mirror of
https://github.com/jxxghp/MoviePilot.git
synced 2026-05-07 13:52:42 +08:00
fix: preserve deepseek reasoning content in tool loops
This commit is contained in:
jxxghp
@@ -3,6 +3,7 @@
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import asyncio
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import inspect
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import time
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from functools import wraps
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from typing import Any, List
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from app.core.config import settings
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@@ -74,6 +75,132 @@ def _get_httpx_proxy_key() -> str:
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return "proxies"
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def _deepseek_thinking_toggle(extra_body: Any) -> bool | None:
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"""
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解析 DeepSeek extra_body 中显式传入的 thinking 开关。
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"""
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if not isinstance(extra_body, dict):
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return None
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thinking = extra_body.get("thinking")
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if not isinstance(thinking, dict):
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return None
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thinking_type = str(thinking.get("type") or "").strip().lower()
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if thinking_type == "enabled":
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return True
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if thinking_type == "disabled":
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return False
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return None
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def _is_deepseek_thinking_enabled(model_name: str | None, extra_body: Any) -> bool:
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"""
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判断本次 DeepSeek 调用是否处于 thinking mode。
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"""
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explicit_toggle = _deepseek_thinking_toggle(extra_body)
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if explicit_toggle is not None:
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return explicit_toggle
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normalized_model_name = str(model_name or "").strip().lower()
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if normalized_model_name == "deepseek-reasoner":
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return True
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if normalized_model_name.startswith("deepseek-v4-"):
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# DeepSeek V4 默认启用 thinking mode，除非显式关闭。
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return True
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return False
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def _extract_input_messages(input_: Any) -> list[Any]:
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"""
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将 chat model 输入还原为原始 BaseMessage 序列。
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"""
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try:
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from langchain_core.messages import convert_to_messages
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return list(convert_to_messages(input_))
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except Exception:
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if isinstance(input_, list):
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return list(input_)
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return []
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def _patch_deepseek_reasoning_content_support():
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"""
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修补 langchain-deepseek 在 tool-call 场景下遗漏 reasoning_content 回传的问题。
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DeepSeek thinking mode 要求：若 assistant 历史消息包含 tool_calls，
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后续请求中必须带回该条消息的顶层 reasoning_content。
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某些 langchain-deepseek 版本虽然能从响应中拿到 reasoning_content，
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但不会在重放消息历史时写回请求载荷，导致 400。
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"""
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try:
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from langchain_deepseek import ChatDeepSeek
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except Exception as err:
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logger.debug(f"跳过 langchain-deepseek reasoning_content 修补：{err}")
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return
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if getattr(ChatDeepSeek, "_moviepilot_reasoning_content_patched", False):
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return
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original_get_request_payload = getattr(ChatDeepSeek, "_get_request_payload", None)
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if not callable(original_get_request_payload):
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logger.warning("langchain-deepseek 缺少 _get_request_payload，无法修补 reasoning_content")
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return
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@wraps(original_get_request_payload)
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def _patched_get_request_payload(self, input_, *, stop=None, **kwargs):
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payload = original_get_request_payload(self, input_, stop=stop, **kwargs)
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try:
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original_messages = _extract_input_messages(input_)
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payload_messages = payload.get("messages") or []
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model_name = getattr(self, "model_name", None) or getattr(
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self, "model", None
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)
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extra_body = kwargs.get("extra_body")
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if extra_body is None:
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extra_body = getattr(self, "extra_body", None)
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if extra_body is None:
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extra_body = getattr(self, "model_kwargs", {}).get("extra_body")
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if not _is_deepseek_thinking_enabled(model_name, extra_body):
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return payload
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for index, message in enumerate(payload_messages):
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if not isinstance(message, dict):
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continue
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if message.get("role") != "assistant":
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continue
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if not message.get("tool_calls"):
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continue
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if message.get("reasoning_content") is not None:
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continue
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reasoning_content = ""
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if index < len(original_messages):
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additional_kwargs = (
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getattr(original_messages[index], "additional_kwargs", None)
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or {}
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)
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if isinstance(additional_kwargs, dict):
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captured_reasoning = additional_kwargs.get("reasoning_content")
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if isinstance(captured_reasoning, str):
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reasoning_content = captured_reasoning
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message["reasoning_content"] = reasoning_content
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except Exception as err:
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logger.warning(
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f"修补 langchain-deepseek reasoning_content 请求载荷时失败，将继续使用原始载荷: {err}"
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)
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return payload
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ChatDeepSeek._get_request_payload = _patched_get_request_payload
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ChatDeepSeek._moviepilot_reasoning_content_patched = True
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logger.debug("已修补 langchain-deepseek thinking tool-call 的 reasoning_content 回传兼容性")
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class LLMHelper:
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"""LLM模型相关辅助功能"""
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@@ -437,6 +564,7 @@ class LLMHelper:
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elif provider_name == "deepseek":
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from langchain_deepseek import ChatDeepSeek
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_patch_deepseek_reasoning_content_support()
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model = ChatDeepSeek(
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model=model_name,
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api_key=api_key_value,
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146
tests/test_langchain_deepseek_compat.py
Normal file
146
tests/test_langchain_deepseek_compat.py
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@@ -0,0 +1,146 @@
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import importlib.util
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import sys
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import unittest
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from pathlib import Path
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from types import ModuleType
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from langchain_core.messages import AIMessage, HumanMessage, ToolMessage
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def _stub_module(name: str, **attrs):
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module = sys.modules.get(name)
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if module is None:
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module = ModuleType(name)
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sys.modules[name] = module
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for key, value in attrs.items():
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setattr(module, key, value)
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return module
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class _DummyLogger:
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def __getattr__(self, _name):
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return lambda *args, **kwargs: None
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def _build_tool_call(name: str = "search", arguments: str = "{}"):
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return [
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{
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"id": "call_1",
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"type": "tool_call",
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"name": name,
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"args": {},
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}
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]
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class _FakeChatDeepSeek:
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def __init__(self, model_name: str, model_kwargs: dict | None = None):
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self.model_name = model_name
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self.model_kwargs = model_kwargs or {}
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def _get_request_payload(self, input_, *, stop=None, **kwargs):
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messages = []
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for message in input_:
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payload_message = {
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"role": message.type,
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"content": message.content,
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}
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if message.type == "human":
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payload_message["role"] = "user"
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elif message.type == "ai":
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payload_message["role"] = "assistant"
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tool_calls = getattr(message, "tool_calls", None)
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if tool_calls:
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payload_message["tool_calls"] = tool_calls
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elif message.type == "tool":
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payload_message["role"] = "tool"
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payload_message["tool_call_id"] = message.tool_call_id
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messages.append(payload_message)
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return {"messages": messages}
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_ORIGINAL_GET_REQUEST_PAYLOAD = _FakeChatDeepSeek._get_request_payload
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sys.modules.pop("app.helper.llm", None)
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_stub_module(
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"app.core.config",
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settings=ModuleType("settings"),
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)
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sys.modules["app.core.config"].settings.LLM_PROVIDER = "deepseek"
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sys.modules["app.core.config"].settings.LLM_MODEL = "deepseek-v4-pro"
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sys.modules["app.core.config"].settings.LLM_API_KEY = "sk-test"
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sys.modules["app.core.config"].settings.LLM_BASE_URL = "https://api.deepseek.com"
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sys.modules["app.core.config"].settings.LLM_THINKING_LEVEL = None
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sys.modules["app.core.config"].settings.LLM_DISABLE_THINKING = False
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sys.modules["app.core.config"].settings.LLM_REASONING_EFFORT = None
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sys.modules["app.core.config"].settings.LLM_TEMPERATURE = 0.1
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sys.modules["app.core.config"].settings.LLM_MAX_CONTEXT_TOKENS = 64
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sys.modules["app.core.config"].settings.PROXY_HOST = None
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_stub_module("app.log", logger=_DummyLogger())
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_stub_module("langchain_deepseek", ChatDeepSeek=_FakeChatDeepSeek)
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module_path = Path(__file__).resolve().parents[1] / "app" / "helper" / "llm.py"
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spec = importlib.util.spec_from_file_location("test_llm_module_for_deepseek_compat", module_path)
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llm_module = importlib.util.module_from_spec(spec)
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assert spec and spec.loader
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spec.loader.exec_module(llm_module)
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class DeepSeekCompatPatchTest(unittest.TestCase):
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def setUp(self):
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_FakeChatDeepSeek._get_request_payload = _ORIGINAL_GET_REQUEST_PAYLOAD
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if hasattr(_FakeChatDeepSeek, "_moviepilot_reasoning_content_patched"):
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delattr(_FakeChatDeepSeek, "_moviepilot_reasoning_content_patched")
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llm_module._patch_deepseek_reasoning_content_support()
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def test_injects_reasoning_content_for_assistant_tool_calls(self):
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llm = _FakeChatDeepSeek("deepseek-v4-pro")
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messages = [
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HumanMessage(content="天气如何？"),
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AIMessage(
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content="",
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tool_calls=_build_tool_call(),
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additional_kwargs={"reasoning_content": "先调用天气工具"},
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),
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ToolMessage(content="晴天", tool_call_id="call_1"),
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]
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payload = llm._get_request_payload(messages)
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self.assertEqual(
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payload["messages"][1]["reasoning_content"],
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"先调用天气工具",
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)
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def test_falls_back_to_empty_reasoning_content_when_missing(self):
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llm = _FakeChatDeepSeek("deepseek-v4-flash")
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messages = [
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HumanMessage(content="天气如何？"),
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AIMessage(content="", tool_calls=_build_tool_call()),
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ToolMessage(content="晴天", tool_call_id="call_1"),
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]
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payload = llm._get_request_payload(messages)
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self.assertIn("reasoning_content", payload["messages"][1])
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self.assertEqual(payload["messages"][1]["reasoning_content"], "")
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def test_skips_injection_when_thinking_is_disabled(self):
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llm = _FakeChatDeepSeek(
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"deepseek-v4-pro",
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model_kwargs={"extra_body": {"thinking": {"type": "disabled"}}},
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)
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messages = [
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HumanMessage(content="天气如何？"),
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AIMessage(
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content="",
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tool_calls=_build_tool_call(),
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additional_kwargs={"reasoning_content": "先调用天气工具"},
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),
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ToolMessage(content="晴天", tool_call_id="call_1"),
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]
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payload = llm._get_request_payload(messages)
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self.assertNotIn("reasoning_content", payload["messages"][1])
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@@ -144,6 +144,7 @@ class LlmHelperTestCallTest(unittest.TestCase):
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def test_get_llm_uses_deepseek_thinking_level_controls(self):
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calls = []
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patch_calls = []
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class _FakeChatDeepSeek:
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def __init__(self, **kwargs):
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@@ -154,6 +155,10 @@ class LlmHelperTestCallTest(unittest.TestCase):
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with patch.dict(
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sys.modules,
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{"langchain_deepseek": SimpleNamespace(ChatDeepSeek=_FakeChatDeepSeek)},
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), patch.object(
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llm_module,
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"_patch_deepseek_reasoning_content_support",
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side_effect=lambda: patch_calls.append(True),
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):
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llm_module.LLMHelper.get_llm(
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provider="deepseek",
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@@ -168,11 +173,13 @@ class LlmHelperTestCallTest(unittest.TestCase):
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calls[0].get("extra_body"),
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{"thinking": {"type": "enabled"}},
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)
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self.assertEqual(patch_calls, [True])
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self.assertEqual(calls[0].get("reasoning_effort"), "max")
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self.assertEqual(calls[0].get("api_base"), "https://api.deepseek.com")
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def test_get_llm_disables_deepseek_thinking_via_thinking_level(self):
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calls = []
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patch_calls = []
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class _FakeChatDeepSeek:
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def __init__(self, **kwargs):
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@@ -183,6 +190,10 @@ class LlmHelperTestCallTest(unittest.TestCase):
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with patch.dict(
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sys.modules,
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{"langchain_deepseek": SimpleNamespace(ChatDeepSeek=_FakeChatDeepSeek)},
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), patch.object(
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llm_module,
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"_patch_deepseek_reasoning_content_support",
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side_effect=lambda: patch_calls.append(True),
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):
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llm_module.LLMHelper.get_llm(
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provider="deepseek",
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@@ -197,6 +208,7 @@ class LlmHelperTestCallTest(unittest.TestCase):
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calls[0].get("extra_body"),
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{"thinking": {"type": "disabled"}},
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)
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self.assertEqual(patch_calls, [True])
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self.assertIsNone(calls[0].get("reasoning_effort"))
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self.assertEqual(calls[0].get("api_base"), "https://proxy.example.com")
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