diff --git a/novel_generator copy.py b/novel_generator copy.py
deleted file mode 100644
index 7505948..0000000
--- a/novel_generator copy.py
+++ /dev/null
@@ -1,830 +0,0 @@
-# novel_generator.py
-# -*- coding: utf-8 -*-
-import os
-import logging
-import re
-import time
-import traceback
-from typing import List, Optional
-
-# langchain 相关
-from langchain_openai import ChatOpenAI, OpenAIEmbeddings
-from langchain_chroma import Chroma
-from chromadb.config import Settings
-from langchain.docstore.document import Document
-
-# nltk、sentence_transformers 及文本处理相关
-import nltk
-import math
-from sentence_transformers import SentenceTransformer
-from sklearn.metrics.pairwise import cosine_similarity
-
-# 工具函数
-from utils import (
- read_file, append_text_to_file, clear_file_content,
- save_string_to_txt
-)
-
-# prompt模板
-from prompt_definitions import (
- # 设定相关
- set_prompt, character_prompt, dark_lines_prompt,
- finalize_setting_prompt, novel_directory_prompt,
-
- # 写作流程相关
- summary_prompt, update_character_state_prompt,
- chapter_outline_prompt, chapter_write_prompt
-)
-
-# Ollama嵌入 (如使用Ollama时需要)
-from embedding_ollama import OllamaEmbeddings
-
-# 用于目录解析章节标题/简介
-from chapter_directory_parser import get_chapter_info_from_directory
-
-
-logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s")
-
-
-# ============ 帮助函数 ============
-def remove_think_tags(text: str) -> str:
- """移除 ... 包裹的内容"""
- return re.sub(r'.*?', '', text, flags=re.DOTALL)
-
-def debug_log(prompt: str, response_content: str):
- logging.info(f"\n[######################################### Prompt #########################################]\n {prompt}\n")
- logging.info(f"\n[######################################### Response #########################################]\n {response_content}\n")
-
-def invoke_with_cleaning(model: ChatOpenAI, prompt: str) -> str:
- """通用封装：调用模型并移除 ... 文本，记录日志后返回"""
- response = model.invoke(prompt)
- if not response:
- logging.warning("No response from model.")
- return ""
- cleaned_text = remove_think_tags(response.content)
- debug_log(prompt, cleaned_text)
- return cleaned_text.strip()
-
-def ensure_openai_base_url_has_v1(url: str) -> str:
- """
- 若用户输入的 url 不包含 '/v1'，则在末尾追加 '/v1'。
- """
- import re
- url = url.strip()
- if not url:
- return url
- if not re.search(r'/v\d+$', url):
- if '/v1' not in url:
- url = url.rstrip('/') + '/v1'
- return url
-
-def is_using_ollama_api(interface_format: str) -> bool:
- return interface_format.lower() == "ollama"
-
-def is_using_ml_studio_api(interface_format: str) -> bool:
- return interface_format.lower() == "ml studio"
-
-
-# ============ 获取 vectorstore 路径 ============
-def get_vectorstore_dir(filepath: str) -> str:
- """
- 返回存储向量库的本地路径：
- 在用户指定的 `filepath` 下创建/使用 'vectorstore' 文件夹。
- """
- return os.path.join(filepath, "vectorstore")
-
-
-# ============ 创建 Embeddings 对象 ============
-def create_embeddings_object(
- api_key: str,
- base_url: str,
- interface_format: str,
- embedding_model_name: str
-):
- """
- 根据 embedding_interface_format，选择 Ollama 或 OpenAIEmbeddings 等不同后端。
- """
- if is_using_ollama_api(interface_format):
- fixed_url = base_url.rstrip("/")
- return OllamaEmbeddings(
- model_name=embedding_model_name,
- base_url=fixed_url
- )
- else:
- # OpenAI 或 ML Studio 均使用 OpenAIEmbeddings，注意 base_url 可能需要 ensure /v1
- fixed_url = ensure_openai_base_url_has_v1(base_url)
- return OpenAIEmbeddings(
- openai_api_key=api_key,
- openai_api_base=fixed_url,
- model=embedding_model_name
- )
-
-
-# ============ 向量库相关操作 ============
-def clear_vector_store(filepath: str) -> bool:
- """
- 返回值表示是否成功清空向量库。
- """
- import shutil
-
- store_dir = get_vectorstore_dir(filepath)
- if not os.path.exists(store_dir):
- logging.info("No vector store found to clear.")
- return False
-
- try:
- if os.path.exists(store_dir):
- shutil.rmtree(store_dir)
- logging.info(f"Vector store directory '{store_dir}' removed.")
- return True
- except Exception as e:
- logging.error(f"程序正在运行，无法删除，请在程序关闭后手动前往 {store_dir} 删除目录。\n {str(e)}")
- traceback.print_exc()
- return False
-
-def init_vector_store(
- api_key: str,
- base_url: str,
- interface_format: str,
- embedding_model_name: str,
- texts: List[str],
- filepath: str
-) -> Chroma:
- """
- 在 filepath 下创建/加载一个 Chroma 向量库并插入 texts。
- """
- store_dir = get_vectorstore_dir(filepath)
- os.makedirs(store_dir, exist_ok=True)
-
- embeddings = create_embeddings_object(
- api_key=api_key,
- base_url=base_url,
- interface_format=interface_format,
- embedding_model_name=embedding_model_name
- )
- documents = [Document(page_content=str(t)) for t in texts]
- vectorstore = Chroma.from_documents(
- documents,
- embedding=embeddings,
- persist_directory=store_dir,
- client_settings=Settings(anonymized_telemetry=False),
- collection_name="novel_collection"
- )
- return vectorstore
-
-
-def load_vector_store(
- api_key: str,
- base_url: str,
- interface_format: str,
- embedding_model_name: str,
- filepath: str
-) -> Optional[Chroma]:
- """
- 读取已存在的 Chroma 向量库。若不存在则返回 None。
- """
- store_dir = get_vectorstore_dir(filepath)
- if not os.path.exists(store_dir):
- logging.info("Vector store not found. Will return None.")
- return None
-
- embeddings = create_embeddings_object(
- api_key=api_key,
- base_url=base_url,
- interface_format=interface_format,
- embedding_model_name=embedding_model_name
- )
- return Chroma(
- persist_directory=store_dir,
- embedding_function=embeddings,
- client_settings=Settings(anonymized_telemetry=False),
- collection_name="novel_collection"
- )
-
-
-def split_by_length(text: str, max_length: int = 500) -> List[str]:
- segments = []
- start_idx = 0
- while start_idx < len(text):
- end_idx = min(start_idx + max_length, len(text))
- segment = text[start_idx:end_idx]
- segments.append(segment.strip())
- start_idx = end_idx
- return segments
-
-
-def split_text_for_vectorstore(chapter_text: str,
- max_length: int = 500,
- similarity_threshold: float = 0.7) -> List[str]:
- """
- 对新的章节文本进行分段后，再用于存入向量库。
- """
- if not chapter_text.strip():
- return []
-
- nltk.download('punkt', quiet=True)
- nltk.download('punkt_tab', quiet=True)
- sentences = nltk.sent_tokenize(chapter_text)
- if not sentences:
- return []
-
- # 先对相近句子进行合并
- model = SentenceTransformer('paraphrase-MiniLM-L6-v2')
- embeddings = model.encode(sentences)
-
- merged_paragraphs = []
- current_sentences = [sentences[0]]
- current_embedding = embeddings[0]
-
- for i in range(1, len(sentences)):
- sim = cosine_similarity([current_embedding], [embeddings[i]])[0][0]
- if sim >= similarity_threshold:
- current_sentences.append(sentences[i])
- current_embedding = (current_embedding + embeddings[i]) / 2.0
- else:
- merged_paragraphs.append(" ".join(current_sentences))
- current_sentences = [sentences[i]]
- current_embedding = embeddings[i]
-
- if current_sentences:
- merged_paragraphs.append(" ".join(current_sentences))
-
- # 再对合并好的段落做 max_length 切分
- final_segments = []
- for para in merged_paragraphs:
- 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)
-
- return final_segments
-
-
-def update_vector_store(
- api_key: str,
- base_url: str,
- new_chapter: str,
- interface_format: str,
- embedding_model_name: str,
- filepath: str
-):
- """
- 将最新章节文本插入到向量库中。若库不存在则初始化。
- """
- splitted_texts = split_text_for_vectorstore(new_chapter)
- if not splitted_texts:
- logging.warning("No valid text to insert into vector store. Skipping.")
- return
-
- store = load_vector_store(
- api_key=api_key,
- base_url=base_url,
- interface_format=interface_format,
- embedding_model_name=embedding_model_name,
- filepath=filepath
- )
- if not store:
- logging.info("Vector store does not exist. Initializing a new one for new chapter...")
- init_vector_store(
- api_key=api_key,
- base_url=base_url,
- interface_format=interface_format,
- embedding_model_name=embedding_model_name,
- texts=splitted_texts,
- filepath=filepath
- )
- return
-
- docs = [Document(page_content=str(t)) for t in splitted_texts]
- store.add_documents(docs)
- logging.info("Vector store updated with the new chapter splitted segments.")
-
-
-def get_relevant_context_from_vector_store(
- api_key: str,
- base_url: str,
- query: str,
- interface_format: str,
- embedding_model_name: str,
- filepath: str,
- k: int = 2
-) -> str:
- """
- 从向量库中检索与 query 最相关的 k 条文本，拼接后返回。
- """
- store = load_vector_store(
- api_key=api_key,
- base_url=base_url,
- interface_format=interface_format,
- embedding_model_name=embedding_model_name,
- filepath=filepath
- )
- if not store:
- logging.info("No vector store found. Returning empty context.")
- return ""
-
- docs = store.similarity_search(query, k=k)
- if not docs:
- logging.info(f"No relevant documents found for query '{query}'. Returning empty context.")
- return ""
-
- combined = "\n".join([d.page_content for d in docs])
- return combined
-
-
-# ============ 1. 生成小说“设定” (Novel_setting.txt) ============
-def Novel_setting_generate(
- api_key: str,
- base_url: str,
- llm_model: str,
- topic: str,
- genre: str,
- number_of_chapters: int,
- word_number: int,
- filepath: str,
- temperature: float = 0.7
-) -> None:
- os.makedirs(filepath, exist_ok=True)
-
- model = ChatOpenAI(
- model=llm_model,
- api_key=api_key,
- base_url=ensure_openai_base_url_has_v1(base_url),
- temperature=temperature
- )
-
- # Step1: 基础设定
- prompt_base = set_prompt.format(
- topic=topic,
- genre=genre,
- number_of_chapters=number_of_chapters,
- word_number=word_number
- )
- base_setting = invoke_with_cleaning(model, prompt_base)
-
- # Step2: 角色设定
- prompt_char = character_prompt.format(
- novel_setting=base_setting
- )
- character_setting = invoke_with_cleaning(model, prompt_char)
-
- # Step3: 暗线/雷点
- prompt_dark = dark_lines_prompt.format(
- character_info=character_setting
- )
- dark_lines = invoke_with_cleaning(model, prompt_dark)
-
- # Step4: 最终整合
- prompt_final = finalize_setting_prompt.format(
- novel_setting_base=base_setting,
- character_setting=character_setting,
- dark_lines=dark_lines
- )
- final_novel_setting = invoke_with_cleaning(model, prompt_final)
-
- filename_set = os.path.join(filepath, "Novel_setting.txt")
- clear_file_content(filename_set)
-
- final_novel_setting_cleaned = final_novel_setting.replace('#', '').replace('*', '')
- save_string_to_txt(final_novel_setting_cleaned, filename_set)
- logging.info("Novel_setting.txt has been generated successfully.")
-
-
-# ============ 2. 生成小说目录 (Novel_directory.txt) ============
-def Novel_directory_generate(
- api_key: str,
- base_url: str,
- llm_model: str,
- number_of_chapters: int,
- filepath: str,
- temperature: float = 0.7
-) -> None:
- filename_set = os.path.join(filepath, "Novel_setting.txt")
- final_novel_setting = read_file(filename_set).strip()
- if not final_novel_setting:
- logging.warning("Novel_setting.txt 内容为空，请先生成小说设定。")
- return
-
- model = ChatOpenAI(
- model=llm_model,
- api_key=api_key,
- base_url=ensure_openai_base_url_has_v1(base_url),
- temperature=temperature
- )
-
- prompt_dir = novel_directory_prompt.format(
- final_novel_setting=final_novel_setting,
- number_of_chapters=number_of_chapters
- )
- final_novel_directory = invoke_with_cleaning(model, prompt_dir)
- if not final_novel_directory.strip():
- logging.warning("Novel_directory生成结果为空。")
- return
-
- filename_dir = os.path.join(filepath, "Novel_directory.txt")
- clear_file_content(filename_dir)
-
- final_novel_directory_cleaned = final_novel_directory.replace('#', '').replace('*', '')
- save_string_to_txt(final_novel_directory_cleaned, filename_dir)
-
- logging.info("Novel_directory.txt has been generated successfully.")
-
-
-# ============ 获取最近 N 章内容，生成短期摘要 ============
-def get_last_n_chapters_text(chapters_dir: str, current_chapter_num: int, n: int = 3) -> List[str]:
- 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)
- if len(texts) < n:
- texts = [''] * (n - len(texts)) + texts
- return texts
-
-def summarize_recent_chapters(
- llm_model: str,
- api_key: str,
- base_url: str,
- temperature: float,
- chapters_text_list: List[str]
-) -> str:
- if not chapters_text_list:
- return ""
- if all(not txt.strip() for txt in chapters_text_list):
- return "暂无摘要。"
-
- model = ChatOpenAI(
- model=llm_model,
- api_key=api_key,
- base_url=ensure_openai_base_url_has_v1(base_url),
- temperature=temperature
- )
-
- combined_text = "\n".join(chapters_text_list)
- prompt = f"""你是一名资深长篇小说写作辅助AI，下面是最近几章的合并文本：
-{combined_text}
-
-请用中文输出不超过500字的摘要，只包含主要剧情进展、角色变化、冲突焦点等要点："""
-
- summary_text = invoke_with_cleaning(model, prompt)
- if not summary_text:
- return (combined_text[:800] + "...") if len(combined_text) > 800 else combined_text
- return summary_text
-
-
-# ============ 剧情要点/冲突 ============
-PLOT_ARCS_PROMPT = """\
-下面是新生成的章节内容:
-{chapter_text}
-
-这里是已记录的剧情要点/未解决冲突(可能为空):
-{old_plot_arcs}
-
-请基于新的章节内容，提炼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。
-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。
-最终输出更新后的剧情要点列表，以帮助后续保持故事整体的一致性和悬念延续。
-"""
-
-def update_plot_arcs(
- chapter_text: str,
- old_plot_arcs: str,
- api_key: str,
- base_url: str,
- model_name: str,
- temperature: float
-) -> str:
- model = ChatOpenAI(
- model=model_name,
- api_key=api_key,
- base_url=ensure_openai_base_url_has_v1(base_url),
- temperature=temperature
- )
- prompt = PLOT_ARCS_PROMPT.format(
- chapter_text=chapter_text,
- old_plot_arcs=old_plot_arcs
- )
- arcs_text = invoke_with_cleaning(model, prompt)
- if not arcs_text:
- logging.warning("update_plot_arcs: No response or empty result.")
- return old_plot_arcs
- return arcs_text
-
-
-# ============ 生成章节草稿 ============
-def generate_chapter_draft(
- novel_settings: 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,
- word_number: int,
- temperature: float,
- novel_novel_directory: str,
- filepath: str,
- interface_format: str,
- embedding_model_name: str,
- embedding_base_url: str,
- embedding_retrieval_k: int = 4
-) -> str:
- # 1) 根据目录解析标题、简介
- chapter_info = get_chapter_info_from_directory(novel_novel_directory, novel_number)
- chapter_title = chapter_info["chapter_title"]
- chapter_brief = chapter_info["chapter_brief"]
-
- # 合并要检索的文本（用户指导 + 章节简介 + 最近摘要）
- combined_query_parts = []
- if user_guidance.strip():
- combined_query_parts.append(user_guidance)
- if chapter_brief.strip():
- combined_query_parts.append(chapter_brief)
- if recent_chapters_summary.strip():
- combined_query_parts.append(recent_chapters_summary)
- # 额外加一个关键字
- combined_query_parts.append("回顾剧情")
-
- merged_query_str = "\n".join(combined_query_parts)
-
- # 2) 从向量库检索上下文
- relevant_context = get_relevant_context_from_vector_store(
- api_key=api_key,
- base_url=embedding_base_url if embedding_base_url else base_url,
- query=merged_query_str,
- interface_format=interface_format,
- embedding_model_name=embedding_model_name,
- filepath=filepath,
- k=embedding_retrieval_k
- )
- if not relevant_context.strip():
- relevant_context = "暂无相关内容。"
-
- # 3) 生成本章大纲
- model = ChatOpenAI(
- model=model_name,
- api_key=api_key,
- base_url=ensure_openai_base_url_has_v1(base_url),
- temperature=temperature
- )
-
- outline_prompt_text = chapter_outline_prompt.format(
- novel_setting=novel_settings,
- character_state=character_state + "\n\n【检索到的上下文】\n" + relevant_context,
- global_summary=global_summary,
- novel_number=novel_number,
- chapter_title=chapter_title,
- chapter_brief=chapter_brief
- )
- outline_prompt_text += f"\n\n【最近几章摘要】\n{recent_chapters_summary}"
- outline_prompt_text += f"\n\n【用户指导】\n{user_guidance if user_guidance else '（无）'}"
-
- chapter_outline = invoke_with_cleaning(model, outline_prompt_text)
-
- 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)
-
- # 4) 生成正文草稿
- writing_prompt_text = chapter_write_prompt.format(
- novel_setting=novel_settings,
- character_state=character_state + "\n\n【检索到的上下文】\n" + relevant_context,
- global_summary=global_summary,
- chapter_outline=chapter_outline,
- word_number=word_number,
- novel_number=novel_number,
- chapter_title=chapter_title,
- chapter_brief=chapter_brief
- )
- writing_prompt_text += f"\n\n【最近几章摘要】\n{recent_chapters_summary}"
- writing_prompt_text += f"\n\n【用户指导】\n{user_guidance if user_guidance else '（无）'}"
-
- chapter_content = invoke_with_cleaning(model, writing_prompt_text)
-
- 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")
- 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
-
-
-# ============ 定稿章节 ============
-def finalize_chapter(
- novel_number: int,
- word_number: int,
- api_key: str,
- base_url: str,
- interface_format: str,
- embedding_model_name: str,
- model_name: str,
- temperature: float,
- filepath: str,
- embedding_base_url: str,
- embedding_api_key: str
-):
- 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
-
- character_state_file = os.path.join(filepath, "character_state.txt")
- global_summary_file = os.path.join(filepath, "global_summary.txt")
- plot_arcs_file = os.path.join(filepath, "plot_arcs.txt")
-
- old_char_state = read_file(character_state_file)
- old_global_summary = read_file(global_summary_file)
- old_plot_arcs = read_file(plot_arcs_file)
-
- # 篇幅不足，二次扩写
- if len(chapter_text) < 0.8 * word_number:
- logging.info("Chapter text is shorter than 80% of desired length. Enriching...")
- 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
- )
- clear_file_content(chapter_file)
- save_string_to_txt(chapter_text, chapter_file)
-
- # 更新全局摘要
- model = ChatOpenAI(
- model=model_name,
- api_key=api_key,
- base_url=ensure_openai_base_url_has_v1(base_url),
- temperature=temperature
- )
-
- def update_global_summary(chapter_text: str, old_summary: str) -> str:
- prompt = summary_prompt.format(
- chapter_text=chapter_text,
- global_summary=old_summary
- )
- return invoke_with_cleaning(model, prompt) or old_summary
-
- new_global_summary = update_global_summary(chapter_text, old_global_summary)
-
- # 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,
- old_state=old_state
- )
- return invoke_with_cleaning(model, prompt) or old_state
-
- new_char_state = update_character_state(chapter_text, old_char_state)
-
- # 更新剧情要点
- new_plot_arcs = update_plot_arcs(
- chapter_text=chapter_text,
- old_plot_arcs=old_plot_arcs,
- api_key=api_key,
- base_url=base_url,
- model_name=model_name,
- temperature=temperature
- )
-
- # 写回文件
- 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)
-
- clear_file_content(plot_arcs_file)
- save_string_to_txt(new_plot_arcs, plot_arcs_file)
-
- # 更新向量库（此时用 embedding_api_key/embedding_base_url）
- update_vector_store(
- api_key=embedding_api_key,
- base_url=embedding_base_url if embedding_base_url else base_url,
- new_chapter=chapter_text,
- interface_format=interface_format,
- embedding_model_name=embedding_model_name,
- filepath=filepath
- )
-
- logging.info(f"Chapter {novel_number} has been finalized.")
-
-
-def enrich_chapter_text(
- chapter_text: str,
- word_number: int,
- api_key: str,
- base_url: str,
- model_name: str,
- temperature: float
-) -> str:
- model = ChatOpenAI(
- model=model_name,
- api_key=api_key,
- base_url=ensure_openai_base_url_has_v1(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。
-
-原章节内容：
-{chapter_text}"""
- enriched_text = invoke_with_cleaning(model, prompt)
- return enriched_text if enriched_text else chapter_text
-
-
-# ============ 导入外部知识文本到向量库 ============
-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，最后按 max_length 二次切分。
- """
- nltk.download('punkt', quiet=True)
- sentences = nltk.sent_tokenize(content)
- if not sentences:
- return []
-
- model = SentenceTransformer('paraphrase-MiniLM-L6-v2')
- embeddings = model.encode(sentences)
-
- merged_paragraphs = []
- current_sentences = [sentences[0]]
- current_embedding = embeddings[0]
-
- for i in range(1, len(sentences)):
- sim = cosine_similarity([current_embedding], [embeddings[i]])[0][0]
- if sim >= similarity_threshold:
- current_sentences.append(sentences[i])
- current_embedding = (current_embedding + embeddings[i]) / 2.0
- else:
- merged_paragraphs.append(" ".join(current_sentences))
- current_sentences = [sentences[i]]
- current_embedding = embeddings[i]
-
- if current_sentences:
- merged_paragraphs.append(" ".join(current_sentences))
-
- final_segments = []
- for para in merged_paragraphs:
- 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)
-
- return final_segments
-
-def import_knowledge_file(
- api_key: str,
- base_url: str,
- interface_format: str,
- embedding_model_name: str,
- file_path: str,
- embedding_base_url: str,
- filepath: str
-):
- logging.info(f"开始导入知识库文件: {file_path}, 接口格式: {interface_format}, 模型: {embedding_model_name}")
- if not os.path.exists(file_path):
- logging.warning(f"知识库文件不存在: {file_path}")
- return
-
- content = read_file(file_path)
- if not content.strip():
- logging.warning("知识库文件内容为空。")
- return
-
- paragraphs = advanced_split_content(content)
-
- # 若向量库不存在则初始化，否则追加
- store = load_vector_store(
- api_key=api_key,
- base_url=base_url if base_url else "http://localhost:11434/v1",
- interface_format=interface_format,
- embedding_model_name=embedding_model_name,
- filepath=filepath
- )
- if not store:
- logging.info("Vector store does not exist. Initializing a new one for knowledge import...")
- init_vector_store(
- api_key=api_key,
- base_url=base_url if base_url else "http://localhost:11434/v1",
- interface_format=interface_format,
- embedding_model_name=embedding_model_name,
- texts=paragraphs,
- filepath=filepath
- )
- else:
- docs = [Document(page_content=str(p)) for p in paragraphs]
- store.add_documents(docs)
- logging.info("知识库文件已成功导入至向量库。")
diff --git a/novel_generator.py b/novel_generator.py
index 5e2a6ed..c02a2a2 100644
--- a/novel_generator.py
+++ b/novel_generator.py
@@ -29,6 +29,7 @@ from prompt_definitions import (
world_building_prompt,
plot_architecture_prompt,
chapter_blueprint_prompt,
+ chunked_chapter_blueprint_prompt,
summary_prompt,
update_character_state_prompt,
chapter_draft_prompt,
@@ -386,6 +387,8 @@ def Novel_architecture_generate(
plot_arch_result = invoke_with_cleaning(llm_adapter, prompt_plot)
final_content = (
+ "#=== 0) 小说设定 ===\n"
+ f"主题：{topic},类型：{genre},篇幅：约{number_of_chapters}章（每章{word_number}字）\n\n"
"#=== 1) 核心种子 ===\n"
f"{core_seed_result}\n\n"
"#=== 2) 角色动力学 ===\n"
@@ -402,7 +405,29 @@ def Novel_architecture_generate(
logging.info("Novel_architecture.txt has been generated successfully.")
-# ============ 2) 生成章节蓝图 ============
+# ============ 计算分块大小的工具函数 ============
+
+def compute_chunk_size(number_of_chapters: int, max_tokens: int) -> int:
+ """
+ 基于“每章约100 tokens”的粗略估算，
+ 再结合当前max_tokens，计算分块大小：
+ chunk_size = (floor(max_tokens/100/10)*10) - 10
+ 并确保 chunk_size 不会小于1或大于实际章节数。
+ """
+ tokens_per_chapter = 100.0
+ ratio = max_tokens / tokens_per_chapter # 8192 / 100 = 81.92
+ # 先取到最接近的10倍
+ ratio_rounded_to_10 = int(ratio // 10) * 10 # => 80
+ # 再减10
+ chunk_size = ratio_rounded_to_10 - 10 # => 70
+ if chunk_size < 1:
+ chunk_size = 1
+ if chunk_size > number_of_chapters:
+ chunk_size = number_of_chapters
+ return chunk_size
+
+
+# ============ 2) 生成章节蓝图（新增分块逻辑） ============
def Chapter_blueprint_generate(
interface_format: str,
@@ -410,9 +435,18 @@ def Chapter_blueprint_generate(
base_url: str,
llm_model: str,
filepath: str,
+ number_of_chapters: int,
temperature: float = 0.7,
max_tokens: int = 2048
) -> None:
+ """
+ 如果章节数小于等于 chunk_size，则直接使用 chapter_blueprint_prompt 一次性生成。
+ 如果章节数较多，则进行分块生成：
+ 1) 首先说明要生成的总章节数
+ 2) 先生成 [1..chunk_size] 的章节
+ 3) 将生成的文本作为已有目录传入，继续生成 [chunk_size+1..] 的章节
+ 4) 最后汇总全部章节目录写入 Novel_directory.txt
+ """
arch_file = os.path.join(filepath, "Novel_architecture.txt")
if not os.path.exists(arch_file):
logging.warning("Novel_architecture.txt not found. Please generate architecture first.")
@@ -423,19 +457,6 @@ def Chapter_blueprint_generate(
logging.warning("Novel_architecture.txt is empty.")
return
- match_chaps = re.search(r'约(\d+)章', architecture_text)
- if match_chaps:
- number_of_chapters = int(match_chaps.group(1))
- else:
- number_of_chapters = 10
-
- # 提取三幕式文本
- plot_arch_text = ""
- pat_plot = r'#=== 4\) 三幕式情节架构 ===\n([\s\S]+)$'
- m = re.search(pat_plot, architecture_text)
- if m:
- plot_arch_text = m.group(1).strip()
-
llm_adapter = create_llm_adapter(
interface_format=interface_format,
base_url=base_url,
@@ -445,20 +466,65 @@ def Chapter_blueprint_generate(
max_tokens=max_tokens
)
- prompt = chapter_blueprint_prompt.format(
- plot_architecture=plot_arch_text,
- number_of_chapters=number_of_chapters
- )
- blueprint_text = invoke_with_cleaning(llm_adapter, prompt)
- if not blueprint_text.strip():
- logging.warning("Chapter blueprint generation result is empty.")
+ # 计算分块大小
+ chunk_size = compute_chunk_size(number_of_chapters, max_tokens)
+ logging.info(f"Number of chapters = {number_of_chapters}, computed chunk_size = {chunk_size}.")
+
+ # 如果一次就可以生成全部
+ if chunk_size >= number_of_chapters:
+ prompt = chapter_blueprint_prompt.format(
+ novel_architecture=architecture_text,
+ number_of_chapters=number_of_chapters
+ )
+ blueprint_text = invoke_with_cleaning(llm_adapter, prompt)
+ if not blueprint_text.strip():
+ logging.warning("Chapter blueprint generation result is empty.")
+ return
+
+ filename_dir = os.path.join(filepath, "Novel_directory.txt")
+ clear_file_content(filename_dir)
+ save_string_to_txt(blueprint_text, filename_dir)
+ logging.info("Novel_directory.txt (chapter blueprint) has been generated successfully (single-shot).")
+ return
+
+ # 否则，分块生成
+ final_blueprint = ""
+ current_start = 1
+ while current_start <= number_of_chapters:
+ current_end = min(current_start + chunk_size - 1, number_of_chapters)
+
+ # 分块提示
+ chunk_prompt = chunked_chapter_blueprint_prompt.format(
+ novel_architecture=architecture_text,
+ chapter_list=final_blueprint, # 已有的章节列表文本
+ number_of_chapters=number_of_chapters,
+ n=current_start,
+ m=current_end
+ )
+ logging.info(f"Generating chapters [{current_start}..{current_end}] in a chunk...")
+
+ chunk_result = invoke_with_cleaning(llm_adapter, chunk_prompt)
+ if not chunk_result.strip():
+ logging.warning(f"Chunk generation for chapters [{current_start}..{current_end}] is empty.")
+ chunk_result = ""
+
+ # 将本次生成的文本拼接到最终结果中
+ if final_blueprint.strip():
+ final_blueprint += "\n\n" + chunk_result
+ else:
+ final_blueprint = chunk_result
+
+ current_start = current_end + 1
+
+ if not final_blueprint.strip():
+ logging.warning("All chunked generation results are empty, cannot create blueprint.")
return
filename_dir = os.path.join(filepath, "Novel_directory.txt")
clear_file_content(filename_dir)
- save_string_to_txt(blueprint_text, filename_dir)
+ save_string_to_txt(final_blueprint.strip(), filename_dir)
- logging.info("Novel_directory.txt (chapter blueprint) has been generated successfully.")
+ logging.info("Novel_directory.txt (chapter blueprint) has been generated successfully (chunked).")
# ============ 3) 生成章节草稿 ============
@@ -610,7 +676,7 @@ def finalize_chapter(
logging.warning(f"Chapter {novel_number} is empty, cannot finalize.")
return
- if len(chapter_text) < 0.6 * word_number:
+ if len(chapter_text) < 0.7 * word_number:
chapter_text = enrich_chapter_text(chapter_text, word_number, api_key, base_url, model_name, temperature, interface_format, max_tokens)
clear_file_content(chapter_file)
save_string_to_txt(chapter_text, chapter_file)
diff --git a/prompt_definitions.py b/prompt_definitions.py
index 72dc37c..c7b5661 100644
--- a/prompt_definitions.py
+++ b/prompt_definitions.py
@@ -121,39 +121,91 @@ plot_architecture_prompt = """\
# =============== 5. 章节目录生成（悬念节奏曲线）===================
chapter_blueprint_prompt = """\
-根据三幕式架构：
-{plot_architecture}
+根据小说架构：\n
+{novel_architecture}
设计{number_of_chapters}章的节奏分布：
-1. 每章需明确：
+1. 章节集群划分：
+- 每3-5章构成一个悬念单元，包含完整的小高潮
+- 单元之间设置"认知过山车"（连续2章紧张→1章缓冲）
+- 关键转折章需预留多视角铺垫
+
+2. 每章需明确：
+- 章节定位（角色/事件/主题等）
- 核心悬念类型（信息差/道德困境/时间压力等）
- 情感基调迁移（如从怀疑→恐惧→决绝）
- 伏笔操作（埋设/强化/回收）
- 认知颠覆强度（1-5级）
-2. 章节集群划分：
-- 每3-5章构成一个悬念单元，包含完整的小高潮
-- 单元之间设置"认知过山车"（连续2章紧张→1章缓冲）
-- 关键转折章需预留多视角铺垫
-
输出格式示例：
第n章 - [标题]
-本章定位：[角色/事件/主题]
-核心作用：[推进/转折/揭示]
-悬念密度：[紧凑/渐进/爆发]
-伏笔操作：埋设(A线索)→强化(B矛盾)
+本章定位：[角色/事件/主题/...]
+核心作用：[推进/转折/揭示/...]
+悬念密度：[紧凑/渐进/爆发/...]
+伏笔操作：埋设(A线索)→强化(B矛盾)...
认知颠覆：★☆☆☆☆
本章简述：[一句话概括]
第n+1章 - [标题]
-本章定位：[角色/事件/主题]
-核心作用：[推进/转折/揭示]
-悬念密度：[紧凑/渐进/爆发]
-伏笔操作：埋设(A线索)→强化(B矛盾)
+本章定位：[角色/事件/主题/...]
+核心作用：[推进/转折/揭示/...]
+悬念密度：[紧凑/渐进/爆发/...]
+伏笔操作：埋设(A线索)→强化(B矛盾)...
认知颠覆：★☆☆☆☆
本章简述：[一句话概括]
-使用精炼语言描述，每章字数控制在100字以内。
+要求：
+- 使用精炼语言描述，每章字数控制在100字以内。
+- 合理安排节奏，确保整体悬念曲线的连贯性。
+- 在生成{number_of_chapters}章前不要出现结局章节。
+
+仅给出最终文本，不要解释任何内容。
+"""
+
+chunked_chapter_blueprint_prompt = """\
+根据小说架构：\n
+{novel_architecture}
+
+需要生成总共{number_of_chapters}章的节奏分布，
+
+当前已有章节目录（若未空则说明是初始生成）：\n
+{chapter_list}
+
+现在请设计第{n}章到第{m}的节奏分布：
+1. 章节集群划分：
+- 每3-5章构成一个悬念单元，包含完整的小高潮
+- 单元之间设置"认知过山车"（连续2章紧张→1章缓冲）
+- 关键转折章需预留多视角铺垫
+
+2. 每章需明确：
+- 章节定位（角色/事件/主题等）
+- 核心悬念类型（信息差/道德困境/时间压力等）
+- 情感基调迁移（如从怀疑→恐惧→决绝）
+- 伏笔操作（埋设/强化/回收）
+- 认知颠覆强度（1-5级）
+
+输出格式示例：
+第n章 - [标题]
+本章定位：[角色/事件/主题/...]
+核心作用：[推进/转折/揭示/...]
+悬念密度：[紧凑/渐进/爆发/...]
+伏笔操作：埋设(A线索)→强化(B矛盾)...
+认知颠覆：★☆☆☆☆
+本章简述：[一句话概括]
+
+第n+1章 - [标题]
+本章定位：[角色/事件/主题/...]
+核心作用：[推进/转折/揭示/...]
+悬念密度：[紧凑/渐进/爆发/...]
+伏笔操作：埋设(A线索)→强化(B矛盾)...
+认知颠覆：★☆☆☆☆
+本章简述：[一句话概括]
+
+要求：
+- 使用精炼语言描述，每章字数控制在100字以内。
+- 合理安排节奏，确保整体悬念曲线的连贯性。
+- 在生成{number_of_chapters}章前不要出现结局章节。
+
仅给出最终文本，不要解释任何内容。
"""
@@ -183,11 +235,11 @@ update_character_state_prompt = """\
这是当前的角色状态文档（可为空）：
{old_state}
-请更新角色状态，内容包括：
+请更新角色状态，内容格式：
角色A属性：
├──物品:
- ├──道具1：描述
- ├──道具2：描述
+ ├──某物(道具)：描述
+ ├──XX长剑(武器)：描述
...
├──能力
├──技能1：描述
@@ -232,8 +284,10 @@ update_character_state_prompt = """\
仅返回更新后的角色状态文本，不要解释任何内容。
"""
-# =============== 8. 章节正文写作（新版） ===================
-chapter_draft_prompt = """\
+# =============== 8. 章节正文写作 ===================
+
+# 8.1 第一章草稿提示
+first_chapter_draft_prompt = """\
即将创作：第 {novel_number} 章《{chapter_title}》
本章定位：{chapter_role}
核心作用：{chapter_purpose}
@@ -252,18 +306,6 @@ chapter_draft_prompt = """\
- 小说设定：
{novel_setting}
-- 全局摘要：
-{global_summary}
-
-- 角色状态：
-{character_state}
-
-前章片段(可能为空)：
-{previous_chapter_excerpt}
-
-本地知识(向量)库检索到的片段(可能为空)：
-{context_excerpt}
-
请完成第 {novel_number} 章的正文，至少设计下方2个具有动态张力的场景：
1. 对话场景：
- 潜台词冲突（表面谈论A，实际博弈B）
@@ -287,5 +329,63 @@ chapter_draft_prompt = """\
- 不使用分章节小标题；
- 不要使用markdown格式。
-用户额外指导(可能未指定)：{user_guidance}
+额外指导(可能未指定)：{user_guidance}
"""
+
+# 8.2 后续章节草稿提示
+next_chapter_draft_prompt = """\
+参考文档：
+- 小说设定：
+{novel_setting}
+
+- 全局摘要：
+{global_summary}
+
+- 角色状态：
+{character_state}
+
+本地知识库检索到的片段：
+{context_excerpt}
+
+即将创作：第 {novel_number} 章《{chapter_title}》
+本章定位：{chapter_role}
+核心作用：{chapter_purpose}
+悬念密度：{suspense_level}
+伏笔操作：{foreshadowing}
+认知颠覆：{plot_twist_level}
+本章简述：{chapter_summary}
+
+可用元素：
+- 核心人物(可能未指定)：{characters_involved}
+- 关键道具(可能未指定)：{key_items}
+- 空间坐标(可能未指定)：{scene_location}
+- 时间压力(可能未指定)：{time_constraint}
+
+前章结尾段：
+{previous_chapter_excerpt}
+
+请从前章结尾处继续完成第 {novel_number} 章的正文，至少设计下方2个具有动态张力的场景：
+1. 对话场景：
+ - 潜台词冲突（表面谈论A，实际博弈B）
+ - 权力关系变化（通过非对称对话长度体现）
+ - 至少1处双关语暗示未来危机
+
+2. 动作场景：
+ - 环境交互细节（至少3个感官描写）
+ - 节奏控制（短句加速+比喻减速）
+ - 动作揭示人物隐藏特质
+
+3. 心理场景：
+ - 认知失调的具体表现（行为矛盾）
+ - 隐喻系统的运用（连接世界观符号）
+ - 决策前的价值天平描写
+
+文末设置一个"钩链转折"：结尾时回收旧悬念/创造新悬念/抛出新危机/颠覆某个认知/神转折等。
+
+格式要求：
+- 仅返回章节正文文本；
+- 不使用分章节小标题；
+- 不要使用markdown格式。
+
+额外指导(可能未指定)：{user_guidance}
+"""
\ No newline at end of file
diff --git a/ui.py b/ui.py
index f5b81cc..22b3bbc 100644
--- a/ui.py
+++ b/ui.py
@@ -794,6 +794,7 @@ 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()
+ number_of_chapters = self.safe_get_int(self.num_chapters_var, 10)
temperature = self.temperature_var.get()
max_tokens = self.max_tokens_var.get()
@@ -803,6 +804,7 @@ class NovelGeneratorGUI:
api_key=api_key,
base_url=base_url,
llm_model=model_name,
+ number_of_chapters=number_of_chapters,
filepath=filepath,
temperature=temperature,
max_tokens=max_tokens