新增功能：自定义知识库、生成指导

允许用户上传自己的知识库（建议在线版API，或上下文长的用户使用）

允许在生成下一章节时加入提前指导

模型Temperature参数支持

.gitignore

@@ -4,5 +4,5 @@
 /build
 /dist
 /.vscode
-__pycache__
+/__pycache__
 config.json

README.md

@@ -20,10 +20,22 @@
 ---
 
 ## **2. 安装依赖**
-**手动安装以下依赖**：
+
+**进入项目目录，执行**：
 ```bash
-pip install openai langchain chromadb langchain_openai langchain_chroma langgraph typing_extensions langchain-community
+pip install -r requirements.txt
 ```
+### 安装语句切分模型punkt（可选，默认程序运行后会自动加载）
+**Python环境终端输入**:
+```bash
+python
+import nltk
+nltk.download('punkt')
+```
+
+等待下载完成(很小，下载很快的)
+
+**至此，环境配置完成。**
 
 ---
 
@@ -82,6 +94,11 @@ pyinstaller --onefile --windowed main.py
 ```
 这样会在 `dist/` 目录下生成 `main.exe`（Windows）或 `main`（Linux/macOS）。
 
+或者使用提供的`main.spec`，执行以下打包指令：
+```bash
+pyinstaller main.spec
+```
+
 ---
 
 ## **6. 使用指南**

config_manager.py

@@ -1,3 +1,5 @@
+# config_manager.py
+# -*- coding: utf-8 -*-
 import json
 import os
 

consistency_checker.py

@@ -1,7 +1,5 @@
-"""
-演示多Agent思路中的“审校Agent”，对最新章节进行简单的一致性或逻辑冲突检查。
-可根据需要进行扩展。
-"""
+# consistency_checker.py
+# -*- coding: utf-8 -*-
 from langchain_openai import ChatOpenAI
 
 CONSISTENCY_PROMPT = """\
@@ -28,7 +26,8 @@ def check_consistency(
     chapter_text: str,
     api_key: str,
     base_url: str,
-    model_name: str
+    model_name: str,
+    temperature: float = 0.3
 ) -> 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。
@@ -43,9 +42,16 @@ def check_consistency(
         model=model_name,
         api_key=api_key,
         base_url=base_url,
-        temperature=0.3
+        temperature=temperature
     )
+    # 调试日志
+    print("\n[ConsistencyChecker] Prompt >>>", prompt)
+
     response = model.invoke(prompt)
     if not response:
         return "审校Agent无回复"
+
+    # 调试日志
+    print("[ConsistencyChecker] Response <<<", response.content.strip())
+
     return response.content.strip()

main.py

@@ -1,3 +1,5 @@
+# main.py
+# -*- coding: utf-8 -*-
 import tkinter as tk
 from ui import NovelGeneratorGUI
 

main.spec

@@ -6,7 +6,19 @@ a = Analysis(
     pathex=[],
     binaries=[],
     datas=[],
-    hiddenimports=['typing_extensions', 'langchain-openai', 'langgraph', 'openai', 'chromadb','langchain-community','pydantic','pydantic.deprecated.decorator'],
+    hiddenimports=['typing_extensions', 
+                    'langchain-openai',
+                    'langgraph', 
+                    'openai', 
+                    'chromadb',
+                    'nltk',
+                    'sentence_transformers',
+                    'scikit-learn',
+                    'langchain-community',
+                    'pydantic',
+                    'pydantic.deprecated.decorator',
+                    'chromadb.utils.embedding_functions.onnx_mini_lm_l6_v2'
+                ],
     hookspath=[],
     hooksconfig={},
     runtime_hooks=[],
@@ -21,7 +33,7 @@ exe = EXE(
     a.scripts,
     [],
     exclude_binaries=True,
-    name='AI_NovelGenerator_V1.0',
+    name='AI_NovelGenerator_V1.1',
     debug=False,
     bootloader_ignore_signals=False,
     strip=False,
@@ -41,5 +53,5 @@ coll = COLLECT(
     strip=False,
     upx=True,
     upx_exclude=[],
-    name='AI_NovelGenerator_V1.0',
+    name='AI_NovelGenerator_V1.1',
 )

novel_generator.py

@@ -1,5 +1,8 @@
+# novel_generator.py
+# -*- coding: utf-8 -*-
 import os
 import logging
+import re
 from typing import Dict, List, Optional
 try:
     from typing import TypedDict  # Python 3.8+ 直接可用；若是3.7可改用 typing_extensions
@@ -12,6 +15,12 @@ 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
+from sklearn.metrics.pairwise import cosine_similarity
+
 from utils import (
     read_file, append_text_to_file, clear_file_content,
     save_string_to_txt
@@ -23,7 +32,7 @@ from prompt_definitions import (
     chapter_outline_prompt, chapter_write_prompt
 )
 
-# ============ 日志配置（可选） ============
+# ============ 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） ============
@@ -37,7 +46,7 @@ def init_vector_store(api_key: str, base_url: str, texts: List[str]) -> Chroma:
     """
     embeddings = OpenAIEmbeddings(
         openai_api_key=api_key,
-        openai_api_base=base_url  # <-- 这里用传进来的 base_url
+        openai_api_base=base_url
     )
     documents = [Document(page_content=t) for t in texts]
     vectorstore = Chroma.from_documents(
@@ -48,18 +57,16 @@ def init_vector_store(api_key: str, base_url: str, texts: List[str]) -> Chroma:
     vectorstore.persist()
     return vectorstore
 
-
 def load_vector_store(api_key: str, base_url: str) -> Optional[Chroma]:
     """读取已存在的向量库。若不存在则返回 None。"""
     if not os.path.exists(VECTOR_STORE_DIR):
         return None
     embeddings = OpenAIEmbeddings(
         openai_api_key=api_key,
-        openai_api_base=base_url  # <-- 使用 base_url
+        openai_api_base=base_url
     )
     return Chroma(persist_directory=VECTOR_STORE_DIR, embedding_function=embeddings)
 
-
 def update_vector_store(api_key: str, base_url: str, new_chapter: str) -> None:
     """将最新章节文本插入到向量库里，用于后续检索参考。若库不存在则初始化。"""
     store = load_vector_store(api_key, base_url)
@@ -72,7 +79,6 @@ def update_vector_store(api_key: str, base_url: str, new_chapter: str) -> None:
     store.add_documents([new_doc])
     store.persist()
 
-
 def get_relevant_context_from_vector_store(api_key: str, base_url: str, query: 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。
@@ -86,7 +92,6 @@ def get_relevant_context_from_vector_store(api_key: str, base_url: str, query: s
     combined = "\n".join([d.page_content for d in docs])
     return combined
 
-
 # ============ 多步生成：设置 & 目录 ============
 
 class OverallState(TypedDict):
@@ -100,7 +105,6 @@ class OverallState(TypedDict):
     final_novel_setting: str
     novel_directory: str
 
-
 def Novel_novel_directory_generate(
     api_key: str,
     base_url: str,
@@ -109,7 +113,8 @@ def Novel_novel_directory_generate(
     genre: str,
     number_of_chapters: int,
     word_number: int,
-    filepath: str
+    filepath: str,
+    temperature: float = 0.7
 ) -> None:
     """
     使用多步流程，生成 Novel_setting.txt 与 Novel_directory.txt 并保存到 filepath。
@@ -122,6 +127,7 @@ def Novel_novel_directory_generate(
     :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)
@@ -129,9 +135,15 @@ def Novel_novel_directory_generate(
     model = ChatOpenAI(
         model=llm_model,
         api_key=api_key,
-        base_url=base_url
+        base_url=base_url,
+        temperature=temperature
     )
 
+    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")
+
     def generate_base_setting(state: OverallState) -> Dict[str, str]:
         prompt = set_prompt.format(
             topic=state["topic"],
@@ -143,6 +155,7 @@ def Novel_novel_directory_generate(
         if not response:
             logging.warning("generate_base_setting: No response.")
             return {"novel_setting_base": ""}
+        debug_log(prompt, response.content)
         return {"novel_setting_base": response.content.strip()}
 
     def generate_character_setting(state: OverallState) -> Dict[str, str]:
@@ -153,6 +166,7 @@ def Novel_novel_directory_generate(
         if not response:
             logging.warning("generate_character_setting: No response.")
             return {"character_setting": ""}
+        debug_log(prompt, response.content)
         return {"character_setting": response.content.strip()}
 
     def generate_dark_lines(state: OverallState) -> Dict[str, str]:
@@ -163,6 +177,7 @@ def Novel_novel_directory_generate(
         if not response:
             logging.warning("generate_dark_lines: No response.")
             return {"dark_lines": ""}
+        debug_log(prompt, response.content)
         return {"dark_lines": response.content.strip()}
 
     def finalize_novel_setting(state: OverallState) -> Dict[str, str]:
@@ -175,6 +190,7 @@ def Novel_novel_directory_generate(
         if not response:
             logging.warning("finalize_novel_setting: No response.")
             return {"final_novel_setting": ""}
+        debug_log(prompt, response.content)
         return {"final_novel_setting": response.content.strip()}
 
     def generate_novel_directory(state: OverallState) -> Dict[str, str]:
@@ -186,6 +202,7 @@ def Novel_novel_directory_generate(
         if not response:
             logging.warning("generate_novel_directory: No response.")
             return {"novel_directory": ""}
+        debug_log(prompt, response.content)
         return {"novel_directory": response.content.strip()}
 
     # 构建状态图
@@ -196,7 +213,6 @@ def Novel_novel_directory_generate(
     graph.add_node("finalize_novel_setting", finalize_novel_setting)
     graph.add_node("generate_novel_directory", generate_novel_directory)
 
-    # 注意修正此处节点名称
     graph.add_edge(START, "generate_base_setting")
     graph.add_edge("generate_base_setting", "generate_character_setting")
     graph.add_edge("generate_character_setting", "generate_dark_lines")
@@ -229,22 +245,132 @@ def Novel_novel_directory_generate(
     filename_set = os.path.join(filepath, "Novel_setting.txt")
     filename_novel_directory = os.path.join(filepath, "Novel_directory.txt")
 
-    # 清理文本（去除多余 # 或 * 等）
+    # 清理文本（可根据需要去除多余字符）
     def clean_text(txt: str) -> str:
         return txt.replace('#', '').replace('*', '')
 
     final_novel_setting_cleaned = clean_text(final_novel_setting)
     final_novel_directory_cleaned = clean_text(final_novel_directory)
 
-    # 以追加方式保存；如果希望覆盖可改为 save_string_to_txt()
     append_text_to_file(final_novel_setting_cleaned, filename_set)
     append_text_to_file(final_novel_directory_cleaned, filename_novel_directory)
 
     logging.info("Novel settings and directory generated successfully.")
 
-
 # ============ 生成章节（每章独立文件） ============
 
+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:
+    """
+    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
+    """
+    total = 0
+    current_unit = 1  # 记录当前单位
+    tmp_val = 0       # 暂存本轮数字
+
+    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:
+    """
+    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。
+    """
+
+    lines = novel_directory_text.splitlines()
+
+    # 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 ...
+    ]
+
+    # 用来存储匹配结果： chapter_num -> title
+    directory_map = {}
+
+    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(
     novel_settings: str,
     novel_novel_directory: str,
@@ -254,19 +380,22 @@ def generate_chapter_with_state(
     novel_number: int,
     filepath: str,
     word_number: int,
-    lastchapter: str
+    lastchapter: str,
+    user_guidance: str = "",
+    temperature: float = 0.7
 ) -> 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) 大纲 -> 正文
-      5) 写入 chapter_{novel_number}.txt, 更新 last_chapter.txt
-      6) 更新向量库
+      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
 
     :param novel_settings:        最终的作品设定（字符串）
-    :param novel_novel_directory: 小说目录信息（此处暂时未使用，可根据需求做扩展）
+    :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
@@ -274,6 +403,8 @@ def generate_chapter_with_state(
     :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
     """
     # 确保文件夹存在
@@ -283,9 +414,15 @@ def generate_chapter_with_state(
         model=model_name,
         api_key=api_key,
         base_url=base_url,
-        temperature=0.9
+        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")
+
     # --- 文件路径定义 ---
     chapters_dir = os.path.join(filepath, "chapters")
     os.makedirs(chapters_dir, exist_ok=True)
@@ -308,6 +445,7 @@ 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():
@@ -325,6 +463,7 @@ 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():
@@ -334,53 +473,78 @@ def generate_chapter_with_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  # <-- 多传一个 base_url
+        api_key, base_url, "回顾剧情", k=2
     )
 
-    # 4) 生成大纲
+    # 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)
+
+    # 5) 生成大纲
     def outline_chapter(
         novel_setting: str,
         char_state: str,
         global_summary: str,
         chap_num: int,
-        extra_context: str
+        extra_context: str,
+        directory_hint: str,
+        user_guide: str
     ) -> str:
-        prompt = chapter_outline_prompt.format(
+        """
+        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(prompt)
+
+        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
+        novel_settings, new_char_state, new_global_summary, novel_number,
+        relevant_context, this_and_related_chapters, user_guidance
     )
 
-    # 5) 生成正文
+    # 6) 生成正文
     def write_chapter(
         novel_setting: str,
         char_state: str,
         global_summary: str,
         outline: str,
         wnum: int,
-        extra_context: str
+        extra_context: str,
+        directory_hint: str,
+        user_guide: str
     ) -> str:
-        prompt = chapter_write_prompt.format(
+        # 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(prompt)
+
+        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(
@@ -389,7 +553,9 @@ def generate_chapter_with_state(
         new_global_summary,
         chap_outline,
         word_number,
-        relevant_context
+        relevant_context,
+        this_and_related_chapters,
+        user_guidance
     )
 
     # 写入文件并更新记录
@@ -407,10 +573,117 @@ def generate_chapter_with_state(
         clear_file_content(global_summary_file)
         save_string_to_txt(new_global_summary, global_summary_file)
 
-        # 6) 更新向量检索库
+        # 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
+
+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
+    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()
+    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]
+
+    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
+    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):
+        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

prompt_definitions.py

@@ -1,3 +1,5 @@
+# prompt_definitions.py
+# -*- coding: utf-8 -*-
 """
 集中存放所有提示词(Prompt)，便于统一管理和修改。
 """
@@ -10,7 +12,7 @@ set_prompt = """\
 1. 小说名称、总字数走向（大致范围即可）。
 2. 小说类型与基调（如：都市、穿越、战争等类型，以及轻松、爆笑、暗黑等基调）。
 3. 写作风格（正式 / 轻松；细腻 / 简洁；抒情 / 客观；叙事视角等）。
-4. 整体世界观（时间背景、地理环境、社会结构、科技或魔法水平、重要历史传说或事件等）。
+4. 整体世界观（时间背景、地理环境、社会结构、科技或魔法水平、重要历史事件等）。
 5. 核心内容梗概（可以使用常见叙事结构，如三幕结构、英雄之旅等）。
 6. 初步的情节安排设想（主线、副线、交织等关键思路）。
 7. 初步的人物关系与主要角色设定（角色定位、主要冲突或关系）。
@@ -58,7 +60,7 @@ novel_directory_prompt = """\
 根据以下最终《小说设定》：
 {final_novel_setting}
 并按照下面的小说目录模板生成 {number_of_chapters} 章的目录，同时确保目录符合小说设定中的叙事结构、角色发展及暗线伏笔。
-目录模板：
+目录模板（示例）：
 第1章 ：< text >
 第2章 ：< text >
 ...
@@ -127,3 +129,4 @@ chapter_write_prompt = """\
 3. 可以着重描写人物心理、环境氛围等，以保证足够长度。
 4. 在结尾部分保留一定悬念或剧情转折，为下一章做铺垫。
 """
+

requirements.txt

@@ -4,3 +4,6 @@ langgraph
 openai
 chromadb
 langchain-community
+sentence_transformers
+scikit-learn
+nltk

ui.py

@@ -1,3 +1,5 @@
+# ui.py
+# -*- coding: utf-8 -*-
 import os
 import tkinter as tk
 from tkinter import ttk, filedialog, scrolledtext, messagebox
@@ -7,7 +9,8 @@ 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
+    generate_chapter_with_state,
+    import_knowledge_file
 )
 from consistency_checker import check_consistency
 
@@ -60,9 +63,9 @@ class NovelGeneratorGUI:
         self.chapter_result = scrolledtext.ScrolledText(chapter_frame, width=80, height=10, foreground="blue")
         self.chapter_result.grid(row=0, column=0, sticky="nsew")
 
-    def build_right_layout(self, ):
+    def build_right_layout(self):
         # 行列配置
-        for i in range(12):
+        for i in range(15):
             self.right_frame.rowconfigure(i, weight=0)
         self.right_frame.columnconfigure(1, weight=1)
 
@@ -81,57 +84,77 @@ class NovelGeneratorGUI:
         self.model_name_var = tk.StringVar(value=self.loaded_config.get("model_name", "gpt-4o-mini"))
         ttk.Entry(self.right_frame, textvariable=self.model_name_var, width=32).grid(row=2, column=1, padx=5, pady=5, sticky="w")
 
-        # 4. 主题(Topic) 多行输入
-        ttk.Label(self.right_frame, text="主题(Topic):").grid(row=3, column=0, padx=5, pady=5, sticky="ne")
+        # 4. Temperature
+        ttk.Label(self.right_frame, text="Temperature:").grid(row=3, column=0, padx=5, pady=5, sticky="e")
+        self.temperature_var = tk.DoubleVar(value=self.loaded_config.get("temperature", 0.7))
+        self.temp_value_label = ttk.Label(self.right_frame, text=f"{self.temperature_var.get():.2f}")
+        self.temp_value_label.grid(row=3, column=2, padx=5, pady=5, sticky="w")
+        
+        temp_scale = ttk.Scale(self.right_frame, from_=0.0, to=1.0, orient=tk.HORIZONTAL, variable=self.temperature_var)
+        temp_scale.grid(row=3, column=1, padx=5, pady=5, sticky="we")
+        def update_temp_label(*args):
+            self.temp_value_label.config(text=f"{self.temperature_var.get():.2f}")
+        self.temperature_var.trace("w", update_temp_label)
+
+        # 5. 主题(Topic) 多行输入
+        ttk.Label(self.right_frame, text="主题(Topic):").grid(row=4, column=0, padx=5, pady=5, sticky="ne")
         self.topic_text = scrolledtext.ScrolledText(self.right_frame, width=32, height=4)
-        self.topic_text.grid(row=3, column=1, padx=5, pady=5, sticky="w")
+        self.topic_text.grid(row=4, column=1, padx=5, pady=5, sticky="w")
         topic_default = self.loaded_config.get("topic", "")
         if topic_default:
             self.topic_text.insert(tk.END, topic_default)
 
-        # 5. 类型(Genre)
-        ttk.Label(self.right_frame, text="类型(Genre):").grid(row=4, column=0, padx=5, pady=5, sticky="e")
+        # 6. 类型(Genre)
+        ttk.Label(self.right_frame, text="类型(Genre):").grid(row=5, column=0, padx=5, pady=5, sticky="e")
         self.genre_var = tk.StringVar(value=self.loaded_config.get("genre", "玄幻"))
-        ttk.Entry(self.right_frame, textvariable=self.genre_var, width=32).grid(row=4, column=1, padx=5, pady=5, sticky="w")
+        ttk.Entry(self.right_frame, textvariable=self.genre_var, width=32).grid(row=5, column=1, padx=5, pady=5, sticky="w")
 
-        # 6. 章节数
-        ttk.Label(self.right_frame, text="章节数:").grid(row=5, column=0, padx=5, pady=5, sticky="e")
+        # 7. 章节数
+        ttk.Label(self.right_frame, text="章节数:").grid(row=6, column=0, padx=5, pady=5, sticky="e")
         self.num_chapters_var = tk.IntVar(value=self.loaded_config.get("num_chapters", 10))
-        ttk.Entry(self.right_frame, textvariable=self.num_chapters_var, width=8).grid(row=5, column=1, padx=5, pady=5, sticky="w")
+        ttk.Entry(self.right_frame, textvariable=self.num_chapters_var, width=8).grid(row=6, column=1, padx=5, pady=5, sticky="w")
 
-        # 7. 每章字数
-        ttk.Label(self.right_frame, text="每章字数:").grid(row=6, column=0, padx=5, pady=5, sticky="e")
+        # 8. 每章字数
+        ttk.Label(self.right_frame, text="每章字数:").grid(row=7, column=0, padx=5, pady=5, sticky="e")
         self.word_number_var = tk.IntVar(value=self.loaded_config.get("word_number", 3000))
-        ttk.Entry(self.right_frame, textvariable=self.word_number_var, width=8).grid(row=6, column=1, padx=5, pady=5, sticky="w")
+        ttk.Entry(self.right_frame, textvariable=self.word_number_var, width=8).grid(row=7, column=1, padx=5, pady=5, sticky="w")
 
-        # 8. 文件保存路径
-        ttk.Label(self.right_frame, text="保存路径:").grid(row=7, column=0, padx=5, pady=5, sticky="e")
+        # 9. 文件保存路径
+        ttk.Label(self.right_frame, text="保存路径:").grid(row=8, column=0, padx=5, pady=5, sticky="e")
         self.filepath_var = tk.StringVar(value=self.loaded_config.get("filepath", ""))
-        ttk.Entry(self.right_frame, textvariable=self.filepath_var, width=32).grid(row=7, column=1, padx=5, pady=5, sticky="w")
-        ttk.Button(self.right_frame, text="浏览...", command=self.browse_folder).grid(row=7, column=2, padx=5, pady=5, sticky="w")
+        ttk.Entry(self.right_frame, textvariable=self.filepath_var, width=32).grid(row=8, column=1, padx=5, pady=5, sticky="w")
+        ttk.Button(self.right_frame, text="浏览...", command=self.browse_folder).grid(row=8, column=2, padx=5, pady=5, sticky="w")
 
         # 保存/加载配置按钮
         config_frame = ttk.Frame(self.right_frame)
-        config_frame.grid(row=8, column=1, sticky="w")
+        config_frame.grid(row=9, column=1, sticky="w")
         ttk.Button(config_frame, text="保存配置", command=self.save_config_btn).grid(row=0, column=0, padx=5)
         ttk.Button(config_frame, text="加载配置", command=self.load_config_btn).grid(row=0, column=1, padx=5)
 
-        # 按钮区域
-        row_base = 9
-        ttk.Label(self.right_frame, text="章节号:").grid(row=row_base, column=0, sticky="e")
+        # 10. 章节号
+        ttk.Label(self.right_frame, text="章节号:").grid(row=10, column=0, sticky="e")
         self.chapter_num_var = tk.IntVar(value=1)
-        ttk.Entry(self.right_frame, textvariable=self.chapter_num_var, width=6).grid(row=row_base, column=1, padx=5, pady=5, sticky="w")
+        ttk.Entry(self.right_frame, textvariable=self.chapter_num_var, width=6).grid(row=10, column=1, padx=5, pady=5, sticky="w")
 
+        # 11. “用户指导” 多行输入
+        ttk.Label(self.right_frame, text="本章指导:").grid(row=11, column=0, padx=5, pady=5, sticky="ne")
+        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
         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+1, column=0, columnspan=2, padx=5, pady=5, sticky="ew")
+        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)
-        self.btn_generate_chapter.grid(row=row_base+2, column=0, columnspan=2, padx=5, pady=5, sticky="ew")
+        self.btn_generate_chapter.grid(row=row_base+1, column=0, columnspan=2, padx=5, pady=5, sticky="ew")
 
-        # 可选：添加一个“一致性审校”按钮
         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+3, column=0, columnspan=2, padx=5, pady=5, sticky="ew")
+        self.btn_check_consistency.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
+        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")
 
     # -------------- 配置管理 --------------
     def load_config_btn(self):
@@ -140,6 +163,7 @@ class NovelGeneratorGUI:
             self.api_key_var.set(cfg.get("api_key", ""))
             self.base_url_var.set(cfg.get("base_url", ""))
             self.model_name_var.set(cfg.get("model_name", ""))
+            self.temperature_var.set(cfg.get("temperature", 0.7))
             self.genre_var.set(cfg.get("genre", ""))
             self.num_chapters_var.set(cfg.get("num_chapters", 10))
             self.word_number_var.set(cfg.get("word_number", 3000))
@@ -158,6 +182,7 @@ class NovelGeneratorGUI:
             "api_key": self.api_key_var.get(),
             "base_url": self.base_url_var.get(),
             "model_name": self.model_name_var.get(),
+            "temperature": self.temperature_var.get(),
             "topic": self.topic_text.get("1.0", tk.END).strip(),
             "genre": self.genre_var.get(),
             "num_chapters": self.num_chapters_var.get(),
@@ -200,6 +225,7 @@ class NovelGeneratorGUI:
                 num_chapters = self.num_chapters_var.get()
                 word_number = self.word_number_var.get()
                 filepath = self.filepath_var.get().strip()
+                temperature = self.temperature_var.get()
 
                 if not filepath:
                     messagebox.showwarning("警告", "请先选择保存文件路径")
@@ -214,7 +240,8 @@ class NovelGeneratorGUI:
                     genre=genre,
                     number_of_chapters=num_chapters,
                     word_number=word_number,
-                    filepath=filepath
+                    filepath=filepath,
+                    temperature=temperature
                 )
                 self.log("✅ 小说设定和目录生成完成。查看 Novel_setting.txt 和 Novel_directory.txt。")
             except Exception as e:
@@ -226,7 +253,7 @@ class NovelGeneratorGUI:
         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"""
+        """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 task():
             self.disable_button(self.btn_generate_chapter)
             try:
@@ -236,6 +263,7 @@ class NovelGeneratorGUI:
                 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()
 
                 # 读取设定 & 目录
                 novel_settings_file = os.path.join(filepath, "Novel_setting.txt")
@@ -253,6 +281,9 @@ class NovelGeneratorGUI:
                     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
+                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(
                     novel_settings=novel_settings,
@@ -263,11 +294,13 @@ class NovelGeneratorGUI:
                     novel_number=novel_number,
                     filepath=filepath,
                     word_number=word_number,
-                    lastchapter=lastchapter
+                    lastchapter=lastchapter,
+                    user_guidance=user_guidance,
+                    temperature=temperature
                 )
 
                 if chapter_text:
-                    self.log(f"✅ 第{novel_number}章内容生成完成。chapter.txt 已更新。")
+                    self.log(f"✅ 第{novel_number}章内容生成完成。chapter_{novel_number}.txt 已更新。")
                     self.chapter_result.delete("1.0", tk.END)
                     self.chapter_result.insert(tk.END, chapter_text)
                     self.chapter_result.see(tk.END)
@@ -291,6 +324,7 @@ class NovelGeneratorGUI:
                 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()
 
                 # 读取关键文件
                 novel_settings_file = os.path.join(filepath, "Novel_setting.txt")
@@ -315,7 +349,8 @@ class NovelGeneratorGUI:
                     chapter_text=last_chapter_text,
                     api_key=api_key,
                     base_url=base_url,
-                    model_name=model_name
+                    model_name=model_name,
+                    temperature=temperature
                 )
                 self.log("审校结果：")
                 self.log(result)
@@ -327,3 +362,29 @@ class NovelGeneratorGUI:
 
         thread = threading.Thread(target=task)
         thread.start()
+
+    def import_knowledge_handler(self):
+        """处理导入知识库文件。"""
+        selected_file = filedialog.askopenfilename(
+            title="选择要导入的知识库文件",
+            filetypes=[("Text Files", "*.txt"), ("All Files", "*.*")]
+        )
+        if selected_file:
+            def task():
+                self.disable_button(self.btn_import_knowledge)
+                try:
+                    self.log(f"开始导入知识库文件: {selected_file}")
+                    import_knowledge_file(
+                        api_key=self.api_key_var.get().strip(),
+                        base_url=self.base_url_var.get().strip(),
+                        file_path=selected_file
+                    )
+                    self.log("✅ 知识库文件导入完成。")
+                except Exception as e:
+                    self.log(f"❌ 导入知识库时出错: {e}")
+                finally:
+                    self.enable_button(self.btn_import_knowledge)
+
+            thread = threading.Thread(target=task)
+            thread.start()
+

utils.py

@@ -1,3 +1,5 @@
+# utils.py
+# -*- coding: utf-8 -*-
 import os
 import json