690 lines
25 KiB
Python
690 lines
25 KiB
Python
# novel_generator.py
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# -*- coding: utf-8 -*-
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import os
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import logging
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import re
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from typing import Dict, List, Optional
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try:
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from typing import TypedDict # Python 3.8+ 直接可用；若是3.7可改用 typing_extensions
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except ImportError:
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from typing_extensions import TypedDict
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from langchain_openai import ChatOpenAI
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from langgraph.graph import StateGraph, START, END
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from langchain_openai import OpenAIEmbeddings
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from langchain_community.vectorstores import Chroma
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from langchain.docstore.document import Document
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#
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import nltk
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import math
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from sentence_transformers import SentenceTransformer
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from sklearn.metrics.pairwise import cosine_similarity
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from utils import (
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read_file, append_text_to_file, clear_file_content,
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save_string_to_txt
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)
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from prompt_definitions import (
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set_prompt, character_prompt, dark_lines_prompt,
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finalize_setting_prompt, novel_directory_prompt,
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summary_prompt, update_character_state_prompt,
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chapter_outline_prompt, chapter_write_prompt
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)
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# ============ 日志配置 ============
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logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s")
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# ============ 向量检索相关函数（Chroma） ============
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VECTOR_STORE_DIR = "vectorstore"
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def init_vector_store(api_key: str, base_url: str, texts: List[str]) -> Chroma:
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"""
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初始化并返回一个Chroma向量库，将传入的文本进行嵌入并保存到本地目录。
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如果不存在该目录，会自动创建。
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"""
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embeddings = OpenAIEmbeddings(
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openai_api_key=api_key,
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openai_api_base=base_url
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)
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documents = [Document(page_content=t) for t in texts]
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vectorstore = Chroma.from_documents(
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documents,
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embedding=embeddings,
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persist_directory=VECTOR_STORE_DIR
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)
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vectorstore.persist()
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return vectorstore
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def load_vector_store(api_key: str, base_url: str) -> Optional[Chroma]:
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"""读取已存在的向量库。若不存在则返回 None。"""
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if not os.path.exists(VECTOR_STORE_DIR):
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return None
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embeddings = OpenAIEmbeddings(
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openai_api_key=api_key,
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openai_api_base=base_url
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)
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return Chroma(persist_directory=VECTOR_STORE_DIR, embedding_function=embeddings)
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def update_vector_store(api_key: str, base_url: str, new_chapter: str) -> None:
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"""将最新章节文本插入到向量库里，用于后续检索参考。若库不存在则初始化。"""
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store = load_vector_store(api_key, base_url)
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if not store:
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logging.info("Vector store does not exist. Initializing a new one...")
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init_vector_store(api_key, base_url, [new_chapter])
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return
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new_doc = Document(page_content=new_chapter)
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store.add_documents([new_doc])
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store.persist()
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def get_relevant_context_from_vector_store(api_key: str, base_url: str, query: str, k: int = 2) -> str:
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"""
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从向量库中检索与 query 最相关的 k 条文本，拼接后返回。
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若向量库不存在则返回空字符串。
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"""
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store = load_vector_store(api_key, base_url)
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if not store:
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logging.warning("Vector store not found. Returning empty context.")
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return ""
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docs = store.similarity_search(query, k=k)
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combined = "\n".join([d.page_content for d in docs])
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return combined
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# ============ 多步生成：设置 & 目录 ============
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class OverallState(TypedDict):
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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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novel_setting_base: str
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character_setting: str
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dark_lines: str
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final_novel_setting: str
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novel_directory: str
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def Novel_novel_directory_generate(
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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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) -> None:
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"""
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使用多步流程，生成 Novel_setting.txt 与 Novel_directory.txt 并保存到 filepath。
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:param api_key: OpenAI API key
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:param base_url: OpenAI API base url
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:param llm_model: 所使用的 LLM 模型名称
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:param topic: 小说主题
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:param genre: 小说类型
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:param number_of_chapters: 章节数
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:param word_number: 单章目标字数
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:param filepath: 存放生成文件的目录路径
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:param temperature: 生成温度
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"""
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# 确保文件夹存在
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os.makedirs(filepath, exist_ok=True)
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model = ChatOpenAI(
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model=llm_model,
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api_key=api_key,
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base_url=base_url,
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temperature=temperature
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)
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def debug_log(prompt: str, response_content: str):
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"""在控制台打印或记录下每次Prompt与Response，[调试]"""
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logging.info(f"\n[Prompt >>>] {prompt}\n")
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logging.info(f"[Response <<<] {response_content}\n")
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def generate_base_setting(state: OverallState) -> Dict[str, str]:
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prompt = set_prompt.format(
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topic=state["topic"],
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genre=state["genre"],
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number_of_chapters=state["number_of_chapters"],
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word_number=state["word_number"]
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)
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response = model.invoke(prompt)
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if not response:
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logging.warning("generate_base_setting: No response.")
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return {"novel_setting_base": ""}
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debug_log(prompt, response.content)
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return {"novel_setting_base": response.content.strip()}
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def generate_character_setting(state: OverallState) -> Dict[str, str]:
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prompt = character_prompt.format(
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novel_setting=state["novel_setting_base"]
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)
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response = model.invoke(prompt)
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if not response:
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logging.warning("generate_character_setting: No response.")
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return {"character_setting": ""}
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debug_log(prompt, response.content)
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return {"character_setting": response.content.strip()}
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def generate_dark_lines(state: OverallState) -> Dict[str, str]:
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prompt = dark_lines_prompt.format(
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character_info=state["character_setting"]
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)
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response = model.invoke(prompt)
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if not response:
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logging.warning("generate_dark_lines: No response.")
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return {"dark_lines": ""}
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debug_log(prompt, response.content)
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return {"dark_lines": response.content.strip()}
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def finalize_novel_setting(state: OverallState) -> Dict[str, str]:
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prompt = finalize_setting_prompt.format(
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novel_setting_base=state["novel_setting_base"],
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character_setting=state["character_setting"],
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dark_lines=state["dark_lines"]
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)
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response = model.invoke(prompt)
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if not response:
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logging.warning("finalize_novel_setting: No response.")
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return {"final_novel_setting": ""}
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debug_log(prompt, response.content)
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return {"final_novel_setting": response.content.strip()}
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def generate_novel_directory(state: OverallState) -> Dict[str, str]:
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prompt = novel_directory_prompt.format(
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final_novel_setting=state["final_novel_setting"],
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number_of_chapters=state["number_of_chapters"]
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)
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response = model.invoke(prompt)
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if not response:
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logging.warning("generate_novel_directory: No response.")
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return {"novel_directory": ""}
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debug_log(prompt, response.content)
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return {"novel_directory": response.content.strip()}
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# 构建状态图
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graph = StateGraph(OverallState)
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graph.add_node("generate_base_setting", generate_base_setting)
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graph.add_node("generate_character_setting", generate_character_setting)
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graph.add_node("generate_dark_lines", generate_dark_lines)
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graph.add_node("finalize_novel_setting", finalize_novel_setting)
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graph.add_node("generate_novel_directory", generate_novel_directory)
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graph.add_edge(START, "generate_base_setting")
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graph.add_edge("generate_base_setting", "generate_character_setting")
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graph.add_edge("generate_character_setting", "generate_dark_lines")
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graph.add_edge("generate_dark_lines", "finalize_novel_setting")
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graph.add_edge("finalize_novel_setting", "generate_novel_directory")
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graph.add_edge("generate_novel_directory", END)
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app = graph.compile()
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input_params = {
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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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result = app.invoke(input_params)
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if not result:
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logging.warning("Novel_novel_directory_generate: invoke() 结果为空，生成失败。")
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return
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final_novel_setting = result.get("final_novel_setting", "")
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final_novel_directory = result.get("novel_directory", "")
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if not final_novel_setting or not final_novel_directory:
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logging.warning("生成失败：缺少 final_novel_setting 或 novel_directory。")
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return
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# 写入文件
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filename_set = os.path.join(filepath, "Novel_setting.txt")
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filename_novel_directory = os.path.join(filepath, "Novel_directory.txt")
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# 清理文本（可根据需要去除多余字符）
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def clean_text(txt: str) -> str:
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return txt.replace('#', '').replace('*', '')
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final_novel_setting_cleaned = clean_text(final_novel_setting)
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final_novel_directory_cleaned = clean_text(final_novel_directory)
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append_text_to_file(final_novel_setting_cleaned, filename_set)
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append_text_to_file(final_novel_directory_cleaned, filename_novel_directory)
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logging.info("Novel settings and directory generated successfully.")
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# ============ 生成章节（每章独立文件） ============
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CHINESE_NUM_MAP = {
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'零': 0, '○': 0, '〇': 0,
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'一': 1, '二': 2, '三': 3, '四': 4, '五': 5,
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'六': 6, '七': 7, '八': 8, '九': 9,
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'十': 10, '百': 100, '千': 1000, '万': 10000
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}
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def chinese_to_arabic(chinese_str: str) -> int:
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"""
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只能处理到万(10000)以内的中文数字，正常小说章节应该够用了
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"""
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total = 0
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current_unit = 1 # 记录当前单位
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tmp_val = 0 # 暂存本轮数字
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for char in reversed(chinese_str):
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if char in CHINESE_NUM_MAP:
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val = CHINESE_NUM_MAP[char]
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if val >= 10:
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if val > current_unit:
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# 如 100, 1000, 10000
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current_unit = val
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else:
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# 比如 “十二” -> 2 * 10 + 1
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# 如果 val <= current_unit, 那么相当于在这个单位下加
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total += tmp_val * val
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tmp_val = 0
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else:
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# 0~9
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tmp_val = tmp_val + val * current_unit
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else:
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# 非中文数字字符，视情况决定怎么处理，这里直接跳过
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pass
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total += tmp_val
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return total
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def parse_chapter_title_from_directory(novel_directory_text: str,
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novel_number: int,
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range_size: int = 1) -> str:
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"""
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从小说目录文本中，提取指定章节（以及前后几章）的目录信息。
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range_size=1，表示获取当前章节、前一章和后一章的目录信息(若存在)。
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支持多种常见的章节格式。
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"""
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lines = novel_directory_text.splitlines()
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# 可以根据需求自行扩展，这里列举了几种常见的章节标题格式，如果模型实在不听话，可以适当调整
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# 每个pattern都应该捕获两个组：
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# 1. chapter_num_str：章节数字(可能是中文也可能是阿拉伯数字)
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# 2. chapter_title ：章节标题（.*）
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patterns = [
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# 1) 第12章 标题
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r"^第\s*([\d]+)\s*章[:：]?\s*(.*)$",
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# 2) 第十二章 标题（中文数字）
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r"^第\s*([零○〇一二三四五六七八九十百千万]+)\s*章[:：]?\s*(.*)$",
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# 3) Chapter 12 标题
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r"^Chapter\s+(\d+)\s*[:：]?\s*(.*)$",
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# 4) Ch 12 标题
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r"^Ch\s+(\d+)\s*[:：]?\s*(.*)$",
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# 5) 第12节 标题
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r"^第\s*([\d]+)\s*节[:：]?\s*(.*)$",
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# 6) 第12话 标题
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r"^第\s*([\d]+)\s*话[:：]?\s*(.*)$",
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# ... 更多模式 ...
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]
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# 用来存储匹配结果： chapter_num -> title
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directory_map = {}
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for line in lines:
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line = line.strip()
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if not line:
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continue
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# 依次尝试每一种pattern
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matched = False
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for pat in patterns:
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match = re.match(pat, line, flags=re.IGNORECASE)
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if match:
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chapter_num_str = match.group(1)
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chapter_title = match.group(2).strip()
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# 如果是中文数字，需要转换
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# 如果是阿拉伯数字，直接转 int 即可
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if re.match(r"^[零○〇一二三四五六七八九十百千万]+$", chapter_num_str):
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chapter_num = chinese_to_arabic(chapter_num_str)
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else:
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chapter_num = int(chapter_num_str)
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directory_map[chapter_num] = chapter_title
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matched = True
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break
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# 如果已经匹配到其中一个pattern，就不需要继续匹配剩余pattern
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if matched:
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continue
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# 收集需要的章节范围
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chapters_info = []
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for cnum in range(novel_number - range_size, novel_number + range_size + 1):
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if cnum in directory_map:
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if cnum == novel_number:
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chapters_info.append(f"【当前】第{cnum}章：{directory_map[cnum]}")
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else:
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chapters_info.append(f"第{cnum}章：{directory_map[cnum]}")
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if chapters_info:
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return "\n".join(chapters_info)
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return ""
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def generate_chapter_with_state(
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novel_settings: str,
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novel_novel_directory: 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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novel_number: int,
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filepath: str,
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word_number: int,
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lastchapter: str,
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user_guidance: str = "",
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temperature: float = 0.7
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) -> str:
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"""
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多步流程:
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1) 更新/创建全局摘要
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2) 更新/生成角色状态文档
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3) 向量检索获取往期上下文
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4) 从Novel_directory.txt中获取当前(和前后几章)的目录信息
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5) 大纲 -> 正文（可结合用户给出的额外指导）
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6) 写入 chapter_{novel_number}.txt, 更新 last_chapter.txt
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7) 更新向量库
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:param novel_settings: 最终的作品设定（字符串）
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:param novel_novel_directory: 小说目录信息
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:param api_key: OpenAI API Key
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:param base_url: OpenAI Base URL
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:param model_name: LLM 模型名称
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:param novel_number: 当前要生成的章节号
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:param filepath: 文件存放的目录
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:param word_number: 单章目标字数
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:param lastchapter: 上一章内容（若为空字符串，表示无上一章）
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:param user_guidance: 用户对当前章节的额外指导或想法
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:param temperature: 生成温度
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:return: 本章生成的正文内容
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"""
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# 确保文件夹存在
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os.makedirs(filepath, exist_ok=True)
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model = ChatOpenAI(
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model=model_name,
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api_key=api_key,
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base_url=base_url,
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temperature=temperature
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)
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# 调试输出函数
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def debug_log(prompt: str, response_content: str):
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"""在控制台打印或记录下每次的 Prompt 与 Response，便于观察生成过程。"""
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logging.info(f"\n[Prompt >>>]\n{prompt}\n")
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logging.info(f"[Response <<<]\n{response_content}\n")
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# --- 文件路径定义 ---
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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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chapter_file = os.path.join(chapters_dir, f"chapter_{novel_number}.txt")
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lastchapter_file = os.path.join(filepath, "last_chapter.txt")
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character_state_file = os.path.join(filepath, "character_state.txt")
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global_summary_file = os.path.join(filepath, "global_summary.txt")
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old_char_state = read_file(character_state_file)
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old_global_summary = read_file(global_summary_file)
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# 1) 更新全局摘要
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def update_global_summary(chapter_text: str, old_summary: str) -> str:
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prompt = summary_prompt.format(
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chapter_text=chapter_text,
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global_summary=old_summary
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)
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response = model.invoke(prompt)
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if not response:
|
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logging.warning("update_global_summary: No response.")
|
||
return old_summary
|
||
debug_log(prompt, response.content)
|
||
return response.content.strip()
|
||
|
||
if lastchapter.strip():
|
||
new_global_summary = update_global_summary(lastchapter, old_global_summary)
|
||
else:
|
||
new_global_summary = old_global_summary
|
||
|
||
# 2) 更新角色状态文档
|
||
def update_character_state(chapter_text: str, old_state: str) -> str:
|
||
prompt = update_character_state_prompt.format(
|
||
chapter_text=chapter_text,
|
||
old_state=old_state
|
||
)
|
||
response = model.invoke(prompt)
|
||
if not response:
|
||
logging.warning("update_character_state: No response.")
|
||
return old_state
|
||
debug_log(prompt, response.content)
|
||
return response.content.strip()
|
||
|
||
if lastchapter.strip():
|
||
new_char_state = update_character_state(lastchapter, old_char_state)
|
||
else:
|
||
new_char_state = old_char_state
|
||
|
||
# 3) 从向量库检索上下文
|
||
relevant_context = get_relevant_context_from_vector_store(
|
||
api_key, base_url, "回顾剧情", k=2
|
||
)
|
||
|
||
# 4) 解析本章及前后章节目录信息
|
||
this_and_related_chapters = parse_chapter_title_from_directory(novel_novel_directory, novel_number, range_size=1)
|
||
|
||
# 5) 生成大纲
|
||
def outline_chapter(
|
||
novel_setting: str,
|
||
char_state: str,
|
||
global_summary: str,
|
||
chap_num: int,
|
||
extra_context: str,
|
||
directory_hint: str,
|
||
user_guide: str
|
||
) -> str:
|
||
"""
|
||
将目录提示以及用户额外指导内容一起放入 Prompt 中。
|
||
"""
|
||
# 适度修改章节提纲提示词，以整合目录信息 & 用户指导
|
||
outline_prompt = (
|
||
chapter_outline_prompt
|
||
+ "\n\n【目录参考】\n" + directory_hint
|
||
+ "\n\n【用户指导】\n" + user_guide
|
||
).format(
|
||
novel_setting=novel_setting,
|
||
character_state=char_state + "\n\n【历史上下文】\n" + extra_context,
|
||
global_summary=global_summary,
|
||
novel_number=chap_num
|
||
)
|
||
|
||
response = model.invoke(outline_prompt)
|
||
if not response:
|
||
logging.warning("outline_chapter: No response.")
|
||
return ""
|
||
debug_log(outline_prompt, response.content)
|
||
return response.content.strip()
|
||
|
||
chap_outline = outline_chapter(
|
||
novel_settings, new_char_state, new_global_summary, novel_number,
|
||
relevant_context, this_and_related_chapters, user_guidance
|
||
)
|
||
|
||
# 6) 生成正文
|
||
def write_chapter(
|
||
novel_setting: str,
|
||
char_state: str,
|
||
global_summary: str,
|
||
outline: str,
|
||
wnum: int,
|
||
extra_context: str,
|
||
directory_hint: str,
|
||
user_guide: str
|
||
) -> str:
|
||
# 同理，整合目录信息和用户指导
|
||
writing_prompt = (
|
||
chapter_write_prompt
|
||
+ "\n\n【目录参考】\n" + directory_hint
|
||
+ "\n\n【用户指导】\n" + user_guide
|
||
).format(
|
||
novel_setting=novel_setting,
|
||
character_state=char_state + "\n\n【历史上下文】\n" + extra_context,
|
||
global_summary=global_summary,
|
||
chapter_outline=outline,
|
||
word_number=wnum
|
||
)
|
||
|
||
response = model.invoke(writing_prompt)
|
||
if not response:
|
||
logging.warning("write_chapter: No response.")
|
||
return ""
|
||
debug_log(writing_prompt, response.content)
|
||
return response.content.strip()
|
||
|
||
chapter_content = write_chapter(
|
||
novel_settings,
|
||
new_char_state,
|
||
new_global_summary,
|
||
chap_outline,
|
||
word_number,
|
||
relevant_context,
|
||
this_and_related_chapters,
|
||
user_guidance
|
||
)
|
||
|
||
# 写入文件并更新记录
|
||
if chapter_content:
|
||
save_string_to_txt(chapter_content, chapter_file)
|
||
|
||
# 更新 last_chapter.txt
|
||
clear_file_content(lastchapter_file)
|
||
save_string_to_txt(chapter_content, lastchapter_file)
|
||
|
||
# 更新角色状态、全局摘要
|
||
clear_file_content(character_state_file)
|
||
save_string_to_txt(new_char_state, character_state_file)
|
||
|
||
clear_file_content(global_summary_file)
|
||
save_string_to_txt(new_global_summary, global_summary_file)
|
||
|
||
# 7) 更新向量检索库
|
||
update_vector_store(api_key, base_url, chapter_content)
|
||
logging.info(f"Chapter {novel_number} generated successfully.")
|
||
else:
|
||
logging.warning(f"Chapter {novel_number} generation failed.")
|
||
|
||
return chapter_content
|
||
|
||
def import_knowledge_file(api_key: str, base_url: str, file_path: str) -> None:
|
||
"""
|
||
将用户选定的文本文件导入到向量库，以便在写作时检索。
|
||
可以在UI中提供按钮来调用此函数。
|
||
"""
|
||
|
||
# 1. 检查文件路径是否有效
|
||
if not os.path.exists(file_path):
|
||
logging.warning(f"知识库文件不存在: {file_path}")
|
||
return
|
||
|
||
# 2. 读取文件内容
|
||
content = read_file(file_path)
|
||
if not content.strip():
|
||
logging.warning("知识库文件内容为空。")
|
||
return
|
||
|
||
# 3. 对内容进行高级切分处理
|
||
paragraphs = advanced_split_content(content)
|
||
|
||
# 4. 加载或初始化向量存储
|
||
store = load_vector_store(api_key, base_url)
|
||
if not store:
|
||
logging.info("Vector store does not exist. Initializing a new one for knowledge import...")
|
||
init_vector_store(api_key, base_url, paragraphs)
|
||
return
|
||
|
||
# 5. 创建Document对象并更新到向量库
|
||
docs = [Document(page_content=p) for p in paragraphs]
|
||
store.add_documents(docs)
|
||
store.persist()
|
||
logging.info("知识库文件已成功导入至向量库。")
|
||
|
||
|
||
def advanced_split_content(content: str,
|
||
similarity_threshold: float = 0.7,
|
||
max_length: int = 500) -> List[str]:
|
||
"""
|
||
将文本先按句子切分，然后根据语义相似度进行合并，最后根据max_length进行二次切分。
|
||
|
||
:param content: 原始文本内容
|
||
:param similarity_threshold: 相邻句子合并的语义相似度阈值，小于此值则会开启新的段落
|
||
:param max_length: 每个段落的最大长度（按字符数计算，超过则进一步拆分）
|
||
:return: 切分好的段落列表
|
||
"""
|
||
|
||
# 1. 按句子切分
|
||
nltk.download('punkt', quiet=True) # 确保 punkt 数据可用
|
||
sentences = nltk.sent_tokenize(content)
|
||
|
||
if not sentences:
|
||
return []
|
||
|
||
# 2. 加载 SentenceTransformer 模型，用于计算语义相似度
|
||
model = SentenceTransformer('paraphrase-MiniLM-L6-v2')
|
||
embeddings = model.encode(sentences)
|
||
|
||
# 3. 根据相邻句子的语义相似度合并段落
|
||
merged_paragraphs = []
|
||
current_sentences = [sentences[0]]
|
||
current_embedding = embeddings[0]
|
||
|
||
for i in range(1, len(sentences)):
|
||
sim = cosine_similarity([current_embedding], [embeddings[i]])[0][0]
|
||
if sim >= similarity_threshold:
|
||
# 语义相似则并入当前段落
|
||
current_sentences.append(sentences[i])
|
||
# 更新current_embedding为合并后的平均值（可选，也可只采用最后一句做比较）
|
||
current_embedding = (current_embedding + embeddings[i]) / 2.0
|
||
else:
|
||
# 语义相似度不足，另起一个新段落
|
||
merged_paragraphs.append(" ".join(current_sentences))
|
||
current_sentences = [sentences[i]]
|
||
current_embedding = embeddings[i]
|
||
|
||
# 把最后一段加进去
|
||
if current_sentences:
|
||
merged_paragraphs.append(" ".join(current_sentences))
|
||
|
||
# 4. 根据最大长度 max_length 做二次拆分，避免段落过长
|
||
final_segments = []
|
||
for para in merged_paragraphs:
|
||
# 如果段落长度超过max_length，进一步切分
|
||
if len(para) > max_length:
|
||
sub_segments = split_by_length(para, max_length=max_length)
|
||
final_segments.extend(sub_segments)
|
||
else:
|
||
final_segments.append(para)
|
||
|
||
# 返回最终段落列表
|
||
return final_segments
|
||
|
||
|
||
def split_by_length(text: str, max_length: int = 500) -> List[str]:
|
||
"""
|
||
将文本按照max_length进行拆分，以避免段落过长。
|
||
这里以字符数为单位进行简单的拆分，也可以改为按词数或token数等。
|
||
"""
|
||
segments = []
|
||
start_idx = 0
|
||
while start_idx < len(text):
|
||
end_idx = min(start_idx + max_length, len(text))
|
||
segment = text[start_idx:end_idx]
|
||
segments.append(segment.strip())
|
||
start_idx = end_idx
|
||
return segments
|