143 lines
5.7 KiB
Python
143 lines
5.7 KiB
Python
"""AI 调用工具封装
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核心机制：健康门闸（Health Gate）
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- ainvoke 抛异常 → 标记故障，全部协程阻塞，单探针退避重试直到成功
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- ainvoke 正常返回 → 放行，后续逻辑不变
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"""
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import asyncio
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import re
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from datetime import datetime
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from typing import Any, Optional
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from json_repair import repair_json
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from langchain_core.language_models import BaseChatModel
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from langchain_core.messages import SystemMessage, HumanMessage
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from snowflake import SnowflakeGenerator
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from app.core.logger import log
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# AI 日志专用雪花ID
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_log_id_gen = SnowflakeGenerator(instance=2)
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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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# ──────────── 健康门闸 ────────────
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_gate_event = asyncio.Event() # clear=故障阻塞中, set=健康放行
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_gate_event.set() # 初始状态：健康
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_gate_probing = False # 是否已有探针在重试
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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: BaseChatModel, system_prompt: str, user_message: str, scene: Optional[str] = None) -> str:
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"""异步调用 LLM，返回原始文本。接口异常时触发门闸阻塞重试直到成功。"""
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global _gate_probing
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while True:
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# 门闸检查：如果当前处于故障状态，阻塞等待直到探针恢复门闸
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await _gate_event.wait()
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try:
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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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content = response.content
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# 记录 AI 调用日志（失败不影响主流程）
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if scene:
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await _save_call_log(scene, system_prompt, user_message, content)
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return content
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except Exception as e:
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# 接口失败 → 关闭门闸
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if _gate_event.is_set():
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_gate_event.clear()
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log.error("AI 接口异常，门闸关闭，全部协程阻塞等待恢复: {}", e)
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if not _gate_probing:
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# 当前协程成为探针，负责退避重试直到成功
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_gate_probing = True
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result = await _probe_until_recover(llm, system_prompt, user_message)
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# 记录 AI 调用日志
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if scene:
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await _save_call_log(scene, system_prompt, user_message, result)
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# 探针成功，直接返回结果（不用再调一次）
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return result
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# 已有探针在工作，回到 while 顶部 await _gate_event.wait() 等待唤醒
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async def _probe_until_recover(llm: BaseChatModel, system_prompt: str, user_message: str) -> str:
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"""探针：固定间隔重试直到接口恢复，返回成功的响应内容，并打开门闸唤醒所有等待协程"""
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global _gate_probing
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delay = 0.5 # 固定间隔 500ms
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attempt = 0
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while True:
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await asyncio.sleep(delay)
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attempt += 1
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try:
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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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# 成功 → 打开门闸，唤醒所有等待协程
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_gate_probing = False
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_gate_event.set()
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log.info("AI 接口恢复，门闸打开，第{}次探测成功", attempt)
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return response.content
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except Exception as e:
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log.warning("AI 接口仍不可用，第{}次探测失败(间隔{}ms): {}", attempt, int(delay * 1000), e)
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async def ai_chat_json(llm: BaseChatModel, system_prompt: str, user_message: str, scene: Optional[str] = None) -> 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, scene=scene)
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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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async def _save_call_log(scene: str, system_prompt: str, user_message: str, response: Optional[str]) -> None:
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"""异步写入 AI 调用日志到 PG，失败只 warning 不影响主流程"""
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try:
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from app.core.database import PgSession
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from sqlalchemy import insert
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from app.models.pg.ai_call_log import AiCallLog
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async with PgSession() as pg:
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await pg.execute(
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insert(AiCallLog).values(
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id=next(_log_id_gen),
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scene=scene,
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system_prompt=system_prompt,
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user_message=user_message,
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response=response,
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created_at=datetime.now(),
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)
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)
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await pg.commit()
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except Exception as e:
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log.warning("AI 调用日志写入失败: {}", e)
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