zk
52 lines
1.8 KiB
Python
52 lines
1.8 KiB
Python
"""AI 调用工具封装"""
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import re
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from typing import Any
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from json_repair import repair_json
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from langchain_openai import ChatOpenAI
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from langchain_core.messages import SystemMessage, HumanMessage
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from app.core.logger import log
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# 匹配 <think>任意内容</think>，用于剥离推理模型的思考过程
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_THINK_RE = re.compile(r"<think>.*?</think>", re.DOTALL | re.IGNORECASE)
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# 匹配 ```json ... ``` 代码块，提取中间的 JSON 内容
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_CODE_BLOCK_RE = re.compile(r"```(?:json\w*)?\s*\n?(.*?)\n?\s*```", re.DOTALL | re.IGNORECASE)
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def parse_llm_json(text: str) -> Any:
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"""解析 AI 输出的 JSON，自动去除思考标签、markdown 代码块，容错处理"""
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# 1. 去掉 <think>...</think> 思考内容
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cleaned = _THINK_RE.sub("", text).strip()
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# 2. 如果有 ```json ... ``` 代码块，只取代码块里的内容
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match = _CODE_BLOCK_RE.search(cleaned)
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if match:
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cleaned = match.group(1).strip()
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# 3. repair_json 容错解析：修复不规范的 JSON（多余逗号、缺引号、非法转义等）
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return repair_json(cleaned, return_objects=True)
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async def ai_chat(llm: ChatOpenAI, system_prompt: str, user_message: str) -> str:
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"""异步调用 LLM，返回原始文本"""
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messages = [
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SystemMessage(content=system_prompt),
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HumanMessage(content=user_message),
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]
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response = await llm.ainvoke(messages)
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return response.content
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async def ai_chat_json(llm: ChatOpenAI, system_prompt: str, user_message: str) -> Any:
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"""异步调用 LLM，返回解析后的 JSON 对象"""
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raw = await ai_chat(llm, system_prompt, user_message)
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if not raw or not raw.strip():
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log.warning("AI 返回为空")
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return None
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try:
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return parse_llm_json(raw)
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except Exception as e:
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log.warning("AI JSON 解析失败: {}, raw={}", e, raw[:200])
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return None
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