konjacpotato
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154 lines
8.1 KiB
Python
154 lines
8.1 KiB
Python
from datetime import datetime
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import json
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from task.manager_task import execute_task
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from config.database import SessionLocal
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from models import SourceContent, Article
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from utils import logger
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from llm import LLMThinkingEngine
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def story_edit_task():
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with SessionLocal() as db:
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# 获取今天的帖子（限定，最多50条）
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today_contents = db.query(SourceContent).filter(
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SourceContent.create_time >= (datetime.today().replace(hour=0, minute=0, second=0, microsecond=0))
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).order_by(SourceContent.create_time.desc()).limit(50).all()
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if len(today_contents) == 0:
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logger.info("story_edit_task finish, content size 0")
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return
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logger.info(f"story_edit_task get {len(today_contents)} contents")
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# 按照帖子正文字数排序
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# 定义提取函数：解析JSON并返回content字段长度
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def get_content_length(item):
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try:
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if not item.content:
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return 0
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data = json.loads(item.content)
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# 安全获取 content 字段，避免 None
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body = data.get('content') or ''
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return len(body)
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except (json.JSONDecodeError, TypeError, AttributeError):
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return 0
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today_contents.sort(key=lambda x: get_content_length(x), reverse=True)
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# 去掉帖子正文字数小于200的帖子
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to_processed_contents = [content for content in today_contents if get_content_length(content) >= 200]
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logger.info(f"story_edit_task after filter content size {len(to_processed_contents)}")
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# 如果没有符合条件的帖子，直接使用字数最多的帖子（即使它的字数小于200）
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if len(to_processed_contents) == 0 and len(today_contents) > 0:
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to_processed_contents = [today_contents[0]]
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# 下面会调用LLM对帖子进行筛选，此处限定所有帖子的正文字数之和不超过10000字（成本安全考虑）
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total_length = sum(get_content_length(content) for content in to_processed_contents)
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if total_length > 10000:
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# 从字数最多的帖子开始，逐步移除，直到总字数不超过10000
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while total_length > 10000 and to_processed_contents:
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removed_content = to_processed_contents.pop()
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total_length -= get_content_length(removed_content)
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# 如果to_processed_contents数量超过2条，则让LLM从中选择2条最适合创作故事的帖子
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# 定义提取函数：解析JSON并返回content内容
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def get_content(item):
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try:
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if not item.content:
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return ""
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data = json.loads(item.content)
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# 安全获取 content 字段，避免 None
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body = data.get('content') or ''
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return body
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except (json.JSONDecodeError, TypeError, AttributeError):
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return ""
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if len(to_processed_contents) > 2:
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llm_engine = LLMThinkingEngine(system_prompt_file="real_estate_story_selection_system_prompt.txt")
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content_list_str = "\n".join([f"{idx+1}. {get_content(content)}" for idx, content in enumerate(to_processed_contents)])
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logger.info(f"story_edit_task LLM selection content list: {content_list_str}")
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selection_result = llm_engine.think(content_list_str)
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logger.info(f"story_edit_task LLM selection result: {selection_result}")
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# 解析LLM的选择结果，提取出数字编号
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selected_indices = []
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for part in selection_result.split(","):
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part = part.strip()
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if part.isdigit():
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idx = int(part) - 1
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if 0 <= idx < len(to_processed_contents):
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selected_indices.append(idx)
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if len(selected_indices) >= 2:
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break
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to_processed_contents = [to_processed_contents[idx] for idx in selected_indices]
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logger.info(f"story_edit_task after LLM selection content size {len(to_processed_contents)}")
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# 下面是对筛选后的帖子进行故事创作，目前先处理一条内容，后续再改成批量处理
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llm_engine = LLMThinkingEngine(system_prompt_file="wechat_official_account_system_prompt.txt")
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for content in to_processed_contents:
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logger.info(f"story_edit_task content id: {content.id}, title: {content.link}, platform: {content.platform}")
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story = llm_engine.think(f"【素材内容】\n{content.content}")
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logger.info(f"story_edit_task content id: {content.id} story: {story}")
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# llm生成的结果有时不是json结构，会在前后增加一些文本，需要提取出json部分进行解析
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try:
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json_start = story.find("{")
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json_end = story.rfind("}") + 1
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if json_start != -1 and json_end != -1:
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story = story[json_start:json_end]
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else:
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logger.warning(f"story_edit_task content id: {content.id} llm生成的结果不是有效的json格式，无法提取故事内容")
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continue
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except json.JSONDecodeError:
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logger.warning(f"story_edit_task content id: {content.id} llm生成的结果不是有效的json格式，无法解析故事内容")
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continue
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# 将生成的故事写入Article表
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json_story = json.loads(story)
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title = json_story.get("title", "无标题")
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article_content = json_story.get("body", "无内容")
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# article_content有连续多个换行的情况，替换成单个换行
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# article_content = "\n".join([line.strip() for line in article_content.splitlines() if line.strip()])
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article = Article(
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title=title,
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keywords=None,
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content=article_content,
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used=False
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)
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db.add(article)
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db.commit()
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# break # 目前先处理一条内容，后续再改成批量处理
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llm_engine = LLMThinkingEngine(system_prompt_file="wei_toutiao_system_prompt.txt")
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for content in to_processed_contents:
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logger.info(f"story_edit_task content id: {content.id}, title: {content.link}, platform: {content.platform}")
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story = llm_engine.think(f"故事素材：{content.content}")
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logger.info(f"story_edit_task content id: {content.id} story: {story}")
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# llm生成的结果有时不是json结构，会在前后增加一些文本，需要提取出json部分进行解析
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try:
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json_start = story.find("{")
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json_end = story.rfind("}") + 1
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if json_start != -1 and json_end != -1:
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story = story[json_start:json_end]
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else:
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logger.warning(f"story_edit_task content id: {content.id} llm生成的结果不是有效的json格式，无法提取故事内容")
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continue
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except json.JSONDecodeError:
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logger.warning(f"story_edit_task content id: {content.id} llm生成的结果不是有效的json格式，无法解析故事内容")
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continue
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# 将生成的故事写入Article表
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try:
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json_story = json.loads(story)
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except json.JSONDecodeError:
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logger.warning(f"story_edit_task content id: {content.id} llm生成的故事内容不是有效的json格式，无法解析故事内容")
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continue
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title = json_story.get("title", "无标题")
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paragraphs = json_story.get("body", ["无内容"])
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article_content = "\n".join(paragraphs)
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article = Article(
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title=title,
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keywords=None,
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content=article_content,
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used=False
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)
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db.add(article)
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db.commit()
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# break # 目前先处理一条内容，后续再改成批量处理
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if __name__ == "__main__":
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execute_task(story_edit_task) |