607 lines
21 KiB
Python
607 lines
21 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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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 ============
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VECTOR_STORE_DIR = "vectorstore"
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def clear_vector_store():
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"""
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清空本地向量库（删除 vectorstore 文件夹）。
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需要在UI中加一个二次确认弹窗，防止误删。
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"""
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if os.path.exists(VECTOR_STORE_DIR):
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try:
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import shutil
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shutil.rmtree(VECTOR_STORE_DIR)
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logging.info("Local vector store has been cleared.")
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except Exception as e:
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logging.warning(f"Failed to remove vector store: {e}")
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else:
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logging.info("No vector store found to clear.")
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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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"""
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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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# ============ 新增：获取最近N章内容，生成短期摘要 ============
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def get_last_n_chapters_text(chapters_dir: str, current_chapter_num: int, n: int = 3) -> List[str]:
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"""
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从指定文件夹中，读取最近 n 章的内容（如果存在），并按从旧到新的顺序返回文本列表。
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不包含当前章，只拿之前的 n 章。
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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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if text:
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texts.append(text)
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return texts
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def summarize_recent_chapters(model: ChatOpenAI, chapters_text_list: List[str]) -> str:
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"""
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将最近几章的文本拼接后，通过模型生成一个相对详细的“短期内容摘要”。
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"""
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if not chapters_text_list:
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return ""
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# 拼接这几章的内容
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combined_text = "\n".join(chapters_text_list)
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# 在这里可以写一个更详细的提示
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prompt = f"""\
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这是最近几章的故事内容，请生成一份详细的短期内容摘要（不少于一章篇幅的细节），用于帮助后续创作时回顾细节。请着重强调发生的事件、角色的心理和关系变化、冲突或悬念等。
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{combined_text}
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"""
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response = model.invoke(prompt)
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if not response:
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return ""
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return response.content.strip()
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# ============ 生成章节草稿 & 定稿 ============
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def generate_chapter_draft(
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novel_settings: str,
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global_summary: str,
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character_state: str,
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recent_chapters_summary: str,
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user_guidance: 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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word_number: int,
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temperature: float,
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novel_novel_directory: str,
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filepath: str
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) -> str:
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"""
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仅生成当前章节的草稿，不更新全局摘要/角色状态/向量库。
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并将生成的内容写到 "chapter_{novel_number}.txt" 覆盖写入。
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同时生成 "outline_{novel_number}.txt" 存储大纲内容。
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recent_chapters_summary: 最近 3 章的“短期内容摘要”
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"""
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# 1) 从向量库检索往期上下文
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relevant_context = get_relevant_context_from_vector_store(
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api_key, base_url, "回顾剧情", k=2
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)
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# 2) 生成大纲（增加 recent_chapters_summary）
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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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# Prompt 拼接
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outline_prompt = (
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chapter_outline_prompt
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+ "\n\n【最近几章摘要】\n" + recent_chapters_summary
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+ "\n\n【用户指导】\n" + (user_guidance if user_guidance else "（无）")
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).format(
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novel_setting=novel_settings,
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character_state=character_state + "\n\n【历史上下文】\n" + relevant_context,
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global_summary=global_summary,
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novel_number=novel_number
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)
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response_outline = model.invoke(outline_prompt)
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if not response_outline:
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logging.warning("outline_chapter: No response.")
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chapter_outline = ""
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else:
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chapter_outline = response_outline.content.strip()
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# 将大纲写到 outline_{novel_number}.txt
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outlines_dir = os.path.join(filepath, "outlines")
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os.makedirs(outlines_dir, exist_ok=True)
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outline_file = os.path.join(outlines_dir, f"outline_{novel_number}.txt")
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clear_file_content(outline_file)
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save_string_to_txt(chapter_outline, outline_file)
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# 3) 生成正文草稿
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writing_prompt = (
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chapter_write_prompt
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+ "\n\n【最近几章摘要】\n" + recent_chapters_summary
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+ "\n\n【用户指导】\n" + (user_guidance if user_guidance else "（无）")
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).format(
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novel_setting=novel_settings,
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character_state=character_state + "\n\n【历史上下文】\n" + relevant_context,
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global_summary=global_summary,
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chapter_outline=chapter_outline,
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word_number=word_number
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)
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response_chapter = model.invoke(writing_prompt)
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if not response_chapter:
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logging.warning("write_chapter: No response.")
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chapter_content = ""
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else:
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chapter_content = response_chapter.content.strip()
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# 4) 覆盖写到 chapter_{novel_number}.txt
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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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clear_file_content(chapter_file)
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save_string_to_txt(chapter_content, chapter_file)
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logging.info(f"[Draft] Chapter {novel_number} generated as a draft.")
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return chapter_content
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def finalize_chapter(
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novel_number: int,
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word_number: int,
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api_key: str,
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base_url: str,
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model_name: str,
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temperature: float,
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filepath: str
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):
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"""
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对当前章节进行定稿：
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1. 读取 chapter_{novel_number}.txt 的最终内容；
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2. 更新全局摘要、角色状态文件；
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3. 如果字数明显少于 word_number 的 80%，则自动调用 enrich_chapter_text 再次扩写；
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4. 更新向量库。
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* 注意：实际应用中，用户也可以再次编辑 chapter_{n}.txt 后再点定稿，这里示例不做 GUI 级别的文本编辑逻辑。
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"""
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# 读取当前章节内容
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chapters_dir = os.path.join(filepath, "chapters")
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chapter_file = os.path.join(chapters_dir, f"chapter_{novel_number}.txt")
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chapter_text = read_file(chapter_file).strip()
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if not chapter_text:
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logging.warning(f"Chapter {novel_number} is empty, cannot finalize.")
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return
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# 读取角色状态 & 全局摘要
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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) 先检查字数是否过少，若少于 80% 则调用 enrich 逻辑
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if len(chapter_text) < 0.8 * word_number:
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logging.info("Chapter text seems shorter than 80% of desired length. Attempting to enrich content...")
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chapter_text = enrich_chapter_text(
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chapter_text=chapter_text,
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word_number=word_number,
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api_key=api_key,
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base_url=base_url,
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model_name=model_name,
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temperature=temperature
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)
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# 覆盖写回文件
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clear_file_content(chapter_file)
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save_string_to_txt(chapter_text, chapter_file)
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logging.info("Chapter text has been enriched and updated.")
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# 2) 更新全局摘要
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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,
|
||
temperature=temperature
|
||
)
|
||
|
||
def update_global_summary(chapter_text: str, old_summary: str) -> str:
|
||
prompt = summary_prompt.format(
|
||
chapter_text=chapter_text,
|
||
global_summary=old_summary
|
||
)
|
||
response = model.invoke(prompt)
|
||
if not response:
|
||
logging.warning("update_global_summary: No response.")
|
||
return old_summary
|
||
return response.content.strip()
|
||
|
||
new_global_summary = update_global_summary(chapter_text, old_global_summary)
|
||
|
||
# 3) 更新角色状态
|
||
def update_character_state(chapter_text: str, old_state: str) -> str:
|
||
prompt = update_character_state_prompt.format(
|
||
chapter_text=chapter_text,
|
||
old_state=old_state
|
||
)
|
||
response = model.invoke(prompt)
|
||
if not response:
|
||
logging.warning("update_character_state: No response.")
|
||
return old_state
|
||
return response.content.strip()
|
||
|
||
new_char_state = update_character_state(chapter_text, old_char_state)
|
||
|
||
# 4) 覆盖写入角色状态文件与全局摘要文件
|
||
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)
|
||
|
||
# 5) 更新向量检索库
|
||
update_vector_store(api_key, base_url, chapter_text)
|
||
|
||
logging.info(f"Chapter {novel_number} has been finalized (summary & state updated, vector store updated).")
|
||
|
||
def enrich_chapter_text(
|
||
chapter_text: str,
|
||
word_number: int,
|
||
api_key: str,
|
||
base_url: str,
|
||
model_name: str,
|
||
temperature: float
|
||
) -> str:
|
||
"""
|
||
当章节篇幅不足时，调用此函数对章节文本进行二次扩写。
|
||
可以让模型补充场景描写、角色心理等，保证与现有文本风格一致。
|
||
"""
|
||
model = ChatOpenAI(
|
||
model=model_name,
|
||
api_key=api_key,
|
||
base_url=base_url,
|
||
temperature=temperature
|
||
)
|
||
prompt = f"""\
|
||
以下是当前章节文本，可能篇幅较短，请在保持剧情连贯的前提下进行扩写，使其更充实、生动，并尽量靠近目标 {word_number} 字数。
|
||
|
||
原章节内容：
|
||
{chapter_text}
|
||
"""
|
||
response = model.invoke(prompt)
|
||
if not response:
|
||
logging.warning("enrich_chapter_text: No response.")
|
||
return chapter_text # 无响应时就返回原文
|
||
return response.content.strip()
|
||
|
||
# ============ 导入外部知识文本 ============
|
||
|
||
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。
|
||
"""
|
||
|
||
# 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。
|
||
"""
|
||
nltk.download('punkt', quiet=True) # 确保 punkt 数据可用
|
||
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))
|
||
|
||
# 按最大长度二次拆分
|
||
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 split_by_length(text: str, max_length: int = 500) -> List[str]:
|
||
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
|