279 lines
11 KiB
Python
279 lines
11 KiB
Python
import json
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import os
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from typing import Optional, Dict, Generator, Any
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import requests
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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 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 _create_requests_session() -> requests.Session:
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session = requests.Session()
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session.trust_env = os.getenv("IMEETING_USE_SYSTEM_PROXY", "").lower() in {"1", "true", "yes", "on"}
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return session
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@staticmethod
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def build_call_params_from_config(config: Optional[Dict[str, Any]] = None) -> Dict[str, Any]:
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config = config or {}
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endpoint_url = config.get("endpoint_url") or SystemConfigService.get_llm_endpoint_url()
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api_key = config.get("api_key")
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if api_key is None:
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api_key = SystemConfigService.get_llm_api_key(config_module.QWEN_API_KEY)
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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": config.get("llm_model_name") or config.get("model") or SystemConfigService.get_llm_model_name(),
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"timeout": int(config.get("llm_timeout") or config.get("timeout") or SystemConfigService.get_llm_timeout()),
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"temperature": float(config.get("llm_temperature") if config.get("llm_temperature") is not None else config.get("temperature", SystemConfigService.get_llm_temperature())),
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"top_p": float(config.get("llm_top_p") if config.get("llm_top_p") is not None else config.get("top_p", SystemConfigService.get_llm_top_p())),
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"max_tokens": int(config.get("llm_max_tokens") or config.get("max_tokens") or SystemConfigService.get_llm_max_tokens()),
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"system_prompt": config.get("llm_system_prompt") or config.get("system_prompt") or SystemConfigService.get_llm_system_prompt(None),
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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 _build_payload(self, prompt: str, stream: bool = False, params: Optional[Dict[str, Any]] = None) -> Dict[str, Any]:
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params = params or self._get_llm_call_params()
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messages = []
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system_prompt = params.get("system_prompt")
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if system_prompt:
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messages.append({"role": "system", "content": system_prompt})
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messages.append({"role": "user", "content": prompt})
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payload = {
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"model": params["model"],
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"messages": 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 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，如果未找到返回默认提示词
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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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# 返回默认提示词
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return self._get_default_prompt(task_type)
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def _get_default_prompt(self, task_name: str) -> str:
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"""获取默认提示词"""
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system_prompt = SystemConfigService.get_llm_system_prompt("请根据提供的内容进行总结和分析。")
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default_prompts = {
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'MEETING_TASK': system_prompt,
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'KNOWLEDGE_TASK': "请根据提供的信息生成知识库文章。",
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}
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return default_prompts.get(task_name, "请根据提供的内容进行总结和分析。")
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def _call_llm_api_stream(self, prompt: str) -> Generator[str, None, None]:
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"""流式调用 OpenAI 兼容大模型API"""
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params = self._get_llm_call_params()
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if not params["api_key"]:
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yield "error: 缺少API Key"
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return
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try:
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session = self._create_requests_session()
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try:
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response = session.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, stream=True),
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timeout=params["timeout"],
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stream=True,
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)
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response.raise_for_status()
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for line in response.iter_lines(decode_unicode=True):
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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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finally:
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session.close()
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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(self, prompt: str) -> Optional[str]:
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"""调用 OpenAI 兼容大模型API（非流式）"""
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params = self._get_llm_call_params()
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return self.call_llm_api_with_config(params, prompt)
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def call_llm_api_with_config(self, params: Dict[str, Any], prompt: str) -> 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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if not params["api_key"]:
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print("调用大模型API错误: 缺少API Key")
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return None
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try:
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session = self._create_requests_session()
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try:
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response = session.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, params=params),
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timeout=params["timeout"],
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)
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response.raise_for_status()
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content = self._extract_response_text(response.json())
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finally:
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session.close()
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if content:
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return content
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print("API调用失败: 返回内容为空")
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return None
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except Exception 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 test_model(self, config: Dict[str, Any], prompt: Optional[str] = None) -> Dict[str, Any]:
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params = self.build_call_params_from_config(config)
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test_prompt = prompt or "请用一句中文回复：LLM测试成功。"
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content = self.call_llm_api_with_config(params, test_prompt)
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if not content:
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raise Exception("模型无有效返回内容")
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return {
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"model": params["model"],
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"endpoint_url": params["endpoint_url"],
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"response_preview": content[:500],
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"used_params": {
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"timeout": params["timeout"],
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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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},
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}
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# 测试代码
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if __name__ == '__main__':
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print("--- 运行LLM服务测试 ---")
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llm_service = LLMService()
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# 测试获取任务提示词
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meeting_prompt = llm_service.get_task_prompt('MEETING_TASK')
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print(f"会议任务提示词: {meeting_prompt[:100]}...")
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knowledge_prompt = llm_service.get_task_prompt('KNOWLEDGE_TASK')
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print(f"知识库任务提示词: {knowledge_prompt[:100]}...")
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print("--- LLM服务测试完成 ---")
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