mirror of
https://github.com/jxxghp/MoviePilot.git
synced 2026-09-05 07:27:15 +08:00
refactor: migrate LLM helper to agent module and add unified LLM API endpoints
- Move LLMHelper and related logic from app.helper.llm to app.agent.llm.helper - Update all imports to reference new LLMHelper location - Introduce app/agent/llm/__init__.py for internal LLM adapter exports - Add llm.py API router with endpoints for model listing, provider auth, and test calls - Remove legacy LLM endpoints from system.py - Update requirements for langchain-anthropic and anthropic - Refactor test_llm_helper_testcall.py for async LLMHelper usage and new import paths
This commit is contained in:
jxxghp
@@ -0,0 +1,773 @@
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"""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 json
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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 langchain_core.messages import AIMessage
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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 as e:
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logger.warning(f"检测 httpx 代理参数失败，默认使用 'proxies'：{e}")
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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 _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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# Resolve original messages so we can extract reasoning_content from
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# additional_kwargs. The parent's payload builder does not propagate
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# this DeepSeek-specific field.
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messages = self._convert_input(input_).to_messages()
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for i, message in enumerate(payload["messages"]):
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if message["role"] == "tool" and isinstance(message["content"], list):
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message["content"] = json.dumps(message["content"])
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elif message["role"] == "assistant":
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if isinstance(message["content"], list):
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# DeepSeek API expects assistant content to be a string,
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# not a list. Extract text blocks and join them, or use
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# empty string if none exist.
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text_parts = [
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block.get("text", "")
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for block in message["content"]
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if isinstance(block, dict) and block.get("type") == "text"
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]
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message["content"] = "".join(text_parts) if text_parts else ""
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# DeepSeek reasoning models require every assistant message to
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# carry a reasoning_content field (even when empty). The value
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# is stored in AIMessage.additional_kwargs by
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# _create_chat_result(); re-inject it into the API payload.
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if (
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"reasoning_content" not in message
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and i < len(messages)
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and isinstance(messages[i], AIMessage)
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):
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message["reasoning_content"] = messages[i].additional_kwargs.get(
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"reasoning_content", ""
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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 _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_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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if not thinking_level or thinking_level in {"off", "auto"}:
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return None
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if thinking_level in {"minimal", "low", "medium", "high"}:
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return "high"
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if thinking_level 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: {thinking_level}")
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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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if not thinking_level or thinking_level == "auto":
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return None
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if thinking_level == "off":
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return "none"
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if thinking_level == "max":
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return "xhigh"
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return thinking_level
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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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) -> 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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if provider_name == "deepseek":
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if thinking_level == "off":
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return {"extra_body": {"thinking": {"type": "disabled"}}}
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if 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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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, 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 in {"openai", "chatgpt"} 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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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, 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:
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"""
|
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判断当前模型是否启用了图片输入能力。
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"""
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return bool(settings.LLM_SUPPORT_IMAGE_INPUT)
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@staticmethod
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def _build_legacy_runtime(
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provider_name: str,
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model_name: str | None,
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api_key: str | None = None,
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base_url: str | None = None,
|
||||
) -> dict[str, Any]:
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||||
"""
|
||||
在 provider 目录不可用时回退到旧的直接构造逻辑。
|
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|
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这主要用于单测 stub 环境以及极端的最小运行环境，正常生产路径仍优先
|
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走 `LLMProviderManager.resolve_runtime()`。
|
||||
"""
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||||
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
|
||||
if not api_key_value:
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raise ValueError("未配置LLM API Key")
|
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|
||||
runtime_name = provider_name if provider_name in {"google", "deepseek"} else "openai_compatible"
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return {
|
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"provider_id": provider_name,
|
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"runtime": runtime_name,
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"model_id": model_name,
|
||||
"api_key": api_key_value,
|
||||
"base_url": base_url_value,
|
||||
"default_headers": None,
|
||||
"use_responses_api": None,
|
||||
"model_record": None,
|
||||
"model_metadata": None,
|
||||
}
|
||||
|
||||
@classmethod
|
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def _resolve_thinking_level(
|
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cls,
|
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thinking_level: str | None = None,
|
||||
disable_thinking: bool | None = None,
|
||||
reasoning_effort: str | None = None,
|
||||
) -> str | None:
|
||||
"""
|
||||
统一兼容新旧 thinking 参数。
|
||||
|
||||
新前端只会传 `thinking_level`，但测试和部分旧调用仍可能带
|
||||
`disable_thinking` / `reasoning_effort`，这里集中做一次归一化。
|
||||
"""
|
||||
|
||||
def _normalize(value: str | None) -> str | None:
|
||||
normalized = str(value or "").strip().lower()
|
||||
if not normalized:
|
||||
return None
|
||||
alias_map = {
|
||||
"none": "off",
|
||||
"disabled": "off",
|
||||
"disable": "off",
|
||||
"enabled": "auto",
|
||||
"enable": "auto",
|
||||
"default": "auto",
|
||||
"dynamic": "auto",
|
||||
}
|
||||
normalized = alias_map.get(normalized, normalized)
|
||||
if normalized in cls._SUPPORTED_THINKING_LEVELS:
|
||||
return normalized
|
||||
logger.warning(f"忽略不支持的思考级别: {value}")
|
||||
return None
|
||||
|
||||
normalized_thinking_level = _normalize(thinking_level)
|
||||
if normalized_thinking_level:
|
||||
return normalized_thinking_level
|
||||
|
||||
legacy_effort = _normalize(reasoning_effort)
|
||||
if disable_thinking:
|
||||
return "off"
|
||||
if disable_thinking is False:
|
||||
return legacy_effort or "auto"
|
||||
return legacy_effort
|
||||
|
||||
@classmethod
|
||||
async def get_llm(
|
||||
cls,
|
||||
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
|
||||
:param provider: LLM提供商，默认为配置项LLM_PROVIDER
|
||||
:param model: 模型名称，默认为配置项LLM_MODEL
|
||||
:param thinking_level: 思考模式级别，默认为 None（即自动判断
|
||||
是否启用思考模式）。支持的级别包括 "off"（关闭）、"auto"（自动）、"minimal"、"low"、"medium"、"high"、"max"/"xhigh"（最大）。
|
||||
不同模型对思考模式的支持和表现不同，具体映射关系请
|
||||
参考代码实现。对于不支持思考模式的模型，该参数将被忽略。
|
||||
:param disable_thinking: 兼容旧参数，若传入则会被转换为新的
|
||||
`thinking_level` 语义。
|
||||
:param reasoning_effort: 兼容旧参数，若传入则会被转换为新的
|
||||
`thinking_level` 语义。
|
||||
:param api_key: API Key，默认为
|
||||
配置项LLM_API_KEY。对于某些提供商（
|
||||
如 DeepSeek），可能需要同时提供 base_url。
|
||||
:param base_url: API Base URL，默认为配置项LLM_BASE_URL。
|
||||
: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
|
||||
normalized_thinking_level = cls._resolve_thinking_level(
|
||||
thinking_level=thinking_level,
|
||||
disable_thinking=disable_thinking,
|
||||
reasoning_effort=reasoning_effort,
|
||||
)
|
||||
try:
|
||||
# 延迟导入，避免单测在最小 stub 环境下 import `llm.py` 时被 provider
|
||||
# 目录依赖链拖住。
|
||||
from app.agent.llm.provider import LLMProviderManager
|
||||
|
||||
runtime = await LLMProviderManager().resolve_runtime(
|
||||
provider_id=provider_name,
|
||||
model=model_name,
|
||||
api_key=api_key,
|
||||
base_url=base_url,
|
||||
)
|
||||
except Exception as err:
|
||||
logger.debug(f"LLM provider 目录不可用，回退到旧运行时逻辑: {err}")
|
||||
runtime = cls._build_legacy_runtime(
|
||||
provider_name=provider_name,
|
||||
model_name=model_name,
|
||||
api_key=api_key,
|
||||
base_url=base_url,
|
||||
)
|
||||
model_name = runtime.get("model_id") or model_name
|
||||
thinking_kwargs = cls._build_thinking_kwargs(
|
||||
provider=provider_name,
|
||||
model=model_name,
|
||||
thinking_level=normalized_thinking_level,
|
||||
)
|
||||
|
||||
if runtime["runtime"] == "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=runtime["api_key"],
|
||||
retries=3,
|
||||
temperature=settings.LLM_TEMPERATURE,
|
||||
streaming=streaming,
|
||||
client_args=client_args,
|
||||
**thinking_kwargs,
|
||||
)
|
||||
elif runtime["runtime"] == "deepseek":
|
||||
from langchain_deepseek import ChatDeepSeek
|
||||
|
||||
_patch_deepseek_reasoning_content_support()
|
||||
model = ChatDeepSeek(
|
||||
model=model_name,
|
||||
api_key=runtime["api_key"],
|
||||
api_base=runtime["base_url"],
|
||||
max_retries=3,
|
||||
temperature=settings.LLM_TEMPERATURE,
|
||||
streaming=streaming,
|
||||
stream_usage=True,
|
||||
**thinking_kwargs,
|
||||
)
|
||||
elif runtime["runtime"] in {"anthropic_compatible", "copilot_anthropic"}:
|
||||
from langchain_anthropic import ChatAnthropic
|
||||
|
||||
model = ChatAnthropic(
|
||||
model=model_name,
|
||||
api_key=runtime["api_key"],
|
||||
base_url=runtime["base_url"],
|
||||
max_retries=3,
|
||||
temperature=settings.LLM_TEMPERATURE,
|
||||
streaming=streaming,
|
||||
stream_usage=True,
|
||||
anthropic_proxy=settings.PROXY_HOST,
|
||||
default_headers=runtime.get("default_headers"),
|
||||
**thinking_kwargs,
|
||||
)
|
||||
else:
|
||||
from langchain_openai import ChatOpenAI
|
||||
|
||||
model = ChatOpenAI(
|
||||
model=model_name,
|
||||
api_key=runtime["api_key"],
|
||||
max_retries=3,
|
||||
base_url=runtime.get("base_url"),
|
||||
temperature=settings.LLM_TEMPERATURE,
|
||||
streaming=streaming,
|
||||
stream_usage=True,
|
||||
openai_proxy=settings.PROXY_HOST,
|
||||
default_headers=runtime.get("default_headers"),
|
||||
use_responses_api=runtime.get("use_responses_api"),
|
||||
**thinking_kwargs,
|
||||
)
|
||||
|
||||
# 优先使用 provider / models.dev 目录中的上下文上限，减少用户手填成本。
|
||||
model_profile = getattr(model, "profile", None)
|
||||
if model_profile:
|
||||
logger.debug(f"使用LLM模型: {model.model}，Profile: {model.profile}")
|
||||
else:
|
||||
model_record = runtime.get("model_record") or {}
|
||||
model_metadata = runtime.get("model_metadata") or {}
|
||||
metadata_limit = model_metadata.get("limit") or {}
|
||||
max_input_tokens = (
|
||||
model_record.get("input_tokens")
|
||||
or model_record.get("context_tokens")
|
||||
or metadata_limit.get("input")
|
||||
or metadata_limit.get("context")
|
||||
or settings.LLM_MAX_CONTEXT_TOKENS * 1000
|
||||
)
|
||||
model.profile = {
|
||||
"max_input_tokens": int(max_input_tokens),
|
||||
}
|
||||
|
||||
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
|
||||
start = time.perf_counter()
|
||||
llm = await 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,
|
||||
base_url=base_url,
|
||||
)
|
||||
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
|
||||
|
||||
async def get_models(
|
||||
self,
|
||||
provider: str,
|
||||
api_key: str | None = None,
|
||||
base_url: str | None = None,
|
||||
force_refresh: bool = False,
|
||||
) -> List[dict[str, Any]]:
|
||||
"""
|
||||
获取模型列表。
|
||||
|
||||
返回值会带上 context/supports_reasoning 等元数据，供前端直接渲染并自动
|
||||
回填上下文大小。
|
||||
"""
|
||||
logger.info(f"获取 {provider} 模型列表...")
|
||||
try:
|
||||
from app.agent.llm.provider import LLMProviderManager
|
||||
|
||||
return await LLMProviderManager().list_models(
|
||||
provider_id=provider,
|
||||
api_key=api_key,
|
||||
base_url=base_url,
|
||||
force_refresh=force_refresh,
|
||||
)
|
||||
except Exception as err:
|
||||
logger.debug(f"LLM provider 目录不可用，回退旧模型列表逻辑: {err}")
|
||||
if provider == "google":
|
||||
return [
|
||||
{"id": model_id, "name": model_id}
|
||||
for model_id in await self._get_google_models(api_key or "")
|
||||
]
|
||||
return [
|
||||
{"id": model_id, "name": model_id}
|
||||
for model_id in await self._get_openai_compatible_models(
|
||||
provider,
|
||||
api_key or "",
|
||||
base_url,
|
||||
)
|
||||
]
|
||||
|
||||
@staticmethod
|
||||
async 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 = await client.aio.models.list()
|
||||
result = [
|
||||
m.name
|
||||
for m in models.page
|
||||
if m.supported_actions and "generateContent" in m.supported_actions
|
||||
]
|
||||
await client.aio.aclose()
|
||||
return result
|
||||
except Exception as e:
|
||||
logger.error(f"获取Google模型列表失败：{e}")
|
||||
raise e
|
||||
|
||||
@staticmethod
|
||||
async 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 AsyncOpenAI
|
||||
|
||||
if provider == "deepseek":
|
||||
base_url = base_url or "https://api.deepseek.com"
|
||||
|
||||
client = AsyncOpenAI(api_key=api_key, base_url=base_url)
|
||||
models = await client.models.list()
|
||||
await client.close()
|
||||
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
|
||||
Reference in New Issue
Block a user