diff --git a/api/services/__pycache__/tweet.cpython-312.pyc b/api/services/__pycache__/tweet.cpython-312.pyc index 7838a58..4858d9b 100644 Binary files a/api/services/__pycache__/tweet.cpython-312.pyc and b/api/services/__pycache__/tweet.cpython-312.pyc differ diff --git a/api/services/tweet.py b/api/services/tweet.py index d7be025..50d1a0b 100644 --- a/api/services/tweet.py +++ b/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) diff --git a/poster/__pycache__/text_generator.cpython-312.pyc b/poster/__pycache__/text_generator.cpython-312.pyc new file mode 100644 index 0000000..2ece277 Binary files /dev/null and b/poster/__pycache__/text_generator.cpython-312.pyc differ diff --git a/tweet/__pycache__/content_generator.cpython-312.pyc b/tweet/__pycache__/content_generator.cpython-312.pyc index f159e17..f253259 100644 Binary files a/tweet/__pycache__/content_generator.cpython-312.pyc and b/tweet/__pycache__/content_generator.cpython-312.pyc differ diff --git a/tweet/__pycache__/content_judger.cpython-312.pyc b/tweet/__pycache__/content_judger.cpython-312.pyc index 93314a5..10cad7b 100644 Binary files a/tweet/__pycache__/content_judger.cpython-312.pyc and b/tweet/__pycache__/content_judger.cpython-312.pyc differ diff --git a/tweet/__pycache__/topic_generator.cpython-312.pyc b/tweet/__pycache__/topic_generator.cpython-312.pyc index 6858240..504ca61 100644 Binary files a/tweet/__pycache__/topic_generator.cpython-312.pyc and b/tweet/__pycache__/topic_generator.cpython-312.pyc differ diff --git a/tweet/content_generator.py b/tweet/content_generator.py index c6adef8..0e3d99e 100644 --- a/tweet/content_generator.py +++ b/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。") diff --git a/tweet/content_judger.py b/tweet/content_judger.py index 7bf5743..b62b25a 100644 --- a/tweet/content_judger.py +++ b/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") diff --git a/tweet/topic_generator.py b/tweet/topic_generator.py index 43a6ec0..c2ebdee 100644 --- a/tweet/topic_generator.py +++ b/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