137 lines
4.3 KiB
Python
137 lines
4.3 KiB
Python
"""
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LLM客户端封装
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统一使用OpenAI格式调用
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"""
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import json
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import re
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from typing import Optional, Dict, Any, List
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from openai import OpenAI
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from ..config import Config
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class LLMClient:
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"""LLM客户端"""
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def __init__(
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self,
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api_key: Optional[str] = None,
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base_url: Optional[str] = None,
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model: Optional[str] = None
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):
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self.api_key = api_key or Config.LLM_API_KEY
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self.base_url = base_url or Config.LLM_BASE_URL
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self.model = model or Config.LLM_MODEL_NAME
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if not self.api_key:
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raise ValueError("LLM_API_KEY 未配置")
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self.client = OpenAI(
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api_key=self.api_key,
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base_url=self.base_url
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)
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def _stub_key(self, messages: list[dict]) -> str:
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user_msg = next((m["content"] for m in reversed(messages) if m.get("role") == "user"), "")
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sys_msg = next((m["content"] for m in messages if m.get("role") == "system"), "")
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# Allow callers to embed an explicit stub_key=... token
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for chunk in user_msg.split():
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if chunk.startswith("stub_key="):
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return chunk[len("stub_key="):]
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import hashlib
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return hashlib.sha256((sys_msg + "|" + user_msg).encode("utf-8")).hexdigest()[:12]
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def _stub_response(self, messages: list[dict]) -> str:
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import json as _json
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return _json.dumps(self._stub_response_json(messages), ensure_ascii=False)
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def _stub_response_json(self, messages: list[dict]) -> dict:
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key = self._stub_key(messages)
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# Deterministic centered Likert + plausible open text
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digit = sum(ord(c) for c in key) % 5 + 1
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return {
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"stub_key": key,
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"responses": {"item_001": digit, "item_002": digit, "item_003": (digit % 5) + 1},
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"confidence": {"item_001": 0.7, "item_002": 0.7, "item_003": 0.6},
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"open_comment": f"stub:{key}",
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}
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def chat(
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self,
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messages: List[Dict[str, str]],
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temperature: float = 0.7,
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max_tokens: int = 4096,
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response_format: Optional[Dict] = None
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) -> str:
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"""
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发送聊天请求
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Args:
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messages: 消息列表
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temperature: 温度参数
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max_tokens: 最大token数
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response_format: 响应格式（如JSON模式）
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Returns:
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模型响应文本
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"""
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from app.config import Config
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if getattr(Config, "LLM_STUB_MODE", False):
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return self._stub_response(messages)
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kwargs = {
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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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if response_format:
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kwargs["response_format"] = response_format
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response = self.client.chat.completions.create(**kwargs)
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content = response.choices[0].message.content
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# 部分模型（如MiniMax M2.5）会在content中包含<think>思考内容，需要移除
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content = re.sub(r'<think>[\s\S]*?</think>', '', content).strip()
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return content
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def chat_json(
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self,
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messages: List[Dict[str, str]],
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temperature: float = 0.3,
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max_tokens: int = 4096
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) -> Dict[str, Any]:
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"""
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发送聊天请求并返回JSON
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Args:
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messages: 消息列表
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temperature: 温度参数
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max_tokens: 最大token数
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Returns:
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解析后的JSON对象
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"""
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from app.config import Config
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if getattr(Config, "LLM_STUB_MODE", False):
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return self._stub_response_json(messages)
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response = self.chat(
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messages=messages,
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temperature=temperature,
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max_tokens=max_tokens,
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response_format={"type": "json_object"}
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)
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# 清理markdown代码块标记
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cleaned_response = response.strip()
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cleaned_response = re.sub(r'^```(?:json)?\s*\n?', '', cleaned_response, flags=re.IGNORECASE)
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cleaned_response = re.sub(r'\n?```\s*$', '', cleaned_response)
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cleaned_response = cleaned_response.strip()
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try:
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return json.loads(cleaned_response)
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except json.JSONDecodeError:
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raise ValueError(f"LLM返回的JSON格式无效: {cleaned_response}")
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