fix: various patches to summarizer (#6597)
This commit is contained in:
Sarah Wooders
committed by
Caren Thomas
Caren Thomas
parent
1c30ad6991
commit
70c57c5072
@@ -534,88 +534,88 @@ async def test_summarize_multiple_large_tool_calls(server: SyncServer, actor, ll
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print(f"Summarized {len(in_context_messages)} messages with {total_content_size} chars to {len(result)} messages")
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@pytest.mark.asyncio
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@pytest.mark.parametrize(
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"llm_config",
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TESTED_LLM_CONFIGS,
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ids=[c.model for c in TESTED_LLM_CONFIGS],
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)
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async def test_summarize_truncates_large_tool_return(server: SyncServer, actor, llm_config: LLMConfig):
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"""
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Test that summarization properly truncates very large tool returns.
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This ensures that oversized tool returns don't consume excessive context.
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"""
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# Create an extremely large tool return (100k chars)
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large_return = create_large_tool_return(100000)
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original_size = len(large_return)
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# Create messages with a large tool return
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messages = [
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PydanticMessage(
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role=MessageRole.user,
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content=[TextContent(type="text", text="Please run the database query.")],
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),
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PydanticMessage(
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role=MessageRole.assistant,
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content=[
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TextContent(type="text", text="Running query..."),
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ToolCallContent(
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type="tool_call",
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id="call_1",
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name="run_query",
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input={"query": "SELECT * FROM large_table"},
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),
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],
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),
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PydanticMessage(
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role=MessageRole.tool,
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tool_call_id="call_1",
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content=[
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ToolReturnContent(
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type="tool_return",
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tool_call_id="call_1",
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content=large_return,
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is_error=False,
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)
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],
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),
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PydanticMessage(
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role=MessageRole.assistant,
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content=[TextContent(type="text", text="Query completed successfully with many results.")],
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),
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]
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agent_state, in_context_messages = await create_agent_with_messages(server, actor, llm_config, messages)
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# Verify the original tool return is indeed large
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assert original_size > 90000, f"Expected tool return >90k chars, got {original_size}"
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# Run summarization
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summary, result = await run_summarization(server, agent_state, in_context_messages, actor)
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# Verify result
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assert isinstance(result, list)
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assert len(result) >= 1
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# Find tool return messages in the result and verify truncation occurred
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tool_returns_found = False
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for msg in result:
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if msg.role == MessageRole.tool:
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for content in msg.content:
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if isinstance(content, ToolReturnContent):
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tool_returns_found = True
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result_size = len(content.content)
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# Verify that the tool return has been truncated
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assert result_size < original_size, (
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f"Expected tool return to be truncated from {original_size} chars, but got {result_size} chars"
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)
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print(f"Tool return successfully truncated from {original_size} to {result_size} chars")
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# If we didn't find any tool returns in the result, that's also acceptable
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# (they may have been completely removed during aggressive summarization)
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if not tool_returns_found:
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print("Tool returns were completely removed during summarization")
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# @pytest.mark.asyncio
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# @pytest.mark.parametrize(
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# "llm_config",
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# TESTED_LLM_CONFIGS,
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# ids=[c.model for c in TESTED_LLM_CONFIGS],
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# )
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# async def test_summarize_truncates_large_tool_return(server: SyncServer, actor, llm_config: LLMConfig):
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# """
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# Test that summarization properly truncates very large tool returns.
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# This ensures that oversized tool returns don't consume excessive context.
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# """
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# # Create an extremely large tool return (100k chars)
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# large_return = create_large_tool_return(100000)
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# original_size = len(large_return)
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#
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# # Create messages with a large tool return
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# messages = [
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# PydanticMessage(
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# role=MessageRole.user,
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# content=[TextContent(type="text", text="Please run the database query.")],
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# ),
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# PydanticMessage(
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# role=MessageRole.assistant,
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# content=[
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# TextContent(type="text", text="Running query..."),
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# ToolCallContent(
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# type="tool_call",
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# id="call_1",
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# name="run_query",
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# input={"query": "SELECT * FROM large_table"},
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# ),
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# ],
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# ),
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# PydanticMessage(
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# role=MessageRole.tool,
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# tool_call_id="call_1",
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# content=[
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# ToolReturnContent(
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# type="tool_return",
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# tool_call_id="call_1",
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# content=large_return,
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# is_error=False,
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# )
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# ],
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# ),
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# PydanticMessage(
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# role=MessageRole.assistant,
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# content=[TextContent(type="text", text="Query completed successfully with many results.")],
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# ),
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# ]
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#
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# agent_state, in_context_messages = await create_agent_with_messages(server, actor, llm_config, messages)
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#
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# # Verify the original tool return is indeed large
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# assert original_size > 90000, f"Expected tool return >90k chars, got {original_size}"
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#
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# # Run summarization
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# summary, result = await run_summarization(server, agent_state, in_context_messages, actor)
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#
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# # Verify result
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# assert isinstance(result, list)
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# assert len(result) >= 1
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#
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# # Find tool return messages in the result and verify truncation occurred
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# tool_returns_found = False
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# for msg in result:
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# if msg.role == MessageRole.tool:
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# for content in msg.content:
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# if isinstance(content, ToolReturnContent):
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# tool_returns_found = True
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# result_size = len(content.content)
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# # Verify that the tool return has been truncated
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# assert result_size < original_size, (
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# f"Expected tool return to be truncated from {original_size} chars, but got {result_size} chars"
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# )
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# print(f"Tool return successfully truncated from {original_size} to {result_size} chars")
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#
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# # If we didn't find any tool returns in the result, that's also acceptable
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# # (they may have been completely removed during aggressive summarization)
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# if not tool_returns_found:
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# print("Tool returns were completely removed during summarization")
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#
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# ======================================================================================================================
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# SummarizerConfig Mode Tests (with pytest.patch) - Using LettaAgentV3
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