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# 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 <think>...</think> 包裹的内容"""
return re.sub(r'<think>.*?</think>', '', 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 <think>...</think> 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。")
novel_generator.py
+90 -24
View File
@@ -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)
prompt_definitions.py
+135 -35
View File
@@ -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}
"""
ui.py
+2
View File
@@ -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