增加了基于目录的文章主题维护，避免偏题
This commit is contained in:
YILING0013
+44
-26
@@ -31,9 +31,15 @@ from prompt_definitions import (
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chapter_outline_prompt, chapter_write_prompt
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)
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# ============ 新增：导入 chapter_directory_parser ============
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from chapter_directory_parser import get_chapter_info_from_directory
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# ============ 日志配置 ============
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logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s")
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def debug_log(prompt: str, response_content: str):
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"""在控制台打印或记录下每次Prompt与Response，[调试]"""
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logging.info(f"\n[Prompt >>>] {prompt}\n")
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logging.info(f"[Response >>>] {response_content}\n")
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# ============ 向量检索相关 ============
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VECTOR_STORE_DIR = "vectorstore"
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@@ -143,10 +149,7 @@ def Novel_novel_directory_generate(
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temperature=temperature
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)
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def debug_log(prompt: str, response_content: str):
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"""在控制台打印或记录下每次Prompt与Response，[调试]"""
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logging.info(f"\n[Prompt >>>] {prompt}\n")
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logging.info(f"[Response >>>] {response_content}\n")
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def generate_base_setting(state: OverallState) -> Dict[str, str]:
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prompt = set_prompt.format(
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@@ -289,13 +292,15 @@ def summarize_recent_chapters(model: ChatOpenAI, chapters_text_list: List[str])
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combined_text = "\n".join(chapters_text_list)
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# 在这里可以写一个更详细的提示
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prompt = f"""\
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这是最近几章的故事内容，请生成一份详细的短期内容摘要（不少于一章篇幅的细节），用于帮助后续创作时回顾细节。请着重强调发生的事件、角色的心理和关系变化、冲突或悬念等。
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这是最近几章的故事内容，请生成一份详细的短期内容摘要（不少于一章篇幅的细节），用于帮助后续创作时回顾细节。
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请着重强调发生的事件、角色的心理和关系变化、冲突或悬念等。
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{combined_text}
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"""
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response = model.invoke(prompt)
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if not response:
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return ""
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debug_log(prompt, response.content)
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return response.content.strip()
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# ============ 生成章节草稿 & 定稿 ============
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@@ -319,16 +324,20 @@ def generate_chapter_draft(
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仅生成当前章节的草稿，不更新全局摘要/角色状态/向量库。
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并将生成的内容写到 "chapter_{novel_number}.txt" 覆盖写入。
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同时生成 "outline_{novel_number}.txt" 存储大纲内容。
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recent_chapters_summary: 最近 3 章的“短期内容摘要”
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"""
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# 0) 根据 novel_number 从 novel_novel_directory 中获取本章标题及简述
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chapter_info = get_chapter_info_from_directory(novel_novel_directory, novel_number)
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chapter_title = chapter_info["chapter_title"]
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chapter_brief = chapter_info["chapter_brief"]
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# 1) 从向量库检索往期上下文
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relevant_context = get_relevant_context_from_vector_store(
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api_key, base_url, "回顾剧情", k=2
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)
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# 2) 生成大纲（增加 recent_chapters_summary）
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# 2) 生成大纲
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model = ChatOpenAI(
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model=model_name,
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api_key=api_key,
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@@ -337,22 +346,26 @@ def generate_chapter_draft(
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)
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# Prompt 拼接
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outline_prompt = (
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chapter_outline_prompt
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+ "\n\n【最近几章摘要】\n" + recent_chapters_summary
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+ "\n\n【用户指导】\n" + (user_guidance if user_guidance else "（无）")
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).format(
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outline_prompt_text = chapter_outline_prompt.format(
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novel_setting=novel_settings,
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character_state=character_state + "\n\n【历史上下文】\n" + relevant_context,
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global_summary=global_summary,
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novel_number=novel_number
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novel_number=novel_number,
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chapter_title=chapter_title,
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chapter_brief=chapter_brief
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)
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response_outline = model.invoke(outline_prompt)
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# 在后面加上用户指导与最近章节摘要（可根据需要灵活组织）
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outline_prompt_text += f"\n\n【本章目录标题与简述】\n标题：{chapter_title}\n简述：{chapter_brief}\n"
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outline_prompt_text += f"\n【最近几章摘要】\n{recent_chapters_summary}"
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outline_prompt_text += f"\n\n【用户指导】\n{user_guidance if user_guidance else '（无）'}"
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response_outline = model.invoke(outline_prompt_text)
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if not response_outline:
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logging.warning("outline_chapter: No response.")
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logging.warning("generate_chapter_draft: outline no response.")
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chapter_outline = ""
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else:
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debug_log(outline_prompt_text, response_outline.content)
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chapter_outline = response_outline.content.strip()
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# 将大纲写到 outline_{novel_number}.txt
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@@ -363,23 +376,27 @@ def generate_chapter_draft(
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save_string_to_txt(chapter_outline, outline_file)
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# 3) 生成正文草稿
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writing_prompt = (
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chapter_write_prompt
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+ "\n\n【最近几章摘要】\n" + recent_chapters_summary
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+ "\n\n【用户指导】\n" + (user_guidance if user_guidance else "（无）")
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).format(
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writing_prompt_text = chapter_write_prompt.format(
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novel_setting=novel_settings,
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character_state=character_state + "\n\n【历史上下文】\n" + relevant_context,
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global_summary=global_summary,
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chapter_outline=chapter_outline,
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word_number=word_number
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word_number=word_number,
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chapter_title=chapter_title,
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chapter_brief=chapter_brief
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)
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response_chapter = model.invoke(writing_prompt)
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# 同样插入用户指导和最近摘要
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writing_prompt_text += f"\n\n【本章目录标题与简述】\n标题：{chapter_title}\n简述：{chapter_brief}\n"
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writing_prompt_text += f"\n【最近几章摘要】\n{recent_chapters_summary}"
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writing_prompt_text += f"\n\n【用户指导】\n{user_guidance if user_guidance else '（无）'}"
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response_chapter = model.invoke(writing_prompt_text)
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if not response_chapter:
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logging.warning("write_chapter: No response.")
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logging.warning("generate_chapter_draft: writing no response.")
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chapter_content = ""
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else:
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debug_log(writing_prompt_text, response_chapter.content)
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chapter_content = response_chapter.content.strip()
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# 4) 覆盖写到 chapter_{novel_number}.txt
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@@ -407,8 +424,6 @@ def finalize_chapter(
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2. 更新全局摘要、角色状态文件；
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3. 如果字数明显少于 word_number 的 80%，则自动调用 enrich_chapter_text 再次扩写；
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4. 更新向量库。
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* 注意：实际应用中，用户也可以再次编辑 chapter_{n}.txt 后再点定稿，这里示例不做 GUI 级别的文本编辑逻辑。
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"""
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# 读取当前章节内容
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chapters_dir = os.path.join(filepath, "chapters")
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@@ -458,6 +473,7 @@ def finalize_chapter(
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if not response:
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logging.warning("update_global_summary: No response.")
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return old_summary
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debug_log(prompt, response.content)
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return response.content.strip()
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new_global_summary = update_global_summary(chapter_text, old_global_summary)
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@@ -472,6 +488,7 @@ def finalize_chapter(
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if not response:
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logging.warning("update_character_state: No response.")
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return old_state
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debug_log(prompt, response.content)
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return response.content.strip()
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new_char_state = update_character_state(chapter_text, old_char_state)
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@@ -516,6 +533,7 @@ def enrich_chapter_text(
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if not response:
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logging.warning("enrich_chapter_text: No response.")
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return chapter_text # 无响应时就返回原文
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debug_log(prompt, response.content)
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return response.content.strip()
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# ============ 导入外部知识文本 ============
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