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YILING0013
2025-02-01 18:49:52 +08:00
parent ed7e74583d
commit eb627fbec9
5 changed files with 107 additions and 34 deletions
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.gitignore
+1
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@@ -4,5 +4,6 @@
/dist
/.vscode
/__pycache__
/markdown
/vectorstore
config.json
consistency_checker.py
+9 -2
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@@ -2,6 +2,7 @@
# -*- coding: utf-8 -*-
from langchain_openai import ChatOpenAI
# ============== 增加对“剧情要点/未解决冲突”进行检查的可选引导 ==============
CONSISTENCY_PROMPT = """\
请检查下面的小说设定与最新章节是否存在明显冲突或不一致之处，如有请列出：
- 小说设定：
@@ -13,10 +14,13 @@ CONSISTENCY_PROMPT = """\
- 全局摘要：
{global_summary}
- 已记录的未解决冲突或剧情要点：
{plot_arcs} # 若为空可能不输出
- 最新章节内容：
{chapter_text}
如果存在冲突或不一致，请说明；否则请返回“无明显冲突”。
如果存在冲突或不一致，请说明；如果在未解决冲突中有被忽略或需要推进的地方，也请提及；否则请返回“无明显冲突”。
"""
def check_consistency(
@@ -27,15 +31,18 @@ def check_consistency(
api_key: str,
base_url: str,
model_name: str,
temperature: float = 0.3
temperature: float = 0.3,
plot_arcs: str = "" # 新增参数，默认空字符串
) -> str:
"""
调用模型做简单的一致性检查。可扩展更多提示或校验规则。
新增: 会额外检查对“未解决冲突或剧情要点”（plot_arcs）的衔接情况。
"""
prompt = CONSISTENCY_PROMPT.format(
novel_setting=novel_setting,
character_state=character_state,
global_summary=global_summary,
plot_arcs=plot_arcs,
chapter_text=chapter_text
)
model = ChatOpenAI(
novel_generator.py
+69 -26
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@@ -36,10 +36,12 @@ from chapter_directory_parser import get_chapter_info_from_directory
# ============ 日志配置 ============
logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s")
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")
"""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")
# ============ 向量检索相关 ============
VECTOR_STORE_DIR = os.path.join(os.getcwd(), "vectorstore")
@@ -156,8 +158,6 @@ def Novel_novel_directory_generate(
temperature=temperature
)
def generate_base_setting(state: OverallState) -> Dict[str, str]:
prompt = set_prompt.format(
topic=state["topic"],
@@ -271,7 +271,7 @@ def Novel_novel_directory_generate(
logging.info("Novel settings and directory generated successfully.")
# ============ 新增：获取最近N章内容，生成短期摘要 ============
# ============ 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]:
"""
@@ -295,9 +295,7 @@ def summarize_recent_chapters(model: ChatOpenAI, chapters_text_list: List[str])
if not chapters_text_list:
return ""
# 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。
@@ -310,6 +308,49 @@ def summarize_recent_chapters(model: ChatOpenAI, chapters_text_list: List[str])
debug_log(prompt, response.content)
return response.content.strip()
# ============ 新增1：记录剧情要点/未解决冲突 ============
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，提炼出本章引入或延续的悬念、冲突、角色暗线等，将其合并到旧的剧情要点中。
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。
最终输出一份更新后的剧情要点列表，以帮助后续保持故事的整体一致性和悬念延续。
"""
def update_plot_arcs(
chapter_text: str,
old_plot_arcs: str,
api_key: str,
base_url: str,
model_name: str,
temperature: float
) -> str:
"""
利用模型分析最新章节文本，提炼或更新“未解决冲突或剧情要点”。
并返回更新后的字符串。
"""
model = ChatOpenAI(
model=model_name,
api_key=api_key,
base_url=base_url,
temperature=temperature
)
prompt = PLOT_ARCS_PROMPT.format(
chapter_text=chapter_text,
old_plot_arcs=old_plot_arcs
)
response = model.invoke(prompt)
if not response:
logging.warning("update_plot_arcs: No response.")
return old_plot_arcs
debug_log(prompt, response.content)
return response.content.strip()
# ============ 生成章节草稿 & 定稿 ============
def generate_chapter_draft(
@@ -331,9 +372,7 @@ def generate_chapter_draft(
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。
recent_chapters_summary: 最近 3 章的“短期内容摘要”
"""
# 0) 根据 novel_number 从 novel_novel_directory 中获取本章标题及简述
chapter_info = get_chapter_info_from_directory(novel_novel_directory, novel_number)
chapter_title = chapter_info["chapter_title"]
@@ -352,7 +391,6 @@ def generate_chapter_draft(
temperature=temperature
)
# Prompt 拼接
outline_prompt_text = chapter_outline_prompt.format(
novel_setting=novel_settings,
character_state=character_state + "\n\n【历史上下文】\n" + relevant_context,
@@ -362,7 +400,6 @@ def generate_chapter_draft(
chapter_brief=chapter_brief
)
# 在后面加上用户指导与最近章节摘要（可根据需要灵活组织）
outline_prompt_text += f"\n\n【本章目录标题与简述】\n标题：{chapter_title}\n简述：{chapter_brief}\n"
outline_prompt_text += f"\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）'}"
@@ -375,7 +412,6 @@ def generate_chapter_draft(
debug_log(outline_prompt_text, response_outline.content)
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")
@@ -393,7 +429,6 @@ def generate_chapter_draft(
chapter_brief=chapter_brief
)
# 同样插入用户指导和最近摘要
writing_prompt_text += f"\n\n【本章目录标题与简述】\n标题：{chapter_title}\n简述：{chapter_brief}\n"
writing_prompt_text += f"\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）'}"
@@ -406,7 +441,6 @@ def generate_chapter_draft(
debug_log(writing_prompt_text, response_chapter.content)
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")
@@ -430,7 +464,8 @@ def finalize_chapter(
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。
4. 更新向量库；
5. 新增：更新剧情要点/未解决冲突 -> plot_arcs.txt
"""
# 读取当前章节内容
chapters_dir = os.path.join(filepath, "chapters")
@@ -440,12 +475,14 @@ def finalize_chapter(
logging.warning(f"Chapter {novel_number} is empty, cannot finalize.")
return
# 读取角色状态 & 全局摘要
# 读取角色状态 & 全局摘要 & 剧情要点
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") # 新增文件
old_char_state = read_file(character_state_file)
old_global_summary = read_file(global_summary_file)
old_plot_arcs = read_file(plot_arcs_file)
# 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:
@@ -500,17 +537,30 @@ def finalize_chapter(
new_char_state = update_character_state(chapter_text, old_char_state)
# 4) 覆盖写入角色状态文件与全局摘要文件
# ============ 新增2: 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
)
# 4) 覆盖写入角色状态文件、全局摘要文件、剧情要点文件
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)
# 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).")
logging.info(f"Chapter {novel_number} has been finalized (summary & state updated, plot arcs updated, vector store updated).")
def enrich_chapter_text(
chapter_text: str,
@@ -549,29 +599,23 @@ 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。
"""
# 1. 检查文件路径是否有效
if not os.path.exists(file_path):
logging.warning(f"知识库文件不存在: {file_path}")
return
# 2. 读取文件内容
content = read_file(file_path)
if not content.strip():
logging.warning("知识库文件内容为空。")
return
# 3. 对内容进行高级切分处理
paragraphs = advanced_split_content(content)
# 4. 加载或初始化向量存储
store = load_vector_store(api_key, base_url)
if not store:
logging.info("Vector store does not exist. Initializing a new one for knowledge import...")
init_vector_store(api_key, base_url, paragraphs)
return
# 5. 创建Document对象并更新到向量库
docs = [Document(page_content=p) for p in paragraphs]
store.add_documents(docs)
store.persist()
@@ -609,7 +653,6 @@ def advanced_split_content(content: str,
if current_sentences:
merged_paragraphs.append(" ".join(current_sentences))
# 按最大长度二次拆分
final_segments = []
for para in merged_paragraphs:
if len(para) > max_length:
prompt_definitions.py
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@@ -5,7 +5,6 @@
"""
# =============== 提示词：设定 & 目录 ===================
set_prompt = """\
请根据主题:{topic}、类型:{genre}、章数:{number_of_chapters}、每章字数:{word_number}来完善小说整体设定。
需要包含以下信息：
@@ -72,7 +71,6 @@ novel_directory_prompt = """\
"""
# =============== 提示词：章节+角色状态流程 ===================
summary_prompt = """\
这是新生成的章节文本:
{chapter_text}
@@ -102,8 +100,6 @@ update_character_state_prompt = """\
使用简洁、易读的方式描述，可用条目或段落表示。保持与旧文档风格一致。
"""
# ------------------ 新增占位符：chapter_title, chapter_brief ------------------
chapter_outline_prompt = """\
以下是当前小说设定与角色状态信息：
- 小说设定：{novel_setting}
ui.py
+28 -2
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@@ -172,6 +172,11 @@ class NovelGeneratorGUI:
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")
# (7) 查看剧情要点
ttk.Button(self.right_frame, text="[查看] 剧情要点", command=self.show_plot_arcs_ui).grid(
row=row_base+6, column=0, columnspan=2, padx=5, pady=5, sticky="ew"
)
# -------------- 配置管理 --------------
def load_config_btn(self):
cfg = load_config(self.config_file)
@@ -360,7 +365,7 @@ class NovelGeneratorGUI:
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）。")
self.log(f"✅ 第{chap_num}章定稿完成（已更新全局摘要、角色状态、剧情要点、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")
@@ -392,10 +397,12 @@ class NovelGeneratorGUI:
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")
plot_arcs_file = os.path.join(filepath, "plot_arcs.txt") # 新增
novel_setting = read_file(novel_settings_file)
character_state = read_file(character_state_file)
global_summary = read_file(global_summary_file)
plot_arcs = read_file(plot_arcs_file) # 新增
# 获取当前章节文本
chap_num = self.chapter_num_var.get()
@@ -415,7 +422,8 @@ class NovelGeneratorGUI:
api_key=api_key,
base_url=base_url,
model_name=model_name,
temperature=temperature
temperature=temperature,
plot_arcs=plot_arcs # 新增传入
)
self.log("审校结果：")
self.log(result)
@@ -467,6 +475,24 @@ class NovelGeneratorGUI:
if first_confirm:
confirmed_clear()
# =========== 新增：在 UI 中查看当前剧情要点 =============
def show_plot_arcs_ui(self):
filepath = self.filepath_var.get().strip()
plot_arcs_file = os.path.join(filepath, "plot_arcs.txt")
if not os.path.exists(plot_arcs_file):
messagebox.showinfo("剧情要点", "当前还未生成任何剧情要点或未解决冲突。")
return
arcs_text = read_file(plot_arcs_file).strip()
if not arcs_text:
arcs_text = "当前没有记录的剧情要点或冲突。"
# 弹出一个简单的弹窗显示
top = tk.Toplevel(self.master)
top.title("剧情要点/未解决冲突")
text_area = scrolledtext.ScrolledText(top, width=60, height=20)
text_area.pack(fill="both", expand=True)
text_area.insert(tk.END, arcs_text)
text_area.config(state=tk.DISABLED)
def get_llm_model(self, model_name, api_key, base_url, temperature):
from langchain_openai import ChatOpenAI
return ChatOpenAI(