Merge pull request #21 from YILING0013/local_dev

Local dev

embedding_ollama.py

@@ -0,0 +1,55 @@
+# embedding_ollama.py
+import requests
+import traceback
+from typing import List
+
+class OllamaEmbeddings:
+    """
+    Ollama 本地服务提供的 Embedding 接口，
+    最终拼出形如: http://localhost:11434/api/embed
+    即 base_url + "/embed"
+    """
+
+    def __init__(self, model_name: str, base_url: str):
+        self.model_name = model_name
+        self.base_url = base_url
+
+    def embed(self, texts: List[str]) -> List[List[float]]:
+        """
+        批量将多段文本转换为embedding向量
+        """
+        embeddings = []
+        for text in texts:
+            embeddings.append(self.embed_single_document(text))
+        return embeddings
+    
+    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+        """
+        兼容langchain的接口写法
+        """
+        return self.embed(texts)
+
+    def embed_query(self, query: str) -> List[float]:
+        """
+        将单条 query 转换为 embedding 向量
+        """
+        return self.embed_single_document(query)
+
+    def embed_single_document(self, text: str) -> List[float]:
+        """
+        调用 Ollama 本地服务接口，获取文本的 embedding。
+        """
+        url = f"{self.base_url}/embed"
+        data = {
+            "model": self.model_name,
+            "prompt": text
+        }
+        try:
+            response = requests.post(url, json=data)
+            response.raise_for_status()
+            result = response.json()
+            if "embedding" not in result:
+                raise ValueError("No 'embedding' field in Ollama response.")
+            return result["embedding"]
+        except requests.exceptions.RequestException as e:
+            raise Exception(f"Ollama embeddings request error: {e}\n{traceback.format_exc()}")

main.spec

@@ -40,7 +40,7 @@ exe = EXE(
     a.scripts,
     [],
     exclude_binaries=True,
-    name='AI_NovelGenerator_V1.2.3',
+    name='AI_NovelGenerator_V1.2.4',
     debug=True,
     bootloader_ignore_signals=False,
     strip=False,
@@ -60,5 +60,5 @@ coll = COLLECT(
     strip=False,
     upx=True,
     upx_exclude=[],
-    name='AI_NovelGenerator_V1.2.3'
+    name='AI_NovelGenerator_V1.2.4'
 )

novel_generator.py

@@ -3,11 +3,9 @@
 import os
 import logging
 import re
+import traceback
 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
-except ImportError:
-    from typing_extensions import TypedDict
+from typing import TypedDict
 
 from langchain_openai import ChatOpenAI
 from langgraph.graph import StateGraph, START, END
@@ -30,20 +28,79 @@ from prompt_definitions import (
     summary_prompt, update_character_state_prompt,
     chapter_outline_prompt, chapter_write_prompt
 )
-
-# ============ 新增：导入 chapter_directory_parser ============
+from embedding_ollama import OllamaEmbeddings
 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]"""
+    """
+    打印prompt和response的辅助函数
+    """
     logging.info(f"\n[Prompt >>>] {prompt}\n")
     logging.info(f"[Response >>>] {response_content}\n")
 
-# ============ 向量检索相关 ============
+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 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()
+
+# ============ 判断接口格式相关 ============
+def is_using_ollama_api(interface_format: str, base_url: str) -> bool:
+    """
+    当 interface_format == "Ollama" 时返回 True
+    """
+    return interface_format.lower() == "ollama"
+
+def is_using_ml_studio_api(interface_format: str, base_url: str) -> bool:
+    """
+    如果用户在下拉里选择了 ML Studio
+    """
+    return interface_format.lower() == "ml studio"
+
+# ============ 创建 Embeddings 对象 ============
+def create_embeddings_object(
+    api_key: str,
+    base_url: str,
+    embed_url: str,
+    interface_format: str,
+    embedding_model_name: str
+):
+    """
+    根据用户在UI中配置的参数，返回对应的 embeddings 对象。
+    - 当 interface_format = "Ollama" => OllamaEmbeddings(...)
+    - 当 interface_format = "OpenAI"/"ML Studio" => OpenAIEmbeddings(...)
+    - 其它情况可扩展
+    """
+    if is_using_ollama_api(interface_format, embed_url):
+        fixed_url = embed_url.rstrip("/")
+        # Ollama embedding接口通常是 /api/embed
+        fixed_url = fixed_url.replace("/v1", "/api")
+        return OllamaEmbeddings(
+            model_name=embedding_model_name,
+            base_url=fixed_url
+        )
+    elif is_using_ml_studio_api(interface_format, base_url):
+        return OpenAIEmbeddings(openai_api_key=api_key, openai_api_base=base_url)
+    else:
+        # 默认使用 OpenAIEmbeddings
+        return OpenAIEmbeddings(openai_api_key=api_key, openai_api_base=base_url)
+
+# ============ 向量库相关 ============
 VECTOR_STORE_DIR = os.path.join(os.getcwd(), "vectorstore")
 if not os.path.exists(VECTOR_STORE_DIR):
     os.makedirs(VECTOR_STORE_DIR)
@@ -51,11 +108,10 @@ if not os.path.exists(VECTOR_STORE_DIR):
 def clear_vector_store():
     """
     清空本地向量库（删除 vectorstore 文件夹内的内容）。
-    需要在UI中加一个二次确认弹窗，防止误删。
     """
     if os.path.exists(VECTOR_STORE_DIR):
-        try:
         import shutil
+        try:
             for filename in os.listdir(VECTOR_STORE_DIR):
                 file_path = os.path.join(VECTOR_STORE_DIR, filename)
                 if os.path.isfile(file_path) or os.path.islink(file_path):
@@ -63,19 +119,29 @@ def clear_vector_store():
                 elif os.path.isdir(file_path):
                     shutil.rmtree(file_path)
             logging.info("Local vector store has been cleared.")
-        except Exception as e:
-            logging.warning(f"Failed to clear vector store: {e}")
+        except Exception:
+            logging.warning(f"Failed to clear vector store:\n{traceback.format_exc()}")
     else:
         logging.info("No vector store found to clear.")
 
-def init_vector_store(api_key: str, base_url: str, texts: List[str]) -> Chroma:
+def init_vector_store(
+    api_key: str,
+    base_url: str, 
+    interface_format: str,
+    embedding_model_name: str,
+    texts: List[str], 
+    embedding_base_url: str = ""
+) -> Chroma:
     """
     初始化并返回一个Chroma向量库，将传入的文本进行嵌入并保存到本地目录。
-    如果不存在该目录，会自动创建。
     """
-    embeddings = OpenAIEmbeddings(
-        openai_api_key=api_key,
-        openai_api_base=base_url
+    embed_url = embedding_base_url if embedding_base_url else base_url
+    embeddings = create_embeddings_object(
+        api_key=api_key,
+        base_url=base_url,
+        embed_url=embed_url,
+        interface_format=interface_format,
+        embedding_model_name=embedding_model_name
     )
     documents = [Document(page_content=t) for t in texts]
     vectorstore = Chroma.from_documents(
@@ -86,43 +152,101 @@ 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。"""
+def load_vector_store(
+    api_key: str,
+    base_url: str,
+    interface_format: str,
+    embedding_model_name: str,
+    embedding_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):
+        logging.info("Vector store not found. Initializing a new one...")
         return None
-    embeddings = OpenAIEmbeddings(
-        openai_api_key=api_key,
-        openai_api_base=base_url
+    embed_url = embedding_base_url if embedding_base_url else base_url
+    embeddings = create_embeddings_object(
+        api_key=api_key,
+        base_url=base_url,
+        embed_url=embed_url,
+        interface_format=interface_format,
+        embedding_model_name=embedding_model_name
     )
     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)
+def update_vector_store(
+    api_key: str, 
+    base_url: str, 
+    new_chapter: str, 
+    interface_format: str,
+    embedding_model_name: str,
+    embedding_base_url: 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=api_key,
+        base_url=base_url,
+        interface_format=interface_format,
+        embedding_model_name=embedding_model_name,
+        embedding_base_url=embedding_base_url
+    )
+    
+    # 如果向量库不存在，初始化它
     if not store:
-        logging.info("Vector store does not exist. Initializing a new one...")
-        init_vector_store(api_key, base_url, [new_chapter])
+        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=[new_chapter],
+            embedding_base_url=embedding_base_url
+        )
         return
 
     new_doc = Document(page_content=new_chapter)
     store.add_documents([new_doc])
     store.persist()
+    logging.info("Vector store updated with the new chapter.")
 
-def get_relevant_context_from_vector_store(api_key: str, base_url: str, query: str, k: int = 2) -> str:
+def get_relevant_context_from_vector_store(
+    api_key: str,
+    base_url: str,
+    query: str,
+    interface_format: str,
+    embedding_model_name: str,
+    embedding_base_url: 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。
-    若向量库不存在则返回空字符串。
+    若向量库不存在或没有足够的内容，则返回空字符串。
     """
-    store = load_vector_store(api_key, base_url)
+    store = load_vector_store(
+        api_key=api_key,
+        base_url=base_url,
+        interface_format=interface_format,
+        embedding_model_name=embedding_model_name,
+        embedding_base_url=embedding_base_url
+    )
+    
+    # 如果向量库为空，直接返回空字符串
     if not store:
-        logging.warning("Vector store not found. Returning empty context.")
+        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
 
 # ============ 多步生成：设置 & 目录 ============
-
 class OverallState(TypedDict):
     topic: str
     genre: str
@@ -148,7 +272,6 @@ def Novel_novel_directory_generate(
     """
     使用多步流程，生成 Novel_setting.txt 与 Novel_directory.txt 并保存到 filepath。
     """
-    # 确保文件夹存在
     os.makedirs(filepath, exist_ok=True)
 
     model = ChatOpenAI(
@@ -165,67 +288,46 @@ def Novel_novel_directory_generate(
             number_of_chapters=state["number_of_chapters"],
             word_number=state["word_number"]
         )
-        response = model.invoke(prompt)
-        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()}
+        result_text = invoke_with_cleaning(model, prompt)
+        return {"novel_setting_base": result_text}
 
     def generate_character_setting(state: OverallState) -> Dict[str, str]:
         prompt = character_prompt.format(
             novel_setting=state["novel_setting_base"]
         )
-        response = model.invoke(prompt)
-        if not response:
-            logging.warning("generate_character_setting: No response.")
-            return {"character_setting": ""}
-        debug_log(prompt, response.content)
-        return {"character_setting": response.content.strip()}
+        result_text = invoke_with_cleaning(model, prompt)
+        return {"character_setting": result_text}
 
     def generate_dark_lines(state: OverallState) -> Dict[str, str]:
         prompt = dark_lines_prompt.format(
             character_info=state["character_setting"]
         )
-        response = model.invoke(prompt)
-        if not response:
-            logging.warning("generate_dark_lines: No response.")
-            return {"dark_lines": ""}
-        debug_log(prompt, response.content)
-        return {"dark_lines": response.content.strip()}
+        result_text = invoke_with_cleaning(model, prompt)
+        return {"dark_lines": result_text}
 
-    def finalize_novel_setting(state: OverallState) -> Dict[str, str]:
+    def finalize_novel_setting_func(state: OverallState) -> Dict[str, str]:
         prompt = finalize_setting_prompt.format(
             novel_setting_base=state["novel_setting_base"],
             character_setting=state["character_setting"],
             dark_lines=state["dark_lines"]
         )
-        response = model.invoke(prompt)
-        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()}
+        result_text = invoke_with_cleaning(model, prompt)
+        return {"final_novel_setting": result_text}
 
-    def generate_novel_directory(state: OverallState) -> Dict[str, str]:
+    def generate_novel_directory_func(state: OverallState) -> Dict[str, str]:
         prompt = novel_directory_prompt.format(
             final_novel_setting=state["final_novel_setting"],
             number_of_chapters=state["number_of_chapters"]
         )
-        response = model.invoke(prompt)
-        if not response:
-            logging.warning("generate_novel_directory: No response.")
-            return {"novel_directory": ""}
-        debug_log(prompt, response.content)
-        return {"novel_directory": response.content.strip()}
+        result_text = invoke_with_cleaning(model, prompt)
+        return {"novel_directory": result_text}
 
-    # 构建状态图
     graph = StateGraph(OverallState)
     graph.add_node("generate_base_setting", generate_base_setting)
     graph.add_node("generate_character_setting", generate_character_setting)
     graph.add_node("generate_dark_lines", generate_dark_lines)
-    graph.add_node("finalize_novel_setting", finalize_novel_setting)
-    graph.add_node("generate_novel_directory", generate_novel_directory)
+    graph.add_node("finalize_novel_setting", finalize_novel_setting_func)
+    graph.add_node("generate_novel_directory", generate_novel_directory_func)
 
     graph.add_edge(START, "generate_base_setting")
     graph.add_edge("generate_base_setting", "generate_character_setting")
@@ -255,24 +357,25 @@ def Novel_novel_directory_generate(
         logging.warning("生成失败：缺少 final_novel_setting 或 novel_directory。")
         return
 
-    # 写入文件
     filename_set = os.path.join(filepath, "Novel_setting.txt")
     filename_novel_directory = os.path.join(filepath, "Novel_directory.txt")
 
-    # 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)
 
-    append_text_to_file(final_novel_setting_cleaned, filename_set)
-    append_text_to_file(final_novel_directory_cleaned, filename_novel_directory)
+    # 改进：写文件时先清空再写入
+    clear_file_content(filename_set)
+    save_string_to_txt(final_novel_setting_cleaned, filename_set)
+
+    clear_file_content(filename_novel_directory)
+    save_string_to_txt(final_novel_directory_cleaned, filename_novel_directory)
 
     logging.info("Novel settings and directory 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]:
     """
     从指定文件夹中，读取最近 n 章的内容（如果存在），并按从旧到新的顺序返回文本列表。
@@ -286,30 +389,45 @@ def get_last_n_chapters_text(chapters_dir: str, current_chapter_num: int, n: int
             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(model: ChatOpenAI, chapters_text_list: List[str]) -> str:
+def summarize_recent_chapters(
+    llm_model: str,
+    api_key: str,
+    base_url: str,
+    temperature: float,
+    chapters_text_list: List[str]
+) -> str:
     """
-    将最近几章的文本拼接后，通过模型生成一个相对详细的“短期内容摘要”。
+    将最近几章文本拼接，通过模型生成相对简要的“短期内容摘要”。
     """
     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=base_url,
+        temperature=temperature
+    )
 
     combined_text = "\n".join(chapters_text_list)
-    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。
-
+    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}
-"""
-    response = model.invoke(prompt)
-    if not response:
-        return ""
-    debug_log(prompt, response.content)
-    return response.content.strip()
 
-# ============ 新增1：记录剧情要点/未解决冲突 ============
+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:
+        # 若模型无响应，就截取一段作为“备选”
+        return combined_text[:800] + "..." if len(combined_text) > 800 else combined_text
+    return summary_text
+
+# ============ 新增：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}
@@ -317,9 +435,9 @@ PLOT_ARCS_PROMPT = """\
 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。
+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。
+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(
@@ -330,10 +448,6 @@ def update_plot_arcs(
     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,
@@ -344,15 +458,13 @@ def update_plot_arcs(
         chapter_text=chapter_text,
         old_plot_arcs=old_plot_arcs
     )
-    response = model.invoke(prompt)
-    if not response:
-        logging.warning("update_plot_arcs: No response.")
+    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
-    debug_log(prompt, response.content)
-    return response.content.strip()
+    return arcs_text
 
 # ============ 生成章节草稿 & 定稿 ============
-
 def generate_chapter_draft(
     novel_settings: str,
     global_summary: str,
@@ -366,24 +478,43 @@ def generate_chapter_draft(
     word_number: int,
     temperature: float,
     novel_novel_directory: str,
-    filepath: str
+    filepath: str,
+    interface_format: str,
+    embedding_model_name: str,
+    embedding_base_url: str
 ) -> str:
     """
-    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。
+    生成当前章节的草稿，不更新全局摘要/角色状态/向量库。
     """
-    # 0) 根据 novel_number 从 novel_novel_directory 中获取本章标题及简述
+    # 根据目录信息获取本章标题、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"]
 
-    # 1) 从向量库检索往期上下文
-    relevant_context = get_relevant_context_from_vector_store(
-        api_key, base_url, "回顾剧情", k=2
+    # 从向量库检索上下文
+    queries = []
+    if user_guidance.strip():
+        queries.append(user_guidance)
+    if chapter_brief.strip():
+        queries.append(chapter_brief)
+    queries.append("回顾剧情")
+
+    relevant_context = ""
+    for q in queries:
+        partial_context = get_relevant_context_from_vector_store(
+            api_key=api_key,
+            base_url=base_url,
+            query=q,
+            interface_format=interface_format,
+            embedding_model_name=embedding_model_name,
+            embedding_base_url=embedding_base_url,
+            k=2
         )
+        if partial_context.strip():
+            relevant_context += "\n" + partial_context
+    if not relevant_context:
+        relevant_context = "暂无相关内容。"
 
-    # 2) 生成大纲
     model = ChatOpenAI(
         model=model_name,
         api_key=api_key,
@@ -391,26 +522,19 @@ def generate_chapter_draft(
         temperature=temperature
     )
 
+    # 1) 生成本章大纲
     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,
+        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【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【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）'}"
 
-    response_outline = model.invoke(outline_prompt_text)
-    if not response_outline:
-        logging.warning("generate_chapter_draft: outline no response.")
-        chapter_outline = ""
-    else:
-        debug_log(outline_prompt_text, response_outline.content)
-        chapter_outline = response_outline.content.strip()
+    chapter_outline = invoke_with_cleaning(model, outline_prompt_text)
 
     outlines_dir = os.path.join(filepath, "outlines")
     os.makedirs(outlines_dir, exist_ok=True)
@@ -418,28 +542,20 @@ def generate_chapter_draft(
     clear_file_content(outline_file)
     save_string_to_txt(chapter_outline, outline_file)
 
-    # 3) 生成正文草稿
+    # 2) 生成正文草稿
     writing_prompt_text = chapter_write_prompt.format(
         novel_setting=novel_settings,
-        character_state=character_state + "\n\n【历史上下文】\n" + relevant_context,
+        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,
         chapter_title=chapter_title,
         chapter_brief=chapter_brief
     )
-
-    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【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）'}"
 
-    response_chapter = model.invoke(writing_prompt_text)
-    if not response_chapter:
-        logging.warning("generate_chapter_draft: writing no response.")
-        chapter_content = ""
-    else:
-        debug_log(writing_prompt_text, response_chapter.content)
-        chapter_content = response_chapter.content.strip()
+    chapter_content = invoke_with_cleaning(model, writing_prompt_text)
 
     chapters_dir = os.path.join(filepath, "chapters")
     os.makedirs(chapters_dir, exist_ok=True)
@@ -455,19 +571,20 @@ def finalize_chapter(
     word_number: int,
     api_key: str,
     base_url: str,
+    interface_format: str,
+    embedding_model_name: str,
     model_name: str,
     temperature: float,
     filepath: str
 ):
     """
     对当前章节进行定稿：
-    1. 读取 chapter_{novel_number}.txt 的最终内容；
-    2. 更新全局摘要、角色状态文件；
-    3. 如果字数明显少于 word_number 的 80%，则自动调用 enrich_chapter_text 再次扩写；
-    4. 更新向量库；
-    5. 新增：更新剧情要点/未解决冲突 -> plot_arcs.txt
+    1. 读取草稿文本
+    2. 若字数太短则再次扩写
+    3. 更新全局摘要、角色状态
+    4. 更新剧情要点
+    5. 更新向量库
     """
-    # 读取当前章节内容
     chapters_dir = os.path.join(filepath, "chapters")
     chapter_file = os.path.join(chapters_dir, f"chapter_{novel_number}.txt")
     chapter_text = read_file(chapter_file).strip()
@@ -475,18 +592,17 @@ def finalize_chapter(
         logging.warning(f"Chapter {novel_number} is empty, cannot finalize.")
         return
 
-    # 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
+    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)
 
-    # 1) 先检查字数是否过少，若少于 80% 则调用 enrich 逻辑
+    # 若篇幅过短，二次扩写
     if len(chapter_text) < 0.8 * word_number:
-        logging.info("Chapter text seems shorter than 80% of desired length. Attempting to enrich content...")
+        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,
@@ -495,12 +611,10 @@ def finalize_chapter(
             model_name=model_name,
             temperature=temperature
         )
-        # 覆盖写回文件
         clear_file_content(chapter_file)
         save_string_to_txt(chapter_text, chapter_file)
-        logging.info("Chapter text has been enriched and updated.")
 
-    # 2) 更新全局摘要
+    # 更新全局摘要
     model = ChatOpenAI(
         model=model_name,
         api_key=api_key,
@@ -513,31 +627,21 @@ def finalize_chapter(
             chapter_text=chapter_text,
             global_summary=old_summary
         )
-        response = model.invoke(prompt)
-        if not response:
-            logging.warning("update_global_summary: No response.")
-            return old_summary
-        debug_log(prompt, response.content)
-        return response.content.strip()
+        return invoke_with_cleaning(model, prompt) or old_summary
 
     new_global_summary = update_global_summary(chapter_text, old_global_summary)
 
-    # 3) 更新角色状态
+    # 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
         )
-        response = model.invoke(prompt)
-        if not response:
-            logging.warning("update_character_state: No response.")
-            return old_state
-        debug_log(prompt, response.content)
-        return response.content.strip()
+        return invoke_with_cleaning(model, prompt) or old_state
 
     new_char_state = update_character_state(chapter_text, old_char_state)
 
-    # ============ 新增2: 更新剧情要点 =============
+    # 更新剧情要点
     new_plot_arcs = update_plot_arcs(
         chapter_text=chapter_text,
         old_plot_arcs=old_plot_arcs,
@@ -547,7 +651,7 @@ def finalize_chapter(
         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
+    # 写回文件
     clear_file_content(character_state_file)
     save_string_to_txt(new_char_state, character_state_file)
 
@@ -557,10 +661,16 @@ def finalize_chapter(
     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)
+    # 更新向量库
+    update_vector_store(
+        api_key=api_key,
+        base_url=base_url,
+        new_chapter=chapter_text,
+        interface_format=interface_format,
+        embedding_model_name=embedding_model_name
+    )
 
-    logging.info(f"Chapter {novel_number} has been finalized (summary & state updated, plot arcs updated, vector store updated).")
+    logging.info(f"Chapter {novel_number} has been finalized.")
 
 def enrich_chapter_text(
     chapter_text: str,
@@ -572,7 +682,6 @@ def enrich_chapter_text(
 ) -> str:
     """
     当章节篇幅不足时，调用此函数对章节文本进行二次扩写。
-    可以让模型补充场景描写、角色心理等，保证与现有文本风格一致。
     """
     model = ChatOpenAI(
         model=model_name,
@@ -580,25 +689,26 @@ def enrich_chapter_text(
         base_url=base_url,
         temperature=temperature
     )
-    prompt = f"""\
-以下是当前章节文本，可能篇幅较短，请在保持剧情连贯的前提下进行扩写，使其更充实、生动，并尽量靠近目标 {word_number} 字数。
+    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}
-"""
-    response = model.invoke(prompt)
-    if not response:
-        logging.warning("enrich_chapter_text: No response.")
-        return chapter_text  # 无响应时就返回原文
-    debug_log(prompt, response.content)
-    return response.content.strip()
+{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 ============
-
-def import_knowledge_file(api_key: str, base_url: str, file_path: str) -> None:
+def import_knowledge_file(
+    api_key: str,
+    base_url: str, 
+    interface_format: str,
+    embedding_model_name: str,
+    file_path: str, 
+    embedding_base_url: 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。
     """
+    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
@@ -608,12 +718,22 @@ def import_knowledge_file(api_key: str, base_url: str, file_path: str) -> None:
         logging.warning("知识库文件内容为空。")
         return
 
+    nltk.download('punkt', quiet=True)
+    nltk.download('punkt_tab', quiet=True)
+
     paragraphs = advanced_split_content(content)
 
-    store = load_vector_store(api_key, base_url)
+    store = load_vector_store(api_key, base_url, interface_format, embedding_model_name, embedding_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)
+        init_vector_store(
+            api_key,
+            base_url,
+            interface_format,
+            embedding_model_name,
+            paragraphs,
+            embedding_base_url
+        )
         return
 
     docs = [Document(page_content=p) for p in paragraphs]
@@ -625,11 +745,10 @@ 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。
+    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。
+    可根据需要微调此逻辑。
     """
-    nltk.download('punkt_tab', quiet=True)
     sentences = nltk.sent_tokenize(content)
-
     if not sentences:
         return []