kx_guo
e8c122e542
- 接入数据库驱动的七维风险评分第一阶段逻辑\n- 增加主模型失败后的 DeepSeek 语义、流式和预防建议兜底\n- 将 AI 预防建议改为基于评分、历史异常和标准规则动态生成\n- 增加无历史案例时的标准库兜底校验和通用规则匹配\n- 修复未知供应商识别及测试占位名称过滤\n- 增加后端滚动日志文件配置和 LLM 请求日志\n- 历史会话直接读取已保存结果，避免重新调用模型和重算风险\n- 补充评分、供应商识别和 LLM 协议测试
418 lines
19 KiB
Python
418 lines
19 KiB
Python
"""Provider-neutral LLM client with a configurable DeepSeek fallback."""
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import json
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import logging
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from collections.abc import AsyncGenerator
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from typing import Any, Literal
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import httpx
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from app.core.config import get_settings
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from app.core.exceptions import AppException
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logger = logging.getLogger("dieya_risk_prediction.llm_client")
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ProviderName = Literal["primary", "fallback"]
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class LLMClient:
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"""Call the primary provider and fail over to DeepSeek when necessary."""
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def __init__(self, transport: httpx.AsyncBaseTransport | None = None) -> None:
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settings = get_settings()
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self.api_key = settings.llm_api_key.get_secret_value() if settings.llm_api_key else ""
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self.base_url = settings.llm_base_url.rstrip("/")
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self.model = settings.llm_model
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self.timeout = settings.llm_timeout_seconds
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self.temperature = settings.llm_temperature
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self.fallback_api_key = (
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settings.llm_fallback_api_key.get_secret_value() if settings.llm_fallback_api_key else ""
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)
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self.fallback_base_url = settings.llm_fallback_base_url.rstrip("/")
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self.fallback_model = settings.llm_fallback_model
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self.fallback_api_mode = settings.llm_fallback_api_mode
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self.fallback_timeout = settings.llm_fallback_timeout_seconds
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self.fallback_max_retries = settings.llm_fallback_max_retries
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self.fallback_reasoning_effort = settings.llm_fallback_reasoning_effort
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self.fallback_max_output_tokens = settings.llm_fallback_max_output_tokens
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self.transport = transport
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@property
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def configured(self) -> bool:
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return bool(self.api_key or self.fallback_api_key)
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@property
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def fallback_configured(self) -> bool:
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return bool(self.fallback_api_key and self.fallback_base_url and self.fallback_model)
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@property
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def endpoint(self) -> str:
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return self._chat_endpoint(self.base_url)
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@staticmethod
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def _chat_endpoint(base_url: str) -> str:
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suffix = "/chat/completions"
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return f"{base_url}{suffix}" if base_url.endswith("/v1") else f"{base_url}/v1{suffix}"
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def _endpoint(self, provider: ProviderName) -> str:
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if provider == "primary":
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return self.endpoint
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if self.fallback_api_mode == "responses":
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return f"{self.fallback_base_url}/responses"
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return self._chat_endpoint(self.fallback_base_url)
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def _provider_config(self, provider: ProviderName) -> tuple[str, str, int]:
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if provider == "primary":
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return self.api_key, self.model, self.timeout
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return self.fallback_api_key, self.fallback_model, self.fallback_timeout
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def _headers(self, provider: ProviderName) -> dict[str, str]:
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api_key, _, _ = self._provider_config(provider)
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if not api_key:
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if provider == "primary" and self.fallback_configured:
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raise AppException("主 LLM 未配置 API Key，将尝试使用兜底模型", code=503, status_code=503)
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raise AppException("未配置 LLM_API_KEY，暂时无法使用 AI 对话", code=503, status_code=503)
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return {
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"Authorization": f"Bearer {api_key}",
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"Content-Type": "application/json",
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"Accept": "text/event-stream",
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}
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async def extract_semantics(
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self,
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text: str,
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history: list[dict[str, str]] | None = None,
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) -> dict[str, Any]:
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"""Extract structured fields, retrying with DeepSeek when the primary fails."""
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messages = [
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{
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"role": "system",
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"content": (
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"你是蝶雅产品风险预估系统的语义理解模块。"
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"只输出合法 JSON，不要输出 Markdown。请输出 json object。抽取用户输入中的意图和风险评估参数。"
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"intent 只能是 greet、general、assessment、supplier、material、cases、"
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"standard、compliance、solution 之一。"
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"字段必须包含 intent, risk_requested, category, specification, packaging, "
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"supplier, factory, stage, confidence。"
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"只有用户明确要求风险评估、风险查询、标准判定、案例检索或预防方案时 risk_requested 才为 true；"
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"用户描述要开发新品并提供规格、容量、包装材质、供应商或加工厂等参数时，"
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"即使没有出现‘风险’二字，也必须将 intent 设为 assessment、risk_requested 设为 true；"
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"问候、闲聊、测试回复、要求原样回复等情况必须为 false。"
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"缺失字段使用 null，confidence 为 0 到 1 的数字。"
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),
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},
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]
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messages.extend(history or [])
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messages.append({"role": "user", "content": text})
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try:
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body = await self._request(messages, provider="primary", stream=False, json_mode=True)
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content = self._response_text(body, provider="primary")
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return self._parse_json(content)
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except Exception as primary_exc: # noqa: BLE001 - fallback must cover provider and parse failures
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logger.warning(
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"LLM semantic request failed provider=primary model=%s error=%s",
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self.model,
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primary_exc.__class__.__name__,
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)
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if not self.fallback_configured:
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raise
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try:
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body = await self._request(messages, provider="fallback", stream=False, json_mode=True)
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content = self._response_text(body, provider="fallback")
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logger.info("LLM semantic request succeeded provider=fallback model=%s", self.fallback_model)
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return self._parse_json(content)
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except Exception as fallback_exc: # noqa: BLE001 - normalize both provider failures
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logger.exception(
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"LLM semantic fallback failed provider=fallback model=%s",
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self.fallback_model,
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)
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raise AppException(
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"主模型与兜底模型均调用失败，请检查网络、模型配置或额度",
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code=503,
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status_code=503,
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) from fallback_exc
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async def stream_reply(self, messages: list[dict[str, str]]) -> AsyncGenerator[str, None]:
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"""Yield reply deltas and fail over only before any primary text is emitted."""
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emitted = False
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try:
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async for delta in self._stream_provider(messages, provider="primary"):
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emitted = True
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yield delta
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return
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except Exception as primary_exc: # noqa: BLE001 - fallback is the public behavior
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logger.warning(
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"LLM stream failed provider=primary model=%s error=%s emitted=%s",
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self.model,
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primary_exc.__class__.__name__,
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emitted,
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)
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if emitted or not self.fallback_configured:
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raise
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logger.info("LLM stream switching provider=primary to provider=fallback model=%s", self.fallback_model)
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async for delta in self._stream_provider(messages, provider="fallback"):
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yield delta
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async def generate_prevention_suggestions(self, context: dict[str, Any]) -> list[str]:
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"""Generate executable prevention actions from the current risk context."""
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messages = [
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{
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"role": "system",
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"content": (
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"你是蝶雅产品风险预估系统的预防方案生成模块。"
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"请根据提供的风险评估数据生成可执行的质量预防建议。"
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"只输出合法 json object，不要输出 Markdown，格式必须是 "
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'{"suggestions":["建议1","建议2"]}。'
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"输出 4 到 6 条建议，每条不超过 80 个汉字。"
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"建议必须优先针对分数较低的风险维度、历史异常频发项和命中的标准规则，"
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"明确检测或管控动作；可以包含责任角色、阶段和验收闭环。"
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"不得虚构数据库没有提供的案例、检测结果、法规条款或 RAG 检索结果。"
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"数据不足时明确写出需要补充的验证动作，不要把猜测写成事实。"
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"不要重复表达，不要输出标题、解释、风险评分或额外字段。"
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),
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},
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{
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"role": "user",
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"content": "以下是本次风险评估数据，请生成预防建议：\n" + json.dumps(
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context, ensure_ascii=False, separators=(",", ":")
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),
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},
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]
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try:
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body = await self._request(messages, provider="primary", stream=False, json_mode=True)
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raw = self._response_text(body, provider="primary")
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return self._parse_suggestions(raw)
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except Exception as primary_exc: # noqa: BLE001 - prevention suggestions have the same failover policy
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logger.warning(
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"LLM prevention suggestion request failed provider=primary model=%s error=%s",
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self.model,
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primary_exc.__class__.__name__,
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)
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if not self.fallback_configured:
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raise
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try:
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body = await self._request(messages, provider="fallback", stream=False, json_mode=True)
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raw = self._response_text(body, provider="fallback")
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suggestions = self._parse_suggestions(raw)
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logger.info(
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"LLM prevention suggestions generated provider=fallback model=%s count=%s",
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self.fallback_model,
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len(suggestions),
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)
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return suggestions
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except Exception as fallback_exc: # noqa: BLE001 - caller owns deterministic fallback behavior
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logger.exception(
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"LLM prevention suggestion fallback failed provider=fallback model=%s",
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self.fallback_model,
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)
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raise AppException(
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"主模型与兜底模型均无法生成预防建议",
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code=503,
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status_code=503,
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) from fallback_exc
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async def _stream_provider(
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self,
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messages: list[dict[str, str]],
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*,
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provider: ProviderName,
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) -> AsyncGenerator[str, None]:
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headers = self._headers(provider)
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_, model, timeout_seconds = self._provider_config(provider)
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is_responses = provider == "fallback" and self.fallback_api_mode == "responses"
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payload = self._payload(messages, provider=provider, stream=True, json_mode=False)
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timeout = httpx.Timeout(timeout_seconds, connect=min(15, timeout_seconds))
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async with httpx.AsyncClient(timeout=timeout, transport=self.transport) as client:
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try:
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async with client.stream("POST", self._endpoint(provider), headers=headers, json=payload) as response:
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if response.status_code >= 400:
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detail = await response.aread()
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raise self._http_failure(response.status_code, detail)
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async for line in response.aiter_lines():
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if not line.startswith("data:"):
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continue
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raw = line[5:].strip()
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if not raw or raw == "[DONE]":
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continue
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try:
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chunk = json.loads(raw)
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except json.JSONDecodeError:
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continue
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delta = self._stream_delta(chunk, responses_api=is_responses)
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if delta:
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yield delta
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except AppException:
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raise
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except httpx.HTTPError as exc:
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raise AppException(
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"LLM 流式连接失败",
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code=503,
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status_code=503,
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) from exc
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logger.info("LLM stream completed provider=%s model=%s", provider, model)
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async def _request(
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self,
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messages: list[dict[str, str]],
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*,
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provider: ProviderName,
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stream: bool,
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json_mode: bool = False,
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) -> dict[str, Any]:
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headers = self._headers(provider)
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payload = self._payload(messages, provider=provider, stream=stream, json_mode=json_mode)
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_, model, timeout_seconds = self._provider_config(provider)
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timeout = httpx.Timeout(timeout_seconds, connect=min(15, timeout_seconds))
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logger.info(
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"LLM request start provider=%s model=%s endpoint=%s stream=%s",
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provider,
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model,
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self._endpoint(provider),
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stream,
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)
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async with httpx.AsyncClient(timeout=timeout, transport=self.transport) as client:
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try:
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response = await client.post(self._endpoint(provider), headers=headers, json=payload)
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if response.status_code >= 400:
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detail = await response.aread()
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raise self._http_failure(response.status_code, detail)
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except AppException:
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raise
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except httpx.HTTPError as exc:
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raise AppException("LLM 语义理解请求失败", code=503, status_code=503) from exc
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try:
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data = response.json()
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except ValueError as exc:
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raise AppException("LLM 返回格式无效", code=503, status_code=503) from exc
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if not isinstance(data, dict):
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raise AppException("LLM 返回格式无效", code=503, status_code=503)
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logger.info("LLM request success provider=%s model=%s status=%s", provider, model, response.status_code)
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return data
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def _payload(
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self,
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messages: list[dict[str, str]],
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*,
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provider: ProviderName,
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stream: bool,
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json_mode: bool,
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) -> dict[str, Any]:
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if provider == "fallback" and self.fallback_api_mode == "responses":
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payload: dict[str, Any] = {
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"model": self.fallback_model,
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"input": messages,
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"stream": stream,
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"max_output_tokens": self.fallback_max_output_tokens,
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}
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if self.fallback_reasoning_effort:
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payload["reasoning_effort"] = self.fallback_reasoning_effort
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return payload
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_, model, _ = self._provider_config(provider)
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payload = {
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"model": model,
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"messages": messages,
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"temperature": self.temperature,
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"stream": stream,
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}
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if json_mode:
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payload["response_format"] = {"type": "json_object"}
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return payload
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@staticmethod
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def _http_failure(status_code: int, detail: bytes) -> AppException:
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return AppException(
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f"LLM 请求失败（HTTP {status_code}）",
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code=503,
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status_code=503,
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details=detail.decode("utf-8", errors="replace")[:500],
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)
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@staticmethod
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def _response_text(body: dict[str, Any], *, provider: ProviderName) -> str:
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if provider == "fallback":
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output_text = body.get("output_text")
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if isinstance(output_text, str) and output_text:
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return output_text
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parts: list[str] = []
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for item in body.get("output") or []:
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if not isinstance(item, dict):
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continue
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content = item.get("content")
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if isinstance(content, str):
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parts.append(content)
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for part in content or []:
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if isinstance(part, dict) and part.get("type") in {"output_text", "text"}:
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parts.append(str(part.get("text") or ""))
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if parts:
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return "".join(parts)
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try:
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content = body["choices"][0]["message"]["content"]
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except (KeyError, IndexError, TypeError) as exc:
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raise AppException("LLM 语义结果缺少 content", code=503, status_code=503) from exc
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if isinstance(content, list):
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content = "".join(str(part.get("text", "")) for part in content if isinstance(part, dict))
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return str(content)
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@staticmethod
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def _stream_delta(chunk: dict[str, Any], *, responses_api: bool) -> str:
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if responses_api:
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event_type = chunk.get("type") or chunk.get("event")
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if event_type in {"response.output_text.delta", "output_text.delta"}:
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return str(chunk.get("delta") or "")
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return ""
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choices = chunk.get("choices") or []
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if not choices:
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return ""
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delta = (choices[0].get("delta") or {}).get("content")
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if isinstance(delta, list):
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delta = "".join(str(part.get("text", "")) for part in delta if isinstance(part, dict))
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return str(delta or "")
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@staticmethod
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def _parse_json(content: str) -> dict[str, Any]:
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cleaned = content.strip()
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if cleaned.startswith("```"):
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cleaned = cleaned.removeprefix("```").removeprefix("json").removesuffix("```").strip()
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try:
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parsed = json.loads(cleaned)
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except json.JSONDecodeError as exc:
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raise AppException("LLM 语义结果不是合法 JSON", code=503, status_code=503) from exc
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if not isinstance(parsed, dict):
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raise AppException("LLM 语义结果格式无效", code=503, status_code=503)
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return parsed
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@staticmethod
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def _parse_suggestions(content: str) -> list[str]:
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"""Parse and normalize the model's suggestion-only JSON response."""
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cleaned = content.strip()
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if cleaned.startswith("```"):
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cleaned = cleaned.removeprefix("```").removeprefix("json").removesuffix("```").strip()
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try:
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parsed = json.loads(cleaned)
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except json.JSONDecodeError as exc:
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raise AppException("LLM 预防建议不是合法 JSON", code=503, status_code=503) from exc
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values = parsed.get("suggestions") if isinstance(parsed, dict) else parsed
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if not isinstance(values, list):
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raise AppException("LLM 预防建议格式无效", code=503, status_code=503)
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suggestions: list[str] = []
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for value in values:
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if not isinstance(value, str):
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continue
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item = " ".join(value.strip().split())
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if item and item not in suggestions:
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suggestions.append(item[:100])
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if not suggestions:
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raise AppException("LLM 未返回有效预防建议", code=503, status_code=503)
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return suggestions[:6]
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