qing
0e9efe4895
Profiles previously had no validation, so config errors surfaced far from their origin and cryptically: - explicit profile missing `model` fell back to the profile NAME as the model (a profile named "local" would send "model": "local") - default profile missing `model` with no model param produced "model": null -> API 400 - missing `api_url` surfaced as requests InvalidURL _resolve_model_profile now runs _validate_profile (dict type + api_url) before reading fields and _require_model on the effective model, raising ValueErrors that name the profile, the missing field, and the config.json path to fix. Bare model names still legally substitute for a default profile's missing model field. Tests: new TestH2SchemaValidation (6 cases: missing model/api_url, null-model via default profile, bare-name substitution, non-dict profile, e2e create_llm_client); full suite 282 passed.
374 lines
16 KiB
Python
374 lines
16 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 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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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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# 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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@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 "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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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 = 0.3,
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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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@abstractmethod
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def stream_chat(self, messages: List[Dict], temperature: float = 0.7,
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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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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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super().__init__(api_key, model, base_url, timeout, config, logger)
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@property
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def name(self) -> str:
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return "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 = 0.3,
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max_tokens: int = 1024, **kwargs) -> str:
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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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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 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 = 0.7,
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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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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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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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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
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if reasoning_enabled and delta.get("reasoning_content"):
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yield {"type": "reasoning", "text": delta["reasoning_content"]}
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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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def resolve_api_key(api_key: Optional[str], config=None,
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env_name: Optional[str] = None,
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allow_missing: bool = False,
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allow_key_file: bool = True) -> str:
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"""解析 API Key：优先显式传入，其次环境变量，最后 ~/.cert/deepseek.key。
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env_name: 档案级环境变量名（来自 models.<name>.api_key_env）；
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None 时回退 config 的 api.api_key_env。
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allow_missing: 本地免密端点（models.<name>.api_key_env 为空串）时，
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允许无 Key，返回空串（Bearer 空头，Ollama/vLLM 忽略）。
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allow_key_file: 是否允许回退读取 ~/.cert/deepseek.key。该文件是 DeepSeek
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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
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时直接报错，绝不把 A 厂商 Key 发给 B 厂商（跨厂商凭据泄漏）。
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"""
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if api_key:
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return api_key
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if env_name is None:
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if config is not None and hasattr(config, "get"):
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env_name = config.get("api.api_key_env")
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env_name = env_name or "DEEPSEEK_API_KEY"
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if not env_name: # 档案显式置空 → 免密端点
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if allow_missing:
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return ""
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raise RuntimeError("模型档案 api_key_env 配置为空且未启用免密")
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env_key = os.environ.get(env_name)
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if env_key:
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return env_key
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if allow_missing:
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return ""
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if not allow_key_file:
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raise RuntimeError(
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f"模型档案端点缺少 API Key：请设置环境变量 {env_name}，"
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f"或将该档案的 api_key_env 配置为空串以启用本地免密端点")
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try:
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home = os.environ.get("HOME", os.environ["USERPROFILE"])
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key_path = os.path.join(home, ".cert", "deepseek.key")
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with open(key_path, "r") as f:
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return f.read().strip()
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except Exception:
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raise RuntimeError(
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f"请设置环境变量 {env_name} 或在配置文件中设置 API Key")
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def _validate_profile(name: str, profile) -> None:
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"""H2：档案类型与 api_url 校验（在读取档案字段之前调用）。"""
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if not isinstance(profile, dict):
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raise ValueError(
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f"模型档案 '{name}' 配置无效：应为对象（字典），"
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f"实际为 {type(profile).__name__}，请检查 config.json 的 models.{name}")
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if not str(profile.get("api_url") or "").strip():
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raise ValueError(
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f"模型档案 '{name}' 缺少 api_url 字段，"
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f"请在 config.json 的 models.{name} 中补齐（OpenAI Chat Completions 端点 URL）")
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def _require_model(name: str, effective_model) -> None:
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"""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）。"""
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if not str(effective_model or "").strip():
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raise ValueError(
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f"模型档案 '{name}' 缺少 model 字段（且未传入裸模型名），"
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f"请在 config.json 的 models.{name} 中补齐，或调用时用 --model 指定模型名")
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def _resolve_model_profile(config, model: Optional[str]):
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"""解析模型档案。返回 (profile_dict, effective_model_name)。
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优先级：model 命中 models.<name> 档案 > api.default_model 档案 > None（回退旧 api 节）。
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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）。
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"""
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if config is None or not hasattr(config, "get"):
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return None, model
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models = config.get("models") or {}
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if not isinstance(models, dict):
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return None, model
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explicit = models.get(model) if model else None
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default_name = config.get("api.default_model")
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default_profile = models.get(default_name) if default_name else None
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if explicit is not None:
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# model 是档案名 → 完整采用该档案（url/key/模型名）；
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# 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）
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_validate_profile(model, explicit)
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effective = explicit.get("model")
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_require_model(model, effective)
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return explicit, effective
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if default_profile is not None:
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# model 是裸名或未指定 → 默认档案的 url/key，模型名被裸名覆盖
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_validate_profile(default_name, default_profile)
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effective = model or default_profile.get("model")
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_require_model(default_name, effective)
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return default_profile, effective
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return None, model
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def create_llm_client(config, api_key: Optional[str] = None,
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logger=None, model: Optional[str] = None) -> LLMClient:
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"""根据配置创建对应厂商的 LLM 客户端。
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模型选择优先级：
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1. model 命中 models 节档案名 → 采用该档案（api_url/model/api_key_env/timeout）
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2. api.default_model 指向的档案 → model 为裸模型名时覆盖档案内模型名
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3. 均未配置 → 回退旧 api 扁平节（deepseek_api_url/deepseek_model）
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provider: deepseek / openai_compatible / openai 均映射到 DeepSeekClient
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（OpenAI Chat Completions 协议兼容实现，厂商差异由子类覆写 _parse_delta）。
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Args:
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config: 配置对象（需提供 get 方法）。
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api_key: 显式传入的 API Key；为 None 时按 resolve_api_key 解析。
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logger: 可选日志对象。
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model: 档案名或裸模型名。
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"""
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profile, model = _resolve_model_profile(config, model)
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env_name = None
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allow_missing = False
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if profile is not None:
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base_url = profile.get("api_url")
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timeout = profile.get("timeout") or config.get("api.timeout", 30)
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env_name = profile.get("api_key_env")
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if env_name == "":
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allow_missing = True # 档案显式空串 → 本地免密端点
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else:
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provider = (config.get("api.provider", "deepseek") or "deepseek").lower()
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base_url = config.get("api.deepseek_api_url")
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model = model or config.get("api.deepseek_model", "deepseek-v4-flash")
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timeout = config.get("api.timeout", 30)
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resolved_key = resolve_api_key(api_key, config, env_name=env_name,
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allow_missing=allow_missing,
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allow_key_file=profile is None)
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if profile is not None:
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provider = "openai_compatible"
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if provider in ("deepseek", "deepseek-chat", "openai_compatible", "openai"):
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client = DeepSeekClient(
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api_key=resolved_key,
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model=model,
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base_url=base_url,
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timeout=timeout,
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config=config,
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logger=logger,
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)
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# 档案级思考模式开关（未配置时回退全局 api.reasoning_enabled）
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if profile is not None:
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client.reasoning_enabled = profile.get(
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"reasoning_enabled", config.get("api.reasoning_enabled", False))
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return client
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# 其他 OpenAI 兼容厂商：可直接复用 DeepSeekClient（继承其协议解析），
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# 或在此接入专有实现。新增厂商时在此扩展即可。
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raise ValueError(f"未知的模型提供商: {provider}") |