qing
53ddcb364f
- CreateSkillTool/InstallSkillTool 改为生成带 .md 扩展名的技能文件，与人格技能文件规范一致 - 同步更新 7_extension_skills.md 模板与工具描述 - 删除旧版 smtp.py（P0-1）并同步审计文档与 README
273 lines
11 KiB
Python
273 lines
11 KiB
Python
# -*- coding: utf-8 -*-
|
|
"""
|
|
大模型客户端抽象层
|
|
|
|
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。
|
|
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：
|
|
1. 新增一个 `LLMClient` 子类实现；
|
|
2. 在 `create_llm_client` 工厂里按 `api.provider` 进行路由。
|
|
|
|
当前内置实现：
|
|
- `DeepSeekClient`：兼容 OpenAI Chat Completions 协议（DeepSeek / Kimi / Qwen / GLM / Doubao 等绝大多数厂商均可复用）。
|
|
|
|
统一流式事件格式（`stream_chat` 逐条 yield 的 dict）：
|
|
{"type": "content", "text": str} # 增量正文
|
|
{"type": "reasoning", "text": str} # 增量思考内容（可选，厂商支持时）
|
|
{"type": "tool_call", "index": int, "id": str, "name": str, "arguments": str} # 工具调用增量
|
|
{"type": "done"} # 流结束
|
|
"""
|
|
|
|
from abc import ABC, abstractmethod
|
|
from typing import Dict, Iterator, List, Optional
|
|
|
|
import json
|
|
import os
|
|
import requests
|
|
|
|
|
|
class LLMClient(ABC):
|
|
"""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。"""
|
|
|
|
def __init__(self, api_key: str, model: str, base_url: str,
|
|
timeout: int, config=None, logger=None):
|
|
self.api_key = api_key
|
|
self.model = model
|
|
self.base_url = base_url
|
|
self.timeout = timeout
|
|
self.config = config # 可选，用于读取代理等动态配置
|
|
self.logger = logger # 可选，用于日志输出
|
|
# 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
|
|
self._current_response = None
|
|
|
|
@property
|
|
def name(self) -> str:
|
|
"""厂商/模型展示名，业务层可用于上下文（如 history 里的 assistant 名）。"""
|
|
return "LLM"
|
|
|
|
# ---------- 工具方法 ----------
|
|
|
|
def _resolve_proxies(self) -> Optional[Dict[str, str]]:
|
|
"""从 config 解析 requests 可用的 proxies 字典（动态代理）。"""
|
|
if self.config is not None and hasattr(self.config, "get_proxy"):
|
|
return self.config.get_proxy()
|
|
return None
|
|
|
|
def _log(self, level: str, msg: str):
|
|
if self.logger is not None and hasattr(self.logger, level):
|
|
try:
|
|
getattr(self.logger, level)(msg)
|
|
except Exception:
|
|
pass
|
|
|
|
def cancel(self):
|
|
"""中断当前流式请求。线程安全，可从任意线程调用。"""
|
|
resp = self._current_response
|
|
self._current_response = None
|
|
if resp is not None:
|
|
try:
|
|
# 先关闭底层 raw socket，强制阻塞的 recv() 立即抛出异常
|
|
if getattr(resp, "raw", None) is not None:
|
|
resp.raw.close()
|
|
except Exception:
|
|
pass
|
|
try:
|
|
resp.close()
|
|
except Exception:
|
|
pass
|
|
|
|
# ---------- 抽象接口 ----------
|
|
|
|
@abstractmethod
|
|
def chat_completion(self, messages: List[Dict], temperature: float = 0.3,
|
|
max_tokens: int = 1024, **kwargs) -> str:
|
|
"""非流式对话，返回完整文本。用于对话压缩等离线摘要场景。"""
|
|
|
|
@abstractmethod
|
|
def stream_chat(self, messages: List[Dict], temperature: float = 0.7,
|
|
max_tokens: int = 8192, tools: Optional[List[Dict]] = None,
|
|
tool_choice: Optional[str] = None,
|
|
reasoning_enabled: bool = False, **extra) -> Iterator[Dict]:
|
|
"""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。"""
|
|
|
|
|
|
class DeepSeekClient(LLMClient):
|
|
"""DeepSeek 客户端（OpenAI Chat Completions 协议兼容）。
|
|
|
|
思考模式通过 `thinking: {"type": "enabled"}` 参数控制，思考内容字段为
|
|
`delta.reasoning_content`。二者均为 DeepSeek 特色，其他 OpenAI 兼容厂商
|
|
若字段不同，可在子类中覆写 `_parse_delta` 归一化。
|
|
"""
|
|
|
|
def __init__(self, api_key: str, model: str, base_url: str,
|
|
timeout: int = 30, config=None, logger=None):
|
|
super().__init__(api_key, model, base_url, timeout, config, logger)
|
|
|
|
@property
|
|
def name(self) -> str:
|
|
return "DeepSeek"
|
|
|
|
def _headers(self) -> Dict[str, str]:
|
|
return {
|
|
"Authorization": f"Bearer {self.api_key}",
|
|
"Content-Type": "application/json",
|
|
}
|
|
|
|
def chat_completion(self, messages: List[Dict], temperature: float = 0.3,
|
|
max_tokens: int = 1024, **kwargs) -> str:
|
|
payload = {
|
|
"model": self.model,
|
|
"messages": messages,
|
|
"temperature": temperature,
|
|
"max_tokens": max_tokens,
|
|
}
|
|
payload.update(kwargs)
|
|
try:
|
|
response = requests.post(
|
|
self.base_url,
|
|
headers=self._headers(),
|
|
json=payload,
|
|
timeout=30,
|
|
proxies=self._resolve_proxies(),
|
|
)
|
|
if response.status_code == 200:
|
|
result = response.json()
|
|
return result["choices"][0]["message"]["content"].strip()
|
|
self._log("error", f"LLM 压缩请求失败: HTTP {response.status_code}")
|
|
return None
|
|
except Exception as e:
|
|
self._log("error", f"LLM 压缩请求异常: {e}")
|
|
return None
|
|
|
|
def stream_chat(self, messages: List[Dict], temperature: float = 0.7,
|
|
max_tokens: int = 8192, tools: Optional[List[Dict]] = None,
|
|
tool_choice: Optional[str] = None,
|
|
reasoning_enabled: bool = False, **extra) -> Iterator[Dict]:
|
|
payload = {
|
|
"model": self.model,
|
|
"messages": messages,
|
|
"temperature": temperature,
|
|
"max_tokens": max_tokens,
|
|
"stream": True,
|
|
}
|
|
# 思考模式
|
|
if reasoning_enabled:
|
|
payload["thinking"] = {"type": "enabled"}
|
|
# 工具调用
|
|
if tools:
|
|
payload["tools"] = tools
|
|
payload["tool_choice"] = tool_choice or "auto"
|
|
payload.update(extra)
|
|
|
|
proxies = self._resolve_proxies()
|
|
response = requests.post(
|
|
self.base_url,
|
|
headers=self._headers(),
|
|
json=payload,
|
|
timeout=self.timeout,
|
|
stream=True,
|
|
proxies=proxies,
|
|
)
|
|
self._current_response = response
|
|
|
|
if response.status_code != 200:
|
|
raise RuntimeError(
|
|
f"HTTP {response.status_code}: {response.text}")
|
|
|
|
try:
|
|
for line in response.iter_lines():
|
|
if not line:
|
|
continue
|
|
line_text = line.decode("utf-8")
|
|
if not line_text.startswith("data: "):
|
|
continue
|
|
data = line_text[6:]
|
|
if data == "[DONE]":
|
|
yield {"type": "done"}
|
|
break
|
|
try:
|
|
chunk = json.loads(data)
|
|
except json.JSONDecodeError as e:
|
|
self._log("error", f"JSON 解析错误: {e}, 数据: {data}")
|
|
continue
|
|
choices = chunk.get("choices", [])
|
|
if not choices:
|
|
continue
|
|
delta = choices[0].get("delta", {})
|
|
for event in self._parse_delta(delta, reasoning_enabled):
|
|
yield event
|
|
finally:
|
|
self._current_response = None
|
|
|
|
def _parse_delta(self, delta: Dict, reasoning_enabled: bool) -> Iterator[Dict]:
|
|
"""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。"""
|
|
# 思考内容
|
|
if reasoning_enabled and delta.get("reasoning_content"):
|
|
yield {"type": "reasoning", "text": delta["reasoning_content"]}
|
|
# 正文
|
|
if delta.get("content"):
|
|
yield {"type": "content", "text": delta["content"]}
|
|
# 工具调用（增量）
|
|
for tc in delta.get("tool_calls", []):
|
|
index = tc.get("index", 0)
|
|
function = tc.get("function", {})
|
|
yield {
|
|
"type": "tool_call",
|
|
"index": index,
|
|
"id": tc.get("id", ""),
|
|
"name": function.get("name", ""),
|
|
"arguments": function.get("arguments", ""),
|
|
}
|
|
|
|
|
|
def resolve_api_key(api_key: Optional[str], config=None) -> str:
|
|
"""解析 API Key：优先显式传入，其次环境变量，最后 ~/.cert/deepseek.key。"""
|
|
if api_key:
|
|
return api_key
|
|
env_name = 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"
|
|
env_key = os.environ.get(env_name)
|
|
if env_key:
|
|
return env_key
|
|
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 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。可通过 `api.provider` 切换厂商。
|
|
|
|
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: 可选模型名覆盖；为 None 时使用配置中的 `api.deepseek_model`。
|
|
子代理等场景可通过此参数显式指定与主代理不同的模型。
|
|
"""
|
|
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)
|
|
|
|
if provider in ("deepseek", "deepseek-chat"):
|
|
return DeepSeekClient(
|
|
api_key=resolved_key,
|
|
model=model,
|
|
base_url=base_url,
|
|
timeout=timeout,
|
|
config=config,
|
|
logger=logger,
|
|
)
|
|
|
|
# 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}") |