进行文件的逻辑拆分
初步对ui.py以及novel_generator.py进行了拆分
This commit is contained in:
YILING0013
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#novel_generator/__init__.py
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from .architecture import Novel_architecture_generate
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from .blueprint import Chapter_blueprint_generate
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from .chapter import generate_chapter_draft, get_last_n_chapters_text
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from .finalization import finalize_chapter, enrich_chapter_text
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from .knowledge import import_knowledge_file
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from .vectorstore_utils import clear_vector_store
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#novel_generator/architecture.py
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# -*- coding: utf-8 -*-
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"""
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小说总体架构生成（Novel_architecture_generate 及相关辅助函数）
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"""
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import os
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import json
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import logging
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import traceback
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from novel_generator.common import invoke_with_cleaning
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from llm_adapters import create_llm_adapter
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from prompt_definitions import (
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core_seed_prompt,
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character_dynamics_prompt,
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world_building_prompt,
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plot_architecture_prompt,
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create_character_state_prompt
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)
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from utils import clear_file_content, save_string_to_txt
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def load_partial_architecture_data(filepath: str) -> dict:
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"""
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从 filepath 下的 partial_architecture.json 读取已有的阶段性数据。
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如果文件不存在或无法解析，返回空 dict。
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"""
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partial_file = os.path.join(filepath, "partial_architecture.json")
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if not os.path.exists(partial_file):
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return {}
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try:
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with open(partial_file, "r", encoding="utf-8") as f:
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data = json.load(f)
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return data
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except Exception as e:
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logging.warning(f"Failed to load partial_architecture.json: {e}")
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return {}
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def save_partial_architecture_data(filepath: str, data: dict):
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"""
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将阶段性数据写入 partial_architecture.json。
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"""
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partial_file = os.path.join(filepath, "partial_architecture.json")
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try:
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with open(partial_file, "w", encoding="utf-8") as f:
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json.dump(data, f, ensure_ascii=False, indent=2)
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except Exception as e:
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logging.warning(f"Failed to save partial_architecture.json: {e}")
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def Novel_architecture_generate(
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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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llm_model: str,
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topic: str,
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genre: str,
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number_of_chapters: int,
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word_number: int,
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filepath: str,
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temperature: float = 0.7,
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max_tokens: int = 2048,
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timeout: int = 600
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) -> None:
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"""
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依次调用:
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1. core_seed_prompt
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2. character_dynamics_prompt
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3. world_building_prompt
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4. plot_architecture_prompt
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若在中间任何一步报错且重试多次失败，则将已经生成的内容写入 partial_architecture.json 并退出；
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下次调用时可从该步骤继续。
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最终输出 Novel_architecture.txt
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新增：
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- 在完成角色动力学设定后，依据该角色体系，使用 create_character_state_prompt 生成初始角色状态表，
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并存储到 character_state.txt，后续维护更新。
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"""
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os.makedirs(filepath, exist_ok=True)
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partial_data = load_partial_architecture_data(filepath)
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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=llm_model,
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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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# Step1: 核心种子
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if "core_seed_result" not in partial_data:
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logging.info("Step1: Generating core_seed_prompt (核心种子) ...")
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prompt_core = core_seed_prompt.format(
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topic=topic,
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genre=genre,
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number_of_chapters=number_of_chapters,
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word_number=word_number
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)
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core_seed_result = invoke_with_cleaning(llm_adapter, prompt_core)
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if not core_seed_result.strip():
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logging.warning("core_seed_prompt generation failed and returned empty.")
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save_partial_architecture_data(filepath, partial_data)
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return
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partial_data["core_seed_result"] = core_seed_result
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save_partial_architecture_data(filepath, partial_data)
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else:
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logging.info("Step1 already done. Skipping...")
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# Step2: 角色动力学
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if "character_dynamics_result" not in partial_data:
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logging.info("Step2: Generating character_dynamics_prompt ...")
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prompt_character = character_dynamics_prompt.format(core_seed=partial_data["core_seed_result"].strip())
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character_dynamics_result = invoke_with_cleaning(llm_adapter, prompt_character)
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if not character_dynamics_result.strip():
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logging.warning("character_dynamics_prompt generation failed.")
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save_partial_architecture_data(filepath, partial_data)
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return
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partial_data["character_dynamics_result"] = character_dynamics_result
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save_partial_architecture_data(filepath, partial_data)
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else:
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logging.info("Step2 already done. Skipping...")
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# 生成初始角色状态
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if "character_dynamics_result" in partial_data and "character_state_result" not in partial_data:
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logging.info("Generating initial character state from character dynamics ...")
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prompt_char_state_init = create_character_state_prompt.format(
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character_dynamics=partial_data["character_dynamics_result"].strip()
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)
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character_state_init = invoke_with_cleaning(llm_adapter, prompt_char_state_init)
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if not character_state_init.strip():
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logging.warning("create_character_state_prompt generation failed.")
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save_partial_architecture_data(filepath, partial_data)
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return
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partial_data["character_state_result"] = character_state_init
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character_state_file = os.path.join(filepath, "character_state.txt")
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clear_file_content(character_state_file)
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save_string_to_txt(character_state_init, character_state_file)
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save_partial_architecture_data(filepath, partial_data)
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logging.info("Initial character state created and saved.")
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# Step3: 世界观
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if "world_building_result" not in partial_data:
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logging.info("Step3: Generating world_building_prompt ...")
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prompt_world = world_building_prompt.format(core_seed=partial_data["core_seed_result"].strip())
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world_building_result = invoke_with_cleaning(llm_adapter, prompt_world)
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if not world_building_result.strip():
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logging.warning("world_building_prompt generation failed.")
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save_partial_architecture_data(filepath, partial_data)
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return
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partial_data["world_building_result"] = world_building_result
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save_partial_architecture_data(filepath, partial_data)
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else:
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logging.info("Step3 already done. Skipping...")
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# Step4: 三幕式情节
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if "plot_arch_result" not in partial_data:
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logging.info("Step4: Generating plot_architecture_prompt ...")
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prompt_plot = plot_architecture_prompt.format(
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core_seed=partial_data["core_seed_result"].strip(),
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character_dynamics=partial_data["character_dynamics_result"].strip(),
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world_building=partial_data["world_building_result"].strip()
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)
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plot_arch_result = invoke_with_cleaning(llm_adapter, prompt_plot)
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if not plot_arch_result.strip():
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logging.warning("plot_architecture_prompt generation failed.")
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save_partial_architecture_data(filepath, partial_data)
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return
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partial_data["plot_arch_result"] = plot_arch_result
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save_partial_architecture_data(filepath, partial_data)
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else:
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logging.info("Step4 already done. Skipping...")
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core_seed_result = partial_data["core_seed_result"]
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character_dynamics_result = partial_data["character_dynamics_result"]
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world_building_result = partial_data["world_building_result"]
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plot_arch_result = partial_data["plot_arch_result"]
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final_content = (
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"#=== 0) 小说设定 ===\n"
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f"主题：{topic},类型：{genre},篇幅：约{number_of_chapters}章（每章{word_number}字）\n\n"
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"#=== 1) 核心种子 ===\n"
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f"{core_seed_result}\n\n"
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"#=== 2) 角色动力学 ===\n"
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f"{character_dynamics_result}\n\n"
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"#=== 3) 世界观 ===\n"
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f"{world_building_result}\n\n"
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"#=== 4) 三幕式情节架构 ===\n"
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f"{plot_arch_result}\n"
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)
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arch_file = os.path.join(filepath, "Novel_architecture.txt")
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clear_file_content(arch_file)
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save_string_to_txt(final_content, arch_file)
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logging.info("Novel_architecture.txt has been generated successfully.")
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partial_arch_file = os.path.join(filepath, "partial_architecture.json")
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if os.path.exists(partial_arch_file):
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os.remove(partial_arch_file)
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logging.info("partial_architecture.json removed (all steps completed).")
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@@ -0,0 +1,169 @@
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#novel_generator/blueprint.py
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# -*- coding: utf-8 -*-
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"""
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章节蓝图生成（Chapter_blueprint_generate 及辅助函数）
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"""
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import os
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import re
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import logging
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from novel_generator.common import invoke_with_cleaning
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from llm_adapters import create_llm_adapter
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from prompt_definitions import chapter_blueprint_prompt, chunked_chapter_blueprint_prompt
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from utils import read_file, clear_file_content, save_string_to_txt
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def compute_chunk_size(number_of_chapters: int, max_tokens: int) -> int:
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"""
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基于“每章约100 tokens”的粗略估算，
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再结合当前max_tokens，计算分块大小：
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chunk_size = (floor(max_tokens/100/10)*10) - 10
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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
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ratio_rounded_to_10 = int(ratio // 10) * 10
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chunk_size = ratio_rounded_to_10 - 10
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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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chunk_size = number_of_chapters
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return chunk_size
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def limit_chapter_blueprint(blueprint_text: str, limit_chapters: int = 100) -> str:
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"""
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从已有章节目录中只取最近的 limit_chapters 章，以避免 prompt 超长。
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"""
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pattern = r"(第\s*\d+\s*章.*?)(?=第\s*\d+\s*章|$)"
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chapters = re.findall(pattern, blueprint_text, flags=re.DOTALL)
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if not chapters:
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return blueprint_text
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if len(chapters) <= limit_chapters:
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return blueprint_text
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selected = chapters[-limit_chapters:]
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return "\n\n".join(selected).strip()
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def Chapter_blueprint_generate(
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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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llm_model: str,
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filepath: str,
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number_of_chapters: int,
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temperature: float = 0.7,
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max_tokens: int = 4096,
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timeout: int = 600
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) -> None:
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"""
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若 Novel_directory.txt 已存在且内容非空，则表示可能是之前的部分生成结果；
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解析其中已有的章节数，从下一个章节继续分块生成；
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对于已有章节目录，传入时仅保留最近100章目录，避免prompt过长。
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否则：
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- 若章节数 <= chunk_size，直接一次性生成
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- 若章节数 > chunk_size，进行分块生成
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生成完成后输出至 Novel_directory.txt。
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"""
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arch_file = os.path.join(filepath, "Novel_architecture.txt")
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if not os.path.exists(arch_file):
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logging.warning("Novel_architecture.txt not found. Please generate architecture first.")
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return
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architecture_text = read_file(arch_file).strip()
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if not architecture_text:
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logging.warning("Novel_architecture.txt is empty.")
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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=llm_model,
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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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filename_dir = os.path.join(filepath, "Novel_directory.txt")
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if not os.path.exists(filename_dir):
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open(filename_dir, "w", encoding="utf-8").close()
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existing_blueprint = read_file(filename_dir).strip()
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chunk_size = compute_chunk_size(number_of_chapters, max_tokens)
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logging.info(f"Number of chapters = {number_of_chapters}, computed chunk_size = {chunk_size}.")
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if existing_blueprint:
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logging.info("Detected existing blueprint content. Will resume chunked generation from that point.")
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pattern = r"第\s*(\d+)\s*章"
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existing_chapter_numbers = re.findall(pattern, existing_blueprint)
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existing_chapter_numbers = [int(x) for x in existing_chapter_numbers if x.isdigit()]
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max_existing_chap = max(existing_chapter_numbers) if existing_chapter_numbers else 0
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logging.info(f"Existing blueprint indicates up to chapter {max_existing_chap} has been generated.")
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final_blueprint = existing_blueprint
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current_start = max_existing_chap + 1
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while current_start <= number_of_chapters:
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current_end = min(current_start + chunk_size - 1, number_of_chapters)
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limited_blueprint = limit_chapter_blueprint(final_blueprint, 100)
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chunk_prompt = chunked_chapter_blueprint_prompt.format(
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novel_architecture=architecture_text,
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chapter_list=limited_blueprint,
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number_of_chapters=number_of_chapters,
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n=current_start,
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m=current_end
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)
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logging.info(f"Generating chapters [{current_start}..{current_end}] in a chunk...")
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chunk_result = invoke_with_cleaning(llm_adapter, chunk_prompt)
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if not chunk_result.strip():
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logging.warning(f"Chunk generation for chapters [{current_start}..{current_end}] is empty.")
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clear_file_content(filename_dir)
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save_string_to_txt(final_blueprint.strip(), filename_dir)
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return
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final_blueprint += "\n\n" + chunk_result.strip()
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clear_file_content(filename_dir)
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save_string_to_txt(final_blueprint.strip(), filename_dir)
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current_start = current_end + 1
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logging.info("All chapters blueprint have been generated (resumed chunked).")
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return
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if chunk_size >= number_of_chapters:
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prompt = chapter_blueprint_prompt.format(
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novel_architecture=architecture_text,
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number_of_chapters=number_of_chapters
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)
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blueprint_text = invoke_with_cleaning(llm_adapter, prompt)
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if not blueprint_text.strip():
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logging.warning("Chapter blueprint generation result is empty.")
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return
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clear_file_content(filename_dir)
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save_string_to_txt(blueprint_text, filename_dir)
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logging.info("Novel_directory.txt (chapter blueprint) has been generated successfully (single-shot).")
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return
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logging.info("Will generate chapter blueprint in chunked mode from scratch.")
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final_blueprint = ""
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current_start = 1
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while current_start <= number_of_chapters:
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current_end = min(current_start + chunk_size - 1, number_of_chapters)
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limited_blueprint = limit_chapter_blueprint(final_blueprint, 100)
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chunk_prompt = chunked_chapter_blueprint_prompt.format(
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novel_architecture=architecture_text,
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chapter_list=limited_blueprint,
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number_of_chapters=number_of_chapters,
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n=current_start,
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m=current_end
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)
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logging.info(f"Generating chapters [{current_start}..{current_end}] in a chunk...")
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chunk_result = invoke_with_cleaning(llm_adapter, chunk_prompt)
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if not chunk_result.strip():
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logging.warning(f"Chunk generation for chapters [{current_start}..{current_end}] is empty.")
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clear_file_content(filename_dir)
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save_string_to_txt(final_blueprint.strip(), filename_dir)
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return
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if final_blueprint.strip():
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final_blueprint += "\n\n" + chunk_result.strip()
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else:
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final_blueprint = chunk_result.strip()
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clear_file_content(filename_dir)
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save_string_to_txt(final_blueprint.strip(), filename_dir)
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current_start = current_end + 1
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logging.info("Novel_directory.txt (chapter blueprint) has been generated successfully (chunked).")
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@@ -0,0 +1,216 @@
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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)
|
||||
else:
|
||||
texts.append("")
|
||||
return texts
|
||||
|
||||
def summarize_recent_chapters(
|
||||
interface_format: str,
|
||||
api_key: str,
|
||||
base_url: str,
|
||||
model_name: str,
|
||||
temperature: float,
|
||||
max_tokens: int,
|
||||
chapters_text_list: list,
|
||||
timeout: int = 600
|
||||
) -> tuple:
|
||||
"""
|
||||
生成 (short_summary, next_chapter_keywords)
|
||||
如果解析失败，则返回 (合并文本, "")
|
||||
"""
|
||||
combined_text = "\n".join(chapters_text_list).strip()
|
||||
if not combined_text:
|
||||
return ("", "")
|
||||
llm_adapter = create_llm_adapter(
|
||||
interface_format=interface_format,
|
||||
base_url=base_url,
|
||||
model_name=model_name,
|
||||
api_key=api_key,
|
||||
temperature=temperature,
|
||||
max_tokens=max_tokens,
|
||||
timeout=timeout
|
||||
)
|
||||
prompt = summarize_recent_chapters_prompt.format(combined_text=combined_text)
|
||||
response_text = invoke_with_cleaning(llm_adapter, prompt)
|
||||
short_summary = ""
|
||||
next_chapter_keywords = ""
|
||||
for line in response_text.splitlines():
|
||||
line = line.strip()
|
||||
if line.startswith("短期摘要:"):
|
||||
short_summary = line.replace("短期摘要:", "").strip()
|
||||
elif line.startswith("下一章关键字:"):
|
||||
next_chapter_keywords = line.replace("下一章关键字:", "").strip()
|
||||
if not short_summary and not next_chapter_keywords:
|
||||
short_summary = response_text
|
||||
return (short_summary, next_chapter_keywords)
|
||||
|
||||
def generate_chapter_draft(
|
||||
api_key: str,
|
||||
base_url: str,
|
||||
model_name: str,
|
||||
filepath: str,
|
||||
novel_number: int,
|
||||
word_number: int,
|
||||
temperature: float,
|
||||
user_guidance: str,
|
||||
characters_involved: str,
|
||||
key_items: str,
|
||||
scene_location: str,
|
||||
time_constraint: str,
|
||||
embedding_api_key: str,
|
||||
embedding_url: str,
|
||||
embedding_interface_format: str,
|
||||
embedding_model_name: str,
|
||||
embedding_retrieval_k: int = 2,
|
||||
interface_format: str = "openai",
|
||||
max_tokens: int = 2048,
|
||||
timeout: int = 600
|
||||
) -> str:
|
||||
"""
|
||||
根据 novel_number 判断是否为第一章。
|
||||
- 若是第一章，则使用 first_chapter_draft_prompt
|
||||
- 否则使用 next_chapter_draft_prompt
|
||||
最终将生成文本存入 chapters/chapter_{novel_number}.txt。
|
||||
"""
|
||||
arch_file = os.path.join(filepath, "Novel_architecture.txt")
|
||||
novel_architecture_text = read_file(arch_file)
|
||||
directory_file = os.path.join(filepath, "Novel_directory.txt")
|
||||
blueprint_text = read_file(directory_file)
|
||||
global_summary_file = os.path.join(filepath, "global_summary.txt")
|
||||
global_summary_text = read_file(global_summary_file)
|
||||
character_state_file = os.path.join(filepath, "character_state.txt")
|
||||
character_state_text = read_file(character_state_file)
|
||||
|
||||
chapter_info = get_chapter_info_from_blueprint(blueprint_text, novel_number)
|
||||
chapter_title = chapter_info["chapter_title"]
|
||||
chapter_role = chapter_info["chapter_role"]
|
||||
chapter_purpose = chapter_info["chapter_purpose"]
|
||||
suspense_level = chapter_info["suspense_level"]
|
||||
foreshadowing = chapter_info["foreshadowing"]
|
||||
plot_twist_level = chapter_info["plot_twist_level"]
|
||||
chapter_summary = chapter_info["chapter_summary"]
|
||||
|
||||
chapters_dir = os.path.join(filepath, "chapters")
|
||||
os.makedirs(chapters_dir, exist_ok=True)
|
||||
|
||||
if novel_number == 1:
|
||||
prompt_text = first_chapter_draft_prompt.format(
|
||||
novel_number=novel_number,
|
||||
word_number=word_number,
|
||||
chapter_title=chapter_title,
|
||||
chapter_role=chapter_role,
|
||||
chapter_purpose=chapter_purpose,
|
||||
suspense_level=suspense_level,
|
||||
foreshadowing=foreshadowing,
|
||||
plot_twist_level=plot_twist_level,
|
||||
chapter_summary=chapter_summary,
|
||||
characters_involved=characters_involved,
|
||||
key_items=key_items,
|
||||
scene_location=scene_location,
|
||||
time_constraint=time_constraint,
|
||||
user_guidance=user_guidance,
|
||||
novel_setting=novel_architecture_text
|
||||
)
|
||||
else:
|
||||
recent_3_texts = get_last_n_chapters_text(chapters_dir, novel_number, n=3)
|
||||
short_summary, next_chapter_keywords = summarize_recent_chapters(
|
||||
interface_format=interface_format,
|
||||
api_key=api_key,
|
||||
base_url=base_url,
|
||||
model_name=model_name,
|
||||
temperature=temperature,
|
||||
max_tokens=max_tokens,
|
||||
chapters_text_list=recent_3_texts,
|
||||
timeout=timeout
|
||||
)
|
||||
previous_chapter_excerpt = ""
|
||||
for text_block in reversed(recent_3_texts):
|
||||
if text_block.strip():
|
||||
if len(text_block) > 1500:
|
||||
previous_chapter_excerpt = text_block[-1500:]
|
||||
else:
|
||||
previous_chapter_excerpt = text_block
|
||||
break
|
||||
from llm_adapters import create_llm_adapter # 避免循环依赖
|
||||
embedding_adapter = create_llm_adapter(
|
||||
interface_format=embedding_interface_format,
|
||||
base_url=embedding_url,
|
||||
model_name=embedding_model_name,
|
||||
api_key=embedding_api_key,
|
||||
temperature=temperature,
|
||||
max_tokens=max_tokens,
|
||||
timeout=timeout
|
||||
)
|
||||
retrieval_query = short_summary + " " + next_chapter_keywords
|
||||
relevant_context = get_relevant_context_from_vector_store(
|
||||
embedding_adapter=embedding_adapter,
|
||||
query=retrieval_query,
|
||||
filepath=filepath,
|
||||
k=embedding_retrieval_k
|
||||
)
|
||||
if not relevant_context.strip():
|
||||
relevant_context = "（无检索到的上下文）"
|
||||
prompt_text = next_chapter_draft_prompt.format(
|
||||
novel_number=novel_number,
|
||||
word_number=word_number,
|
||||
chapter_title=chapter_title,
|
||||
chapter_role=chapter_role,
|
||||
chapter_purpose=chapter_purpose,
|
||||
suspense_level=suspense_level,
|
||||
foreshadowing=foreshadowing,
|
||||
plot_twist_level=plot_twist_level,
|
||||
chapter_summary=chapter_summary,
|
||||
characters_involved=characters_involved,
|
||||
key_items=key_items,
|
||||
scene_location=scene_location,
|
||||
time_constraint=time_constraint,
|
||||
user_guidance=user_guidance,
|
||||
novel_setting=novel_architecture_text,
|
||||
global_summary=global_summary_text,
|
||||
character_state=character_state_text,
|
||||
context_excerpt=relevant_context,
|
||||
previous_chapter_excerpt=previous_chapter_excerpt
|
||||
)
|
||||
|
||||
llm_adapter = create_llm_adapter(
|
||||
interface_format=interface_format,
|
||||
base_url=base_url,
|
||||
model_name=model_name,
|
||||
api_key=api_key,
|
||||
temperature=temperature,
|
||||
max_tokens=max_tokens,
|
||||
timeout=timeout
|
||||
)
|
||||
|
||||
chapter_content = invoke_with_cleaning(llm_adapter, prompt_text)
|
||||
if not chapter_content.strip():
|
||||
logging.warning("Generated chapter draft is empty.")
|
||||
chapter_file = os.path.join(chapters_dir, f"chapter_{novel_number}.txt")
|
||||
clear_file_content(chapter_file)
|
||||
save_string_to_txt(chapter_content, chapter_file)
|
||||
logging.info(f"[Draft] Chapter {novel_number} generated as a draft.")
|
||||
return chapter_content
|
||||
@@ -0,0 +1,59 @@
|
||||
#novel_generator/common.py
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
通用重试、清洗、日志工具
|
||||
"""
|
||||
import logging
|
||||
import re
|
||||
import time
|
||||
import traceback
|
||||
|
||||
def call_with_retry(func, max_retries=3, sleep_time=2, fallback_return=None, **kwargs):
|
||||
"""
|
||||
通用的重试机制封装。
|
||||
:param func: 要执行的函数
|
||||
:param max_retries: 最大重试次数
|
||||
:param sleep_time: 重试前的等待秒数
|
||||
:param fallback_return: 如果多次重试仍失败时的返回值
|
||||
:param kwargs: 传给func的命名参数
|
||||
:return: func的结果，若失败则返回 fallback_return
|
||||
"""
|
||||
for attempt in range(1, max_retries + 1):
|
||||
try:
|
||||
return func(**kwargs)
|
||||
except Exception as e:
|
||||
logging.warning(f"[call_with_retry] Attempt {attempt} failed with error: {e}")
|
||||
traceback.print_exc()
|
||||
if attempt < max_retries:
|
||||
time.sleep(sleep_time)
|
||||
else:
|
||||
logging.error("Max retries reached, returning fallback_return.")
|
||||
return fallback_return
|
||||
|
||||
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 debug_log(prompt: str, response_content: str):
|
||||
logging.info(
|
||||
f"\n[######################################### Prompt #########################################]\n{prompt}\n"
|
||||
)
|
||||
logging.info(
|
||||
f"\n[######################################### Response #########################################]\n{response_content}\n"
|
||||
)
|
||||
|
||||
def invoke_with_cleaning(llm_adapter, prompt: str) -> str:
|
||||
"""
|
||||
调用 LLM，增加重试和清洗逻辑
|
||||
如果多次失败，则返回空字符串以继续流程，而不是中断。
|
||||
"""
|
||||
def _invoke(prompt):
|
||||
return llm_adapter.invoke(prompt)
|
||||
|
||||
response = call_with_retry(func=_invoke, max_retries=3, fallback_return="", prompt=prompt)
|
||||
if not response:
|
||||
logging.warning("No response from model after retry. Return empty.")
|
||||
return ""
|
||||
cleaned_text = remove_think_tags(response)
|
||||
debug_log(prompt, cleaned_text)
|
||||
return cleaned_text.strip()
|
||||
@@ -0,0 +1,121 @@
|
||||
#novel_generator/finalization.py
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
定稿章节和扩写章节（finalize_chapter、enrich_chapter_text）
|
||||
"""
|
||||
import os
|
||||
import logging
|
||||
from llm_adapters import create_llm_adapter
|
||||
from prompt_definitions import summary_prompt, update_character_state_prompt
|
||||
from novel_generator.common import invoke_with_cleaning
|
||||
from utils import read_file, clear_file_content, save_string_to_txt
|
||||
from novel_generator.vectorstore_utils import update_vector_store
|
||||
|
||||
def finalize_chapter(
|
||||
novel_number: int,
|
||||
word_number: int,
|
||||
api_key: str,
|
||||
base_url: str,
|
||||
model_name: str,
|
||||
temperature: float,
|
||||
filepath: str,
|
||||
embedding_api_key: str,
|
||||
embedding_url: str,
|
||||
embedding_interface_format: str,
|
||||
embedding_model_name: str,
|
||||
interface_format: str,
|
||||
max_tokens: int,
|
||||
timeout: int = 600
|
||||
):
|
||||
"""
|
||||
对指定章节做最终处理：更新全局摘要、更新角色状态、插入向量库等。
|
||||
默认无需再做扩写操作，若有需要可在外部调用 enrich_chapter_text 处理后再定稿。
|
||||
"""
|
||||
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()
|
||||
if not chapter_text:
|
||||
logging.warning(f"Chapter {novel_number} is empty, cannot finalize.")
|
||||
return
|
||||
|
||||
global_summary_file = os.path.join(filepath, "global_summary.txt")
|
||||
old_global_summary = read_file(global_summary_file)
|
||||
character_state_file = os.path.join(filepath, "character_state.txt")
|
||||
old_character_state = read_file(character_state_file)
|
||||
|
||||
llm_adapter = create_llm_adapter(
|
||||
interface_format=interface_format,
|
||||
base_url=base_url,
|
||||
model_name=model_name,
|
||||
api_key=api_key,
|
||||
temperature=temperature,
|
||||
max_tokens=max_tokens,
|
||||
timeout=timeout
|
||||
)
|
||||
|
||||
prompt_summary = summary_prompt.format(
|
||||
chapter_text=chapter_text,
|
||||
global_summary=old_global_summary
|
||||
)
|
||||
new_global_summary = invoke_with_cleaning(llm_adapter, prompt_summary)
|
||||
if not new_global_summary.strip():
|
||||
new_global_summary = old_global_summary
|
||||
|
||||
prompt_char_state = update_character_state_prompt.format(
|
||||
chapter_text=chapter_text,
|
||||
old_state=old_character_state
|
||||
)
|
||||
new_char_state = invoke_with_cleaning(llm_adapter, prompt_char_state)
|
||||
if not new_char_state.strip():
|
||||
new_char_state = old_character_state
|
||||
|
||||
clear_file_content(global_summary_file)
|
||||
save_string_to_txt(new_global_summary, global_summary_file)
|
||||
clear_file_content(character_state_file)
|
||||
save_string_to_txt(new_char_state, character_state_file)
|
||||
|
||||
update_vector_store(
|
||||
embedding_adapter=create_llm_adapter(
|
||||
interface_format=embedding_interface_format,
|
||||
base_url=embedding_url,
|
||||
model_name=embedding_model_name,
|
||||
api_key=embedding_api_key,
|
||||
temperature=temperature,
|
||||
max_tokens=max_tokens,
|
||||
timeout=timeout
|
||||
),
|
||||
new_chapter=chapter_text,
|
||||
filepath=filepath
|
||||
)
|
||||
|
||||
logging.info(f"Chapter {novel_number} has been finalized.")
|
||||
|
||||
def enrich_chapter_text(
|
||||
chapter_text: str,
|
||||
word_number: int,
|
||||
api_key: str,
|
||||
base_url: str,
|
||||
model_name: str,
|
||||
temperature: float,
|
||||
interface_format: str,
|
||||
max_tokens: int,
|
||||
timeout: int=600
|
||||
) -> str:
|
||||
"""
|
||||
对章节文本进行扩写，使其更接近 word_number 字数，保持剧情连贯。
|
||||
"""
|
||||
llm_adapter = create_llm_adapter(
|
||||
interface_format=interface_format,
|
||||
base_url=base_url,
|
||||
model_name=model_name,
|
||||
api_key=api_key,
|
||||
temperature=temperature,
|
||||
max_tokens=max_tokens,
|
||||
timeout=timeout
|
||||
)
|
||||
prompt = f"""以下章节文本较短，请在保持剧情连贯的前提下进行扩写，使其更充实，接近 {word_number} 字左右：
|
||||
原内容：
|
||||
{chapter_text}
|
||||
"""
|
||||
enriched_text = invoke_with_cleaning(llm_adapter, prompt)
|
||||
return enriched_text if enriched_text else chapter_text
|
||||
@@ -0,0 +1,93 @@
|
||||
#novel_generator/knowledge.py
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
知识文件导入至向量库（advanced_split_content、import_knowledge_file）
|
||||
"""
|
||||
import os
|
||||
import logging
|
||||
import re
|
||||
import traceback
|
||||
import nltk
|
||||
from sentence_transformers import SentenceTransformer
|
||||
from sklearn.metrics.pairwise import cosine_similarity
|
||||
from utils import read_file
|
||||
from novel_generator.vectorstore_utils import load_vector_store, init_vector_store
|
||||
from langchain.docstore.document import Document
|
||||
|
||||
def advanced_split_content(content: str, similarity_threshold: float = 0.7, max_length: int = 500) -> list:
|
||||
nltk.download('punkt', quiet=True)
|
||||
nltk.download('punkt_tab', quiet=True)
|
||||
sentences = nltk.sent_tokenize(content)
|
||||
if not sentences:
|
||||
return []
|
||||
model = SentenceTransformer('paraphrase-MiniLM-L6-v2')
|
||||
embeddings = model.encode(sentences)
|
||||
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:
|
||||
current_sentences.append(sentences[i])
|
||||
current_embedding = (current_embedding + embeddings[i]) / 2.0
|
||||
else:
|
||||
merged_paragraphs.append(" ".join(current_sentences))
|
||||
current_sentences = [sentences[i]]
|
||||
current_embedding = embeddings[i]
|
||||
if current_sentences:
|
||||
merged_paragraphs.append(" ".join(current_sentences))
|
||||
final_segments = []
|
||||
for para in merged_paragraphs:
|
||||
if len(para) > max_length:
|
||||
sub_segments = []
|
||||
start_idx = 0
|
||||
while start_idx < len(para):
|
||||
end_idx = min(start_idx + max_length, len(para))
|
||||
segment = para[start_idx:end_idx].strip()
|
||||
sub_segments.append(segment)
|
||||
start_idx = end_idx
|
||||
final_segments.extend(sub_segments)
|
||||
else:
|
||||
final_segments.append(para)
|
||||
return final_segments
|
||||
|
||||
def import_knowledge_file(
|
||||
embedding_api_key: str,
|
||||
embedding_url: str,
|
||||
embedding_interface_format: str,
|
||||
embedding_model_name: str,
|
||||
file_path: str,
|
||||
filepath: str
|
||||
):
|
||||
logging.info(f"开始导入知识库文件: {file_path}, 接口格式: {embedding_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
|
||||
content = read_file(file_path)
|
||||
if not content.strip():
|
||||
logging.warning("知识库文件内容为空。")
|
||||
return
|
||||
paragraphs = advanced_split_content(content)
|
||||
from llm_adapters import create_embedding_adapter
|
||||
embedding_adapter = create_embedding_adapter(
|
||||
embedding_interface_format,
|
||||
embedding_api_key,
|
||||
embedding_url if embedding_url else "http://localhost:11434/api",
|
||||
embedding_model_name
|
||||
)
|
||||
store = load_vector_store(embedding_adapter, filepath)
|
||||
if not store:
|
||||
logging.info("Vector store does not exist or load failed. Initializing a new one for knowledge import...")
|
||||
store = init_vector_store(embedding_adapter, paragraphs, filepath)
|
||||
if store:
|
||||
logging.info("知识库文件已成功导入至向量库(新初始化)。")
|
||||
else:
|
||||
logging.warning("知识库导入失败，跳过。")
|
||||
else:
|
||||
try:
|
||||
docs = [Document(page_content=str(p)) for p in paragraphs]
|
||||
store.add_documents(docs)
|
||||
logging.info("知识库文件已成功导入至向量库(追加模式)。")
|
||||
except Exception as e:
|
||||
logging.warning(f"知识库导入失败: {e}")
|
||||
traceback.print_exc()
|
||||
@@ -0,0 +1,228 @@
|
||||
#novel_generator/vectorstore_utils.py
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
向量库相关操作（初始化、更新、检索、清空、文本切分等）
|
||||
"""
|
||||
import os
|
||||
import logging
|
||||
import traceback
|
||||
import nltk
|
||||
from langchain_chroma import Chroma
|
||||
from chromadb.config import Settings
|
||||
from langchain.docstore.document import Document
|
||||
from sentence_transformers import SentenceTransformer
|
||||
from sklearn.metrics.pairwise import cosine_similarity
|
||||
from .common import call_with_retry
|
||||
|
||||
def get_vectorstore_dir(filepath: str) -> str:
|
||||
"""获取 vectorstore 路径"""
|
||||
return os.path.join(filepath, "vectorstore")
|
||||
|
||||
def clear_vector_store(filepath: str) -> bool:
|
||||
"""清空 清空向量库"""
|
||||
import shutil
|
||||
store_dir = get_vectorstore_dir(filepath)
|
||||
if not os.path.exists(store_dir):
|
||||
logging.info("No vector store found to clear.")
|
||||
return False
|
||||
try:
|
||||
shutil.rmtree(store_dir)
|
||||
logging.info(f"Vector store directory '{store_dir}' removed.")
|
||||
return True
|
||||
except Exception as e:
|
||||
logging.error(f"无法删除向量库文件夹，请关闭程序后手动删除 {store_dir}。\n {str(e)}")
|
||||
traceback.print_exc()
|
||||
return False
|
||||
|
||||
def init_vector_store(embedding_adapter, texts, filepath: str):
|
||||
"""
|
||||
在 filepath 下创建/加载一个 Chroma 向量库并插入 texts。
|
||||
如果Embedding失败，则返回 None，不中断任务。
|
||||
"""
|
||||
from langchain.embeddings.base import Embeddings as LCEmbeddings
|
||||
|
||||
store_dir = get_vectorstore_dir(filepath)
|
||||
os.makedirs(store_dir, exist_ok=True)
|
||||
documents = [Document(page_content=str(t)) for t in texts]
|
||||
|
||||
try:
|
||||
class LCEmbeddingWrapper(LCEmbeddings):
|
||||
def embed_documents(self, texts):
|
||||
return call_with_retry(
|
||||
func=embedding_adapter.embed_documents,
|
||||
max_retries=3,
|
||||
fallback_return=[],
|
||||
texts=texts
|
||||
)
|
||||
def embed_query(self, query: str):
|
||||
res = call_with_retry(
|
||||
func=embedding_adapter.embed_query,
|
||||
max_retries=3,
|
||||
fallback_return=[],
|
||||
query=query
|
||||
)
|
||||
return res
|
||||
|
||||
chroma_embedding = LCEmbeddingWrapper()
|
||||
vectorstore = Chroma.from_documents(
|
||||
documents,
|
||||
embedding=chroma_embedding,
|
||||
persist_directory=store_dir,
|
||||
client_settings=Settings(anonymized_telemetry=False),
|
||||
collection_name="novel_collection"
|
||||
)
|
||||
return vectorstore
|
||||
except Exception as e:
|
||||
logging.warning(f"Init vector store failed: {e}")
|
||||
traceback.print_exc()
|
||||
return None
|
||||
|
||||
def load_vector_store(embedding_adapter, filepath: str):
|
||||
"""
|
||||
读取已存在的 Chroma 向量库。若不存在则返回 None。
|
||||
如果加载失败（embedding 或IO问题），则返回 None。
|
||||
"""
|
||||
from langchain.embeddings.base import Embeddings as LCEmbeddings
|
||||
store_dir = get_vectorstore_dir(filepath)
|
||||
if not os.path.exists(store_dir):
|
||||
logging.info("Vector store not found. Will return None.")
|
||||
return None
|
||||
|
||||
try:
|
||||
class LCEmbeddingWrapper(LCEmbeddings):
|
||||
def embed_documents(self, texts):
|
||||
return call_with_retry(
|
||||
func=embedding_adapter.embed_documents,
|
||||
max_retries=3,
|
||||
fallback_return=[],
|
||||
texts=texts
|
||||
)
|
||||
def embed_query(self, query: str):
|
||||
res = call_with_retry(
|
||||
func=embedding_adapter.embed_query,
|
||||
max_retries=3,
|
||||
fallback_return=[],
|
||||
query=query
|
||||
)
|
||||
return res
|
||||
|
||||
chroma_embedding = LCEmbeddingWrapper()
|
||||
return Chroma(
|
||||
persist_directory=store_dir,
|
||||
embedding_function=chroma_embedding,
|
||||
client_settings=Settings(anonymized_telemetry=False),
|
||||
collection_name="novel_collection"
|
||||
)
|
||||
except Exception as e:
|
||||
logging.warning(f"Failed to load vector store: {e}")
|
||||
traceback.print_exc()
|
||||
return None
|
||||
|
||||
def split_by_length(text: str, max_length: int = 500):
|
||||
"""按照 max_length 切分文本"""
|
||||
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
|
||||
|
||||
def split_text_for_vectorstore(chapter_text: str, max_length: int = 500, similarity_threshold: float = 0.7):
|
||||
"""
|
||||
对新的章节文本进行分段后，再用于存入向量库。
|
||||
先句子切分 -> 语义相似度合并 -> 再按 max_length 切分。
|
||||
"""
|
||||
if not chapter_text.strip():
|
||||
return []
|
||||
|
||||
nltk.download('punkt', quiet=True)
|
||||
nltk.download('punkt_tab', quiet=True)
|
||||
sentences = nltk.sent_tokenize(chapter_text)
|
||||
if not sentences:
|
||||
return []
|
||||
|
||||
model = SentenceTransformer('paraphrase-MiniLM-L6-v2')
|
||||
embeddings = model.encode(sentences)
|
||||
|
||||
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:
|
||||
current_sentences.append(sentences[i])
|
||||
current_embedding = (current_embedding + embeddings[i]) / 2.0
|
||||
else:
|
||||
merged_paragraphs.append(" ".join(current_sentences))
|
||||
current_sentences = [sentences[i]]
|
||||
current_embedding = embeddings[i]
|
||||
|
||||
if current_sentences:
|
||||
merged_paragraphs.append(" ".join(current_sentences))
|
||||
|
||||
final_segments = []
|
||||
for para in merged_paragraphs:
|
||||
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)
|
||||
|
||||
return final_segments
|
||||
|
||||
def update_vector_store(embedding_adapter, new_chapter: str, filepath: str):
|
||||
"""
|
||||
将最新章节文本插入到向量库中。
|
||||
若库不存在则初始化；若初始化/更新失败，则跳过。
|
||||
"""
|
||||
from utils import read_file, clear_file_content, save_string_to_txt
|
||||
splitted_texts = split_text_for_vectorstore(new_chapter)
|
||||
if not splitted_texts:
|
||||
logging.warning("No valid text to insert into vector store. Skipping.")
|
||||
return
|
||||
|
||||
store = load_vector_store(embedding_adapter, filepath)
|
||||
if not store:
|
||||
logging.info("Vector store does not exist or failed to load. Initializing a new one for new chapter...")
|
||||
store = init_vector_store(embedding_adapter, splitted_texts, filepath)
|
||||
if not store:
|
||||
logging.warning("Init vector store failed, skip embedding.")
|
||||
else:
|
||||
logging.info("New vector store created successfully.")
|
||||
return
|
||||
|
||||
try:
|
||||
docs = [Document(page_content=str(t)) for t in splitted_texts]
|
||||
store.add_documents(docs)
|
||||
logging.info("Vector store updated with the new chapter splitted segments.")
|
||||
except Exception as e:
|
||||
logging.warning(f"Failed to update vector store: {e}")
|
||||
traceback.print_exc()
|
||||
|
||||
def get_relevant_context_from_vector_store(embedding_adapter, query: str, filepath: 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。
|
||||
如果向量库加载/检索失败，则返回空字符串。
|
||||
最终只返回最多2000字符的检索片段。
|
||||
"""
|
||||
store = load_vector_store(embedding_adapter, filepath)
|
||||
if not store:
|
||||
logging.info("No vector store found or load failed. Returning empty context.")
|
||||
return ""
|
||||
|
||||
try:
|
||||
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])
|
||||
if len(combined) > 2000:
|
||||
combined = combined[:2000]
|
||||
return combined
|
||||
except Exception as e:
|
||||
logging.warning(f"Similarity search failed: {e}")
|
||||
traceback.print_exc()
|
||||
return ""
|
||||
Reference in New Issue
Block a user