150 lines
4.5 KiB
Python
150 lines
4.5 KiB
Python
"""大模型调用客户端：OpenAI / DeepSeek / 阿里百炼 qwen / OpenAI 兼容。"""
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from __future__ import annotations
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import httpx
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PROVIDER_PRESETS = {
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"openai": {
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"label": "OpenAI",
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"base_url": "https://api.openai.com/v1",
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"model": "gpt-4o-mini",
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},
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"deepseek": {
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"label": "DeepSeek",
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"base_url": "https://api.deepseek.com/v1",
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"model": "deepseek-chat",
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},
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"qwen": {
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"label": "阿里千问（百炼）",
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"base_url": "https://dashscope.aliyuncs.com/compatible-mode/v1",
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"model": "qwen-plus",
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},
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"openai_compatible": {
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"label": "OpenAI 兼容（自定义）",
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"base_url": "",
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"model": "",
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},
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}
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def mask_key(api_key: str) -> str:
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if not api_key:
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return ""
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if len(api_key) <= 8:
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return "****"
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return f"****{api_key[-4:]}"
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def _content_text(value) -> str:
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"""兼容 content 为字符串或 OpenAI 多段数组（[{"type":"text","text":...}]）。"""
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if value is None:
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return ""
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if isinstance(value, str):
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return value
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if isinstance(value, list):
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chunks: list[str] = []
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for part in value:
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if isinstance(part, dict):
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text = part.get("text")
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if isinstance(text, str):
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chunks.append(text)
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else:
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chunks.append(str(part))
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return "".join(chunks)
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if isinstance(value, dict):
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text = value.get("text")
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return text if isinstance(text, str) else ""
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return str(value)
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def _message_text(message: dict) -> str:
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"""OpenAI 兼容网关常见差异：正文可能在 content 或 reasoning/reasoning_content。"""
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text = _content_text(message.get("content"))
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if text.strip():
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return text.strip()
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for key in ("reasoning_content", "reasoning"):
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fallback = _content_text(message.get(key))
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if fallback.strip():
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return fallback.strip()
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refusal = message.get("refusal")
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if isinstance(refusal, str) and refusal.strip():
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raise ValueError(f"模型拒绝回答：{refusal[:200]}")
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return ""
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def chat_completion(
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*,
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base_url: str,
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api_key: str,
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model: str,
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messages: list[dict[str, str]],
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temperature: float = 0.3,
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max_tokens: int = 1600,
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timeout: float = 120.0,
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) -> str:
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if not base_url or not api_key or not model:
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raise ValueError("请先完整填写模型配置（地址、API Key、模型名）。")
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endpoint = f"{base_url.rstrip('/')}/chat/completions"
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payload = {
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"model": 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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headers = {
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"Authorization": f"Bearer {api_key}",
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"Content-Type": "application/json",
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}
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try:
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with httpx.Client(timeout=timeout) as client:
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response = client.post(endpoint, json=payload, headers=headers)
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except httpx.HTTPError as exc:
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raise ValueError(f"无法连接模型服务：{exc.__class__.__name__}") from exc
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if response.status_code != 200:
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detail = response.text[:300]
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raise ValueError(f"模型服务返回 {response.status_code}：{detail}")
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try:
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data = response.json()
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message = data["choices"][0].get("message")
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if not isinstance(message, dict):
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raise KeyError("message")
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content = _message_text(message)
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if not content:
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finish_reason = data["choices"][0].get("finish_reason")
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raise ValueError(
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"模型没有返回可见文本"
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f"（finish_reason={finish_reason}）。"
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"若模型启用了推理模式，请提高 max_tokens 或缩短提示。"
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)
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return content
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except ValueError:
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raise
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except (KeyError, IndexError, TypeError) as exc:
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raise ValueError("模型返回格式不符合 Chat Completions 规范") from exc
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def quick_test(
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*,
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base_url: str,
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api_key: str,
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model: str,
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temperature: float = 0.0,
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) -> str:
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return chat_completion(
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base_url=base_url,
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api_key=api_key,
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model=model,
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temperature=temperature,
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max_tokens=256,
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timeout=45.0,
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messages=[
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{
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"role": "system",
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"content": "你是连通性测试助手，只回复两个字：正常",
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},
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{"role": "user", "content": "请确认服务可用。"},
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],
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)
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