565 lines
21 KiB
Python
565 lines
21 KiB
Python
import json
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import os
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from typing import Optional, Dict, Generator, Any, List
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import httpx
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import app.core.config as config_module
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from app.core.database import get_db_connection
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from app.services.system_config_service import SystemConfigService
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class LLMServiceError(Exception):
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"""LLM 调用失败时抛出的结构化异常。"""
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def __init__(self, message: str, *, status_code: Optional[int] = None):
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super().__init__(message)
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self.message = message
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self.status_code = status_code
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class LLMService:
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"""LLM服务 - 专注于大模型API调用和提示词管理"""
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@staticmethod
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def _use_system_proxy() -> bool:
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return os.getenv("IMEETING_USE_SYSTEM_PROXY", "").lower() in {"1", "true", "yes", "on"}
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@staticmethod
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def _create_httpx_client() -> httpx.Client:
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return httpx.Client(
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trust_env=LLMService._use_system_proxy()
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)
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@staticmethod
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def _coerce_int(value: Any, default: int, minimum: Optional[int] = None) -> int:
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try:
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normalized = int(value)
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except (TypeError, ValueError):
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normalized = default
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if minimum is not None:
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normalized = max(minimum, normalized)
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return normalized
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@staticmethod
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def _coerce_float(value: Any, default: float) -> float:
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try:
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return float(value)
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except (TypeError, ValueError):
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return default
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@staticmethod
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def _build_timeout(timeout_seconds: int) -> httpx.Timeout:
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normalized_timeout = max(1, int(timeout_seconds))
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connect_timeout = min(10.0, float(normalized_timeout))
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return httpx.Timeout(
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connect=connect_timeout,
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read=float(normalized_timeout),
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write=float(normalized_timeout),
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pool=connect_timeout,
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)
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@staticmethod
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def _normalize_api_key(api_key: Optional[Any]) -> Optional[str]:
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if api_key is None:
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return None
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normalized = str(api_key).strip()
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return normalized or None
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@staticmethod
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def _normalize_model_code(model_code: Optional[Any]) -> Optional[str]:
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if model_code is None:
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return None
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normalized = str(model_code).strip()
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return normalized or None
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@classmethod
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def build_call_params_from_config(cls, config: Optional[Dict[str, Any]] = None) -> Dict[str, Any]:
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config = config or {}
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endpoint_url = str(config.get("endpoint_url") or SystemConfigService.get_llm_endpoint_url() or "").strip()
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api_key = cls._normalize_api_key(config.get("api_key"))
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if api_key is None:
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api_key = cls._normalize_api_key(SystemConfigService.get_llm_api_key(config_module.QWEN_API_KEY))
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default_model = SystemConfigService.get_llm_model_name()
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default_timeout = SystemConfigService.get_llm_timeout()
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default_temperature = SystemConfigService.get_llm_temperature()
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default_top_p = SystemConfigService.get_llm_top_p()
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default_max_tokens = SystemConfigService.get_llm_max_tokens()
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default_system_prompt = SystemConfigService.get_llm_system_prompt(None)
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return {
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"endpoint_url": endpoint_url,
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"api_key": api_key,
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"model": str(
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config.get("llm_model_name")
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or config.get("model")
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or config.get("model_name")
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or default_model
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).strip(),
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"timeout": cls._coerce_int(
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config.get("llm_timeout")
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or config.get("timeout")
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or config.get("time_out")
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or default_timeout,
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default_timeout,
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minimum=1,
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),
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"temperature": cls._coerce_float(
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config.get("llm_temperature") if config.get("llm_temperature") is not None else config.get("temperature"),
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default_temperature,
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),
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"top_p": cls._coerce_float(
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config.get("llm_top_p") if config.get("llm_top_p") is not None else config.get("top_p"),
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default_top_p,
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),
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"max_tokens": cls._coerce_int(
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config.get("llm_max_tokens") or config.get("max_tokens") or default_max_tokens,
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default_max_tokens,
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minimum=1,
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),
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"system_prompt": config.get("llm_system_prompt") or config.get("system_prompt") or default_system_prompt,
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}
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def _get_llm_call_params(self) -> Dict[str, Any]:
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"""
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获取 OpenAI 兼容接口调用参数
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Returns:
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Dict: 包含 endpoint_url、api_key、model、timeout、temperature、top_p、max_tokens 的参数字典
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"""
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return self.build_call_params_from_config()
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@staticmethod
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def _build_chat_url(endpoint_url: str) -> str:
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base_url = (endpoint_url or "").rstrip("/")
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if base_url.endswith("/chat/completions"):
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return base_url
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return f"{base_url}/chat/completions"
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@staticmethod
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def _build_headers(api_key: Optional[str]) -> Dict[str, str]:
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headers = {"Content-Type": "application/json"}
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if api_key:
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headers["Authorization"] = f"Bearer {api_key}"
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return headers
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def _normalize_messages(
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self,
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prompt: Optional[str] = None,
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messages: Optional[List[Dict[str, Any]]] = None,
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) -> List[Dict[str, str]]:
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normalized_messages: List[Dict[str, str]] = []
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if messages is not None:
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for message in messages:
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if not isinstance(message, dict):
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continue
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role = str(message.get("role") or "").strip()
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if not role:
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continue
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content = self._normalize_content(message.get("content"))
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if not content:
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continue
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normalized_messages.append({"role": role, "content": content})
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return normalized_messages
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if prompt is not None:
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prompt_content = self._normalize_content(prompt)
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if prompt_content:
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normalized_messages.append({"role": "user", "content": prompt_content})
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return normalized_messages
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@staticmethod
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def _merge_system_messages(
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messages: List[Dict[str, str]],
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base_system_prompt: Optional[str],
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) -> List[Dict[str, str]]:
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merged_messages: List[Dict[str, str]] = []
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merged_system_parts: List[str] = []
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if isinstance(base_system_prompt, str) and base_system_prompt.strip():
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merged_system_parts.append(base_system_prompt.strip())
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index = 0
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while index < len(messages) and messages[index].get("role") == "system":
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content = str(messages[index].get("content") or "").strip()
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if content:
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merged_system_parts.append(content)
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index += 1
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if merged_system_parts:
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merged_messages.append({"role": "system", "content": "\n\n".join(merged_system_parts)})
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merged_messages.extend(messages[index:])
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return merged_messages
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def _build_payload(
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self,
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prompt: Optional[str] = None,
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messages: Optional[List[Dict[str, Any]]] = None,
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stream: bool = False,
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params: Optional[Dict[str, Any]] = None,
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) -> Dict[str, Any]:
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params = params or self._get_llm_call_params()
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normalized_messages = self._normalize_messages(prompt=prompt, messages=messages)
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normalized_messages = self._merge_system_messages(normalized_messages, params.get("system_prompt"))
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if not normalized_messages:
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raise ValueError("缺少 prompt 或 messages")
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payload = {
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"model": params["model"],
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"messages": normalized_messages,
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"temperature": params["temperature"],
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"top_p": params["top_p"],
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"max_tokens": params["max_tokens"],
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"stream": stream,
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}
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return payload
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@staticmethod
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def _normalize_content(content: Any) -> str:
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if isinstance(content, str):
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return content
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if isinstance(content, list):
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texts = []
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for item in content:
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if isinstance(item, str):
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texts.append(item)
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elif isinstance(item, dict):
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text = item.get("text")
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if text:
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texts.append(text)
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return "".join(texts)
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return ""
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def _extract_response_text(self, data: Dict[str, Any]) -> str:
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choices = data.get("choices") or []
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if not choices:
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return ""
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first_choice = choices[0] or {}
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message = first_choice.get("message") or {}
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content = message.get("content")
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if content:
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return self._normalize_content(content)
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delta = first_choice.get("delta") or {}
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delta_content = delta.get("content")
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if delta_content:
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return self._normalize_content(delta_content)
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return ""
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def _validate_call_params(self, params: Dict[str, Any]) -> Optional[str]:
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if not params.get("endpoint_url"):
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return "缺少 endpoint_url"
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if not params.get("model"):
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return "缺少 model"
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if not params.get("api_key"):
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return "缺少API Key"
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return None
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@staticmethod
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def _extract_error_message_from_response(response: httpx.Response) -> str:
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try:
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payload = response.json()
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except ValueError:
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payload = None
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if isinstance(payload, dict):
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error = payload.get("error")
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if isinstance(error, dict):
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parts = [
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str(error.get("message") or "").strip(),
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str(error.get("type") or "").strip(),
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str(error.get("code") or "").strip(),
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]
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message = " / ".join(part for part in parts if part)
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if message:
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return message
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if isinstance(error, str) and error.strip():
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return error.strip()
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message = payload.get("message")
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if isinstance(message, str) and message.strip():
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return message.strip()
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text = (response.text or "").strip()
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return text[:500] if text else f"HTTP {response.status_code}"
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def get_call_params_by_model_code(self, model_code: Optional[str] = None) -> Dict[str, Any]:
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normalized_model_code = self._normalize_model_code(model_code)
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if not normalized_model_code:
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return self._get_llm_call_params()
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runtime_config = SystemConfigService.get_model_runtime_config(normalized_model_code)
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if not runtime_config:
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raise LLMServiceError(f"指定模型不可用: {normalized_model_code}")
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return self.build_call_params_from_config(runtime_config)
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def _resolve_call_params(
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self,
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model_code: Optional[str] = None,
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config: Optional[Dict[str, Any]] = None,
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) -> Dict[str, Any]:
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if config is not None:
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return self.build_call_params_from_config(config)
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return self.get_call_params_by_model_code(model_code)
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def get_task_prompt(self, task_type: str, cursor=None, prompt_id: Optional[int] = None) -> str:
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"""
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统一的提示词获取方法
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Args:
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task_type: 任务类型，如 'MEETING_TASK', 'KNOWLEDGE_TASK' 等
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cursor: 数据库游标，如果传入则使用，否则创建新连接
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prompt_id: 可选的提示词ID，如果指定则使用该提示词，否则使用默认提示词
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Returns:
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str: 提示词内容
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"""
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# 如果指定了 prompt_id，直接获取该提示词
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if prompt_id:
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query = """
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SELECT content
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FROM prompts
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WHERE id = %s AND task_type = %s AND is_active = TRUE
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LIMIT 1
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"""
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params = (prompt_id, task_type)
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else:
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# 否则获取默认提示词
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query = """
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SELECT content
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FROM prompts
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WHERE task_type = %s
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AND is_default = TRUE
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AND is_active = TRUE
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LIMIT 1
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"""
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params = (task_type,)
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if cursor:
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cursor.execute(query, params)
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result = cursor.fetchone()
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if result:
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return result['content'] if isinstance(result, dict) else result[0]
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else:
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with get_db_connection() as connection:
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cursor = connection.cursor(dictionary=True)
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cursor.execute(query, params)
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result = cursor.fetchone()
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if result:
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return result['content']
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prompt_label = f"ID={prompt_id}" if prompt_id else f"task_type={task_type} 的默认模版"
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raise LLMServiceError(f"未找到可用提示词模版：{prompt_label}")
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def stream_llm_api(
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self,
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prompt: Optional[str] = None,
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model_code: Optional[str] = None,
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config: Optional[Dict[str, Any]] = None,
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messages: Optional[List[Dict[str, Any]]] = None,
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) -> Generator[str, None, None]:
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"""流式调用 OpenAI 兼容大模型API。"""
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try:
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params = self._resolve_call_params(model_code=model_code, config=config)
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validation_error = self._validate_call_params(params)
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if validation_error:
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yield f"error: {validation_error}"
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return
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timeout = self._build_timeout(params["timeout"])
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with self._create_httpx_client() as client:
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with client.stream(
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"POST",
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self._build_chat_url(params["endpoint_url"]),
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headers=self._build_headers(params["api_key"]),
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json=self._build_payload(prompt=prompt, messages=messages, stream=True, params=params),
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timeout=timeout,
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) as response:
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response.raise_for_status()
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for line in response.iter_lines():
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if not line or not line.startswith("data:"):
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continue
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data_line = line[5:].strip()
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if not data_line or data_line == "[DONE]":
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continue
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try:
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data = json.loads(data_line)
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except json.JSONDecodeError:
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continue
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new_content = self._extract_response_text(data)
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if new_content:
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yield new_content
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except LLMServiceError as e:
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error_msg = e.message or str(e)
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print(f"流式调用大模型API错误: {error_msg}")
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yield f"error: {error_msg}"
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except httpx.HTTPStatusError as e:
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detail = self._extract_error_message_from_response(e.response)
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error_msg = f"流式调用大模型API错误: HTTP {e.response.status_code} - {detail}"
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print(error_msg)
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yield f"error: {error_msg}"
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except httpx.TimeoutException:
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error_msg = f"流式调用大模型API超时: timeout={params['timeout']}s"
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print(error_msg)
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yield f"error: {error_msg}"
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except httpx.RequestError as e:
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error_msg = f"流式调用大模型API网络错误: {e}"
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print(error_msg)
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yield f"error: {error_msg}"
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except Exception as e:
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error_msg = f"流式调用大模型API错误: {e}"
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print(error_msg)
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yield f"error: {error_msg}"
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def _call_llm_api_stream(self, prompt: str) -> Generator[str, None, None]:
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"""兼容旧调用入口。"""
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return self.stream_llm_api(prompt)
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def call_llm_api(
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self,
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prompt: Optional[str] = None,
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model_code: Optional[str] = None,
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config: Optional[Dict[str, Any]] = None,
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messages: Optional[List[Dict[str, Any]]] = None,
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) -> Optional[str]:
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"""调用 OpenAI 兼容大模型API（非流式）。"""
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try:
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return self.call_llm_api_or_raise(
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prompt=prompt,
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model_code=model_code,
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config=config,
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messages=messages,
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)
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except LLMServiceError as e:
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print(f"调用大模型API错误: {e}")
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return None
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def call_llm_api_or_raise(
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self,
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prompt: Optional[str] = None,
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model_code: Optional[str] = None,
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config: Optional[Dict[str, Any]] = None,
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messages: Optional[List[Dict[str, Any]]] = None,
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) -> str:
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"""调用 OpenAI 兼容大模型API（非流式），失败时抛出结构化异常。"""
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params = self._resolve_call_params(model_code=model_code, config=config)
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return self.call_llm_api_with_config_or_raise(params, prompt=prompt, messages=messages)
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def call_llm_api_messages(
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self,
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messages: List[Dict[str, Any]],
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model_code: Optional[str] = None,
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config: Optional[Dict[str, Any]] = None,
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) -> Optional[str]:
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"""使用多消息结构调用 OpenAI 兼容大模型API（非流式）。"""
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return self.call_llm_api(prompt=None, model_code=model_code, config=config, messages=messages)
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def call_llm_api_messages_or_raise(
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self,
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messages: List[Dict[str, Any]],
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model_code: Optional[str] = None,
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config: Optional[Dict[str, Any]] = None,
|
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) -> str:
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"""使用多消息结构调用 OpenAI 兼容大模型API（非流式），失败时抛出结构化异常。"""
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return self.call_llm_api_or_raise(prompt=None, model_code=model_code, config=config, messages=messages)
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def call_llm_api_with_config(
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self,
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params: Dict[str, Any],
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prompt: Optional[str] = None,
|
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messages: Optional[List[Dict[str, Any]]] = None,
|
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) -> Optional[str]:
|
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"""使用指定配置调用 OpenAI 兼容大模型API（非流式）"""
|
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try:
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return self.call_llm_api_with_config_or_raise(params, prompt=prompt, messages=messages)
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except LLMServiceError as e:
|
||
print(f"调用大模型API错误: {e}")
|
||
return None
|
||
|
||
def call_llm_api_with_config_or_raise(
|
||
self,
|
||
params: Dict[str, Any],
|
||
prompt: Optional[str] = None,
|
||
messages: Optional[List[Dict[str, Any]]] = None,
|
||
) -> str:
|
||
"""使用指定配置调用 OpenAI 兼容大模型API（非流式），失败时抛出结构化异常。"""
|
||
validation_error = self._validate_call_params(params)
|
||
if validation_error:
|
||
raise LLMServiceError(validation_error)
|
||
|
||
timeout = self._build_timeout(params["timeout"])
|
||
try:
|
||
with self._create_httpx_client() as client:
|
||
response = client.post(
|
||
self._build_chat_url(params["endpoint_url"]),
|
||
headers=self._build_headers(params["api_key"]),
|
||
json=self._build_payload(prompt=prompt, messages=messages, params=params),
|
||
timeout=timeout,
|
||
)
|
||
response.raise_for_status()
|
||
content = self._extract_response_text(response.json())
|
||
if content:
|
||
return content
|
||
raise LLMServiceError("API调用失败: 返回内容为空")
|
||
except httpx.HTTPStatusError as e:
|
||
detail = self._extract_error_message_from_response(e.response)
|
||
raise LLMServiceError(
|
||
f"HTTP {e.response.status_code} - {detail}",
|
||
status_code=e.response.status_code,
|
||
) from e
|
||
except httpx.TimeoutException:
|
||
raise LLMServiceError(f"调用超时: timeout={params['timeout']}s")
|
||
except httpx.RequestError as e:
|
||
raise LLMServiceError(f"网络错误: {e}") from e
|
||
except Exception as e:
|
||
raise LLMServiceError(str(e)) from e
|
||
|
||
def _call_llm_api(self, prompt: str) -> Optional[str]:
|
||
"""兼容旧调用入口。"""
|
||
return self.call_llm_api(prompt)
|
||
|
||
def test_model(self, config: Dict[str, Any], prompt: Optional[str] = None) -> Dict[str, Any]:
|
||
params = self.build_call_params_from_config(config)
|
||
test_prompt = prompt or "请用一句中文回复：LLM测试成功。"
|
||
content = self.call_llm_api_with_config_or_raise(params, test_prompt)
|
||
if not content:
|
||
raise Exception("模型无有效返回内容")
|
||
|
||
return {
|
||
"model": params["model"],
|
||
"endpoint_url": params["endpoint_url"],
|
||
"response_preview": content[:500],
|
||
"used_params": {
|
||
"timeout": params["timeout"],
|
||
"temperature": params["temperature"],
|
||
"top_p": params["top_p"],
|
||
"max_tokens": params["max_tokens"],
|
||
},
|
||
}
|
||
|
||
|
||
# 测试代码
|
||
if __name__ == '__main__':
|
||
print("--- 运行LLM服务测试 ---")
|
||
llm_service = LLMService()
|
||
|
||
# 测试获取任务提示词
|
||
meeting_prompt = llm_service.get_task_prompt('MEETING_TASK')
|
||
print(f"会议任务提示词: {meeting_prompt[:100]}...")
|
||
|
||
knowledge_prompt = llm_service.get_task_prompt('KNOWLEDGE_TASK')
|
||
print(f"知识库任务提示词: {knowledge_prompt[:100]}...")
|
||
|
||
print("--- LLM服务测试完成 ---")
|