#!/usr/bin/env python # -*- coding: utf-8 -*- import os import time import random import argparse import json from datetime import datetime import sys sys.path.append('/root/autodl-tmp') # 从本地模块导入 from TravelContentCreator.core.ai_agent import AI_Agent from TravelContentCreator.core.topic_parser import TopicParser from TravelContentCreator.utils.resource_loader import ResourceLoader def generate_topics(ai_agent, system_prompt, user_prompt, output_dir, temperature=0.2, top_p=0.5, presence_penalty=1.5): """生成选题列表""" print("开始生成选题...") # 记录开始时间 time_start = time.time() # 生成选题 result, system_prompt, user_prompt, file_folder, file_name, tokens, time_cost = ai_agent.work( system_prompt, user_prompt, "", temperature, top_p, presence_penalty ) # 计算总耗时 time_end = time.time() print(f"选题生成完成，耗时：{time_end - time_start}秒") # 生成唯一ID run_id = datetime.now().strftime("%Y-%m-%d_%H-%M-%S") # 保存原始结果 os.makedirs(output_dir, exist_ok=True) with open(os.path.join(output_dir, f"{run_id}.txt"), "w", encoding="utf-8") as f: f.write(f"result: {result}\n") f.write(f"system_prompt: {system_prompt}\n") f.write(f"user_prompt: {user_prompt}\n") f.write(f"tokens: {tokens}\n") f.write(f"time: {time_cost}s\n") # 解析选题 result_list = TopicParser.parse_topics(result) success, json_path = TopicParser.save_topics(result_list, output_dir, run_id, result) if not success: print("选题解析失败，请检查AI输出格式") return None print(f"成功解析并保存{len(result_list)}个选题到{json_path}") return json_path, run_id, result_list def generate_content(ai_agent, system_prompt, topics, output_dir, run_id, prompts_dir, resource_dir, variants=2, temperature=0.3, start_index=0, end_index=None): """根据选题生成内容""" if not topics: print("没有选题，无法生成内容") return # 确定处理范围 if end_index is None or end_index > len(topics): end_index = len(topics) topics_to_process = topics[start_index:end_index] print(f"准备处理{len(topics_to_process)}个选题...") # 创建汇总文件 summary_file = ResourceLoader.create_summary_file(output_dir, run_id, len(topics_to_process)) # 处理每个选题 processed_results = [] for i, item in enumerate(topics_to_process): print(f"处理第 {i+1}/{len(topics_to_process)} 篇文章") # 构建提示词 user_prompt = ResourceLoader.build_user_prompt(item, prompts_dir, resource_dir) print(f"完成提示词构建，长度为 {len(user_prompt)} 字符") print(user_prompt) with open("test.txt", "w", encoding="utf-8") as f: f.write(user_prompt) try: # 为每个选题生成多个变体 for j in range(variants): print(f" 正在生成变体 {j+1}/{variants}") # 添加随机停顿，避免请求过于频繁 time.sleep(random.uniform(0.1, 0.5)) # 生成文章 result, _, _, _, _, tokens, time_cost = ai_agent.work( system_prompt, user_prompt, "", temperature, 0.5, 1.5 ) print(f" 生成完成，tokens: {tokens}, 耗时: {time_cost}s") processed_results.append(result) # 保存到单独文件 filepath = ResourceLoader.save_article( result, user_prompt, output_dir, run_id, i+1, j+1 ) if filepath: print(f" 结果已保存到: {filepath}") # 更新汇总文件 (仅保存第一个变体到汇总文件) if j == 0: ResourceLoader.update_summary(summary_file, i+1, user_prompt, result) except Exception as e: print(f"处理选题时出错: {e}") print(f"完成{len(processed_results)}篇文章生成，汇总文件：{summary_file}") return processed_results def prepare_topic_generation( select_date, select_num, system_prompt_path, user_prompt_path, base_url="vllm", model_name="qwenQWQ", api_key="EMPTY", gen_prompts_path="/root/autodl-tmp/TravelContentCreator/genPrompts", resource_dir="/root/autodl-tmp/TravelContentCreator/resource", output_dir="/root/autodl-tmp/TravelContentCreator/result" ): """准备选题生成的环境和参数""" # 创建AI Agent ai_agent = AI_Agent(base_url, model_name, api_key) # 加载系统提示词 with open(system_prompt_path, "r", encoding="utf-8") as f: system_prompt = f.read() # 构建用户提示词 user_prompt = "你拥有的创作资料如下：\n" # 加载genPrompts目录下的文件 gen_prompts_list = os.listdir(gen_prompts_path) for gen_prompt_folder in gen_prompts_list: folder_path = os.path.join(gen_prompts_path, gen_prompt_folder) # 检查是否为目录 if os.path.isdir(folder_path): gen_prompts = os.listdir(folder_path) user_prompt += f"{gen_prompt_folder}\n{gen_prompts}\n" # 加载source文档内的目标文件 ## 其实只会有Object和Product两个文件夹 ## 所以可以简化代码 for dir in resource_dir: source_type = dir["type"] # source_num = dir["num"] source_file_path = dir["file_path"] for file in source_file_path: with open(file, "r", encoding="utf-8") as f: user_prompt += f"{source_type}信息:\n{file.split('/')[-1]}\n{f.read()}\n\n" # 加载日期信息 dateline_path = os.path.join(os.path.dirname(user_prompt_path), "2025各月节日宣传节点时间表.md") if os.path.exists(dateline_path): with open(dateline_path, "r", encoding="utf-8") as f: dateline = f.read() user_prompt += f"\n{dateline}" # 加载用户提示词模板 with open(user_prompt_path, "r", encoding="utf-8") as f: user_prompt += f.read() # 添加选题数量和日期 user_prompt += f"\n选题数量：{select_num}\n选题日期：{select_date}\n" return ai_agent, system_prompt, user_prompt, output_dir def main(): """主函数入口""" args = { "date": "4月17日", "num": 5, "model": "qwenQWQ", "api_url": "vllm", "api_key": "EMPTY", "topic_system_prompt": "/root/autodl-tmp/TravelContentCreator/SelectPrompt/systemPrompt.txt", "topic_user_prompt": "/root/autodl-tmp/TravelContentCreator/SelectPrompt/userPrompt.txt", "content_system_prompt": "/root/autodl-tmp/TravelContentCreator/genPrompts/systemPrompt.txt", "resource_dir": [{ "type": "Object", "num": 4, "file_path": ["/root/autodl-tmp/TravelContentCreator/resource/Object/景点信息-尚书第.txt", "/root/autodl-tmp/TravelContentCreator/resource/Object/景点信息-明清园.txt", "/root/autodl-tmp/TravelContentCreator/resource/Object/景点信息-泰宁古城.txt", "/root/autodl-tmp/TravelContentCreator/resource/Object/景点信息-甘露寺.txt" ]}, { "type": "Product", "num": 0, "file_path": [] } ], "prompts_dir": "/root/autodl-tmp/TravelContentCreator/genPrompts", "output_dir": "/root/autodl-tmp/TravelContentCreator/result", "variants": 2, "topic_temperature": 0.2, "content_temperature": 0.3 } if True: # 1. 首先生成选题 ai_agent, system_prompt, user_prompt, output_dir = prepare_topic_generation( args["date"], args["num"], args["topic_system_prompt"], args["topic_user_prompt"], args["api_url"], args["model"], args["api_key"], args["prompts_dir"], args["resource_dir"], args["output_dir"] ) json_path, run_id, topics = generate_topics( ai_agent, system_prompt, user_prompt, args["output_dir"], args["topic_temperature"], 0.5, 1.5 ) print(topics) # raise Exception("选题生成失败，退出程序") if not json_path or not topics: print("选题生成失败，退出程序") return # 2. 然后生成内容 print("\n开始根据选题生成内容...") # 加载内容生成的系统提示词 content_system_prompt = ResourceLoader.load_system_prompt(args["content_system_prompt"]) if not content_system_prompt: print("内容生成系统提示词为空，使用选题生成的系统提示词") content_system_prompt = system_prompt # 直接使用同一个AI Agent实例 generate_content( ai_agent, content_system_prompt, topics, args["output_dir"], run_id, args["prompts_dir"], args["resource_dir"], args["variants"], args["content_temperature"] ) if __name__ == "__main__": main()