Completely refactor the LLM integration method to easily replace the LLM used by each module and optimize the retransmission mechanism.

2025-10-09 13:45:39 +08:00
parent ce74f00137
commit 154b29c0d7
73 changed files with 942 additions and 51758 deletions
@@ -1,95 +1,89 @@
 """
-Report Engine LLM基类
-定义所有LLM实现的基础接口
+Unified OpenAI-compatible LLM client for the Report Engine, with retry support.
 """

-from abc import ABC, abstractmethod
-from typing import Dict, Any, Optional
+import os
+import sys
+from typing import Any, Dict, Optional
+
+from openai import OpenAI
+
+current_dir = os.path.dirname(os.path.abspath(__file__))
+project_root = os.path.dirname(os.path.dirname(current_dir))
+utils_dir = os.path.join(project_root, "utils")
+if utils_dir not in sys.path:
+    sys.path.append(utils_dir)
+
+try:
+    from retry_helper import with_retry, LLM_RETRY_CONFIG
+except ImportError:
+    def with_retry(config=None):
+        def decorator(func):
+            return func
+        return decorator
+
+    LLM_RETRY_CONFIG = None


-class BaseLLM(ABC):
-    """LLM基类"""
-    
-    def __init__(self, api_key: str, model_name: Optional[str] = None):
-        """
-        初始化LLM客户端
-        
-        Args:
-            api_key: API密钥
-            model_name: 模型名称
-        """
+class LLMClient:
+    """Minimal wrapper around the OpenAI-compatible chat completion API."""
+
+    def __init__(self, api_key: str, model_name: str, base_url: Optional[str] = None):
+        if not api_key:
+            raise ValueError("Report Engine LLM API key is required.")
+        if not model_name:
+            raise ValueError("Report Engine model name is required.")
+
        self.api_key = api_key
+        self.base_url = base_url
        self.model_name = model_name
-    
-    @abstractmethod
+        self.provider = model_name
+        timeout_fallback = os.getenv("LLM_REQUEST_TIMEOUT") or os.getenv("REPORT_ENGINE_REQUEST_TIMEOUT") or "180"
+        try:
+            self.timeout = float(timeout_fallback)
+        except ValueError:
+            self.timeout = 300.0
+
+        client_kwargs: Dict[str, Any] = {
+            "api_key": api_key,
+            "max_retries": 0,
+        }
+        if base_url:
+            client_kwargs["base_url"] = base_url
+        self.client = OpenAI(**client_kwargs)
+
+    @with_retry(LLM_RETRY_CONFIG)
    def invoke(self, system_prompt: str, user_prompt: str, **kwargs) -> str:
-        """
-        调用LLM生成回复
-        
-        Args:
-            system_prompt: 系统提示词
-            user_prompt: 用户输入
-            **kwargs: 其他参数
-            
-        Returns:
-            生成的回复文本
-        """
-        pass
-    
-    @abstractmethod
-    def get_model_info(self) -> Dict[str, Any]:
-        """
-        获取当前模型信息
-        
-        Returns:
-            模型信息字典
-        """
-        pass
-    
-    @abstractmethod
-    def get_default_model(self) -> str:
-        """
-        获取默认模型名称
-        
-        Returns:
-            默认模型名称
-        """
-        pass
-    
-    def validate_response(self, response: str) -> str:
-        """
-        验证和清理响应内容
-        
-        Args:
-            response: 原始响应
-            
-        Returns:
-            清理后的响应
-        """
-        if not response:
+        messages = [
+            {"role": "system", "content": system_prompt},
+            {"role": "user", "content": user_prompt},
+        ]
+
+        allowed_keys = {"temperature", "top_p", "presence_penalty", "frequency_penalty", "stream"}
+        extra_params = {key: value for key, value in kwargs.items() if key in allowed_keys and value is not None}
+
+        timeout = kwargs.pop("timeout", self.timeout)
+
+        response = self.client.chat.completions.create(
+            model=self.model_name,
+            messages=messages,
+            timeout=timeout,
+            **extra_params,
+        )
+
+        if response.choices and response.choices[0].message:
+            return self.validate_response(response.choices[0].message.content)
+        return ""
+
+    @staticmethod
+    def validate_response(response: Optional[str]) -> str:
+        if response is None:
            return ""
-        
-        # 移除多余的空白字符
-        response = response.strip()
-        
-        # 确保响应不为空
-        if not response:
-            return "抱歉，生成的内容为空。"
-        
-        return response
-    
-    def estimate_tokens(self, text: str) -> int:
-        """
-        估算文本的token数量（简单实现）
-        
-        Args:
-            text: 输入文本
-            
-        Returns:
-            估算的token数量
-        """
-        # 简单估算：中文字符按1.5个token计算，英文单词按1个token计算
-        chinese_chars = len([c for c in text if '\u4e00' <= c <= '\u9fff'])
-        english_words = len(text.split()) - chinese_chars
-        
-        return int(chinese_chars * 1.5 + english_words)
+        return response.strip()
+
+    def get_model_info(self) -> Dict[str, Any]:
+        return {
+            "provider": self.provider,
+            "model": self.model_name,
+            "api_base": self.base_url or "default",
+        }