From 5a0db9b82be4cda63eeb9d48bb3d9c3f9507bcd4 Mon Sep 17 00:00:00 2001 From: YILING0013 Date: Fri, 31 Jan 2025 19:57:44 +0800 Subject: [PATCH] =?UTF-8?q?=E6=96=B0=E8=AE=BE=E6=83=B3?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .gitignore | 4 +- main.spec | 16 +- novel_generator.py | 505 +++++++++++++++++++-------------------------- ui.py | 179 +++++++++++----- 4 files changed, 359 insertions(+), 345 deletions(-) diff --git a/.gitignore b/.gitignore index f2bce34..1780e96 100644 --- a/.gitignore +++ b/.gitignore @@ -1,8 +1,8 @@ /Novel_Src -/vectorstore -/.conda +/.venv /build /dist /.vscode /__pycache__ +/vectorstore config.json diff --git a/main.spec b/main.spec index 57e1776..63fdbfd 100644 --- a/main.spec +++ b/main.spec @@ -1,11 +1,13 @@ # -*- mode: python ; coding: utf-8 -*- - +from PyInstaller.utils.hooks import collect_submodules a = Analysis( ['main.py'], pathex=[], binaries=[], - datas=[], + datas=[ + ('vectorstore', 'vectorstore') + ], hiddenimports=['typing_extensions', 'langchain-openai', 'langgraph', @@ -17,7 +19,15 @@ a = Analysis( 'langchain-community', 'pydantic', 'pydantic.deprecated.decorator', - 'chromadb.utils.embedding_functions.onnx_mini_lm_l6_v2' + *collect_submodules('chromadb'), + 'chromadb.utils.embedding_functions.onnx_mini_lm_l6_v2', + 'chromadb.telemetry.product.posthog', + 'chromadb.api.segment', + 'chromadb.db.impl', + 'chromadb.db.impl.sqlite', + 'chromadb.migrations', + 'chromadb.migrations.embeddings_queue' + ], hookspath=[], hooksconfig={}, diff --git a/novel_generator.py b/novel_generator.py index 35d07b6..47db654 100644 --- a/novel_generator.py +++ b/novel_generator.py @@ -15,7 +15,6 @@ from langchain_openai import OpenAIEmbeddings from langchain_community.vectorstores import Chroma from langchain.docstore.document import Document -# import nltk import math from sentence_transformers import SentenceTransformer @@ -35,10 +34,25 @@ from prompt_definitions import ( # ============ 日志配置 ============ logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s") -# ============ 向量检索相关函数（Chroma） ============ +# ============ 向量检索相关 ============ VECTOR_STORE_DIR = "vectorstore" +def clear_vector_store(): + """ + 清空本地向量库（删除 vectorstore 文件夹）。 + 需要在UI中加一个二次确认弹窗，防止误删。 + """ + if os.path.exists(VECTOR_STORE_DIR): + try: + import shutil + shutil.rmtree(VECTOR_STORE_DIR) + logging.info("Local vector store has been cleared.") + except Exception as e: + logging.warning(f"Failed to remove vector store: {e}") + else: + logging.info("No vector store found to clear.") + def init_vector_store(api_key: str, base_url: str, texts: List[str]) -> Chroma: """ 初始化并返回一个Chroma向量库，将传入的文本进行嵌入并保存到本地目录。 @@ -118,16 +132,6 @@ def Novel_novel_directory_generate( ) -> None: """ 使用多步流程，生成 Novel_setting.txt 与 Novel_directory.txt 并保存到 filepath。 - - :param api_key: OpenAI API key - :param base_url: OpenAI API base url - :param llm_model: 所使用的 LLM 模型名称 - :param topic: 小说主题 - :param genre: 小说类型 - :param number_of_chapters: 章节数 - :param word_number: 单章目标字数 - :param filepath: 存放生成文件的目录路径 - :param temperature: 生成温度 """ # 确保文件夹存在 os.makedirs(filepath, exist_ok=True) @@ -142,7 +146,7 @@ def Novel_novel_directory_generate( def debug_log(prompt: str, response_content: str): """在控制台打印或记录下每次Prompt与Response，[调试]""" logging.info(f"\n[Prompt >>>] {prompt}\n") - logging.info(f"[Response <<<] {response_content}\n") + logging.info(f"[Response >>>] {response_content}\n") def generate_base_setting(state: OverallState) -> Dict[str, str]: prompt = set_prompt.format( @@ -257,159 +261,74 @@ def Novel_novel_directory_generate( logging.info("Novel settings and directory generated successfully.") -# ============ 生成章节（每章独立文件） ============ +# ============ 新增：获取最近N章内容，生成短期摘要 ============ -CHINESE_NUM_MAP = { - '零': 0, '○': 0, '〇': 0, - '一': 1, '二': 2, '三': 3, '四': 4, '五': 5, - '六': 6, '七': 7, '八': 8, '九': 9, - '十': 10, '百': 100, '千': 1000, '万': 10000 -} - -def chinese_to_arabic(chinese_str: str) -> int: +def get_last_n_chapters_text(chapters_dir: str, current_chapter_num: int, n: int = 3) -> List[str]: """ - 只能处理到万(10000)以内的中文数字，正常小说章节应该够用了 + 从指定文件夹中，读取最近 n 章的内容（如果存在），并按从旧到新的顺序返回文本列表。 + 不包含当前章，只拿之前的 n 章。 """ - total = 0 - current_unit = 1 # 记录当前单位 - tmp_val = 0 # 暂存本轮数字 + texts = [] + start_chap = max(1, current_chapter_num - n) + for c in range(start_chap, current_chapter_num): + chap_file = os.path.join(chapters_dir, f"chapter_{c}.txt") + if os.path.exists(chap_file): + text = read_file(chap_file).strip() + if text: + texts.append(text) + return texts - for char in reversed(chinese_str): - if char in CHINESE_NUM_MAP: - val = CHINESE_NUM_MAP[char] - if val >= 10: - if val > current_unit: - # 如 100, 1000, 10000 - current_unit = val - else: - # 比如 “十二” -> 2 * 10 + 1 - # 如果 val <= current_unit, 那么相当于在这个单位下加 - total += tmp_val * val - tmp_val = 0 - else: - # 0~9 - tmp_val = tmp_val + val * current_unit - else: - # 非中文数字字符，视情况决定怎么处理，这里直接跳过 - pass - - total += tmp_val - return total - -def parse_chapter_title_from_directory(novel_directory_text: str, - novel_number: int, - range_size: int = 1) -> str: +def summarize_recent_chapters(model: ChatOpenAI, chapters_text_list: List[str]) -> str: """ - 从小说目录文本中，提取指定章节（以及前后几章）的目录信息。 - range_size=1，表示获取当前章节、前一章和后一章的目录信息(若存在)。 - 支持多种常见的章节格式。 + 将最近几章的文本拼接后，通过模型生成一个相对详细的“短期内容摘要”。 """ + if not chapters_text_list: + return "" - lines = novel_directory_text.splitlines() + # 拼接这几章的内容 + combined_text = "\n".join(chapters_text_list) + # 在这里可以写一个更详细的提示 + prompt = f"""\ +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。 - # 可以根据需求自行扩展，这里列举了几种常见的章节标题格式，如果模型实在不听话，可以适当调整 - # 每个pattern都应该捕获两个组： - # 1. chapter_num_str：章节数字(可能是中文也可能是阿拉伯数字) - # 2. chapter_title ：章节标题（.*） - patterns = [ - # 1) 第12章 标题 - r"^第\s*([\d]+)\s*章[:：]?\s*(.*)$", - # 2) 第十二章 标题（中文数字） - r"^第\s*([零○〇一二三四五六七八九十百千万]+)\s*章[:：]?\s*(.*)$", - # 3) Chapter 12 标题 - r"^Chapter\s+(\d+)\s*[:：]?\s*(.*)$", - # 4) Ch 12 标题 - r"^Ch\s+(\d+)\s*[:：]?\s*(.*)$", - # 5) 第12节 标题 - r"^第\s*([\d]+)\s*节[:：]?\s*(.*)$", - # 6) 第12话 标题 - r"^第\s*([\d]+)\s*话[:：]?\s*(.*)$", - # ... 更多模式 ... - ] +{combined_text} +""" + response = model.invoke(prompt) + if not response: + return "" + return response.content.strip() - # 用来存储匹配结果： chapter_num -> title - directory_map = {} +# ============ 生成章节草稿 & 定稿 ============ - for line in lines: - line = line.strip() - if not line: - continue - - # 依次尝试每一种pattern - matched = False - for pat in patterns: - match = re.match(pat, line, flags=re.IGNORECASE) - if match: - chapter_num_str = match.group(1) - chapter_title = match.group(2).strip() - - # 如果是中文数字，需要转换 - # 如果是阿拉伯数字，直接转 int 即可 - if re.match(r"^[零○〇一二三四五六七八九十百千万]+$", chapter_num_str): - chapter_num = chinese_to_arabic(chapter_num_str) - else: - chapter_num = int(chapter_num_str) - - directory_map[chapter_num] = chapter_title - matched = True - break - - # 如果已经匹配到其中一个pattern，就不需要继续匹配剩余pattern - if matched: - continue - - # 收集需要的章节范围 - chapters_info = [] - for cnum in range(novel_number - range_size, novel_number + range_size + 1): - if cnum in directory_map: - if cnum == novel_number: - chapters_info.append(f"【当前】第{cnum}章：{directory_map[cnum]}") - else: - chapters_info.append(f"第{cnum}章：{directory_map[cnum]}") - - if chapters_info: - return "\n".join(chapters_info) - return "" - -def generate_chapter_with_state( +def generate_chapter_draft( novel_settings: str, - novel_novel_directory: str, + global_summary: str, + character_state: str, + recent_chapters_summary: str, + user_guidance: str, api_key: str, base_url: str, model_name: str, novel_number: int, - filepath: str, word_number: int, - lastchapter: str, - user_guidance: str = "", - temperature: float = 0.7 + temperature: float, + novel_novel_directory: str, + filepath: str ) -> str: """ - 多步流程: - 1) 更新/创建全局摘要 - 2) 更新/生成角色状态文档 - 3) 向量检索获取往期上下文 - 4) 从Novel_directory.txt中获取当前(和前后几章)的目录信息 - 5) 大纲 -> 正文（可结合用户给出的额外指导） - 6) 写入 chapter_{novel_number}.txt, 更新 last_chapter.txt - 7) 更新向量库 + 仅生成当前章节的草稿，不更新全局摘要/角色状态/向量库。 + 并将生成的内容写到 "chapter_{novel_number}.txt" 覆盖写入。 + 同时生成 "outline_{novel_number}.txt" 存储大纲内容。 - :param novel_settings: 最终的作品设定（字符串） - :param novel_novel_directory: 小说目录信息 - :param api_key: OpenAI API Key - :param base_url: OpenAI Base URL - :param model_name: LLM 模型名称 - :param novel_number: 当前要生成的章节号 - :param filepath: 文件存放的目录 - :param word_number: 单章目标字数 - :param lastchapter: 上一章内容（若为空字符串，表示无上一章） - :param user_guidance: 用户对当前章节的额外指导或想法 - :param temperature: 生成温度 - :return: 本章生成的正文内容 + recent_chapters_summary: 最近 3 章的“短期内容摘要” """ - # 确保文件夹存在 - os.makedirs(filepath, exist_ok=True) + # 1) 从向量库检索往期上下文 + relevant_context = get_relevant_context_from_vector_store( + api_key, base_url, "回顾剧情", k=2 + ) + + # 2) 生成大纲（增加 recent_chapters_summary） model = ChatOpenAI( model=model_name, api_key=api_key, @@ -417,25 +336,119 @@ def generate_chapter_with_state( temperature=temperature ) - # 调试输出函数 - def debug_log(prompt: str, response_content: str): - """在控制台打印或记录下每次的 Prompt 与 Response，便于观察生成过程。""" - logging.info(f"\n[Prompt >>>]\n{prompt}\n") - logging.info(f"[Response <<<]\n{response_content}\n") + # Prompt 拼接 + outline_prompt = ( + chapter_outline_prompt + + "\n\n【最近几章摘要】\n" + recent_chapters_summary + + "\n\n【用户指导】\n" + (user_guidance if user_guidance else "（无）") + ).format( + novel_setting=novel_settings, + character_state=character_state + "\n\n【历史上下文】\n" + relevant_context, + global_summary=global_summary, + novel_number=novel_number + ) - # --- 文件路径定义 --- + response_outline = model.invoke(outline_prompt) + if not response_outline: + logging.warning("outline_chapter: No response.") + chapter_outline = "" + else: + chapter_outline = response_outline.content.strip() + + # 将大纲写到 outline_{novel_number}.txt + outlines_dir = os.path.join(filepath, "outlines") + os.makedirs(outlines_dir, exist_ok=True) + outline_file = os.path.join(outlines_dir, f"outline_{novel_number}.txt") + clear_file_content(outline_file) + save_string_to_txt(chapter_outline, outline_file) + + # 3) 生成正文草稿 + writing_prompt = ( + chapter_write_prompt + + "\n\n【最近几章摘要】\n" + recent_chapters_summary + + "\n\n【用户指导】\n" + (user_guidance if user_guidance else "（无）") + ).format( + novel_setting=novel_settings, + character_state=character_state + "\n\n【历史上下文】\n" + relevant_context, + global_summary=global_summary, + chapter_outline=chapter_outline, + word_number=word_number + ) + + response_chapter = model.invoke(writing_prompt) + if not response_chapter: + logging.warning("write_chapter: No response.") + chapter_content = "" + else: + chapter_content = response_chapter.content.strip() + + # 4) 覆盖写到 chapter_{novel_number}.txt chapters_dir = os.path.join(filepath, "chapters") os.makedirs(chapters_dir, exist_ok=True) - chapter_file = os.path.join(chapters_dir, f"chapter_{novel_number}.txt") - lastchapter_file = os.path.join(filepath, "last_chapter.txt") + clear_file_content(chapter_file) + save_string_to_txt(chapter_content, chapter_file) + + logging.info(f"[Draft] Chapter {novel_number} generated as a draft.") + return chapter_content + +def finalize_chapter( + novel_number: int, + word_number: int, + api_key: str, + base_url: str, + model_name: str, + temperature: float, + filepath: str +): + """ + 对当前章节进行定稿： + 1. 读取 chapter_{novel_number}.txt 的最终内容； + 2. 更新全局摘要、角色状态文件； + 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； + 4. 更新向量库。 + + * 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。 + """ + # 读取当前章节内容 + chapters_dir = os.path.join(filepath, "chapters") + chapter_file = os.path.join(chapters_dir, f"chapter_{novel_number}.txt") + chapter_text = read_file(chapter_file).strip() + if not chapter_text: + logging.warning(f"Chapter {novel_number} is empty, cannot finalize.") + return + + # 读取角色状态 & 全局摘要 character_state_file = os.path.join(filepath, "character_state.txt") global_summary_file = os.path.join(filepath, "global_summary.txt") old_char_state = read_file(character_state_file) old_global_summary = read_file(global_summary_file) - # 1) 更新全局摘要 + # 1) 先检查字数是否过少，若少于 80% 则调用 enrich 逻辑 + if len(chapter_text) < 0.8 * word_number: + logging.info("Chapter text seems shorter than 80% of desired length. Attempting to enrich content...") + chapter_text = enrich_chapter_text( + chapter_text=chapter_text, + word_number=word_number, + api_key=api_key, + base_url=base_url, + model_name=model_name, + temperature=temperature + ) + # 覆盖写回文件 + clear_file_content(chapter_file) + save_string_to_txt(chapter_text, chapter_file) + logging.info("Chapter text has been enriched and updated.") + + # 2) 更新全局摘要 + model = ChatOpenAI( + model=model_name, + api_key=api_key, + base_url=base_url, + temperature=temperature + ) + def update_global_summary(chapter_text: str, old_summary: str) -> str: prompt = summary_prompt.format( chapter_text=chapter_text, @@ -445,15 +458,11 @@ def generate_chapter_with_state( if not response: logging.warning("update_global_summary: No response.") return old_summary - debug_log(prompt, response.content) return response.content.strip() - if lastchapter.strip(): - new_global_summary = update_global_summary(lastchapter, old_global_summary) - else: - new_global_summary = old_global_summary + new_global_summary = update_global_summary(chapter_text, old_global_summary) - # 2) 更新角色状态文档 + # 3) 更新角色状态 def update_character_state(chapter_text: str, old_state: str) -> str: prompt = update_character_state_prompt.format( chapter_text=chapter_text, @@ -463,128 +472,57 @@ def generate_chapter_with_state( if not response: logging.warning("update_character_state: No response.") return old_state - debug_log(prompt, response.content) return response.content.strip() - if lastchapter.strip(): - new_char_state = update_character_state(lastchapter, old_char_state) - else: - new_char_state = old_char_state + new_char_state = update_character_state(chapter_text, old_char_state) - # 3) 从向量库检索上下文 - relevant_context = get_relevant_context_from_vector_store( - api_key, base_url, "回顾剧情", k=2 + # 4) 覆盖写入角色状态文件与全局摘要文件 + clear_file_content(character_state_file) + save_string_to_txt(new_char_state, character_state_file) + + clear_file_content(global_summary_file) + save_string_to_txt(new_global_summary, global_summary_file) + + # 5) 更新向量检索库 + update_vector_store(api_key, base_url, chapter_text) + + logging.info(f"Chapter {novel_number} has been finalized (summary & state updated, vector store updated).") + +def enrich_chapter_text( + chapter_text: str, + word_number: int, + api_key: str, + base_url: str, + model_name: str, + temperature: float +) -> str: + """ + 当章节篇幅不足时，调用此函数对章节文本进行二次扩写。 + 可以让模型补充场景描写、角色心理等，保证与现有文本风格一致。 + """ + model = ChatOpenAI( + model=model_name, + api_key=api_key, + base_url=base_url, + temperature=temperature ) + prompt = f"""\ +以下是当前章节文本，可能篇幅较短，请在保持剧情连贯的前提下进行扩写，使其更充实、生动，并尽量靠近目标 {word_number} 字数。 - # 4) 解析本章及前后章节目录信息 - this_and_related_chapters = parse_chapter_title_from_directory(novel_novel_directory, novel_number, range_size=1) +原章节内容： +{chapter_text} +""" + response = model.invoke(prompt) + if not response: + logging.warning("enrich_chapter_text: No response.") + return chapter_text # 无响应时就返回原文 + return response.content.strip() - # 5) 生成大纲 - def outline_chapter( - novel_setting: str, - char_state: str, - global_summary: str, - chap_num: int, - extra_context: str, - directory_hint: str, - user_guide: str - ) -> str: - """ - 将目录提示以及用户额外指导内容一起放入 Prompt 中。 - """ - # 适度修改章节提纲提示词，以整合目录信息 & 用户指导 - outline_prompt = ( - chapter_outline_prompt - + "\n\n【目录参考】\n" + directory_hint - + "\n\n【用户指导】\n" + user_guide - ).format( - novel_setting=novel_setting, - character_state=char_state + "\n\n【历史上下文】\n" + extra_context, - global_summary=global_summary, - novel_number=chap_num - ) - - response = model.invoke(outline_prompt) - if not response: - logging.warning("outline_chapter: No response.") - return "" - debug_log(outline_prompt, response.content) - return response.content.strip() - - chap_outline = outline_chapter( - novel_settings, new_char_state, new_global_summary, novel_number, - relevant_context, this_and_related_chapters, user_guidance - ) - - # 6) 生成正文 - def write_chapter( - novel_setting: str, - char_state: str, - global_summary: str, - outline: str, - wnum: int, - extra_context: str, - directory_hint: str, - user_guide: str - ) -> str: - # 同理，整合目录信息和用户指导 - writing_prompt = ( - chapter_write_prompt - + "\n\n【目录参考】\n" + directory_hint - + "\n\n【用户指导】\n" + user_guide - ).format( - novel_setting=novel_setting, - character_state=char_state + "\n\n【历史上下文】\n" + extra_context, - global_summary=global_summary, - chapter_outline=outline, - word_number=wnum - ) - - response = model.invoke(writing_prompt) - if not response: - logging.warning("write_chapter: No response.") - return "" - debug_log(writing_prompt, response.content) - return response.content.strip() - - chapter_content = write_chapter( - novel_settings, - new_char_state, - new_global_summary, - chap_outline, - word_number, - relevant_context, - this_and_related_chapters, - user_guidance - ) - - # 写入文件并更新记录 - if chapter_content: - save_string_to_txt(chapter_content, chapter_file) - - # 更新 last_chapter.txt - clear_file_content(lastchapter_file) - save_string_to_txt(chapter_content, lastchapter_file) - - # 更新角色状态、全局摘要 - clear_file_content(character_state_file) - save_string_to_txt(new_char_state, character_state_file) - - clear_file_content(global_summary_file) - save_string_to_txt(new_global_summary, global_summary_file) - - # 7) 更新向量检索库 - update_vector_store(api_key, base_url, chapter_content) - logging.info(f"Chapter {novel_number} generated successfully.") - else: - logging.warning(f"Chapter {novel_number} generation failed.") - - return chapter_content +# ============ 导入外部知识文本 ============ def import_knowledge_file(api_key: str, base_url: str, file_path: str) -> None: """ 将用户选定的文本文件导入到向量库，以便在写作时检索。 - 可以在UI中提供按钮来调用此函数。 """ # 1. 检查文件路径是否有效 @@ -614,31 +552,21 @@ def import_knowledge_file(api_key: str, base_url: str, file_path: str) -> None: store.persist() logging.info("知识库文件已成功导入至向量库。") - def advanced_split_content(content: str, similarity_threshold: float = 0.7, max_length: int = 500) -> List[str]: """ 将文本先按句子切分，然后根据语义相似度进行合并，最后根据max_length进行二次切分。 - - :param content: 原始文本内容 - :param similarity_threshold: 相邻句子合并的语义相似度阈值，小于此值则会开启新的段落 - :param max_length: 每个段落的最大长度（按字符数计算，超过则进一步拆分） - :return: 切分好的段落列表 """ - - # 1. 按句子切分 nltk.download('punkt', quiet=True) # 确保 punkt 数据可用 sentences = nltk.sent_tokenize(content) if not sentences: return [] - # 2. 加载 SentenceTransformer 模型，用于计算语义相似度 model = SentenceTransformer('paraphrase-MiniLM-L6-v2') embeddings = model.encode(sentences) - # 3. 根据相邻句子的语义相似度合并段落 merged_paragraphs = [] current_sentences = [sentences[0]] current_embedding = embeddings[0] @@ -646,39 +574,28 @@ def advanced_split_content(content: str, for i in range(1, len(sentences)): sim = cosine_similarity([current_embedding], [embeddings[i]])[0][0] if sim >= similarity_threshold: - # 语义相似则并入当前段落 current_sentences.append(sentences[i]) - # 更新current_embedding为合并后的平均值（可选，也可只采用最后一句做比较） current_embedding = (current_embedding + embeddings[i]) / 2.0 else: - # 语义相似度不足，另起一个新段落 merged_paragraphs.append(" ".join(current_sentences)) current_sentences = [sentences[i]] current_embedding = embeddings[i] - # 把最后一段加进去 if current_sentences: merged_paragraphs.append(" ".join(current_sentences)) - # 4. 根据最大长度 max_length 做二次拆分，避免段落过长 + # 按最大长度二次拆分 final_segments = [] for para in merged_paragraphs: - # 如果段落长度超过max_length，进一步切分 if len(para) > max_length: sub_segments = split_by_length(para, max_length=max_length) final_segments.extend(sub_segments) else: final_segments.append(para) - # 返回最终段落列表 return final_segments - def split_by_length(text: str, max_length: int = 500) -> List[str]: - """ - 将文本按照max_length进行拆分，以避免段落过长。 - 这里以字符数为单位进行简单的拆分，也可以改为按词数或token数等。 - """ segments = [] start_idx = 0 while start_idx < len(text): diff --git a/ui.py b/ui.py index 4af9c40..aa8199c 100644 --- a/ui.py +++ b/ui.py @@ -9,8 +9,12 @@ from config_manager import load_config, save_config from utils import read_file from novel_generator import ( Novel_novel_directory_generate, - generate_chapter_with_state, - import_knowledge_file + generate_chapter_draft, + finalize_chapter, + import_knowledge_file, + clear_vector_store, + get_last_n_chapters_text, + summarize_recent_chapters ) from consistency_checker import check_consistency @@ -65,7 +69,7 @@ class NovelGeneratorGUI: def build_right_layout(self): # 行列配置 - for i in range(15): + for i in range(20): self.right_frame.rowconfigure(i, weight=0) self.right_frame.columnconfigure(1, weight=1) @@ -141,20 +145,32 @@ class NovelGeneratorGUI: self.user_guide_text = scrolledtext.ScrolledText(self.right_frame, width=32, height=4) self.user_guide_text.grid(row=11, column=1, padx=5, pady=5, sticky="w") - # 按钮区域 row_base = 12 + # ============ 功能按钮 ============ + + # (1) 生成设定 & 目录 self.btn_generate_full = ttk.Button(self.right_frame, text="1. 生成设定 & 目录", command=self.generate_full_novel) self.btn_generate_full.grid(row=row_base, column=0, columnspan=2, padx=5, pady=5, sticky="ew") - self.btn_generate_chapter = ttk.Button(self.right_frame, text="2. 生成单章(含角色状态)", command=self.generate_chapter_text) + # (2) 生成章节草稿 + self.btn_generate_chapter = ttk.Button(self.right_frame, text="2. 生成章节草稿", command=self.generate_chapter_draft_ui) self.btn_generate_chapter.grid(row=row_base+1, column=0, columnspan=2, padx=5, pady=5, sticky="ew") - self.btn_check_consistency = ttk.Button(self.right_frame, text="3. 一致性审校", command=self.do_consistency_check) - self.btn_check_consistency.grid(row=row_base+2, column=0, columnspan=2, padx=5, pady=5, sticky="ew") + # (3) 定稿当前章节 + self.btn_finalize_chapter = ttk.Button(self.right_frame, text="3. 定稿当前章节", command=self.finalize_chapter_ui) + self.btn_finalize_chapter.grid(row=row_base+2, column=0, columnspan=2, padx=5, pady=5, sticky="ew") - # 增加一个按钮来导入自定义知识库文件 + # (4) 一致性审校 + self.btn_check_consistency = ttk.Button(self.right_frame, text="4. 一致性审校", command=self.do_consistency_check) + self.btn_check_consistency.grid(row=row_base+3, column=0, columnspan=2, padx=5, pady=5, sticky="ew") + + # (5) 导入知识库文件 self.btn_import_knowledge = ttk.Button(self.right_frame, text="导入知识库", command=self.import_knowledge_handler) - self.btn_import_knowledge.grid(row=row_base+3, column=0, columnspan=2, padx=5, pady=5, sticky="ew") + self.btn_import_knowledge.grid(row=row_base+4, column=0, columnspan=2, padx=5, pady=5, sticky="ew") + + # (6) 清空向量库 + self.btn_clear_vectorstore = ttk.Button(self.right_frame, text="清空向量库", command=self.clear_vectorstore_handler) + self.btn_clear_vectorstore.grid(row=row_base+5, column=0, columnspan=2, padx=5, pady=5, sticky="ew") # -------------- 配置管理 -------------- def load_config_btn(self): @@ -205,7 +221,7 @@ class NovelGeneratorGUI: self.log_text.insert(tk.END, message + "\n") self.log_text.see(tk.END) - # -------------- 核心功能按钮 -------------- + # -------------- 功能 -------------- def disable_button(self, btn): btn.config(state=tk.DISABLED) @@ -252,69 +268,114 @@ class NovelGeneratorGUI: thread = threading.Thread(target=task) thread.start() - def generate_chapter_text(self): - """多步生成章节：维护全局摘要+角色状态文档，向量检索辅助，并结合目录信息和用户指导。""" + def generate_chapter_draft_ui(self): + """生成当前章节的草稿""" def task(): self.disable_button(self.btn_generate_chapter) try: api_key = self.api_key_var.get().strip() base_url = self.base_url_var.get().strip() model_name = self.model_name_var.get().strip() - novel_number = self.chapter_num_var.get() - filepath = self.filepath_var.get().strip() - word_number = self.word_number_var.get() temperature = self.temperature_var.get() + filepath = self.filepath_var.get().strip() - # 读取设定 & 目录 novel_settings_file = os.path.join(filepath, "Novel_setting.txt") - novel_novel_directory_file = os.path.join(filepath, "Novel_directory.txt") - last_chapter_file = os.path.join(filepath, "last_chapter.txt") - novel_settings = read_file(novel_settings_file) - novel_novel_directory = read_file(novel_novel_directory_file) - lastchapter = read_file(last_chapter_file) - if not novel_settings.strip(): self.log("⚠️ 未找到 Novel_setting.txt，请先生成设定。") return - if not novel_novel_directory.strip(): - self.log("⚠️ 未找到 Novel_directory.txt，请先生成目录。") - return - # 用户对当前章节的指导 + character_state_file = os.path.join(filepath, "character_state.txt") + character_state = read_file(character_state_file) + global_summary_file = os.path.join(filepath, "global_summary.txt") + global_summary = read_file(global_summary_file) + novel_directory_file = os.path.join(filepath, "Novel_directory.txt") + novel_directory = read_file(novel_directory_file) + + chap_num = self.chapter_num_var.get() + word_number = self.word_number_var.get() user_guidance = self.user_guide_text.get("1.0", tk.END).strip() - self.log(f"开始生成第{novel_number}章内容(含角色状态文档更新)...") - chapter_text = generate_chapter_with_state( + # 获取最近3章文本，生成短期摘要 + chapters_dir = os.path.join(filepath, "chapters") + recent_3_texts = get_last_n_chapters_text(chapters_dir, chap_num, n=3) + # 用当前模型生成一个较为详细的最近剧情摘要 + model_obj = self.get_llm_model(model_name, api_key, base_url, temperature) + recent_chapters_summary = summarize_recent_chapters(model_obj, recent_3_texts) + + self.log(f"开始生成第{chap_num}章草稿...") + draft_text = generate_chapter_draft( novel_settings=novel_settings, - novel_novel_directory=novel_novel_directory, + global_summary=global_summary, + character_state=character_state, + recent_chapters_summary=recent_chapters_summary, + user_guidance=user_guidance, api_key=api_key, base_url=base_url, model_name=model_name, - novel_number=novel_number, - filepath=filepath, + novel_number=chap_num, word_number=word_number, - lastchapter=lastchapter, - user_guidance=user_guidance, - temperature=temperature + temperature=temperature, + novel_novel_directory=novel_directory, + filepath=filepath ) - - if chapter_text: - self.log(f"✅ 第{novel_number}章内容生成完成。chapter_{novel_number}.txt 已更新。") + if draft_text: + self.log(f"✅ 第{chap_num}章草稿生成完成。请在左侧查看。") self.chapter_result.delete("1.0", tk.END) - self.chapter_result.insert(tk.END, chapter_text) + self.chapter_result.insert(tk.END, draft_text) self.chapter_result.see(tk.END) else: - self.log("⚠️ 本章生成失败或无内容。") + self.log("⚠️ 本章草稿生成失败或无内容。") except Exception as e: - self.log(f"❌ 生成章节内容时出错: {e}") + self.log(f"❌ 生成章节草稿时出错: {e}") finally: self.enable_button(self.btn_generate_chapter) thread = threading.Thread(target=task) thread.start() + def finalize_chapter_ui(self): + """定稿当前章节：更新全局摘要、角色状态、向量库等""" + def task(): + self.disable_button(self.btn_finalize_chapter) + try: + api_key = self.api_key_var.get().strip() + base_url = self.base_url_var.get().strip() + model_name = self.model_name_var.get().strip() + temperature = self.temperature_var.get() + filepath = self.filepath_var.get().strip() + + chap_num = self.chapter_num_var.get() + word_number = self.word_number_var.get() + + self.log(f"开始定稿第{chap_num}章...") + finalize_chapter( + novel_number=chap_num, + word_number=word_number, + api_key=api_key, + base_url=base_url, + model_name=model_name, + temperature=temperature, + filepath=filepath + ) + self.log(f"✅ 第{chap_num}章定稿完成（已更新全局摘要、角色状态、向量库）。") + + # 读取定稿后的文本显示 + chap_file = os.path.join(filepath, "chapters", f"chapter_{chap_num}.txt") + final_text = read_file(chap_file) + self.chapter_result.delete("1.0", tk.END) + self.chapter_result.insert(tk.END, final_text) + self.chapter_result.see(tk.END) + + except Exception as e: + self.log(f"❌ 定稿章节时出错: {e}") + finally: + self.enable_button(self.btn_finalize_chapter) + + thread = threading.Thread(target=task) + thread.start() + def do_consistency_check(self): """使用审校Agent对最新章节进行简单一致性或冲突检查""" def task(): @@ -323,22 +384,25 @@ class NovelGeneratorGUI: api_key = self.api_key_var.get().strip() base_url = self.base_url_var.get().strip() model_name = self.model_name_var.get().strip() - filepath = self.filepath_var.get().strip() temperature = self.temperature_var.get() + filepath = self.filepath_var.get().strip() # 读取关键文件 novel_settings_file = os.path.join(filepath, "Novel_setting.txt") character_state_file = os.path.join(filepath, "character_state.txt") global_summary_file = os.path.join(filepath, "global_summary.txt") - last_chapter_file = os.path.join(filepath, "last_chapter.txt") novel_setting = read_file(novel_settings_file) character_state = read_file(character_state_file) global_summary = read_file(global_summary_file) - last_chapter_text = read_file(last_chapter_file) - if not last_chapter_text.strip(): - self.log("⚠️ last_chapter.txt 为空，暂无可检查的章节文本。") + # 获取当前章节文本 + chap_num = self.chapter_num_var.get() + chap_file = os.path.join(filepath, "chapters", f"chapter_{chap_num}.txt") + chapter_text = read_file(chap_file) + + if not chapter_text.strip(): + self.log("⚠️ 当前章节文件为空或不存在，无法审校。") return self.log("开始一致性审校...") @@ -346,7 +410,7 @@ class NovelGeneratorGUI: novel_setting=novel_setting, character_state=character_state, global_summary=global_summary, - chapter_text=last_chapter_text, + chapter_text=chapter_text, api_key=api_key, base_url=base_url, model_name=model_name, @@ -388,3 +452,26 @@ class NovelGeneratorGUI: thread = threading.Thread(target=task) thread.start() + def clear_vectorstore_handler(self): + """ + 清空向量库按钮：弹出二次确认，若确认则执行 clear_vector_store()。 + """ + def confirmed_clear(): + # 再次确认 + second_confirm = messagebox.askyesno("二次确认", "你确定真的要删除所有向量数据吗？此操作不可恢复！") + if second_confirm: + clear_vector_store() + self.log("已清空向量库。") + + first_confirm = messagebox.askyesno("警告", "确定要清空本地向量库吗？此操作不可恢复！") + if first_confirm: + confirmed_clear() + + def get_llm_model(self, model_name, api_key, base_url, temperature): + from langchain_openai import ChatOpenAI + return ChatOpenAI( + model=model_name, + api_key=api_key, + base_url=base_url, + temperature=temperature + )