989fb4deaa
Only treat messages[0] as the initial system prompt when determining the summarization range. Previously, the code scanned the entire context for the first system-role message, which caused failures when the only system message was a mid-conversation injection (e.g. "The user has been quiet"). In that case summary_start exceeded summary_end, producing an empty range and "No messages to summarize" errors. Fixes #4286
1231 lines
51 KiB
Python
1231 lines
51 KiB
Python
#
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# Copyright (c) 2024–2025, Daily
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#
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# SPDX-License-Identifier: BSD 2-Clause License
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#
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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
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from pipecat.frames.frames import LLMContextSummaryRequestFrame
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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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LLMAutoContextSummarizationConfig,
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LLMContextSummarizationConfig,
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LLMContextSummarizationUtil,
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LLMContextSummaryConfig,
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)
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class TestContextSummarizationMixin(unittest.TestCase):
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"""Tests for LLMContextSummarizationUtil."""
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def test_estimate_tokens_simple_text(self):
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"""Test token estimation with simple text."""
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# Simple sentence: "Hello world" = 11 chars / 4 = 2.75 -> 2 tokens
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tokens = LLMContextSummarizationUtil.estimate_tokens("Hello world")
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self.assertEqual(tokens, 2)
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# More words: "This is a test message" = 22 chars / 4 = 5.5 -> 5 tokens
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tokens = LLMContextSummarizationUtil.estimate_tokens("This is a test message")
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self.assertEqual(tokens, 5)
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def test_estimate_tokens_empty(self):
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"""Test token estimation with empty text."""
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tokens = LLMContextSummarizationUtil.estimate_tokens("")
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self.assertEqual(tokens, 0)
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def test_estimate_context_tokens(self):
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"""Test context token estimation."""
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context = LLMContext()
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# Empty context
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self.assertEqual(LLMContextSummarizationUtil.estimate_context_tokens(context), 0)
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# Add messages
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context.add_message({"role": "system", "content": "You are helpful"}) # ~4 words
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context.add_message({"role": "user", "content": "Hello"}) # ~1 word
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context.add_message({"role": "assistant", "content": "Hi there"}) # ~2 words
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# Each message has ~10 token overhead
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# Total content: ~7 words * 1.3 = ~9 tokens
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# Total overhead: 3 * 10 = 30 tokens
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# Expected: ~39 tokens
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total = LLMContextSummarizationUtil.estimate_context_tokens(context)
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self.assertGreater(total, 30) # At least overhead
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self.assertLess(total, 50) # Not too much
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def test_get_messages_to_summarize_basic(self):
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"""Test basic message extraction for summarization."""
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context = LLMContext()
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# Add messages
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context.add_message({"role": "system", "content": "System prompt"})
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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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context.add_message({"role": "assistant", "content": "Response 2"})
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context.add_message({"role": "user", "content": "Message 3"})
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context.add_message({"role": "assistant", "content": "Response 3"})
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# Keep last 2 messages
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result = LLMContextSummarizationUtil.get_messages_to_summarize(context, 2)
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# Get first system message from context
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first_system = None
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for msg in context.messages:
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if msg.get("role") == "system":
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first_system = msg
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break
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# Should get system message
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self.assertIsNotNone(first_system)
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self.assertEqual(first_system["content"], "System prompt")
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# Should get middle messages (indices 1-4)
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self.assertEqual(len(result.messages), 4)
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self.assertEqual(result.messages[0]["content"], "Message 1")
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self.assertEqual(result.messages[-1]["content"], "Response 2")
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# Last index should be 4 (0-indexed)
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self.assertEqual(result.last_summarized_index, 4)
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def test_get_messages_to_summarize_no_system(self):
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"""Test message extraction when there's no system message."""
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context = LLMContext()
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# Add messages without system prompt
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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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context.add_message({"role": "assistant", "content": "Response 2"})
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# Keep last 1 message
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result = LLMContextSummarizationUtil.get_messages_to_summarize(context, 1)
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# Get first system message from context
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first_system = None
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for msg in context.messages:
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if msg.get("role") == "system":
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first_system = msg
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break
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# Should have no system message
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self.assertIsNone(first_system)
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# Should get first 3 messages
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self.assertEqual(len(result.messages), 3)
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self.assertEqual(result.last_summarized_index, 2)
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def test_get_messages_to_summarize_insufficient(self):
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"""Test when there aren't enough messages to summarize."""
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context = LLMContext()
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# Add only 2 messages
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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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# Try to keep 2 messages (same as total)
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result = LLMContextSummarizationUtil.get_messages_to_summarize(context, 2)
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# Should return empty
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self.assertEqual(len(result.messages), 0)
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self.assertEqual(result.last_summarized_index, -1)
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def test_get_messages_to_summarize_mid_conversation_system_message(self):
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"""Test that a system message mid-conversation is included in summarization.
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Regression test for #4286: when the only system message is a mid-conversation
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injection (not at index 0), get_messages_to_summarize should start from index 0,
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not after the mid-conversation system message.
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"""
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context = LLMContext()
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# No system message at index 0 (using system_instruction instead)
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context.add_message({"role": "user", "content": "Hello"})
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context.add_message({"role": "assistant", "content": "Hi there"})
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context.add_message({"role": "user", "content": "Tell me a joke"})
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context.add_message({"role": "assistant", "content": "Why did the chicken..."})
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# Mid-conversation system injection (e.g. "The user has been quiet")
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context.add_message({"role": "system", "content": "The user has been quiet for a while"})
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context.add_message({"role": "user", "content": "Latest message"})
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context.add_message({"role": "assistant", "content": "Latest response"})
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# Keep last 2 messages
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result = LLMContextSummarizationUtil.get_messages_to_summarize(context, 2)
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# Should summarize from index 0 (no system preamble to skip)
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# through index 4, keeping last 2 messages
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self.assertEqual(len(result.messages), 5)
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self.assertEqual(result.messages[0]["content"], "Hello")
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# The mid-conversation system message should be included in summarization
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self.assertEqual(result.messages[4]["content"], "The user has been quiet for a while")
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self.assertEqual(result.last_summarized_index, 4)
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def test_get_messages_to_summarize_system_at_index_0_with_mid_system(self):
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"""Test that only messages[0] system message is preserved, not later ones."""
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context = LLMContext()
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context.add_message({"role": "system", "content": "You are helpful"})
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context.add_message({"role": "user", "content": "Hello"})
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context.add_message({"role": "assistant", "content": "Hi"})
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# Mid-conversation system injection
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context.add_message({"role": "system", "content": "The user seems frustrated"})
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context.add_message({"role": "user", "content": "Help me"})
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context.add_message({"role": "assistant", "content": "Sure"})
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# Keep last 2 messages
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result = LLMContextSummarizationUtil.get_messages_to_summarize(context, 2)
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# Should skip index 0 (system preamble), summarize indices 1-3
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self.assertEqual(len(result.messages), 3)
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self.assertEqual(result.messages[0]["content"], "Hello")
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# Mid-conversation system message is summarized, not treated as preamble
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self.assertEqual(result.messages[2]["content"], "The user seems frustrated")
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self.assertEqual(result.last_summarized_index, 3)
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def test_format_messages_for_summary(self):
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"""Test message formatting for summary."""
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messages = [
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{"role": "user", "content": "Hello"},
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{"role": "assistant", "content": "Hi there"},
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{"role": "user", "content": "How are you?"},
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]
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transcript = LLMContextSummarizationUtil.format_messages_for_summary(messages)
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self.assertIn("USER: Hello", transcript)
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self.assertIn("ASSISTANT: Hi there", transcript)
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self.assertIn("USER: How are you?", transcript)
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def test_format_messages_with_list_content(self):
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"""Test formatting messages with list content."""
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messages = [
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{
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"role": "user",
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"content": [
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{"type": "text", "text": "First part"},
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{"type": "text", "text": "Second part"},
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],
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}
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]
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transcript = LLMContextSummarizationUtil.format_messages_for_summary(messages)
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self.assertIn("USER: First part Second part", transcript)
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class TestLLMContextSummaryConfig(unittest.TestCase):
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"""Tests for LLMContextSummaryConfig."""
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def test_default_config(self):
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"""Test default configuration values."""
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config = LLMContextSummaryConfig()
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self.assertEqual(config.target_context_tokens, 6000)
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self.assertEqual(config.min_messages_after_summary, 4)
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self.assertIsNone(config.summarization_prompt)
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def test_custom_config(self):
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"""Test custom configuration."""
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config = LLMContextSummaryConfig(
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target_context_tokens=2000,
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min_messages_after_summary=4,
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summarization_prompt="Custom prompt",
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)
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self.assertEqual(config.target_context_tokens, 2000)
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self.assertEqual(config.min_messages_after_summary, 4)
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self.assertEqual(config.summary_prompt, "Custom prompt")
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def test_summary_prompt_property(self):
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"""Test summary_prompt property uses default when None."""
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config = LLMContextSummaryConfig()
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self.assertIn("summarizing a conversation", config.summary_prompt.lower())
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config_with_custom = LLMContextSummaryConfig(summarization_prompt="Custom")
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self.assertEqual(config_with_custom.summary_prompt, "Custom")
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class TestLLMAutoContextSummarizationConfig(unittest.TestCase):
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"""Tests for LLMAutoContextSummarizationConfig."""
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def test_default_config(self):
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"""Test default configuration values."""
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config = LLMAutoContextSummarizationConfig()
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self.assertEqual(config.max_context_tokens, 8000)
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self.assertEqual(config.max_unsummarized_messages, 20)
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self.assertEqual(config.summary_config.target_context_tokens, 6000)
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self.assertEqual(config.summary_config.min_messages_after_summary, 4)
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def test_custom_config(self):
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"""Test custom configuration."""
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config = LLMAutoContextSummarizationConfig(
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max_context_tokens=2500,
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max_unsummarized_messages=15,
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summary_config=LLMContextSummaryConfig(
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target_context_tokens=2000,
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min_messages_after_summary=4,
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summarization_prompt="Custom prompt",
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),
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)
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self.assertEqual(config.max_context_tokens, 2500)
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self.assertEqual(config.max_unsummarized_messages, 15)
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self.assertEqual(config.summary_config.target_context_tokens, 2000)
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self.assertEqual(config.summary_config.min_messages_after_summary, 4)
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self.assertEqual(config.summary_config.summary_prompt, "Custom prompt")
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def test_target_tokens_auto_adjusted(self):
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"""Test that target_context_tokens is auto-adjusted when it exceeds max."""
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config = LLMAutoContextSummarizationConfig(
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max_context_tokens=1000,
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summary_config=LLMContextSummaryConfig(target_context_tokens=9000),
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)
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self.assertLessEqual(config.summary_config.target_context_tokens, config.max_context_tokens)
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def test_max_context_tokens_none(self):
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"""Test that max_context_tokens can be None when max_unsummarized_messages is set."""
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config = LLMAutoContextSummarizationConfig(
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max_context_tokens=None,
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max_unsummarized_messages=20,
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)
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self.assertIsNone(config.max_context_tokens)
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self.assertEqual(config.max_unsummarized_messages, 20)
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def test_max_unsummarized_messages_none(self):
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"""Test that max_unsummarized_messages can be None when max_context_tokens is set."""
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config = LLMAutoContextSummarizationConfig(
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max_context_tokens=8000,
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max_unsummarized_messages=None,
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)
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self.assertEqual(config.max_context_tokens, 8000)
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self.assertIsNone(config.max_unsummarized_messages)
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def test_both_none_raises(self):
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"""Test that setting both thresholds to None raises ValueError."""
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with self.assertRaises(ValueError) as cm:
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LLMAutoContextSummarizationConfig(
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max_context_tokens=None,
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max_unsummarized_messages=None,
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)
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self.assertIn("at least one", str(cm.exception).lower())
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def test_target_tokens_not_auto_adjusted_when_max_none(self):
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"""Test that target_context_tokens is not auto-adjusted when max_context_tokens is None."""
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config = LLMAutoContextSummarizationConfig(
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max_context_tokens=None,
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max_unsummarized_messages=10,
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summary_config=LLMContextSummaryConfig(target_context_tokens=9000),
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)
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# target_context_tokens should remain unchanged since there's no max to compare against
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self.assertEqual(config.summary_config.target_context_tokens, 9000)
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class TestLLMContextSummarizationConfigDeprecated(unittest.TestCase):
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"""Tests for deprecated LLMContextSummarizationConfig."""
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def test_emits_deprecation_warning(self):
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"""Test that instantiating the deprecated config emits a DeprecationWarning."""
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with self.assertWarns(DeprecationWarning):
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LLMContextSummarizationConfig()
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def test_to_auto_config(self):
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"""Test conversion to the new LLMAutoContextSummarizationConfig."""
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import warnings
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with warnings.catch_warnings():
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warnings.simplefilter("ignore", DeprecationWarning)
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old_config = LLMContextSummarizationConfig(
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max_context_tokens=2500,
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target_context_tokens=2000,
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max_unsummarized_messages=15,
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min_messages_after_summary=4,
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summarization_prompt="Custom",
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)
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new_config = old_config.to_auto_config()
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self.assertIsInstance(new_config, LLMAutoContextSummarizationConfig)
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self.assertEqual(new_config.max_context_tokens, 2500)
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self.assertEqual(new_config.max_unsummarized_messages, 15)
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self.assertEqual(new_config.summary_config.target_context_tokens, 2000)
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self.assertEqual(new_config.summary_config.min_messages_after_summary, 4)
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self.assertEqual(new_config.summary_config.summarization_prompt, "Custom")
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class TestFunctionCallHandling(unittest.TestCase):
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"""Tests for function call handling in summarization."""
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def test_function_call_in_progress_not_summarized(self):
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"""Test that messages with function calls in progress are not summarized."""
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context = LLMContext()
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# Add messages including a function call without result
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context.add_message({"role": "system", "content": "System prompt"})
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context.add_message({"role": "user", "content": "What time is it?"})
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context.add_message(
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{
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"role": "assistant",
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"content": "",
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"tool_calls": [
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{
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"id": "call_123",
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"type": "function",
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"function": {"name": "get_time", "arguments": "{}"},
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}
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],
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}
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)
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# No tool result yet - function call is in progress
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context.add_message({"role": "user", "content": "Latest message"})
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# Try to keep last 1 message
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result = LLMContextSummarizationUtil.get_messages_to_summarize(context, 1)
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# Should only get the first user message, stopping before the function call
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self.assertEqual(len(result.messages), 1)
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self.assertEqual(result.messages[0]["content"], "What time is it?")
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self.assertEqual(result.last_summarized_index, 1)
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def test_completed_function_call_can_be_summarized(self):
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"""Test that completed function calls can be summarized."""
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context = LLMContext()
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# Add messages including a complete function call sequence
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context.add_message({"role": "system", "content": "System prompt"})
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context.add_message({"role": "user", "content": "What time is it?"})
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context.add_message(
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{
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"role": "assistant",
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"content": "",
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"tool_calls": [
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{
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"id": "call_123",
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"type": "function",
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"function": {"name": "get_time", "arguments": "{}"},
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}
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],
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}
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)
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# Tool result completes the function call
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context.add_message(
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{"role": "tool", "tool_call_id": "call_123", "content": '{"time": "10:30 AM"}'}
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)
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context.add_message({"role": "assistant", "content": "It's 10:30 AM"})
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context.add_message({"role": "user", "content": "Latest message"})
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# Try to keep last 1 message
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result = LLMContextSummarizationUtil.get_messages_to_summarize(context, 1)
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# Should get all messages except the last one (complete function call is included)
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self.assertEqual(len(result.messages), 4)
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self.assertEqual(result.messages[0]["content"], "What time is it?")
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self.assertEqual(result.messages[-1]["content"], "It's 10:30 AM")
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self.assertEqual(result.last_summarized_index, 4)
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def test_multiple_function_calls_in_progress(self):
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"""Test handling of multiple function calls in progress."""
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context = LLMContext()
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|
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# Add messages with multiple function calls
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context.add_message({"role": "system", "content": "System prompt"})
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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": "What's the time and date?"})
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context.add_message(
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{
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"role": "assistant",
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"content": "",
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"tool_calls": [
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{
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"id": "call_time",
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"type": "function",
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"function": {"name": "get_time", "arguments": "{}"},
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},
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{
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"id": "call_date",
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"type": "function",
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"function": {"name": "get_date", "arguments": "{}"},
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},
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],
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}
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)
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# Only one tool result - other call still in progress
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context.add_message(
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{"role": "tool", "tool_call_id": "call_time", "content": '{"time": "10:30 AM"}'}
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)
|
||
context.add_message({"role": "user", "content": "Latest message"})
|
||
|
||
# Try to keep last 1 message
|
||
result = LLMContextSummarizationUtil.get_messages_to_summarize(context, 1)
|
||
|
||
# Should stop before the function call that's in progress
|
||
# Messages to summarize: indices 1, 2, 3 (stops before index 4 where incomplete call is)
|
||
self.assertEqual(len(result.messages), 3)
|
||
self.assertEqual(result.last_summarized_index, 3)
|
||
|
||
def test_multiple_completed_function_calls(self):
|
||
"""Test that multiple completed function calls can be summarized."""
|
||
context = LLMContext()
|
||
|
||
# Add messages with multiple completed function calls
|
||
context.add_message({"role": "system", "content": "System prompt"})
|
||
context.add_message({"role": "user", "content": "What's the time and date?"})
|
||
context.add_message(
|
||
{
|
||
"role": "assistant",
|
||
"content": "",
|
||
"tool_calls": [
|
||
{
|
||
"id": "call_time",
|
||
"type": "function",
|
||
"function": {"name": "get_time", "arguments": "{}"},
|
||
},
|
||
{
|
||
"id": "call_date",
|
||
"type": "function",
|
||
"function": {"name": "get_date", "arguments": "{}"},
|
||
},
|
||
],
|
||
}
|
||
)
|
||
# Both tool results provided
|
||
context.add_message(
|
||
{"role": "tool", "tool_call_id": "call_time", "content": '{"time": "10:30 AM"}'}
|
||
)
|
||
context.add_message(
|
||
{
|
||
"role": "tool",
|
||
"tool_call_id": "call_date",
|
||
"content": '{"date": "January 1, 2024"}',
|
||
}
|
||
)
|
||
context.add_message({"role": "assistant", "content": "It's 10:30 AM on January 1, 2024"})
|
||
context.add_message({"role": "user", "content": "Latest message"})
|
||
|
||
# Try to keep last 1 message
|
||
result = LLMContextSummarizationUtil.get_messages_to_summarize(context, 1)
|
||
|
||
# Should get all messages except the last one (all function calls completed)
|
||
self.assertEqual(len(result.messages), 5)
|
||
self.assertEqual(result.last_summarized_index, 5)
|
||
|
||
def test_sequential_function_calls_mixed_completion(self):
|
||
"""Test sequential function calls with mixed completion states."""
|
||
context = LLMContext()
|
||
|
||
# Add messages with sequential function calls
|
||
context.add_message({"role": "system", "content": "System prompt"})
|
||
|
||
# First function call - completed
|
||
context.add_message({"role": "user", "content": "What time is it?"})
|
||
context.add_message(
|
||
{
|
||
"role": "assistant",
|
||
"content": "",
|
||
"tool_calls": [
|
||
{
|
||
"id": "call_1",
|
||
"type": "function",
|
||
"function": {"name": "get_time", "arguments": "{}"},
|
||
}
|
||
],
|
||
}
|
||
)
|
||
context.add_message(
|
||
{"role": "tool", "tool_call_id": "call_1", "content": '{"time": "10:30 AM"}'}
|
||
)
|
||
context.add_message({"role": "assistant", "content": "It's 10:30 AM"})
|
||
|
||
# Second function call - in progress
|
||
context.add_message({"role": "user", "content": "What's the date?"})
|
||
context.add_message(
|
||
{
|
||
"role": "assistant",
|
||
"content": "",
|
||
"tool_calls": [
|
||
{
|
||
"id": "call_2",
|
||
"type": "function",
|
||
"function": {"name": "get_date", "arguments": "{}"},
|
||
}
|
||
],
|
||
}
|
||
)
|
||
# No result for call_2 yet
|
||
context.add_message({"role": "user", "content": "Latest message"})
|
||
|
||
# Try to keep last 1 message
|
||
result = LLMContextSummarizationUtil.get_messages_to_summarize(context, 1)
|
||
|
||
# Should get messages up to and including the first completed function call
|
||
# but stop before the second function call that's in progress
|
||
# Messages to summarize: indices 1, 2, 3, 4, 5 (stops before index 6 where incomplete call is)
|
||
self.assertEqual(len(result.messages), 5)
|
||
self.assertEqual(result.messages[-1]["content"], "What's the date?")
|
||
self.assertEqual(result.last_summarized_index, 5)
|
||
|
||
def test_function_call_formatting_in_transcript(self):
|
||
"""Test that function calls are properly formatted in transcript."""
|
||
|
||
messages = [
|
||
{"role": "user", "content": "What time is it?"},
|
||
{
|
||
"role": "assistant",
|
||
"content": "",
|
||
"tool_calls": [
|
||
{
|
||
"id": "call_123",
|
||
"type": "function",
|
||
"function": {"name": "get_time", "arguments": "{}"},
|
||
}
|
||
],
|
||
},
|
||
{"role": "tool", "tool_call_id": "call_123", "content": '{"time": "10:30 AM"}'},
|
||
{"role": "assistant", "content": "It's 10:30 AM"},
|
||
]
|
||
|
||
transcript = LLMContextSummarizationUtil.format_messages_for_summary(messages)
|
||
|
||
# Check that function call is included
|
||
self.assertIn("TOOL_CALL: get_time({})", transcript)
|
||
# Check that tool result is included
|
||
self.assertIn('TOOL_RESULT[call_123]: {"time": "10:30 AM"}', transcript)
|
||
|
||
def test_no_function_calls(self):
|
||
"""Test that summarization works normally without function calls."""
|
||
context = LLMContext()
|
||
|
||
# Add normal conversation without function calls
|
||
context.add_message({"role": "system", "content": "System prompt"})
|
||
context.add_message({"role": "user", "content": "Hello"})
|
||
context.add_message({"role": "assistant", "content": "Hi"})
|
||
context.add_message({"role": "user", "content": "How are you?"})
|
||
context.add_message({"role": "assistant", "content": "I'm good"})
|
||
context.add_message({"role": "user", "content": "Latest message"})
|
||
|
||
# Try to keep last 1 message
|
||
result = LLMContextSummarizationUtil.get_messages_to_summarize(context, 1)
|
||
|
||
# Should get all messages except the last one
|
||
self.assertEqual(len(result.messages), 4)
|
||
self.assertEqual(result.last_summarized_index, 4)
|
||
|
||
|
||
class TestSummaryGenerationExceptions(unittest.IsolatedAsyncioTestCase):
|
||
"""Tests for summary generation exception handling."""
|
||
|
||
async def test_generate_summary_raises_on_no_messages(self):
|
||
"""Test that _generate_summary raises RuntimeError when there are no messages to summarize."""
|
||
llm_service = LLMService()
|
||
context = LLMContext()
|
||
|
||
# Add only one message (system), which isn't enough to summarize
|
||
context.add_message({"role": "system", "content": "System prompt"})
|
||
|
||
frame = LLMContextSummaryRequestFrame(
|
||
request_id="test",
|
||
context=context,
|
||
min_messages_to_keep=1,
|
||
target_context_tokens=1000,
|
||
summarization_prompt="Summarize this",
|
||
)
|
||
|
||
with self.assertRaises(RuntimeError) as cm:
|
||
await llm_service._generate_summary(frame)
|
||
|
||
self.assertEqual(str(cm.exception), "No messages to summarize")
|
||
|
||
async def test_generate_summary_raises_on_no_run_inference(self):
|
||
"""Test that _generate_summary raises RuntimeError when run_inference is not implemented."""
|
||
# Create a minimal LLM service - base class raises NotImplementedError
|
||
llm_service = LLMService()
|
||
|
||
context = LLMContext()
|
||
context.add_message({"role": "user", "content": "Message 1"})
|
||
context.add_message({"role": "assistant", "content": "Response 1"})
|
||
context.add_message({"role": "user", "content": "Message 2"})
|
||
|
||
frame = LLMContextSummaryRequestFrame(
|
||
request_id="test",
|
||
context=context,
|
||
min_messages_to_keep=1,
|
||
target_context_tokens=1000,
|
||
summarization_prompt="Summarize this",
|
||
)
|
||
|
||
with self.assertRaises(RuntimeError) as cm:
|
||
await llm_service._generate_summary(frame)
|
||
|
||
self.assertIn("does not implement run_inference", str(cm.exception))
|
||
self.assertIn("LLMService", str(cm.exception))
|
||
|
||
async def test_generate_summary_raises_on_empty_response(self):
|
||
"""Test that _generate_summary raises RuntimeError when LLM returns empty summary."""
|
||
llm_service = LLMService()
|
||
# Mock run_inference to return None
|
||
llm_service.run_inference = AsyncMock(return_value=None)
|
||
|
||
context = LLMContext()
|
||
context.add_message({"role": "user", "content": "Message 1"})
|
||
context.add_message({"role": "assistant", "content": "Response 1"})
|
||
context.add_message({"role": "user", "content": "Message 2"})
|
||
|
||
frame = LLMContextSummaryRequestFrame(
|
||
request_id="test",
|
||
context=context,
|
||
min_messages_to_keep=1,
|
||
target_context_tokens=1000,
|
||
summarization_prompt="Summarize this",
|
||
)
|
||
|
||
with self.assertRaises(RuntimeError) as cm:
|
||
await llm_service._generate_summary(frame)
|
||
|
||
self.assertEqual(str(cm.exception), "LLM returned empty summary")
|
||
|
||
async def test_generate_summary_task_handles_exceptions(self):
|
||
"""Test that _generate_summary_task properly handles exceptions from _generate_summary."""
|
||
llm_service = LLMService()
|
||
|
||
# Mock broadcast_frame to capture the result
|
||
broadcast_calls = []
|
||
|
||
async def mock_broadcast(frame_class, **kwargs):
|
||
broadcast_calls.append((frame_class, kwargs))
|
||
|
||
llm_service.broadcast_frame = mock_broadcast
|
||
|
||
# Mock push_error
|
||
llm_service.push_error = AsyncMock()
|
||
|
||
context = LLMContext()
|
||
context.add_message({"role": "system", "content": "System prompt"})
|
||
|
||
frame = LLMContextSummaryRequestFrame(
|
||
request_id="test_123",
|
||
context=context,
|
||
min_messages_to_keep=1,
|
||
target_context_tokens=1000,
|
||
summarization_prompt="Summarize this",
|
||
)
|
||
|
||
# Execute the task
|
||
await llm_service._generate_summary_task(frame)
|
||
|
||
# Verify broadcast_frame was called with error
|
||
self.assertEqual(len(broadcast_calls), 1)
|
||
frame_class, kwargs = broadcast_calls[0]
|
||
self.assertEqual(kwargs["request_id"], "test_123")
|
||
self.assertEqual(kwargs["summary"], "")
|
||
self.assertEqual(kwargs["last_summarized_index"], -1)
|
||
self.assertEqual(
|
||
kwargs["error"], "Error generating context summary: No messages to summarize"
|
||
)
|
||
|
||
# Verify push_error was called
|
||
llm_service.push_error.assert_called_once()
|
||
|
||
async def test_generate_summary_success(self):
|
||
"""Test that _generate_summary returns successfully with valid input."""
|
||
llm_service = LLMService()
|
||
# Mock run_inference to return a summary
|
||
llm_service.run_inference = AsyncMock(return_value="This is a summary of the conversation")
|
||
|
||
context = LLMContext()
|
||
context.add_message({"role": "user", "content": "Message 1"})
|
||
context.add_message({"role": "assistant", "content": "Response 1"})
|
||
context.add_message({"role": "user", "content": "Message 2"})
|
||
|
||
frame = LLMContextSummaryRequestFrame(
|
||
request_id="test",
|
||
context=context,
|
||
min_messages_to_keep=1,
|
||
target_context_tokens=1000,
|
||
summarization_prompt="Summarize this",
|
||
)
|
||
|
||
summary, last_index = await llm_service._generate_summary(frame)
|
||
|
||
self.assertEqual(summary, "This is a summary of the conversation")
|
||
self.assertGreater(last_index, -1)
|
||
self.assertEqual(last_index, 1) # Should be the index of the last summarized message
|
||
|
||
async def test_generate_summary_task_timeout(self):
|
||
"""Test that _generate_summary_task handles timeout correctly."""
|
||
llm_service = LLMService()
|
||
|
||
# Mock _generate_summary to hang
|
||
async def slow_summary(frame):
|
||
await asyncio.sleep(10)
|
||
return ("summary", 1)
|
||
|
||
llm_service._generate_summary = slow_summary
|
||
|
||
broadcast_calls = []
|
||
|
||
async def mock_broadcast(frame_class, **kwargs):
|
||
broadcast_calls.append((frame_class, kwargs))
|
||
|
||
llm_service.broadcast_frame = mock_broadcast
|
||
llm_service.push_error = AsyncMock()
|
||
|
||
context = LLMContext()
|
||
context.add_message({"role": "user", "content": "Message 1"})
|
||
context.add_message({"role": "assistant", "content": "Response 1"})
|
||
context.add_message({"role": "user", "content": "Message 2"})
|
||
|
||
frame = LLMContextSummaryRequestFrame(
|
||
request_id="timeout_test",
|
||
context=context,
|
||
min_messages_to_keep=1,
|
||
target_context_tokens=1000,
|
||
summarization_prompt="Summarize this",
|
||
summarization_timeout=0.1, # Very short timeout
|
||
)
|
||
|
||
await llm_service._generate_summary_task(frame)
|
||
|
||
# Should have broadcast an error result
|
||
self.assertEqual(len(broadcast_calls), 1)
|
||
_, kwargs = broadcast_calls[0]
|
||
self.assertEqual(kwargs["request_id"], "timeout_test")
|
||
self.assertEqual(kwargs["summary"], "")
|
||
self.assertEqual(kwargs["last_summarized_index"], -1)
|
||
# error is None for timeout path (push_error is called instead)
|
||
self.assertIsNone(kwargs["error"])
|
||
|
||
# push_error should have been called with timeout message
|
||
llm_service.push_error.assert_called_once()
|
||
call_args = llm_service.push_error.call_args
|
||
error_msg = call_args.kwargs.get("error_msg") or call_args.args[0]
|
||
self.assertIn("timed out", error_msg)
|
||
|
||
|
||
class TestDedicatedLLMSummarization(unittest.IsolatedAsyncioTestCase):
|
||
"""Tests for dedicated LLM summarization in LLMContextSummarizer."""
|
||
|
||
async def asyncSetUp(self):
|
||
from pipecat.utils.asyncio.task_manager import TaskManager, TaskManagerParams
|
||
|
||
self.task_manager = TaskManager()
|
||
self.task_manager.setup(TaskManagerParams(loop=asyncio.get_running_loop()))
|
||
|
||
def _create_context_and_config(self, dedicated_llm):
|
||
"""Create a context with enough messages and a config with a dedicated LLM."""
|
||
context = LLMContext()
|
||
for i in range(10):
|
||
context.add_message(
|
||
{"role": "user", "content": f"Test message {i} that adds tokens to context."}
|
||
)
|
||
|
||
config = LLMAutoContextSummarizationConfig(
|
||
max_context_tokens=50, # Very low to trigger easily
|
||
summary_config=LLMContextSummaryConfig(
|
||
llm=dedicated_llm,
|
||
summarization_timeout=5.0,
|
||
),
|
||
)
|
||
return context, config
|
||
|
||
async def test_dedicated_llm_success(self):
|
||
"""Test that dedicated LLM generates summary and applies result."""
|
||
from pipecat.processors.aggregators.llm_context_summarizer import LLMContextSummarizer
|
||
|
||
dedicated_llm = LLMService()
|
||
dedicated_llm._generate_summary = AsyncMock(return_value=("Dedicated summary", 5))
|
||
|
||
context, config = self._create_context_and_config(dedicated_llm)
|
||
original_message_count = len(context.messages)
|
||
summarizer = LLMContextSummarizer(context=context, config=config)
|
||
await summarizer.setup(self.task_manager)
|
||
|
||
# Track whether on_request_summarization event fires (it should NOT)
|
||
event_fired = False
|
||
|
||
@summarizer.event_handler("on_request_summarization")
|
||
async def on_request_summarization(summarizer, frame):
|
||
nonlocal event_fired
|
||
event_fired = True
|
||
|
||
# Trigger summarization via LLM response start
|
||
from pipecat.frames.frames import LLMFullResponseStartFrame
|
||
|
||
await summarizer.process_frame(LLMFullResponseStartFrame())
|
||
|
||
# Wait for the background task to complete
|
||
await asyncio.sleep(0.1)
|
||
|
||
# The event should NOT have fired (dedicated LLM handles it internally)
|
||
self.assertFalse(event_fired)
|
||
|
||
# Verify the dedicated LLM was called
|
||
dedicated_llm._generate_summary.assert_called_once()
|
||
|
||
# Verify summary was applied to context (message count should decrease)
|
||
self.assertLess(len(context.messages), original_message_count)
|
||
|
||
# Verify summary message is present
|
||
summary_messages = [
|
||
msg for msg in context.messages if "Conversation summary:" in msg.get("content", "")
|
||
]
|
||
self.assertEqual(len(summary_messages), 1)
|
||
self.assertIn("Dedicated summary", summary_messages[0]["content"])
|
||
|
||
await summarizer.cleanup()
|
||
|
||
async def test_dedicated_llm_timeout(self):
|
||
"""Test that dedicated LLM timeout produces error and clears state."""
|
||
from pipecat.processors.aggregators.llm_context_summarizer import LLMContextSummarizer
|
||
|
||
dedicated_llm = LLMService()
|
||
|
||
async def slow_summary(frame):
|
||
await asyncio.sleep(10)
|
||
return ("summary", 1)
|
||
|
||
dedicated_llm._generate_summary = slow_summary
|
||
|
||
context, config = self._create_context_and_config(dedicated_llm)
|
||
config.summary_config.summarization_timeout = 0.1 # Very short timeout
|
||
summarizer = LLMContextSummarizer(context=context, config=config)
|
||
await summarizer.setup(self.task_manager)
|
||
|
||
original_message_count = len(context.messages)
|
||
|
||
# Trigger summarization
|
||
from pipecat.frames.frames import LLMFullResponseStartFrame
|
||
|
||
await summarizer.process_frame(LLMFullResponseStartFrame())
|
||
|
||
# Wait for the background task to complete (timeout + some buffer)
|
||
await asyncio.sleep(0.3)
|
||
|
||
# Context should be unchanged (timeout = error = no summary applied)
|
||
self.assertEqual(len(context.messages), original_message_count)
|
||
|
||
# Summarization state should be cleared so new requests can be made
|
||
self.assertFalse(summarizer._summarization_in_progress)
|
||
|
||
await summarizer.cleanup()
|
||
|
||
async def test_dedicated_llm_exception(self):
|
||
"""Test that dedicated LLM exceptions produce error and clear state."""
|
||
from pipecat.processors.aggregators.llm_context_summarizer import LLMContextSummarizer
|
||
|
||
dedicated_llm = LLMService()
|
||
dedicated_llm._generate_summary = AsyncMock(
|
||
side_effect=RuntimeError("LLM connection failed")
|
||
)
|
||
|
||
context, config = self._create_context_and_config(dedicated_llm)
|
||
summarizer = LLMContextSummarizer(context=context, config=config)
|
||
await summarizer.setup(self.task_manager)
|
||
|
||
original_message_count = len(context.messages)
|
||
|
||
# Trigger summarization
|
||
from pipecat.frames.frames import LLMFullResponseStartFrame
|
||
|
||
await summarizer.process_frame(LLMFullResponseStartFrame())
|
||
|
||
# Wait for the background task to complete
|
||
await asyncio.sleep(0.1)
|
||
|
||
# Context should be unchanged (exception = error = no summary applied)
|
||
self.assertEqual(len(context.messages), original_message_count)
|
||
|
||
# Summarization state should be cleared
|
||
self.assertFalse(summarizer._summarization_in_progress)
|
||
|
||
await summarizer.cleanup()
|
||
|
||
async def test_dedicated_llm_does_not_emit_event(self):
|
||
"""Test that summarizer does NOT emit on_request_summarization when dedicated LLM is set."""
|
||
from pipecat.processors.aggregators.llm_context_summarizer import LLMContextSummarizer
|
||
|
||
dedicated_llm = LLMService()
|
||
dedicated_llm._generate_summary = AsyncMock(return_value=("Summary", 1))
|
||
|
||
context, config = self._create_context_and_config(dedicated_llm)
|
||
summarizer = LLMContextSummarizer(context=context, config=config)
|
||
await summarizer.setup(self.task_manager)
|
||
|
||
event_fired = False
|
||
|
||
@summarizer.event_handler("on_request_summarization")
|
||
async def on_request_summarization(summarizer, frame):
|
||
nonlocal event_fired
|
||
event_fired = True
|
||
|
||
from pipecat.frames.frames import LLMFullResponseStartFrame
|
||
|
||
await summarizer.process_frame(LLMFullResponseStartFrame())
|
||
await asyncio.sleep(0.1)
|
||
|
||
self.assertFalse(event_fired)
|
||
|
||
await summarizer.cleanup()
|
||
|
||
async def test_no_dedicated_llm_emits_event(self):
|
||
"""Test that summarizer emits on_request_summarization when no dedicated LLM."""
|
||
from pipecat.processors.aggregators.llm_context_summarizer import LLMContextSummarizer
|
||
|
||
context = LLMContext()
|
||
for i in range(10):
|
||
context.add_message(
|
||
{"role": "user", "content": f"Test message {i} that adds tokens to context."}
|
||
)
|
||
|
||
config = LLMAutoContextSummarizationConfig(max_context_tokens=50)
|
||
summarizer = LLMContextSummarizer(context=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
|
||
|
||
from pipecat.frames.frames import LLMFullResponseStartFrame
|
||
|
||
await summarizer.process_frame(LLMFullResponseStartFrame())
|
||
|
||
self.assertIsNotNone(request_frame)
|
||
self.assertIsInstance(request_frame, LLMContextSummaryRequestFrame)
|
||
|
||
await summarizer.cleanup()
|
||
|
||
|
||
class TestOrphanedToolResponseDetection(unittest.TestCase):
|
||
"""Tests that tool responses in the kept range are treated as orphans.
|
||
|
||
The scan in _get_function_calls_in_progress_index is bounded by summary_end,
|
||
so a tool response that falls in the kept portion (>= summary_end) never
|
||
resolves its matching tool call. This ensures the assistant+tool_calls
|
||
message and all its responses stay together in the kept range.
|
||
"""
|
||
|
||
def test_tool_response_in_kept_range_is_treated_as_orphan(self):
|
||
"""Tool response in the kept range causes the tool call to be kept too."""
|
||
context = LLMContext()
|
||
context.add_message({"role": "system", "content": "System prompt"}) # idx 0
|
||
context.add_message({"role": "user", "content": "Hello"}) # idx 1
|
||
context.add_message( # idx 2: assistant with tool_call
|
||
{
|
||
"role": "assistant",
|
||
"content": "",
|
||
"tool_calls": [
|
||
{
|
||
"id": "call_1",
|
||
"type": "function",
|
||
"function": {"name": "fn", "arguments": "{}"},
|
||
}
|
||
],
|
||
}
|
||
)
|
||
context.add_message(
|
||
{"role": "tool", "tool_call_id": "call_1", "content": "result"}
|
||
) # idx 3 (kept)
|
||
context.add_message({"role": "user", "content": "Thanks"}) # idx 4 (kept)
|
||
|
||
# Keep 2: summary_end=3. The tool response at idx 3 is outside the scan
|
||
# range → call_1 stays pending → boundary moves back to idx 2.
|
||
result = LLMContextSummarizationUtil.get_messages_to_summarize(context, 2)
|
||
self.assertEqual(result.last_summarized_index, 1)
|
||
self.assertEqual(result.messages[-1]["content"], "Hello")
|
||
|
||
def test_tool_response_in_summarized_range_is_not_orphan(self):
|
||
"""Tool response within the summarized range correctly resolves its call."""
|
||
context = LLMContext()
|
||
context.add_message({"role": "system", "content": "System prompt"}) # idx 0
|
||
context.add_message({"role": "user", "content": "Hello"}) # idx 1
|
||
context.add_message( # idx 2: assistant with tool_call
|
||
{
|
||
"role": "assistant",
|
||
"content": "",
|
||
"tool_calls": [
|
||
{
|
||
"id": "call_1",
|
||
"type": "function",
|
||
"function": {"name": "fn", "arguments": "{}"},
|
||
}
|
||
],
|
||
}
|
||
)
|
||
context.add_message(
|
||
{"role": "tool", "tool_call_id": "call_1", "content": "result"}
|
||
) # idx 3
|
||
context.add_message({"role": "assistant", "content": "Done"}) # idx 4
|
||
context.add_message({"role": "user", "content": "Thanks"}) # idx 5 (kept)
|
||
|
||
# Keep 1: summary_end=5. Both the tool call (idx 2) and its response
|
||
# (idx 3) are within the scan range → resolved → no adjustment.
|
||
result = LLMContextSummarizationUtil.get_messages_to_summarize(context, 1)
|
||
self.assertEqual(result.last_summarized_index, 4)
|
||
self.assertEqual(len(result.messages), 4)
|
||
|
||
def test_partial_responses_in_kept_range_moves_back(self):
|
||
"""When only some tool responses are in the kept range the whole group is kept."""
|
||
context = LLMContext()
|
||
context.add_message({"role": "system", "content": "System prompt"}) # idx 0
|
||
context.add_message({"role": "user", "content": "Hello"}) # idx 1
|
||
context.add_message( # idx 2: assistant with two tool_calls
|
||
{
|
||
"role": "assistant",
|
||
"content": "",
|
||
"tool_calls": [
|
||
{
|
||
"id": "call_a",
|
||
"type": "function",
|
||
"function": {"name": "fn_a", "arguments": "{}"},
|
||
},
|
||
{
|
||
"id": "call_b",
|
||
"type": "function",
|
||
"function": {"name": "fn_b", "arguments": "{}"},
|
||
},
|
||
],
|
||
}
|
||
)
|
||
context.add_message(
|
||
{"role": "tool", "tool_call_id": "call_a", "content": "result_a"}
|
||
) # idx 3
|
||
context.add_message(
|
||
{"role": "tool", "tool_call_id": "call_b", "content": "result_b"}
|
||
) # idx 4 (kept)
|
||
context.add_message({"role": "user", "content": "Thanks"}) # idx 5 (kept)
|
||
|
||
# Keep 2: summary_end=4. call_a is resolved (idx 3 is in scan range) but
|
||
# call_b's response (idx 4) is outside → call_b stays pending →
|
||
# function_call_start=2 → boundary moves back to idx 2.
|
||
result = LLMContextSummarizationUtil.get_messages_to_summarize(context, 2)
|
||
self.assertEqual(result.last_summarized_index, 1)
|
||
self.assertEqual(result.messages[-1]["content"], "Hello")
|
||
|
||
def test_non_adjacent_orphan_in_kept_range_moves_back(self):
|
||
"""Orphaned tool response deeper in the kept range (not at the boundary) is detected."""
|
||
context = LLMContext()
|
||
context.add_message({"role": "system", "content": "System prompt"}) # idx 0
|
||
context.add_message({"role": "user", "content": "Hello"}) # idx 1
|
||
context.add_message( # idx 2: assistant with two tool_calls
|
||
{
|
||
"role": "assistant",
|
||
"content": "",
|
||
"tool_calls": [
|
||
{
|
||
"id": "call_a",
|
||
"type": "function",
|
||
"function": {"name": "fn_a", "arguments": "{}"},
|
||
},
|
||
{
|
||
"id": "call_b",
|
||
"type": "function",
|
||
"function": {"name": "fn_b", "arguments": "{}"},
|
||
},
|
||
],
|
||
}
|
||
)
|
||
context.add_message(
|
||
{"role": "tool", "tool_call_id": "call_a", "content": "result_a"}
|
||
) # idx 3
|
||
context.add_message({"role": "user", "content": "Intermediate"}) # idx 4 (kept)
|
||
context.add_message(
|
||
{"role": "tool", "tool_call_id": "call_b", "content": "result_b"}
|
||
) # idx 5 (kept) — NOT adjacent to the boundary
|
||
context.add_message({"role": "user", "content": "Latest"}) # idx 6 (kept)
|
||
|
||
# Keep 3: summary_end=4. call_b's response is at idx 5, two hops into
|
||
# the kept range. The scan stops at idx 4, so call_b is never resolved →
|
||
# function_call_start=2 → boundary moves back to idx 2.
|
||
result = LLMContextSummarizationUtil.get_messages_to_summarize(context, 3)
|
||
self.assertEqual(result.last_summarized_index, 1)
|
||
self.assertEqual(result.messages[-1]["content"], "Hello")
|
||
|
||
|
||
class TestLLMSpecificMessageHandling(unittest.TestCase):
|
||
"""Tests that LLMSpecificMessage objects are correctly skipped in summarization."""
|
||
|
||
def test_estimate_context_tokens_skips_specific_messages(self):
|
||
"""Test that estimate_context_tokens skips LLMSpecificMessage objects."""
|
||
context = LLMContext()
|
||
context.add_message({"role": "user", "content": "Hello"})
|
||
context.add_message(LLMSpecificMessage(llm="google", message={}))
|
||
context.add_message({"role": "assistant", "content": "Hi there"})
|
||
|
||
tokens_with_specific = LLMContextSummarizationUtil.estimate_context_tokens(context)
|
||
|
||
context_without = LLMContext()
|
||
context_without.add_message({"role": "user", "content": "Hello"})
|
||
context_without.add_message({"role": "assistant", "content": "Hi there"})
|
||
tokens_without = LLMContextSummarizationUtil.estimate_context_tokens(context_without)
|
||
|
||
self.assertEqual(tokens_with_specific, tokens_without)
|
||
|
||
def test_get_messages_to_summarize_with_specific_messages(self):
|
||
"""Test that get_messages_to_summarize handles LLMSpecificMessage objects."""
|
||
context = LLMContext()
|
||
context.add_message({"role": "system", "content": "System prompt"})
|
||
context.add_message(LLMSpecificMessage(llm="google", message={}))
|
||
context.add_message({"role": "user", "content": "Message 1"})
|
||
context.add_message({"role": "assistant", "content": "Response 1"})
|
||
context.add_message(LLMSpecificMessage(llm="google", message={}))
|
||
context.add_message({"role": "user", "content": "Message 2"})
|
||
context.add_message({"role": "assistant", "content": "Response 2"})
|
||
|
||
result = LLMContextSummarizationUtil.get_messages_to_summarize(context, 2)
|
||
|
||
self.assertEqual(len(result.messages), 4)
|
||
self.assertEqual(result.last_summarized_index, 4)
|
||
|
||
def test_format_messages_skips_specific_messages(self):
|
||
"""Test that format_messages_for_summary skips LLMSpecificMessage objects."""
|
||
messages = [
|
||
{"role": "user", "content": "Hello"},
|
||
LLMSpecificMessage(llm="google", message={}),
|
||
{"role": "assistant", "content": "Hi there"},
|
||
]
|
||
|
||
transcript = LLMContextSummarizationUtil.format_messages_for_summary(messages)
|
||
|
||
self.assertIn("USER: Hello", transcript)
|
||
self.assertIn("ASSISTANT: Hi there", transcript)
|
||
|
||
def test_function_call_tracking_skips_specific_messages(self):
|
||
"""Test that _get_function_calls_in_progress_index skips LLMSpecificMessage."""
|
||
messages = [
|
||
{"role": "user", "content": "What time is it?"},
|
||
LLMSpecificMessage(llm="google", message={}),
|
||
{
|
||
"role": "assistant",
|
||
"content": "",
|
||
"tool_calls": [
|
||
{
|
||
"id": "call_123",
|
||
"type": "function",
|
||
"function": {"name": "get_time", "arguments": "{}"},
|
||
}
|
||
],
|
||
},
|
||
LLMSpecificMessage(llm="google", message={}),
|
||
{"role": "tool", "tool_call_id": "call_123", "content": '{"time": "10:30 AM"}'},
|
||
]
|
||
|
||
result = LLMContextSummarizationUtil._get_earliest_function_call_not_resolved_in_range(
|
||
messages, 0, len(messages)
|
||
)
|
||
self.assertEqual(result, -1)
|
||
|
||
|
||
if __name__ == "__main__":
|
||
unittest.main()
|