qing
d53eba77f0
FailoverClient.stream_chat now walks the chain but only fails over while no event has been produced yet — replay before the first reasoning/content/tool_call event is invisible to the user. Once any event is out, failures propagate directly (replay would duplicate frontend output and corrupt tool-call accumulation). DeepSeekClient.stream_chat classifies non-200 responses: 429/5xx raise TransientLLMError (failover-eligible), auth/param 4xx keep raising RuntimeError. Chain exhaustion re-raises the last exception so the existing rollback/error handling in think_and_respond catches it; an all-cooling chain raises RuntimeError. Cancel-flag guard: LLMClient.cancel() sets _cancelled because the RequestException raised by force-closing the connection is indistinguishable from a real network fault. FailoverClient.cancel() is now defined explicitly (overriding __getattr__ delegation), broadcasts to every client in the chain and sets the wrapper flag; stream_chat/chat_completion reset it per call and suppress failover when it is set — otherwise interrupting would make the system hop to another vendor and keep generating against the user's intent. Tests: new TestL4bStreamFailover (12 cases: failover before first event on TransientLLMError/ConnectionError, no failover after first event, cancel never fails over on both paths, cancel broadcast, chain exhaustion, all-cooling, cooldown skip, success isolation, transient/ auth status classification). Full suite 342 passed.
612 lines
29 KiB
Python
612 lines
29 KiB
Python
# -*- coding: utf-8 -*-
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"""
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大模型客户端抽象层
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为 aibox 提供统一的 LLM 调用接口，将「调用哪个厂商 / 哪个模型」与业务逻辑解耦。
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业务层只依赖 `LLMClient` 抽象方法与统一的流式事件格式，切换厂商只需：
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1. 新增一个 `LLMClient` 子类实现；
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2. 在 `create_llm_client` 工厂里按 `api.provider` 进行路由。
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当前内置实现：
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- `DeepSeekClient`：兼容 OpenAI Chat Completions 协议（DeepSeek / Kimi / Qwen / GLM / Doubao 等绝大多数厂商均可复用）。
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统一流式事件格式（`stream_chat` 逐条 yield 的 dict）：
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{"type": "content", "text": str} # 增量正文
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{"type": "reasoning", "text": str} # 增量思考内容（可选，厂商支持时）
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{"type": "tool_call", "index": int, "id": str, "name": str, "arguments": str} # 工具调用增量
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{"type": "done"} # 流结束
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"""
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from abc import ABC, abstractmethod
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from typing import Dict, Iterator, List, Optional
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import json
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import os
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import time
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import requests
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class LLMClient(ABC):
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"""大模型客户端抽象基类。子类需实现流式/非流式两种调用，并归一化为统一事件格式。"""
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def __init__(self, api_key: str, model: str, base_url: str,
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timeout: int, config=None, logger=None,
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reasoning_enabled: bool = None,
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display_name: str = None, default_temperature: float = None):
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self.api_key = api_key
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self.model = model
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self.base_url = base_url
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self.timeout = timeout
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self.config = config # 可选，用于读取代理等动态配置
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self.logger = logger # 可选，用于日志输出
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# M3：档案级思考模式开关为构造契约；None = 未配置（消费方回退全局 api.reasoning_enabled）
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self.reasoning_enabled = reasoning_enabled
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# L1：展示名（默认取档案名），业务层上下文（如 history 的 assistant 名）不再误标 "DeepSeek"
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self._display_name = display_name
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# L3：档案级默认采样温度；None = 未配置（方法参数未显式传入时回退硬编码默认值）
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self.default_temperature = default_temperature
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# 当前活动的 HTTP 响应引用，供 cancel() 强制关闭底层连接以中断阻塞读取
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self._current_response = None
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# L4b：用户中断标志——cancel() 强关连接引发的异常与真实网络故障不可区分，
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# FailoverClient 据此标志跳过转移（否则用户越中断、系统越换厂商重试）
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self._cancelled = False
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@property
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def name(self) -> str:
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"""厂商/模型展示名，业务层可用于上下文（如 history 里的 assistant 名）。"""
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return self._display_name or "LLM"
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# ---------- 工具方法 ----------
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def _resolve_proxies(self) -> Optional[Dict[str, str]]:
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"""从 config 解析 requests 可用的 proxies 字典（动态代理）。"""
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if self.config is not None and hasattr(self.config, "get_proxy"):
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return self.config.get_proxy()
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return None
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def _log(self, level: str, msg: str):
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if self.logger is not None and hasattr(self.logger, level):
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try:
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getattr(self.logger, level)(msg)
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except Exception:
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pass
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def cancel(self):
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"""中断当前流式请求。线程安全，可从任意线程调用。"""
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self._cancelled = True
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resp = self._current_response
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self._current_response = None
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if resp is not None:
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try:
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# 先关闭底层 raw socket，强制阻塞的 recv() 立即抛出异常
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if getattr(resp, "raw", None) is not None:
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resp.raw.close()
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except Exception:
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pass
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try:
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resp.close()
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except Exception:
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pass
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# ---------- 抽象接口 ----------
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@abstractmethod
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def chat_completion(self, messages: List[Dict], temperature: float = None,
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max_tokens: int = 1024, **kwargs) -> str:
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"""非流式对话，返回完整文本。用于对话压缩等离线摘要场景。
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temperature=None 时按 default_temperature（档案级）→ 0.3 回退。"""
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@abstractmethod
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def stream_chat(self, messages: List[Dict], temperature: float = None,
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max_tokens: int = 8192, tools: Optional[List[Dict]] = None,
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tool_choice: Optional[str] = None,
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reasoning_enabled: bool = False, **extra) -> Iterator[Dict]:
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"""流式对话，逐条 yield 统一事件格式的 dict。业务层据此驱动输出/工具调用。
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temperature=None 时按 default_temperature（档案级）→ 0.7 回退。"""
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class DeepSeekClient(LLMClient):
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"""DeepSeek 客户端（OpenAI Chat Completions 协议兼容）。
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思考模式通过 `thinking: {"type": "enabled"}` 参数控制，思考内容字段为
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`delta.reasoning_content`。二者均为 DeepSeek 特色，其他 OpenAI 兼容厂商
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若字段不同，可在子类中覆写 `_parse_delta` 归一化。
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"""
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def __init__(self, api_key: str, model: str, base_url: str,
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timeout: int = 30, config=None, logger=None,
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reasoning_enabled: bool = None,
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display_name: str = None, default_temperature: float = None):
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super().__init__(api_key, model, base_url, timeout, config, logger,
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reasoning_enabled=reasoning_enabled,
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display_name=display_name,
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default_temperature=default_temperature)
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@property
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def name(self) -> str:
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# L1：档案命中时展示档案名（或档案 display_name），旧扁平节保持 "DeepSeek"
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return self._display_name or "DeepSeek"
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def _headers(self) -> Dict[str, str]:
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return {
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"Authorization": f"Bearer {self.api_key}",
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"Content-Type": "application/json",
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}
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def chat_completion(self, messages: List[Dict], temperature: float = None,
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max_tokens: int = 1024, **kwargs) -> str:
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# L3：None → 档案级 default_temperature → 硬编码默认（与旧版行为一致）
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if temperature is None:
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temperature = self.default_temperature if self.default_temperature is not None else 0.3
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payload = {
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"model": self.model,
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"messages": messages,
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"temperature": temperature,
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"max_tokens": max_tokens,
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}
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payload.update(kwargs)
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try:
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response = requests.post(
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self.base_url,
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headers=self._headers(),
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json=payload,
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timeout=self.timeout,
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proxies=self._resolve_proxies(),
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)
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if response.status_code == 200:
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result = response.json()
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return result["choices"][0]["message"]["content"].strip()
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if response.status_code == 429 or response.status_code >= 500:
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# L4a：瞬时故障显式抛出——FailoverClient 据此转移下一档案；
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# 无包装器时由调用方 catch-all 接住，下游仍走失败分支（语义等价）
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raise TransientLLMError(
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f"LLM 请求瞬时失败: HTTP {response.status_code}: {response.text[:200]}")
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self._log("error", f"LLM 压缩请求失败: HTTP {response.status_code}")
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return None
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except TransientLLMError:
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raise # L4a：瞬时故障上抛供 FailoverClient 转移，不吞
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except (requests.exceptions.ConnectionError, requests.exceptions.Timeout):
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raise # L4a：连接类瞬时故障同样上抛（原被吞成 None，无法转移）
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except Exception as e:
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self._log("error", f"LLM 压缩请求异常: {e}")
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return None
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def stream_chat(self, messages: List[Dict], temperature: float = None,
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max_tokens: int = 8192, tools: Optional[List[Dict]] = None,
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tool_choice: Optional[str] = None,
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reasoning_enabled: bool = False, **extra) -> Iterator[Dict]:
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# L3：None → 档案级 default_temperature → 硬编码默认（与旧版行为一致）
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if temperature is None:
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temperature = self.default_temperature if self.default_temperature is not None else 0.7
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payload = {
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"model": self.model,
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"messages": messages,
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"temperature": temperature,
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"max_tokens": max_tokens,
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"stream": True,
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}
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# 思考模式
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if reasoning_enabled:
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payload["thinking"] = {"type": "enabled"}
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# 工具调用
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if tools:
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payload["tools"] = tools
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payload["tool_choice"] = tool_choice or "auto"
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payload.update(extra)
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proxies = self._resolve_proxies()
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response = requests.post(
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self.base_url,
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headers=self._headers(),
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json=payload,
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timeout=self.timeout,
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stream=True,
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proxies=proxies,
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)
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self._current_response = response
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if response.status_code != 200:
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if response.status_code == 429 or response.status_code >= 500:
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# L4b：瞬时故障显式分类（此时尚未产出任何事件，可安全转移重放）
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raise TransientLLMError(
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f"LLM 流式请求瞬时失败: HTTP {response.status_code}: {response.text[:200]}")
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raise RuntimeError(
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f"HTTP {response.status_code}: {response.text}")
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try:
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for line in response.iter_lines():
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if not line:
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continue
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line_text = line.decode("utf-8")
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if not line_text.startswith("data: "):
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continue
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data = line_text[6:]
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if data == "[DONE]":
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yield {"type": "done"}
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break
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try:
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chunk = json.loads(data)
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except json.JSONDecodeError as e:
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self._log("error", f"JSON 解析错误: {e}, 数据: {data}")
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continue
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choices = chunk.get("choices", [])
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if not choices:
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continue
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delta = choices[0].get("delta", {})
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for event in self._parse_delta(delta, reasoning_enabled):
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yield event
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finally:
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self._current_response = None
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# L5：思考内容的常见厂商字段别名（首个命中的非空字段生效）
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REASONING_FIELDS = ("reasoning_content", "reasoning", "thinking", "thinking_content")
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def _parse_delta(self, delta: Dict, reasoning_enabled: bool) -> Iterator[Dict]:
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"""将单条 delta 归一化为统一事件。子类覆写以适配不同厂商字段。"""
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# 思考内容：DeepSeek reasoning_content / OpenRouter reasoning /
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# 部分 GLM thinking(_content) —— 此前仅识别 DeepSeek 字段，其他厂商思考流静默丢失
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if reasoning_enabled:
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for field in self.REASONING_FIELDS:
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value = delta.get(field)
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if value:
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yield {"type": "reasoning", "text": value}
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break
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# 正文
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if delta.get("content"):
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yield {"type": "content", "text": delta["content"]}
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# 工具调用（增量）
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for tc in delta.get("tool_calls", []):
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index = tc.get("index", 0)
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function = tc.get("function", {})
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yield {
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"type": "tool_call",
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"index": index,
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"id": tc.get("id", ""),
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"name": function.get("name", ""),
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"arguments": function.get("arguments", ""),
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}
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class TransientLLMError(RuntimeError):
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"""瞬时 LLM 故障（HTTP 429/5xx）——FailoverClient 据此转移下一档案（L4a）。"""
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class FailoverClient:
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"""跨档案失败转移包装器（L4a）。
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持有序 client 链（主档案 + fallback 档案），对外实现与 LLMClient 相同的
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消费面（未显式定义的属性经 __getattr__ 委托主 client）。
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L4a 范围——chat_completion（非流式幂等）按链转移：
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- 转移：HTTP 429/5xx（TransientLLMError）、连接失败/超时（requests 连接类异常）
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- 不转移：认证/参数类 4xx（底层返回 None，直接透传——配置问题应响亮暴露）
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失败档案进入冷却期（cooldown_seconds，默认 60s），期间直接跳过，
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避免每次请求都撞刚失败的端点。链耗尽返回 None（与 chat_completion 失败契约一致）。
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L4b 范围——stream_chat 仅**首事件前**故障转移（连接失败/HTTP 429/5xx，
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尚未产出任何 reasoning/content/tool_call 事件，重放对用户不可见）；
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已产出事件后失败直接上抛（重放会导致前端重复输出/工具增量错乱）；
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用户中断（cancel() 置位）引发的连接异常绝不转移。
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"""
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def __init__(self, clients: List, logger=None, cooldown_seconds: float = 60.0):
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self._clients = list(clients)
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self.logger = logger
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self.cooldown_seconds = cooldown_seconds
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self._fail_at = {} # client.name → 失败时刻（time.monotonic）
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self._cancelled = False
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def __getattr__(self, item):
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# 未显式定义的属性/方法委托主 client（name/model/api_key/cancel 等）；
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# 下划线属性直接报错，防 __init__ 前访问 self._clients 的无限递归
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if item.startswith("_"):
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raise AttributeError(item)
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return getattr(self._clients[0], item)
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def _log(self, level: str, msg: str):
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if self.logger is not None and hasattr(self.logger, level):
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try:
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getattr(self.logger, level)(msg)
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except Exception:
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pass
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def _cooling(self, client) -> bool:
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t = self._fail_at.get(client.name)
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return t is not None and (time.monotonic() - t) < self.cooldown_seconds
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def _mark_fail(self, client):
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self._fail_at[client.name] = time.monotonic()
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def cancel(self):
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"""L4b：中断链上全部 client（任一可能正在流式中）并置自身中断标志。
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显式定义以覆盖 __getattr__ 委托——包装器必须同时通知整条链。
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"""
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self._cancelled = True
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for client in self._clients:
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try:
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client.cancel()
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except Exception:
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pass
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def chat_completion(self, messages: List[Dict], temperature: float = None,
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max_tokens: int = 1024, **kwargs):
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self._cancelled = False # 新一轮请求，清除上一轮中断标志
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last_exc = None
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tried = 0
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for client in self._clients:
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if self._cooling(client):
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self._log("info", f"模型档案 {client.name} 处于失败冷却期，跳过")
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continue
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tried += 1
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try:
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result = client.chat_completion(messages, temperature=temperature,
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max_tokens=max_tokens, **kwargs)
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if result is not None:
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return result
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# None：认证/参数类 4xx 或响应异常——不可转移，直接透传
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return None
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except (TransientLLMError, requests.exceptions.RequestException) as e:
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if self._cancelled:
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return None # 用户中断引发的异常：不转移
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last_exc = e
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self._mark_fail(client)
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self._log("warning", f"模型档案 {client.name} 瞬时故障（{e}），转移下一档案")
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self._log("error", f"全部 {len(self._clients)} 个模型档案均不可用（尝试 {tried} 个）：{last_exc}")
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return None
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def stream_chat(self, messages: List[Dict], temperature: float = None,
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max_tokens: int = 8192, tools: Optional[List[Dict]] = None,
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tool_choice: Optional[str] = None,
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reasoning_enabled: bool = False, **extra) -> Iterator[Dict]:
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"""L4b：流式转移——仅首事件前故障可安全重放（对用户不可见）。"""
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self._cancelled = False # 新一轮请求，清除上一轮中断标志
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last_exc = None
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for client in self._clients:
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if self._cooling(client):
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self._log("info", f"模型档案 {client.name} 处于失败冷却期，跳过")
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continue
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produced_any = False
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try:
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for event in client.stream_chat(
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messages, temperature=temperature, max_tokens=max_tokens,
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tools=tools, tool_choice=tool_choice,
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reasoning_enabled=reasoning_enabled, **extra):
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produced_any = True
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yield event
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return # 自然结束（含 done 事件）视为成功
|
||
except (TransientLLMError, requests.exceptions.RequestException) as e:
|
||
if self._cancelled:
|
||
raise # 用户中断（cancel 强关连接）：绝不转移
|
||
if produced_any:
|
||
raise # 已向消费方产出事件，重放会重复输出/工具增量错乱
|
||
last_exc = e
|
||
self._mark_fail(client)
|
||
self._log("warning",
|
||
f"模型档案 {client.name} 流式瞬时故障（首事件前，{e}），转移下一档案")
|
||
if last_exc is not None:
|
||
raise last_exc # 链耗尽：抛最后异常，由既有错误处理（回滚/报错）接住
|
||
raise RuntimeError(
|
||
f"全部 {len(self._clients)} 个模型档案处于失败冷却期，暂不可用")
|
||
|
||
|
||
def resolve_api_key(api_key: Optional[str], config=None,
|
||
env_name: Optional[str] = None,
|
||
allow_missing: bool = False,
|
||
allow_key_file: bool = True) -> str:
|
||
"""解析 API Key：优先显式传入，其次环境变量，最后 ~/.cert/deepseek.key。
|
||
|
||
env_name: 档案级环境变量名（来自 models.<name>.api_key_env）；
|
||
None 时回退 config 的 api.api_key_env。
|
||
allow_missing: 本地免密端点（models.<name>.api_key_env 为空串）时，
|
||
允许无 Key，返回空串（Bearer 空头，Ollama/vLLM 忽略）。
|
||
allow_key_file: 是否允许回退读取 ~/.cert/deepseek.key。该文件是 DeepSeek
|
||
专属凭据，模型档案端点（其他厂商）必须传 False——环境变量取不到
|
||
时直接报错，绝不把 A 厂商 Key 发给 B 厂商（跨厂商凭据泄漏）。
|
||
"""
|
||
if api_key:
|
||
return api_key
|
||
if env_name is None:
|
||
if config is not None and hasattr(config, "get"):
|
||
env_name = config.get("api.api_key_env")
|
||
env_name = env_name or "DEEPSEEK_API_KEY"
|
||
if not env_name: # 档案显式置空 → 免密端点
|
||
if allow_missing:
|
||
return ""
|
||
raise RuntimeError("模型档案 api_key_env 配置为空且未启用免密")
|
||
env_key = os.environ.get(env_name)
|
||
if env_key:
|
||
return env_key
|
||
if allow_missing:
|
||
return ""
|
||
if not allow_key_file:
|
||
raise RuntimeError(
|
||
f"模型档案端点缺少 API Key：请设置环境变量 {env_name}，"
|
||
f"或将该档案的 api_key_env 配置为空串以启用本地免密端点")
|
||
try:
|
||
home = os.environ.get("HOME", os.environ["USERPROFILE"])
|
||
key_path = os.path.join(home, ".cert", "deepseek.key")
|
||
with open(key_path, "r") as f:
|
||
return f.read().strip()
|
||
except Exception:
|
||
raise RuntimeError(
|
||
f"请设置环境变量 {env_name} 或在配置文件中设置 API Key")
|
||
|
||
|
||
def _validate_profile(name: str, profile) -> None:
|
||
"""H2：档案类型与 api_url 校验（在读取档案字段之前调用）。"""
|
||
if not isinstance(profile, dict):
|
||
raise ValueError(
|
||
f"模型档案 '{name}' 配置无效：应为对象（字典），"
|
||
f"实际为 {type(profile).__name__}，请检查 config.json 的 models.{name}")
|
||
if not str(profile.get("api_url") or "").strip():
|
||
raise ValueError(
|
||
f"模型档案 '{name}' 缺少 api_url 字段，"
|
||
f"请在 config.json 的 models.{name} 中补齐（OpenAI Chat Completions 端点 URL）")
|
||
fb = profile.get("fallback")
|
||
if fb is not None and (not isinstance(fb, list) or not all(isinstance(x, str) for x in fb)):
|
||
raise ValueError(
|
||
f"模型档案 '{name}' 的 fallback 字段无效：应为档案名字符串列表"
|
||
f"（如 [\"kimi\", \"local\"]），实际为 {type(fb).__name__}，"
|
||
f"请检查 config.json 的 models.{name}")
|
||
|
||
|
||
def _require_model(name: str, effective_model) -> None:
|
||
"""H2：有效模型名校验——缺 model 会把档案名当模型名发给 API（或 "model": null → 400）。"""
|
||
if not str(effective_model or "").strip():
|
||
raise ValueError(
|
||
f"模型档案 '{name}' 缺少 model 字段（且未传入裸模型名），"
|
||
f"请在 config.json 的 models.{name} 中补齐，或调用时用 --model 指定模型名")
|
||
|
||
|
||
def _resolve_model_profile(config, model: Optional[str]):
|
||
"""解析模型档案。返回 (profile_dict, effective_model_name, profile_name)。
|
||
|
||
优先级：model 命中 models.<name> 档案 > api.default_model 档案 > None（回退旧 api 节）。
|
||
model 未命中档案时视为裸模型名（同端点换模型，用默认档案的 url/key，模型名用裸名）。
|
||
|
||
L2 前缀逃逸（档案名与裸模型名歧义时使用）：
|
||
- "model:NAME" 强制按裸模型名解析（档案恰好叫 NAME 时仍走裸名语义）
|
||
- "profile:NAME" 强制按档案名解析（不存在则报错，不再静默落回裸名语义）
|
||
"""
|
||
if config is None or not hasattr(config, "get"):
|
||
return None, model, None
|
||
models = config.get("models") or {}
|
||
if not isinstance(models, dict):
|
||
return None, model, None
|
||
|
||
force_bare = False
|
||
if isinstance(model, str) and ":" in model:
|
||
prefix, _, rest = model.partition(":")
|
||
if prefix == "model":
|
||
if not rest.strip():
|
||
raise ValueError("model: 前缀后为空，请提供裸模型名，如 model:deepseek-chat")
|
||
model, force_bare = rest, True
|
||
elif prefix == "profile":
|
||
if not rest.strip():
|
||
raise ValueError("profile: 前缀后为空，请提供档案名，如 profile:kimi")
|
||
prof = models.get(rest)
|
||
if prof is None:
|
||
raise ValueError(
|
||
f"模型档案 '{rest}' 不存在，可用档案：{', '.join(models) or '（无）'}")
|
||
_validate_profile(rest, prof)
|
||
effective = prof.get("model")
|
||
_require_model(rest, effective)
|
||
return prof, effective, rest
|
||
|
||
explicit = models.get(model) if (model and not force_bare) else None
|
||
default_name = config.get("api.default_model")
|
||
default_profile = models.get(default_name) if default_name else None
|
||
|
||
if explicit is not None:
|
||
# model 是档案名 → 完整采用该档案（url/key/模型名）；
|
||
# 档案缺 model 属配置错误，直接报错（不再把档案名当模型名发出）
|
||
_validate_profile(model, explicit)
|
||
effective = explicit.get("model")
|
||
_require_model(model, effective)
|
||
return explicit, effective, model
|
||
if default_profile is not None:
|
||
# model 是裸名或未指定 → 默认档案的 url/key，模型名被裸名覆盖
|
||
_validate_profile(default_name, default_profile)
|
||
effective = model or default_profile.get("model")
|
||
_require_model(default_name, effective)
|
||
return default_profile, effective, default_name
|
||
return None, model, None
|
||
|
||
|
||
def _construct_profile_client(config, profile: Dict, profile_name: str,
|
||
effective_model: str, logger=None,
|
||
api_key: Optional[str] = None) -> LLMClient:
|
||
"""L4a：按档案构造单个 DeepSeekClient（主路径与 fallback 链复用，不处理 fallback 字段）。"""
|
||
env_name = profile.get("api_key_env")
|
||
allow_missing = (env_name == "") # 档案显式空串 → 本地免密端点
|
||
resolved_key = resolve_api_key(api_key, config, env_name=env_name,
|
||
allow_missing=allow_missing, allow_key_file=False)
|
||
return DeepSeekClient(
|
||
api_key=resolved_key,
|
||
model=effective_model,
|
||
base_url=profile.get("api_url"),
|
||
timeout=profile.get("timeout") or config.get("api.timeout", 30),
|
||
config=config,
|
||
logger=logger,
|
||
reasoning_enabled=profile.get("reasoning_enabled",
|
||
config.get("api.reasoning_enabled", False)),
|
||
display_name=profile.get("display_name") or profile_name,
|
||
default_temperature=profile.get("temperature"),
|
||
)
|
||
|
||
|
||
def create_llm_client(config, api_key: Optional[str] = None,
|
||
logger=None, model: Optional[str] = None) -> LLMClient:
|
||
"""根据配置创建对应厂商的 LLM 客户端。
|
||
|
||
模型选择优先级：
|
||
1. model 命中 models 节档案名 → 采用该档案（api_url/model/api_key_env/timeout）
|
||
2. api.default_model 指向的档案 → model 为裸模型名时覆盖档案内模型名
|
||
3. 均未配置 → 回退旧 api 扁平节（deepseek_api_url/deepseek_model）
|
||
L2 前缀逃逸："model:NAME" 强制裸名 / "profile:NAME" 强制档案（--model 同样支持）。
|
||
|
||
provider: deepseek / openai_compatible / openai 均映射到 DeepSeekClient
|
||
（OpenAI Chat Completions 协议兼容实现，厂商差异由子类覆写 _parse_delta）。
|
||
|
||
Args:
|
||
config: 配置对象（需提供 get 方法）。
|
||
api_key: 显式传入的 API Key；为 None 时按 resolve_api_key 解析。
|
||
logger: 可选日志对象。
|
||
model: 档案名或裸模型名（支持 model:/profile: 前缀逃逸）。
|
||
"""
|
||
profile, model, profile_name = _resolve_model_profile(config, model)
|
||
|
||
if profile is None:
|
||
# 旧 api 扁平节（唯一允许回退 ~/.cert/deepseek.key 的路径，H1）
|
||
provider = (config.get("api.provider", "deepseek") or "deepseek").lower()
|
||
base_url = config.get("api.deepseek_api_url")
|
||
model = model or config.get("api.deepseek_model", "deepseek-v4-flash")
|
||
timeout = config.get("api.timeout", 30)
|
||
resolved_key = resolve_api_key(api_key, config, env_name=None,
|
||
allow_missing=False, allow_key_file=True)
|
||
else:
|
||
provider = "openai_compatible"
|
||
primary = _construct_profile_client(config, profile, profile_name,
|
||
model, logger=logger, api_key=api_key)
|
||
fallback_names = profile.get("fallback") or []
|
||
if not fallback_names:
|
||
return primary
|
||
# L4a：构造 fallback 链（一层，不递归——fallback 档案自身的 fallback 字段被忽略）
|
||
models = config.get("models") or {}
|
||
chain = [primary]
|
||
for fb_name in fallback_names:
|
||
if fb_name == profile_name:
|
||
raise ValueError(
|
||
f"模型档案 '{profile_name}' 的 fallback 不能包含自身，"
|
||
f"请检查 config.json 的 models.{profile_name}.fallback")
|
||
fb_profile = models.get(fb_name)
|
||
if fb_profile is None:
|
||
raise ValueError(
|
||
f"模型档案 '{profile_name}' 的 fallback 指向不存在的档案 '{fb_name}'，"
|
||
f"可用档案：{', '.join(models) or '（无）'}")
|
||
try:
|
||
chain.append(_construct_profile_client(
|
||
config, fb_profile, fb_name, fb_profile.get("model"),
|
||
logger=logger))
|
||
except RuntimeError as e:
|
||
# 环境问题（如 fallback 档案缺 API Key）：跳过该 fallback 并告警，
|
||
# 不阻断主档案可用性（配置结构错误在上方已硬性拦截）
|
||
if logger is not None and hasattr(logger, "warning"):
|
||
logger.warning(f"fallback 档案 '{fb_name}' 构造失败，已忽略：{e}")
|
||
return FailoverClient(chain, logger=logger)
|
||
|
||
if provider in ("deepseek", "deepseek-chat", "openai_compatible", "openai"):
|
||
client = DeepSeekClient(
|
||
api_key=resolved_key,
|
||
model=model,
|
||
base_url=base_url,
|
||
timeout=timeout,
|
||
config=config,
|
||
logger=logger,
|
||
)
|
||
return client
|
||
|
||
# 其他 OpenAI 兼容厂商：可直接复用 DeepSeekClient（继承其协议解析），
|
||
# 或在此接入专有实现。新增厂商时在此扩展即可。
|
||
raise ValueError(f"未知的模型提供商: {provider}") |