进行文件的逻辑拆分
初步对ui.py以及novel_generator.py进行了拆分
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YILING0013
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#novel_generator/chapter.py
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# -*- coding: utf-8 -*-
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"""
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章节草稿生成及获取历史章节文本、短期摘要等
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"""
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import os
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import logging
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from nltk import download
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from llm_adapters import create_llm_adapter
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from prompt_definitions import first_chapter_draft_prompt, next_chapter_draft_prompt, summarize_recent_chapters_prompt
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from chapter_directory_parser import get_chapter_info_from_blueprint
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from novel_generator.common import invoke_with_cleaning
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from utils import read_file, clear_file_content, save_string_to_txt
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from novel_generator.vectorstore_utils import get_relevant_context_from_vector_store
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def get_last_n_chapters_text(chapters_dir: str, current_chapter_num: int, n: int = 3) -> list:
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"""
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从目录 chapters_dir 中获取最近 n 章的文本内容，返回文本列表。
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"""
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texts = []
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start_chap = max(1, current_chapter_num - n)
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for c in range(start_chap, current_chapter_num):
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chap_file = os.path.join(chapters_dir, f"chapter_{c}.txt")
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if os.path.exists(chap_file):
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text = read_file(chap_file).strip()
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texts.append(text)
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else:
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texts.append("")
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return texts
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def summarize_recent_chapters(
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interface_format: str,
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api_key: str,
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base_url: str,
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model_name: str,
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temperature: float,
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max_tokens: int,
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chapters_text_list: list,
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timeout: int = 600
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) -> tuple:
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"""
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生成 (short_summary, next_chapter_keywords)
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如果解析失败，则返回 (合并文本, "")
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"""
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combined_text = "\n".join(chapters_text_list).strip()
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if not combined_text:
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return ("", "")
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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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model_name=model_name,
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api_key=api_key,
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temperature=temperature,
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max_tokens=max_tokens,
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timeout=timeout
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)
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prompt = summarize_recent_chapters_prompt.format(combined_text=combined_text)
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response_text = invoke_with_cleaning(llm_adapter, prompt)
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short_summary = ""
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next_chapter_keywords = ""
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for line in response_text.splitlines():
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line = line.strip()
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if line.startswith("短期摘要:"):
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short_summary = line.replace("短期摘要:", "").strip()
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elif line.startswith("下一章关键字:"):
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next_chapter_keywords = line.replace("下一章关键字:", "").strip()
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if not short_summary and not next_chapter_keywords:
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short_summary = response_text
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return (short_summary, next_chapter_keywords)
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def generate_chapter_draft(
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api_key: str,
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base_url: str,
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model_name: str,
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filepath: str,
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novel_number: int,
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word_number: int,
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temperature: float,
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user_guidance: str,
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characters_involved: str,
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key_items: str,
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scene_location: str,
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time_constraint: str,
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embedding_api_key: str,
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embedding_url: str,
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embedding_interface_format: str,
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embedding_model_name: str,
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embedding_retrieval_k: int = 2,
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interface_format: str = "openai",
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max_tokens: int = 2048,
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timeout: int = 600
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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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最终将生成文本存入 chapters/chapter_{novel_number}.txt。
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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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directory_file = os.path.join(filepath, "Novel_directory.txt")
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blueprint_text = read_file(directory_file)
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global_summary_file = os.path.join(filepath, "global_summary.txt")
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global_summary_text = read_file(global_summary_file)
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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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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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chapter_purpose = chapter_info["chapter_purpose"]
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suspense_level = chapter_info["suspense_level"]
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foreshadowing = chapter_info["foreshadowing"]
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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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chapters_dir = os.path.join(filepath, "chapters")
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os.makedirs(chapters_dir, exist_ok=True)
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if novel_number == 1:
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prompt_text = first_chapter_draft_prompt.format(
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novel_number=novel_number,
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word_number=word_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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)
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else:
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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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timeout=timeout
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)
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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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from llm_adapters import create_llm_adapter # 避免循环依赖
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embedding_adapter = create_llm_adapter(
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interface_format=embedding_interface_format,
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base_url=embedding_url,
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model_name=embedding_model_name,
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api_key=embedding_api_key,
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temperature=temperature,
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max_tokens=max_tokens,
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timeout=timeout
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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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prompt_text = next_chapter_draft_prompt.format(
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novel_number=novel_number,
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word_number=word_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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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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model_name=model_name,
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api_key=api_key,
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temperature=temperature,
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max_tokens=max_tokens,
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timeout=timeout
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)
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chapter_content = invoke_with_cleaning(llm_adapter, prompt_text)
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if not chapter_content.strip():
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logging.warning("Generated chapter draft is empty.")
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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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logging.info(f"[Draft] Chapter {novel_number} generated as a draft.")
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return chapter_content
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