拆分第一章和后续章节，添加承接提示词
This commit is contained in:
YILING0013
+97
-57
@@ -32,7 +32,8 @@ from prompt_definitions import (
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chunked_chapter_blueprint_prompt,
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summary_prompt,
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update_character_state_prompt,
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chapter_draft_prompt,
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first_chapter_draft_prompt,
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next_chapter_draft_prompt,
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summarize_recent_chapters_prompt
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)
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@@ -415,11 +416,11 @@ def compute_chunk_size(number_of_chapters: int, max_tokens: int) -> int:
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并确保 chunk_size 不会小于1或大于实际章节数。
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"""
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tokens_per_chapter = 100.0
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ratio = max_tokens / tokens_per_chapter # 8192 / 100 = 81.92
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ratio = max_tokens / tokens_per_chapter # 例如：8192 / 100 = 81.92
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# 先取到最接近的10倍
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ratio_rounded_to_10 = int(ratio // 10) * 10 # => 80
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# 再减10
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chunk_size = ratio_rounded_to_10 - 10 # => 70
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chunk_size = ratio_rounded_to_10 - 10 # => 70
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if chunk_size < 1:
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chunk_size = 1
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if chunk_size > number_of_chapters:
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@@ -527,7 +528,7 @@ def Chapter_blueprint_generate(
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logging.info("Novel_directory.txt (chapter blueprint) has been generated successfully (chunked).")
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# ============ 3) 生成章节草稿 ============
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# ============ 3) 生成章节草稿（分「第一章」与「后续章节」） ============
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def generate_chapter_draft(
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api_key: str,
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@@ -550,6 +551,11 @@ def generate_chapter_draft(
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interface_format: str = "openai",
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max_tokens: int = 2048
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) -> str:
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"""
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根据 novel_number 判断是否为第一章。
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- 若是第一章，则使用 first_chapter_draft_prompt
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- 否则使用 next_chapter_draft_prompt
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"""
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arch_file = os.path.join(filepath, "Novel_architecture.txt")
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novel_architecture_text = read_file(arch_file)
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@@ -562,6 +568,7 @@ def generate_chapter_draft(
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character_state_file = os.path.join(filepath, "character_state.txt")
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character_state_text = read_file(character_state_file)
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# 获取本章在目录中的信息
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chapter_info = get_chapter_info_from_blueprint(blueprint_text, novel_number)
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chapter_title = chapter_info["chapter_title"]
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chapter_role = chapter_info["chapter_role"]
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@@ -571,68 +578,97 @@ def generate_chapter_draft(
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plot_twist_level = chapter_info["plot_twist_level"]
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chapter_summary = chapter_info["chapter_summary"]
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# 准备章节目录文件夹
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chapters_dir = os.path.join(filepath, "chapters")
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os.makedirs(chapters_dir, exist_ok=True)
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recent_3_texts = get_last_n_chapters_text(chapters_dir, novel_number, n=3)
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short_summary, next_chapter_keywords = summarize_recent_chapters(
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interface_format=interface_format,
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api_key=api_key,
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base_url=base_url,
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model_name=model_name,
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temperature=temperature,
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max_tokens=max_tokens,
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chapters_text_list=recent_3_texts
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)
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# 如果是第一章，不需要前情检索与前章结尾
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if novel_number == 1:
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# 使用第一章提示词
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prompt_text = first_chapter_draft_prompt.format(
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novel_number=novel_number,
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chapter_title=chapter_title,
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chapter_role=chapter_role,
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chapter_purpose=chapter_purpose,
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suspense_level=suspense_level,
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foreshadowing=foreshadowing,
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plot_twist_level=plot_twist_level,
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chapter_summary=chapter_summary,
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previous_chapter_excerpt = ""
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for text_block in reversed(recent_3_texts):
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if text_block.strip():
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if len(text_block) > 1500:
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previous_chapter_excerpt = text_block[-1500:]
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else:
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previous_chapter_excerpt = text_block
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break
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characters_involved=characters_involved,
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key_items=key_items,
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scene_location=scene_location,
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time_constraint=time_constraint,
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user_guidance=user_guidance,
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embedding_adapter = create_embedding_adapter(
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embedding_interface_format,
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embedding_api_key,
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embedding_url,
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embedding_model_name
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)
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retrieval_query = short_summary + " " + next_chapter_keywords
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relevant_context = get_relevant_context_from_vector_store(
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embedding_adapter=embedding_adapter,
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query=retrieval_query,
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filepath=filepath,
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k=embedding_retrieval_k
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)
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if not relevant_context.strip():
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relevant_context = "（无检索到的上下文）"
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novel_setting=novel_architecture_text
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)
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prompt_text = chapter_draft_prompt.format(
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novel_number=novel_number,
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chapter_title=chapter_title,
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chapter_role=chapter_role,
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chapter_purpose=chapter_purpose,
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suspense_level=suspense_level,
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foreshadowing=foreshadowing,
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plot_twist_level=plot_twist_level,
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chapter_summary=chapter_summary,
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else:
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# 若不是第一章，则先获取最近几章文本，并做摘要与检索
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recent_3_texts = get_last_n_chapters_text(chapters_dir, novel_number, n=3)
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short_summary, next_chapter_keywords = summarize_recent_chapters(
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interface_format=interface_format,
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api_key=api_key,
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base_url=base_url,
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model_name=model_name,
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temperature=temperature,
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max_tokens=max_tokens,
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chapters_text_list=recent_3_texts
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)
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characters_involved=characters_involved,
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key_items=key_items,
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scene_location=scene_location,
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time_constraint=time_constraint,
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user_guidance=user_guidance,
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# 从最近章节中获取最后一段内容作为前章结尾
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previous_chapter_excerpt = ""
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for text_block in reversed(recent_3_texts):
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if text_block.strip():
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if len(text_block) > 1500:
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previous_chapter_excerpt = text_block[-1500:]
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else:
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previous_chapter_excerpt = text_block
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break
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novel_setting=novel_architecture_text,
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global_summary=global_summary_text,
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character_state=character_state_text,
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previous_chapter_excerpt=previous_chapter_excerpt,
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context_excerpt=relevant_context
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)
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# 从向量库检索上下文
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embedding_adapter = create_embedding_adapter(
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embedding_interface_format,
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embedding_api_key,
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embedding_url,
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embedding_model_name
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)
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retrieval_query = short_summary + " " + next_chapter_keywords
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relevant_context = get_relevant_context_from_vector_store(
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embedding_adapter=embedding_adapter,
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query=retrieval_query,
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filepath=filepath,
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k=embedding_retrieval_k
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)
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if not relevant_context.strip():
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relevant_context = "（无检索到的上下文）"
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# 使用后续章节提示词
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prompt_text = next_chapter_draft_prompt.format(
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novel_number=novel_number,
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chapter_title=chapter_title,
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chapter_role=chapter_role,
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chapter_purpose=chapter_purpose,
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suspense_level=suspense_level,
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foreshadowing=foreshadowing,
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plot_twist_level=plot_twist_level,
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chapter_summary=chapter_summary,
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characters_involved=characters_involved,
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key_items=key_items,
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scene_location=scene_location,
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time_constraint=time_constraint,
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user_guidance=user_guidance,
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novel_setting=novel_architecture_text,
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global_summary=global_summary_text,
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character_state=character_state_text,
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context_excerpt=relevant_context,
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previous_chapter_excerpt=previous_chapter_excerpt
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)
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# 调用LLM生成
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llm_adapter = create_llm_adapter(
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interface_format=interface_format,
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base_url=base_url,
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@@ -645,6 +681,7 @@ def generate_chapter_draft(
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if not chapter_content.strip():
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logging.warning("Generated chapter draft is empty.")
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# 保存章节文本
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chapter_file = os.path.join(chapters_dir, f"chapter_{novel_number}.txt")
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clear_file_content(chapter_file)
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save_string_to_txt(chapter_content, chapter_file)
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@@ -652,6 +689,7 @@ def generate_chapter_draft(
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logging.info(f"[Draft] Chapter {novel_number} generated as a draft.")
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return chapter_content
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# ============ 4) 定稿章节 ============
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def finalize_chapter(
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@@ -676,6 +714,7 @@ def finalize_chapter(
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logging.warning(f"Chapter {novel_number} is empty, cannot finalize.")
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return
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# 如果内容过短，则尝试扩写
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if len(chapter_text) < 0.7 * word_number:
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chapter_text = enrich_chapter_text(chapter_text, word_number, api_key, base_url, model_name, temperature, interface_format, max_tokens)
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clear_file_content(chapter_file)
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@@ -716,6 +755,7 @@ def finalize_chapter(
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clear_file_content(character_state_file)
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save_string_to_txt(new_char_state, character_state_file)
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# 更新向量库
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embedding_adapter = create_embedding_adapter(
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embedding_interface_format,
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embedding_api_key,
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