105 lines
3.1 KiB
Python
105 lines
3.1 KiB
Python
from __future__ import annotations
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from copy import deepcopy
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from typing import Any, cast
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from unittest.mock import patch
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import pytest
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from src.config import ModelConfig
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from src.llm import tool_loop
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from src.llm.runtime import AttemptPlan
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from src.llm.tool_loop import execute_tool_loop
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from src.llm.types import HonchoLLMCallResponse, ProviderClient
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def _make_plan() -> AttemptPlan:
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return AttemptPlan(
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provider="openai",
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model="deepseek-v4-pro",
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client=cast(ProviderClient, object()),
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thinking_budget_tokens=None,
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reasoning_effort=None,
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selected_config=ModelConfig(
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model="deepseek-v4-pro",
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transport="openai",
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),
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attempt=1,
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retry_attempts=1,
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is_fallback=False,
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)
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@pytest.mark.asyncio
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async def test_tool_loop_replays_reasoning_content_on_continuation() -> None:
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calls: list[list[dict[str, Any]]] = []
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responses = iter(
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[
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HonchoLLMCallResponse(
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content="",
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output_tokens=5,
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finish_reasons=["tool_calls"],
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tool_calls_made=[
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{
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"id": "call_1",
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"name": "search",
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"input": {"query": "honcho"},
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}
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],
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thinking_content="DeepSeek reasoning",
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),
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HonchoLLMCallResponse(
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content="done",
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output_tokens=3,
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finish_reasons=["stop"],
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tool_calls_made=[],
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),
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]
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)
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async def fake_call(*_args: Any, **kwargs: Any) -> HonchoLLMCallResponse[Any]:
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calls.append(deepcopy(kwargs["messages"]))
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return next(responses)
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async def execute_search(_name: str, _input: dict[str, Any]) -> str:
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return "result"
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with patch.object(tool_loop, "honcho_llm_call_inner", new=fake_call):
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result = await execute_tool_loop(
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prompt="hi",
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max_tokens=64,
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messages=[{"role": "user", "content": "hi"}],
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tools=[
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{
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"name": "search",
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"description": "Search",
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"input_schema": {"type": "object"},
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}
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],
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tool_choice="auto",
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tool_executor=execute_search,
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max_tool_iterations=5,
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response_model=None,
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json_mode=False,
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temperature=None,
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stop_seqs=None,
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verbosity=None,
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enable_retry=False,
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retry_attempts=1,
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max_input_tokens=None,
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get_attempt_plan=_make_plan,
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before_retry_callback=lambda _retry_state: None,
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stream_final=False,
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telemetry=None,
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)
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assert isinstance(result, HonchoLLMCallResponse)
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assert len(calls) == 2
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assert calls[1][1]["reasoning_content"] == "DeepSeek reasoning"
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assert calls[1][1]["tool_calls"][0]["function"]["name"] == "search"
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assert calls[1][2] == {
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"role": "tool",
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"tool_call_id": "call_1",
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"content": "result",
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}
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