mirror of
https://github.com/jxxghp/MoviePilot.git
synced 2026-05-19 10:29:29 +08:00
751 lines
26 KiB
Python
751 lines
26 KiB
Python
"""LLM模型相关辅助功能"""
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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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from app.log import logger
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class LLMTestError(RuntimeError):
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"""LLM 测试调用异常，附带请求耗时。"""
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def __init__(self, message: str, duration_ms: int | None = None):
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super().__init__(message)
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self.duration_ms = duration_ms
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class LLMTestTimeout(TimeoutError):
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"""LLM 测试调用超时，附带请求耗时。"""
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def __init__(self, message: str, duration_ms: int | None = None):
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super().__init__(message)
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self.duration_ms = duration_ms
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def _patch_gemini_thought_signature():
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"""
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修复 langchain-google-genai 中 Gemini 2.5 思考模型的 thought_signature 兼容问题。
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langchain-google-genai 的 _is_gemini_3_or_later() 仅检查 "gemini-3"，
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导致 Gemini 2.5 思考模型（如 gemini-2.5-flash、gemini-2.5-pro）在工具调用时
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缺少 thought_signature 而报错 400。
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此补丁将检查范围扩展到 Gemini 2.5 模型。
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"""
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try:
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import langchain_google_genai.chat_models as _cm
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# 仅在未修补时执行
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if getattr(_cm, "_thought_signature_patched", False):
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return
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def _patched_is_gemini_3_or_later(model_name: str) -> bool:
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if not model_name:
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return False
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name = model_name.lower().replace("models/", "")
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# Gemini 2.5 思考模型也需要 thought_signature 支持
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return "gemini-3" in name or "gemini-2.5" in name
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_cm._is_gemini_3_or_later = _patched_is_gemini_3_or_later
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_cm._thought_signature_patched = True
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logger.debug(
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"已修补 langchain-google-genai thought_signature 兼容性（覆盖 Gemini 2.5 模型）"
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)
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except Exception as e:
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logger.warning(f"修补 langchain-google-genai thought_signature 失败: {e}")
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def _get_httpx_proxy_key() -> str:
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"""
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获取当前 httpx 版本支持的代理参数名。
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httpx < 0.28 使用 "proxies"（复数），>= 0.28 使用 "proxy"（单数）。
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google-genai SDK 会静默过滤掉不在 httpx.Client.__init__ 签名中的参数，
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因此必须使用与当前 httpx 版本匹配的参数名。
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"""
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try:
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import httpx
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params = inspect.signature(httpx.Client.__init__).parameters
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if "proxy" in params:
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return "proxy"
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return "proxies"
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except Exception:
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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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_SUPPORTED_THINKING_LEVELS = frozenset(
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{"off", "auto", "minimal", "low", "medium", "high", "max", "xhigh"}
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)
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@staticmethod
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def _should_disable_thinking(disable_thinking: bool | None = None) -> bool:
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"""
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判断本次调用是否应尝试关闭模型思考能力。
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"""
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if disable_thinking is not None:
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return bool(disable_thinking)
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return bool(getattr(settings, "LLM_DISABLE_THINKING", False))
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@staticmethod
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def _normalize_model_name(model_name: str | None) -> str:
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"""
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统一清理模型名称，便于按模型族做能力映射。
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"""
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return (model_name or "").strip().lower()
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@classmethod
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def _normalize_thinking_level_value(cls, value: str | None) -> str | None:
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"""
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统一清理思考级别/强度值，并兼容常见别名。
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"""
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if value is None:
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return None
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normalized = str(value).strip().lower()
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if not normalized:
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return None
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alias_map = {
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"none": "off",
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"disabled": "off",
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"disable": "off",
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"enabled": "auto",
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"enable": "auto",
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"default": "auto",
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"dynamic": "auto",
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}
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return alias_map.get(normalized, normalized)
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@classmethod
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def _normalize_thinking_level(
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cls, thinking_level: str | None = None
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) -> str | None:
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"""
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统一清理 thinking_level 配置。
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"""
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value = (
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thinking_level
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if thinking_level is not None
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else getattr(settings, "LLM_THINKING_LEVEL", None)
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)
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normalized = cls._normalize_thinking_level_value(value)
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if not normalized:
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return None
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if normalized not in cls._SUPPORTED_THINKING_LEVELS:
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logger.warning(f"忽略不支持的 thinking_level 配置: {normalized}")
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return None
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return normalized
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@classmethod
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def _normalize_reasoning_effort(
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cls, reasoning_effort: str | None = None
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) -> str | None:
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"""
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统一清理 legacy reasoning_effort 配置。
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"""
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value = (
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reasoning_effort
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if reasoning_effort is not None
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else getattr(settings, "LLM_REASONING_EFFORT", None)
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)
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return cls._normalize_thinking_level(value)
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@classmethod
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def _resolve_thinking_level(
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cls,
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thinking_level: str | None = None,
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disable_thinking: bool | None = None,
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reasoning_effort: str | None = None,
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) -> str:
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"""
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统一解析本次调用的思考配置。
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优先级：
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1. 新字段 `thinking_level`
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2. 本次调用传入的 legacy 字段
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3. 已保存的新字段 `LLM_THINKING_LEVEL`
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4. 已保存的 legacy 字段
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"""
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explicit_level = cls._normalize_thinking_level(thinking_level)
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if explicit_level:
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return explicit_level
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explicit_effort = (
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cls._normalize_reasoning_effort(reasoning_effort)
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if reasoning_effort is not None
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else None
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)
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if disable_thinking is not None or reasoning_effort is not None:
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if disable_thinking is not None and bool(disable_thinking):
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return "off"
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return explicit_effort or "auto"
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configured_level = cls._normalize_thinking_level(
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getattr(settings, "LLM_THINKING_LEVEL", None)
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)
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if configured_level:
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return configured_level
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legacy_disable = getattr(settings, "LLM_DISABLE_THINKING", None)
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legacy_effort = cls._normalize_reasoning_effort(
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getattr(settings, "LLM_REASONING_EFFORT", None)
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)
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if legacy_disable is not None:
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return "off" if bool(legacy_disable) else (legacy_effort or "auto")
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return legacy_effort or "off"
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@classmethod
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def _normalize_deepseek_reasoning_effort(
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cls, thinking_level: str | None = None
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) -> str | None:
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"""
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DeepSeek 文档当前建议使用 high/max；兼容常见 effort 别名。
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"""
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normalized = cls._normalize_thinking_level(thinking_level)
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if not normalized or normalized in {"off", "auto"}:
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return None
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if normalized in {"minimal", "low", "medium", "high"}:
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return "high"
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if normalized in {"max", "xhigh"}:
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return "max"
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logger.warning(f"忽略不支持的 DeepSeek reasoning_effort 配置: {normalized}")
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return None
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@classmethod
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def _normalize_openai_reasoning_effort(
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cls, thinking_level: str | None = None
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) -> str | None:
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"""
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OpenAI reasoning_effort 支持更细粒度的 effort，统一做最近似映射。
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"""
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normalized = cls._normalize_thinking_level(thinking_level)
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if not normalized or normalized == "auto":
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return None
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if normalized == "off":
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return "none"
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if normalized == "max":
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return "xhigh"
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return normalized
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@classmethod
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def _build_google_thinking_kwargs(
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cls, model_name: str, thinking_level: str
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) -> dict[str, Any]:
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"""
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Gemini 3 使用 thinking_level；Gemini 2.5 使用 thinking_budget。
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"""
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if not model_name or thinking_level == "auto":
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return {}
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if "gemini-2.5" in model_name:
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if thinking_level == "off":
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if "pro" in model_name:
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# Gemini 2.5 Pro 官方不支持完全关闭思考，回退到最小预算。
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return {
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"thinking_budget": 128,
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"include_thoughts": False,
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}
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return {
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"thinking_budget": 0,
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"include_thoughts": False,
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}
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budget_map = {
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"minimal": 512,
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"low": 1024,
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"medium": 4096,
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"high": 8192,
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"max": 24576,
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"xhigh": 24576,
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}
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budget = budget_map.get(thinking_level)
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return (
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{
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"thinking_budget": budget,
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"include_thoughts": False,
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}
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if budget is not None
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else {}
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)
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if "gemini-3" in model_name:
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level_map = {
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"off": "minimal",
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"minimal": "minimal",
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"low": "low",
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"medium": "medium",
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"high": "high",
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"max": "high",
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"xhigh": "high",
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}
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google_level = level_map.get(thinking_level)
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return (
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{
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"thinking_level": google_level,
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"include_thoughts": False,
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}
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if google_level
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else {}
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)
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return {}
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@classmethod
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def _build_kimi_thinking_kwargs(
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cls, model_name: str, thinking_level: str
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) -> dict[str, Any]:
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"""
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Kimi 当前公开文档仅支持思考开关，不支持显式深度调节。
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"""
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if model_name.startswith("kimi-k2-thinking"):
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return {}
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if thinking_level == "off":
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return {"extra_body": {"thinking": {"type": "disabled"}}}
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return {}
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@classmethod
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def _build_thinking_kwargs(
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cls,
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provider: str,
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model: str | None,
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thinking_level: str | None = None,
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disable_thinking: bool | None = None,
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reasoning_effort: str | None = None,
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) -> dict[str, Any]:
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"""
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按 provider/model 生成思考模式相关参数。
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优先使用 LangChain/OpenAI SDK 已支持的原生字段；仅在 provider
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明确要求自定义请求体时，才回退到 extra_body。
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"""
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provider_name = (provider or "").strip().lower()
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model_name = cls._normalize_model_name(model)
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resolved_thinking_level = cls._resolve_thinking_level(
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thinking_level=thinking_level,
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disable_thinking=disable_thinking,
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reasoning_effort=reasoning_effort,
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)
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if provider_name == "deepseek":
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if resolved_thinking_level == "off":
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return {"extra_body": {"thinking": {"type": "disabled"}}}
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if resolved_thinking_level == "auto":
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return {}
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kwargs: dict[str, Any] = {"extra_body": {"thinking": {"type": "enabled"}}}
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deepseek_effort = cls._normalize_deepseek_reasoning_effort(
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resolved_thinking_level
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)
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if deepseek_effort:
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kwargs["reasoning_effort"] = deepseek_effort
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return kwargs
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if model_name.startswith(("kimi-k2.5", "kimi-k2.6", "kimi-k2-thinking")):
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return cls._build_kimi_thinking_kwargs(model_name, resolved_thinking_level)
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if not model_name:
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return {}
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# OpenAI 原生推理模型优先走 LangChain 内置 reasoning_effort。
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if provider_name == "openai" and model_name.startswith(
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("gpt-5", "o1", "o3", "o4")
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):
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openai_effort = cls._normalize_openai_reasoning_effort(
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resolved_thinking_level
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)
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return {"reasoning_effort": openai_effort} if openai_effort else {}
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# Gemini 使用 google-genai / langchain-google-genai 内置思考控制参数。
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if provider_name == "google":
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return cls._build_google_thinking_kwargs(
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model_name, resolved_thinking_level
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)
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return {}
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@staticmethod
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||
def supports_image_input() -> bool:
|
||
"""
|
||
判断当前模型是否启用了图片输入能力。
|
||
"""
|
||
return bool(settings.LLM_SUPPORT_IMAGE_INPUT)
|
||
|
||
@staticmethod
|
||
def get_llm(
|
||
streaming: bool = False,
|
||
provider: str | None = None,
|
||
model: str | None = None,
|
||
thinking_level: str | None = None,
|
||
disable_thinking: bool | None = None,
|
||
reasoning_effort: str | None = None,
|
||
api_key: str | None = None,
|
||
base_url: str | None = None,
|
||
):
|
||
"""
|
||
获取LLM实例
|
||
:param streaming: 是否启用流式输出
|
||
:return: LLM实例
|
||
"""
|
||
provider_name = str(
|
||
provider if provider is not None else settings.LLM_PROVIDER
|
||
).lower()
|
||
model_name = model if model is not None else settings.LLM_MODEL
|
||
api_key_value = api_key if api_key is not None else settings.LLM_API_KEY
|
||
base_url_value = base_url if base_url is not None else settings.LLM_BASE_URL
|
||
thinking_kwargs = LLMHelper._build_thinking_kwargs(
|
||
provider=provider_name,
|
||
model=model_name,
|
||
thinking_level=thinking_level,
|
||
disable_thinking=disable_thinking,
|
||
reasoning_effort=reasoning_effort,
|
||
)
|
||
|
||
if not api_key_value:
|
||
raise ValueError("未配置LLM API Key")
|
||
|
||
if provider_name == "google":
|
||
# 修补 Gemini 2.5 思考模型的 thought_signature 兼容性
|
||
_patch_gemini_thought_signature()
|
||
|
||
# 统一使用 langchain-google-genai 原生接口
|
||
# 不使用 OpenAI 兼容端点，因其不支持 Gemini 思考模型的 thought_signature，
|
||
# 会导致工具调用时报错 400
|
||
from langchain_google_genai import ChatGoogleGenerativeAI
|
||
|
||
client_args = None
|
||
if settings.PROXY_HOST:
|
||
proxy_key = _get_httpx_proxy_key()
|
||
client_args = {proxy_key: settings.PROXY_HOST}
|
||
|
||
model = ChatGoogleGenerativeAI(
|
||
model=model_name,
|
||
api_key=api_key_value,
|
||
retries=3,
|
||
temperature=settings.LLM_TEMPERATURE,
|
||
streaming=streaming,
|
||
client_args=client_args,
|
||
**thinking_kwargs,
|
||
)
|
||
elif provider_name == "deepseek":
|
||
from langchain_deepseek import ChatDeepSeek
|
||
|
||
_patch_deepseek_reasoning_content_support()
|
||
model = ChatDeepSeek(
|
||
model=model_name,
|
||
api_key=api_key_value,
|
||
api_base=base_url_value,
|
||
max_retries=3,
|
||
temperature=settings.LLM_TEMPERATURE,
|
||
streaming=streaming,
|
||
stream_usage=True,
|
||
**thinking_kwargs,
|
||
)
|
||
else:
|
||
from langchain_openai import ChatOpenAI
|
||
|
||
model = ChatOpenAI(
|
||
model=model_name,
|
||
api_key=api_key_value,
|
||
max_retries=3,
|
||
base_url=base_url_value,
|
||
temperature=settings.LLM_TEMPERATURE,
|
||
streaming=streaming,
|
||
stream_usage=True,
|
||
openai_proxy=settings.PROXY_HOST,
|
||
**thinking_kwargs,
|
||
)
|
||
|
||
# 检查是否有profile
|
||
if hasattr(model, "profile") and model.profile:
|
||
logger.debug(f"使用LLM模型: {model.model}，Profile: {model.profile}")
|
||
else:
|
||
model.profile = {
|
||
"max_input_tokens": settings.LLM_MAX_CONTEXT_TOKENS
|
||
* 1000, # 转换为token单位
|
||
}
|
||
|
||
return model
|
||
|
||
@staticmethod
|
||
def _extract_text_content(content) -> str:
|
||
"""
|
||
从响应内容中提取纯文本，仅保留真实文本块。
|
||
"""
|
||
if content is None:
|
||
return ""
|
||
if isinstance(content, str):
|
||
return content
|
||
if isinstance(content, list):
|
||
text_parts = []
|
||
for block in content:
|
||
if isinstance(block, str):
|
||
text_parts.append(block)
|
||
continue
|
||
|
||
if isinstance(block, dict) or hasattr(block, "get"):
|
||
block_type = block.get("type")
|
||
if block.get("thought") or block_type in (
|
||
"thinking",
|
||
"reasoning_content",
|
||
"reasoning",
|
||
"thought",
|
||
):
|
||
continue
|
||
if block_type == "text":
|
||
text_parts.append(block.get("text", ""))
|
||
continue
|
||
if not block_type and isinstance(block.get("text"), str):
|
||
text_parts.append(block.get("text", ""))
|
||
return "".join(text_parts)
|
||
if isinstance(content, dict) or hasattr(content, "get"):
|
||
if content.get("thought"):
|
||
return ""
|
||
if content.get("type") == "text":
|
||
return content.get("text", "")
|
||
if not content.get("type") and isinstance(content.get("text"), str):
|
||
return content.get("text", "")
|
||
return ""
|
||
|
||
@staticmethod
|
||
async def test_current_settings(
|
||
prompt: str = "请只回复 OK",
|
||
timeout: int = 20,
|
||
provider: str | None = None,
|
||
model: str | None = None,
|
||
thinking_level: str | None = None,
|
||
disable_thinking: bool | None = None,
|
||
reasoning_effort: str | None = None,
|
||
api_key: str | None = None,
|
||
base_url: str | None = None,
|
||
) -> dict:
|
||
"""
|
||
使用当前已保存配置执行一次最小 LLM 调用。
|
||
"""
|
||
provider_name = provider if provider is not None else settings.LLM_PROVIDER
|
||
model_name = model if model is not None else settings.LLM_MODEL
|
||
api_key_value = api_key if api_key is not None else settings.LLM_API_KEY
|
||
base_url_value = base_url if base_url is not None else settings.LLM_BASE_URL
|
||
start = time.perf_counter()
|
||
llm = LLMHelper.get_llm(
|
||
streaming=False,
|
||
provider=provider_name,
|
||
model=model_name,
|
||
thinking_level=thinking_level,
|
||
disable_thinking=disable_thinking,
|
||
reasoning_effort=reasoning_effort,
|
||
api_key=api_key_value,
|
||
base_url=base_url_value,
|
||
)
|
||
try:
|
||
response = await asyncio.wait_for(llm.ainvoke(prompt), timeout=timeout)
|
||
except TimeoutError as err:
|
||
duration_ms = round((time.perf_counter() - start) * 1000)
|
||
raise LLMTestTimeout("LLM 调用超时", duration_ms=duration_ms) from err
|
||
except Exception as err:
|
||
duration_ms = round((time.perf_counter() - start) * 1000)
|
||
raise LLMTestError(str(err), duration_ms=duration_ms) from err
|
||
|
||
reply_text = LLMHelper._extract_text_content(
|
||
getattr(response, "content", response)
|
||
).strip()
|
||
duration_ms = round((time.perf_counter() - start) * 1000)
|
||
|
||
data = {
|
||
"provider": provider_name,
|
||
"model": model_name,
|
||
"duration_ms": duration_ms,
|
||
}
|
||
if reply_text:
|
||
data["reply_preview"] = reply_text[:120]
|
||
return data
|
||
|
||
def get_models(
|
||
self, provider: str, api_key: str, base_url: str = None
|
||
) -> List[str]:
|
||
"""获取模型列表"""
|
||
logger.info(f"获取 {provider} 模型列表...")
|
||
if provider == "google":
|
||
return self._get_google_models(api_key)
|
||
else:
|
||
return self._get_openai_compatible_models(provider, api_key, base_url)
|
||
|
||
@staticmethod
|
||
def _get_google_models(api_key: str) -> List[str]:
|
||
"""获取Google模型列表（使用 google-genai SDK v1）"""
|
||
try:
|
||
from google import genai
|
||
from google.genai.types import HttpOptions
|
||
|
||
http_options = None
|
||
if settings.PROXY_HOST:
|
||
proxy_key = _get_httpx_proxy_key()
|
||
proxy_args = {proxy_key: settings.PROXY_HOST}
|
||
http_options = HttpOptions(
|
||
client_args=proxy_args,
|
||
async_client_args=proxy_args,
|
||
)
|
||
|
||
client = genai.Client(api_key=api_key, http_options=http_options)
|
||
models = client.models.list()
|
||
return [
|
||
m.name
|
||
for m in models
|
||
if m.supported_actions and "generateContent" in m.supported_actions
|
||
]
|
||
except Exception as e:
|
||
logger.error(f"获取Google模型列表失败：{e}")
|
||
raise e
|
||
|
||
@staticmethod
|
||
def _get_openai_compatible_models(
|
||
provider: str, api_key: str, base_url: str = None
|
||
) -> List[str]:
|
||
"""获取OpenAI兼容模型列表"""
|
||
try:
|
||
from openai import OpenAI
|
||
|
||
if provider == "deepseek":
|
||
base_url = base_url or "https://api.deepseek.com"
|
||
|
||
client = OpenAI(api_key=api_key, base_url=base_url)
|
||
models = client.models.list()
|
||
return [model.id for model in models.data]
|
||
except Exception as e:
|
||
logger.error(f"获取 {provider} 模型列表失败：{e}")
|
||
raise e
|