增加了审核api

api/services/tweet.py

@@ -155,7 +155,7 @@ class TweetService:
         system_prompt, user_prompt = self.prompt_builder.build_judge_prompt(topic, content)
         
         # 审核内容
-        judged_data = await self.content_judger.judge_content(content, topic)
+        judged_data = await self.content_judger.judge_content_with_prompt(content, topic, system_prompt, user_prompt)
         judge_success = judged_data.get('judge_success', False)
         
         # 生成请求ID
@@ -213,7 +213,8 @@ class TweetService:
                 continue
                 
             try:
-                judged_data = await self.content_judger.judge_content(content, topic)
+                system_prompt, user_prompt = self.prompt_builder.build_judge_prompt(topic, content)
+                judged_data = await self.content_judger.judge_content_with_prompt(content, topic, system_prompt, user_prompt)
                 judged_contents[topic_index] = judged_data
             except Exception as e:
                 logger.critical(f"为选题 {topic_index} 处理内容审核时发生意外错误: {e}", exc_info=True)

tweet/content_generator.py

@@ -7,7 +7,7 @@
 
 import logging
 import json
-from typing import Dict, Any
+from typing import Dict, Any, Tuple, Optional
 
 from core.ai import AIAgent
 from core.config import ConfigManager, GenerateTopicConfig, GenerateContentConfig
@@ -78,6 +78,61 @@ class ContentGenerator:
 
         # 3. 解析和保存结果
         content_data = process_llm_json_text(raw_result)
+        if content_data:
+            self.output_manager.save_json(content_data, "article.json", subdir=output_dir.name)
+            logger.info(f"成功为选题 {topic_index} 生成并保存内容。")
+            return content_data
+        else:
+            logger.error(f"解析内容JSON失败 for {topic_index}")
+            return {"error": "JSONDecodeError", "raw_content": raw_result}
+            
+    async def generate_content_with_prompt(self, topic: Dict[str, Any], system_prompt: str, user_prompt: str) -> Dict[str, Any]:
+        """
+        使用已构建的提示词生成内容
+
+        Args:
+            topic: 选题信息字典
+            system_prompt: 已构建好的系统提示词
+            user_prompt: 已构建好的用户提示词
+
+        Returns:
+            包含生成内容的字典
+        """
+        topic_index = topic.get('index', 'N/A')
+        logger.info(f"使用预构建提示词为选题 {topic_index} 生成内容...")
+        
+        # 保存提示以供调试
+        output_dir = self.output_manager.get_topic_dir(topic_index)
+        self.output_manager.save_text(system_prompt, "content_system_prompt.txt", subdir=output_dir.name)
+        self.output_manager.save_text(user_prompt, "content_user_prompt.txt", subdir=output_dir.name)
+
+        # 获取模型参数
+        model_params = {}
+        if hasattr(self.content_config, 'model') and isinstance(self.content_config.model, dict):
+            model_params = {
+                'temperature': self.content_config.model.get('temperature'),
+                'top_p': self.content_config.model.get('top_p'),
+                'presence_penalty': self.content_config.model.get('presence_penalty')
+            }
+            # 移除None值
+            model_params = {k: v for k, v in model_params.items() if v is not None}
+
+        # 调用AI
+        try:
+            raw_result, _, _, _ = await self.ai_agent.generate_text(
+                system_prompt=system_prompt,
+                user_prompt=user_prompt,
+                use_stream=True,
+                stage="内容生成",
+                **model_params
+            )
+            self.output_manager.save_text(raw_result, "content_raw_response.txt", subdir=output_dir.name)
+        except Exception as e:
+            logger.critical(f"为选题 {topic_index} 生成内容时AI调用失败: {e}", exc_info=True)
+            return {"error": str(e)}
+
+        # 解析和保存结果
+        content_data = process_llm_json_text(raw_result)
         if content_data:
             self.output_manager.save_json(content_data, "article.json", subdir=output_dir.name)
             logger.info(f"成功为选题 {topic_index} 生成并保存内容。")

tweet/content_judger.py

@@ -75,6 +75,88 @@ class ContentJudger:
             topic=topic
         )
         
+        # 保存提示词
+        if self.output_manager:
+            self.output_manager.save_text(system_prompt, f"{topic_dir}/judger_system_prompt.txt")
+            self.output_manager.save_text(user_prompt, f"{topic_dir}/judger_user_prompt.txt")
+
+        # 获取模型参数
+        model_params = {}
+        if hasattr(self.content_config, 'judger_model') and isinstance(self.content_config.judger_model, dict):
+            model_params = {
+                'temperature': self.content_config.judger_model.get('temperature'),
+                'top_p': self.content_config.judger_model.get('top_p'),
+                'presence_penalty': self.content_config.judger_model.get('presence_penalty')
+            }
+            # 移除None值
+            model_params = {k: v for k, v in model_params.items() if v is not None}
+
+        # 2. 调用AI进行审核
+        try:
+            raw_result, _, _, _ = await self.ai_agent.generate_text(
+                system_prompt=system_prompt,
+                user_prompt=user_prompt,
+                use_stream=True,
+                stage="内容审核",
+                **model_params
+            )
+            
+            # 保存原始响应
+            if self.output_manager:
+                self.output_manager.save_text(raw_result, f"{topic_dir}/judger_raw_response.txt")
+                
+        except Exception as e:
+            logger.critical(f"内容审核时AI调用失败: {e}", exc_info=True)
+            return {"judge_success": False, "error": str(e)}
+
+        # 3. 解析结果
+        judged_data = process_llm_json_text(raw_result)
+        if judged_data and isinstance(judged_data, dict) and "title" in judged_data and "content" in judged_data:
+            judged_data["judge_success"] = True
+            
+            # 直接使用原始内容中的标签
+            if original_tag:
+                judged_data["tag"] = original_tag
+            # 如果原始内容中没有标签，则使用默认标签
+            logger.info(f"内容审核成功完成，使用标签: {judged_data.get('tag', [])}")
+            
+            # 保存审核后的内容
+            if self.output_manager:
+                self.output_manager.save_json(judged_data, f"{topic_dir}/article_judged.json")
+                
+            return judged_data
+        else:
+            logger.warning(f"审核响应JSON格式不正确或缺少键")
+            return {"judge_success": False, "error": "Invalid JSON response", "raw_response": raw_result} 
+
+    async def judge_content_with_prompt(self, generated_content: Union[str, Dict[str, Any]], topic: Dict[str, Any], system_prompt: str, user_prompt: str) -> Dict[str, Any]:
+        """
+        使用预构建的提示词审核生成的内容
+
+        Args:
+            generated_content: 已生成的原始内容（JSON字符串或字典对象）
+            topic: 与内容相关的原始选题字典
+            system_prompt: 系统提示词
+            user_prompt: 用户提示词
+
+        Returns:
+            一个包含审核结果的字典
+        """
+        logger.info("开始使用预构建提示词审核生成的内容...")
+        
+        # 获取主题索引，用于保存文件
+        topic_index = topic.get('index', 'unknown')
+        topic_dir = f"topic_{topic_index}"
+        
+        # 从原始内容中提取tag
+        original_tag = []
+        original_content = process_llm_json_text(generated_content)
+        if original_content and isinstance(original_content, dict) and "tag" in original_content:
+            original_tag = original_content.get("tag", [])
+            logger.info(f"从原始内容中提取到标签: {original_tag}")
+        else:
+            logger.warning("从原始内容提取标签失败")
+        
         # 保存提示词
         if self.output_manager:
             self.output_manager.save_text(system_prompt, f"{topic_dir}/judger_system_prompt.txt")

tweet/topic_generator.py

@@ -6,7 +6,7 @@
 """
 
 import logging
-from typing import Dict, Any, List, Optional
+from typing import Dict, Any, List, Optional, Tuple
 
 from core.ai import AIAgent
 from core.config import ConfigManager, GenerateTopicConfig
@@ -90,5 +90,59 @@ class TopicGenerator:
         logger.info(f"成功生成并保存 {len(topics)} 个选题")
         
         return topics
+        
+    async def generate_topics_with_prompt(self, system_prompt: str, user_prompt: str) -> Optional[List[Dict[str, Any]]]:
+        """
+        使用预构建的提示词生成选题
+        
+        Args:
+            system_prompt: 已构建好的系统提示词
+            user_prompt: 已构建好的用户提示词
+            
+        Returns:
+            生成的选题列表，如果失败则返回None
+        """
+        logger.info("使用预构建提示词开始执行选题生成流程...")
+        
+        # 保存提示以供调试
+        self.output_manager.save_text(system_prompt, "topic_system_prompt.txt")
+        self.output_manager.save_text(user_prompt, "topic_user_prompt.txt")
+        
+        # 获取模型参数
+        model_params = {}
+        if hasattr(self.config, 'model') and isinstance(self.config.model, dict):
+            model_params = {
+                'temperature': self.config.model.get('temperature'),
+                'top_p': self.config.model.get('top_p'),
+                'presence_penalty': self.config.model.get('presence_penalty')
+            }
+            # 移除None值
+            model_params = {k: v for k, v in model_params.items() if v is not None}
+        
+        # 调用AI生成
+        try:
+            raw_result, _, _, _ = await self.ai_agent.generate_text(
+                system_prompt=system_prompt,
+                user_prompt=user_prompt,
+                use_stream=True,
+                stage="选题生成",
+                **model_params
+            )
+            self.output_manager.save_text(raw_result, "topics_raw_response.txt")
+        except Exception as e:
+            logger.critical(f"AI调用失败，无法生成选题: {e}", exc_info=True)
+            return None
+            
+        # 解析结果
+        topics = self.parser.parse(raw_result)
+        if not topics:
+            logger.error("未能从AI响应中解析出任何有效选题")
+            return None
+            
+        # 保存结果
+        self.output_manager.save_json(topics, "topics.json")
+        logger.info(f"成功生成并保存 {len(topics)} 个选题")
+        
+        return topics