_summarized_index=7,
|
||||
)
|
||||
await summarizer.process_frame(summary_result)
|
||||
|
||||
# Allow async event handler to complete
|
||||
await asyncio.sleep(0.05)
|
||||
|
||||
# Verify event was fired
|
||||
self.assertIsNotNone(applied_event)
|
||||
self.assertIsInstance(applied_event, SummaryAppliedEvent)
|
||||
self.assertEqual(applied_event.original_message_count, original_count)
|
||||
|
||||
# After summarization: system + summary + 3 recent = 5
|
||||
self.assertEqual(applied_event.new_message_count, 5)
|
||||
|
||||
# Summarized messages: indices 1-7 = 7 messages (excluding system at index 0)
|
||||
self.assertEqual(applied_event.summarized_message_count, 7)
|
||||
|
||||
# Preserved: system (1) + recent messages after index 7 (3) = 4
|
||||
self.assertEqual(applied_event.preserved_message_count, 4)
|
||||
|
||||
await summarizer.cleanup()
|
||||
|
||||
async def test_on_summary_applied_not_fired_on_error(self):
|
||||
"""Test that on_summary_applied event is NOT fired when summarization fails."""
|
||||
config = LLMContextSummarizationConfig(max_context_tokens=50, min_messages_after_summary=2)
|
||||
|
||||
summarizer = LLMContextSummarizer(context=self.context, config=config)
|
||||
await summarizer.setup(self.task_manager)
|
||||
|
||||
for i in range(10):
|
||||
self.context.add_message({"role": "user", "content": "Test message."})
|
||||
|
||||
request_frame = None
|
||||
applied_event = None
|
||||
|
||||
@summarizer.event_handler("on_request_summarization")
|
||||
async def on_request_summarization(summarizer, frame):
|
||||
nonlocal request_frame
|
||||
request_frame = frame
|
||||
|
||||
@summarizer.event_handler("on_summary_applied")
|
||||
async def on_summary_applied(summarizer, event):
|
||||
nonlocal applied_event
|
||||
applied_event = event
|
||||
|
||||
await summarizer.process_frame(LLMFullResponseStartFrame())
|
||||
|
||||
# Send a result with an error
|
||||
error_result = LLMContextSummaryResultFrame(
|
||||
request_id=request_frame.request_id,
|
||||
summary="",
|
||||
last_summarized_index=-1,
|
||||
error="Summarization timed out",
|
||||
)
|
||||
await summarizer.process_frame(error_result)
|
||||
|
||||
await asyncio.sleep(0.05)
|
||||
|
||||
# Event should NOT have fired
|
||||
self.assertIsNone(applied_event)
|
||||
|
||||
await summarizer.cleanup()
|
||||
|
||||
async def test_request_frame_includes_timeout(self):
|
||||
"""Test that the request frame includes the configured summarization_timeout."""
|
||||
config = LLMContextSummarizationConfig(
|
||||
max_context_tokens=50,
|
||||
summarization_timeout=60.0,
|
||||
)
|
||||
|
||||
summarizer = LLMContextSummarizer(context=self.context, config=config)
|
||||
await summarizer.setup(self.task_manager)
|
||||
|
||||
request_frame = None
|
||||
|
||||
@summarizer.event_handler("on_request_summarization")
|
||||
async def on_request_summarization(summarizer, frame):
|
||||
nonlocal request_frame
|
||||
request_frame = frame
|
||||
|
||||
for i in range(10):
|
||||
self.context.add_message({"role": "user", "content": "Test message to add tokens."})
|
||||
|
||||
await summarizer.process_frame(LLMFullResponseStartFrame())
|
||||
|
||||
self.assertIsNotNone(request_frame)
|
||||
self.assertEqual(request_frame.summarization_timeout, 60.0)
|
||||
|
||||
await summarizer.cleanup()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
unittest.main()
|
||||
|
||||
Reference in New Issue
Block a user