小优化
This commit is contained in:
YILING0013
+69
-26
@@ -36,10 +36,12 @@ 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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"""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 = os.path.join(os.getcwd(), "vectorstore")
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@@ -156,8 +158,6 @@ def Novel_novel_directory_generate(
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temperature=temperature
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)
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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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topic=state["topic"],
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@@ -271,7 +271,7 @@ def Novel_novel_directory_generate(
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logging.info("Novel settings and directory generated successfully.")
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# ============ 新增：获取最近N章内容，生成短期摘要 ============
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# ============ 获取最近N章内容，生成短期摘要 ============
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def get_last_n_chapters_text(chapters_dir: str, current_chapter_num: int, n: int = 3) -> List[str]:
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"""
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@@ -295,9 +295,7 @@ def summarize_recent_chapters(model: ChatOpenAI, chapters_text_list: List[str])
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if not chapters_text_list:
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return ""
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# 拼接这几章的内容
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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。
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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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@@ -310,6 +308,49 @@ def summarize_recent_chapters(model: ChatOpenAI, chapters_text_list: List[str])
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debug_log(prompt, response.content)
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return response.content.strip()
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# ============ 新增1：记录剧情要点/未解决冲突 ============
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PLOT_ARCS_PROMPT = """\
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下面是新生成的章节内容:
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{chapter_text}
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这里是已记录的剧情要点/未解决冲突(可能为空):
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{old_plot_arcs}
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请基于新的章节内容，提炼出本章引入或延续的悬念、冲突、角色暗线等，将其合并到旧的剧情要点中。
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若有新的冲突则添加，若有已解决/不再重要的冲突可标注或移除。
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最终输出一份更新后的剧情要点列表，以帮助后续保持故事的整体一致性和悬念延续。
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"""
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def update_plot_arcs(
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chapter_text: str,
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old_plot_arcs: str,
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api_key: str,
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base_url: str,
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model_name: str,
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temperature: float
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) -> str:
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"""
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利用模型分析最新章节文本，提炼或更新“未解决冲突或剧情要点”。
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并返回更新后的字符串。
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"""
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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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base_url=base_url,
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temperature=temperature
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)
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prompt = PLOT_ARCS_PROMPT.format(
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chapter_text=chapter_text,
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old_plot_arcs=old_plot_arcs
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)
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response = model.invoke(prompt)
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if not response:
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logging.warning("update_plot_arcs: No response.")
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return old_plot_arcs
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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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def generate_chapter_draft(
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@@ -331,9 +372,7 @@ 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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@@ -352,7 +391,6 @@ def generate_chapter_draft(
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temperature=temperature
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)
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# Prompt 拼接
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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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@@ -362,7 +400,6 @@ def generate_chapter_draft(
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chapter_brief=chapter_brief
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)
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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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@@ -375,7 +412,6 @@ def generate_chapter_draft(
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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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outlines_dir = os.path.join(filepath, "outlines")
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os.makedirs(outlines_dir, exist_ok=True)
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outline_file = os.path.join(outlines_dir, f"outline_{novel_number}.txt")
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@@ -393,7 +429,6 @@ def generate_chapter_draft(
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chapter_brief=chapter_brief
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)
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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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@@ -406,7 +441,6 @@ def generate_chapter_draft(
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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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chapters_dir = os.path.join(filepath, "chapters")
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os.makedirs(chapters_dir, exist_ok=True)
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chapter_file = os.path.join(chapters_dir, f"chapter_{novel_number}.txt")
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@@ -430,7 +464,8 @@ def finalize_chapter(
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1. 读取 chapter_{novel_number}.txt 的最终内容；
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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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4. 更新向量库；
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5. 新增：更新剧情要点/未解决冲突 -> plot_arcs.txt
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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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@@ -440,12 +475,14 @@ def finalize_chapter(
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logging.warning(f"Chapter {novel_number} is empty, cannot finalize.")
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return
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# 读取角色状态 & 全局摘要
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# 读取角色状态 & 全局摘要 & 剧情要点
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character_state_file = os.path.join(filepath, "character_state.txt")
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global_summary_file = os.path.join(filepath, "global_summary.txt")
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plot_arcs_file = os.path.join(filepath, "plot_arcs.txt") # 新增文件
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old_char_state = read_file(character_state_file)
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old_global_summary = read_file(global_summary_file)
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old_plot_arcs = read_file(plot_arcs_file)
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# 1) 先检查字数是否过少，若少于 80% 则调用 enrich 逻辑
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if len(chapter_text) < 0.8 * word_number:
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@@ -500,17 +537,30 @@ def finalize_chapter(
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new_char_state = update_character_state(chapter_text, old_char_state)
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# 4) 覆盖写入角色状态文件与全局摘要文件
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# ============ 新增2: 更新剧情要点 =============
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new_plot_arcs = update_plot_arcs(
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chapter_text=chapter_text,
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old_plot_arcs=old_plot_arcs,
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api_key=api_key,
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base_url=base_url,
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model_name=model_name,
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temperature=temperature
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)
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# 4) 覆盖写入角色状态文件、全局摘要文件、剧情要点文件
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clear_file_content(character_state_file)
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save_string_to_txt(new_char_state, character_state_file)
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clear_file_content(global_summary_file)
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save_string_to_txt(new_global_summary, global_summary_file)
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clear_file_content(plot_arcs_file)
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save_string_to_txt(new_plot_arcs, plot_arcs_file)
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# 5) 更新向量检索库
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update_vector_store(api_key, base_url, chapter_text)
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logging.info(f"Chapter {novel_number} has been finalized (summary & state updated, vector store updated).")
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logging.info(f"Chapter {novel_number} has been finalized (summary & state updated, plot arcs updated, vector store updated).")
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def enrich_chapter_text(
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chapter_text: str,
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@@ -549,29 +599,23 @@ def import_knowledge_file(api_key: str, base_url: str, file_path: str) -> None:
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"""
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将用户选定的文本文件导入到向量库，以便在写作时检索。
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"""
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# 1. 检查文件路径是否有效
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if not os.path.exists(file_path):
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logging.warning(f"知识库文件不存在: {file_path}")
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return
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# 2. 读取文件内容
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content = read_file(file_path)
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if not content.strip():
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logging.warning("知识库文件内容为空。")
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return
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# 3. 对内容进行高级切分处理
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paragraphs = advanced_split_content(content)
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# 4. 加载或初始化向量存储
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store = load_vector_store(api_key, base_url)
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if not store:
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logging.info("Vector store does not exist. Initializing a new one for knowledge import...")
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init_vector_store(api_key, base_url, paragraphs)
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return
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# 5. 创建Document对象并更新到向量库
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docs = [Document(page_content=p) for p in paragraphs]
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store.add_documents(docs)
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store.persist()
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@@ -609,7 +653,6 @@ def advanced_split_content(content: str,
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if current_sentences:
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merged_paragraphs.append(" ".join(current_sentences))
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# 按最大长度二次拆分
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final_segments = []
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for para in merged_paragraphs:
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if len(para) > max_length:
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