中途保存
This commit is contained in:
YILING0013
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# embedding_adapters.py
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# -*- coding: utf-8 -*-
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import logging
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import requests
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import traceback
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from typing import List
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from langchain_openai import OpenAIEmbeddings
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def ensure_openai_base_url_has_v1(url: str) -> str:
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"""
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若用户输入的 url 不包含 '/v1'，则在末尾追加 '/v1'。
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"""
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import re
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url = url.strip()
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if not url:
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return url
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if not re.search(r'/v\d+$', url):
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if '/v1' not in url:
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url = url.rstrip('/') + '/v1'
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return url
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class BaseEmbeddingAdapter:
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"""
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Embedding 接口统一基类
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"""
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def embed_documents(self, texts: List[str]) -> List[List[float]]:
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raise NotImplementedError
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def embed_query(self, query: str) -> List[float]:
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raise NotImplementedError
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class OpenAIEmbeddingAdapter(BaseEmbeddingAdapter):
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"""
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基于 OpenAIEmbeddings（或兼容接口）的适配器
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"""
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def __init__(self, api_key: str, base_url: str, model_name: str):
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self._embedding = OpenAIEmbeddings(
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openai_api_key=api_key,
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openai_api_base=ensure_openai_base_url_has_v1(base_url),
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model=model_name
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)
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def embed_documents(self, texts: List[str]) -> List[List[float]]:
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return self._embedding.embed_documents(texts)
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def embed_query(self, query: str) -> List[float]:
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return self._embedding.embed_query(query)
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class OllamaEmbeddingAdapter(BaseEmbeddingAdapter):
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"""
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Ollama Embedding，示例中和之前的 embedding_ollama.py 类似
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其接口路径往往为 /api/embeddings
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"""
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def __init__(self, model_name: str, base_url: str):
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self.model_name = model_name
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self.base_url = base_url.rstrip("/")
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def embed_documents(self, texts: List[str]) -> List[List[float]]:
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embeddings = []
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for text in texts:
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vec = self._embed_single(text)
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embeddings.append(vec)
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return embeddings
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def embed_query(self, query: str) -> List[float]:
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return self._embed_single(query)
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def _embed_single(self, text: str) -> List[float]:
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"""
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调用 Ollama 本地服务 /api/embeddings 接口，获取文本 embedding
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"""
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# 如果 base_url 中已含 /api/embeddings，可直接用；否则拼上 /api/embeddings
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url = self.base_url
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if "api/embeddings" not in url:
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url = f"{url}/api/embeddings"
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data = {
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"model": self.model_name,
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"prompt": text
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}
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try:
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response = requests.post(url, json=data)
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response.raise_for_status()
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result = response.json()
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if "embedding" not in result:
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raise ValueError("No 'embedding' field in Ollama response.")
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return result["embedding"]
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except requests.exceptions.RequestException as e:
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logging.error(f"Ollama embeddings request error: {e}\n{traceback.format_exc()}")
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return []
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class MLStudioEmbeddingAdapter(BaseEmbeddingAdapter):
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def __init__(self, api_key: str, base_url: str, model_name: str):
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self._embedding = OpenAIEmbeddings(
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openai_api_key=api_key,
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openai_api_base=ensure_openai_base_url_has_v1(base_url),
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model=model_name
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)
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def embed_documents(self, texts: List[str]) -> List[List[float]]:
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return self._embedding.embed_documents(texts)
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def embed_query(self, query: str) -> List[float]:
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return self._embedding.embed_query(query)
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def create_embedding_adapter(
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interface_format: str,
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api_key: str,
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base_url: str,
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model_name: str
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) -> BaseEmbeddingAdapter:
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"""
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工厂函数：根据 interface_format 返回不同的 embedding 适配器实例
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"""
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if interface_format.lower() == "openai":
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return OpenAIEmbeddingAdapter(api_key, base_url, model_name)
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elif interface_format.lower() == "ollama":
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return OllamaEmbeddingAdapter(model_name, base_url)
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elif interface_format.lower() == "ml studio":
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return MLStudioEmbeddingAdapter(api_key, base_url, model_name)
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else:
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raise ValueError(f"Unknown embedding interface_format: {interface_format}")
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+148
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# llm_adapters.py
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# -*- coding: utf-8 -*-
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import logging
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from typing import Optional
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from langchain_openai import ChatOpenAI
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def ensure_openai_base_url_has_v1(url: str) -> str:
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"""
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若用户输入的 url 不包含 '/v1'，则在末尾追加 '/v1'。
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"""
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import re
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url = url.strip()
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if not url:
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return url
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if not re.search(r'/v\d+$', url):
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if '/v1' not in url:
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url = url.rstrip('/') + '/v1'
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return url
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class BaseLLMAdapter:
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"""
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统一的 LLM 接口基类，为不同后端（OpenAI、Ollama、ML Studio 等）提供一致的方法签名。
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"""
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def invoke(self, prompt: str) -> str:
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raise NotImplementedError("Subclasses must implement .invoke(prompt) method.")
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class DeepSeekAdapter(BaseLLMAdapter):
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"""
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适配官方/OpenAI兼容接口（使用 langchain.ChatOpenAI）
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"""
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def __init__(self, api_key: str, base_url: str, model_name: str, max_tokens:int, temperature: float = 0.7):
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self.base_url = ensure_openai_base_url_has_v1(base_url)
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self.api_key = api_key
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self.model_name = model_name
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self.max_tokens = max_tokens
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self.temperature = temperature
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self._client = ChatOpenAI(
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model=self.model_name,
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api_key=self.api_key,
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base_url=self.base_url,
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max_tokens=self.max_tokens,
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temperature=self.temperature
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)
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def invoke(self, prompt: str) -> str:
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response = self._client.invoke(prompt)
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if not response:
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logging.warning("No response from DeepSeekAdapter.")
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return ""
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return response.content
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class OpenAIAdapter(BaseLLMAdapter):
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"""
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适配官方/OpenAI兼容接口（使用 langchain.ChatOpenAI）
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"""
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def __init__(self, api_key: str, base_url: str, model_name: str, max_tokens:int, temperature: float = 0.7):
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self.base_url = ensure_openai_base_url_has_v1(base_url)
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self.api_key = api_key
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self.model_name = model_name
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self.max_tokens = max_tokens
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self.temperature = temperature
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self._client = ChatOpenAI(
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model=self.model_name,
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api_key=self.api_key,
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base_url=self.base_url,
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max_tokens=self.max_tokens,
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temperature=self.temperature
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)
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def invoke(self, prompt: str) -> str:
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response = self._client.invoke(prompt)
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if not response:
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logging.warning("No response from OpenAIAdapter.")
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return ""
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return response.content
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class OllamaAdapter(BaseLLMAdapter):
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"""
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Ollama 同样有一个 OpenAI-like /v1/chat 接口，可直接使用 ChatOpenAI。
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但是通常 Ollama 默认本地服务在 http://localhost:11434，如果符合OpenAI风格即可直接传参。
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"""
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def __init__(self, api_key: str, base_url: str, model_name: str, max_tokens:int, temperature: float = 0.7):
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self.base_url = ensure_openai_base_url_has_v1(base_url)
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self.api_key = api_key
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self.model_name = model_name
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self.max_tokens = max_tokens
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self.temperature = temperature
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self._client = ChatOpenAI(
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model=self.model_name,
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api_key=self.api_key,
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base_url=self.base_url,
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max_tokens=self.max_tokens,
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temperature=self.temperature
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)
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def invoke(self, prompt: str) -> str:
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response = self._client.invoke(prompt)
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if not response:
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logging.warning("No response from OllamaAdapter.")
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return ""
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return response.content
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class MLStudioAdapter(BaseLLMAdapter):
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def __init__(self, api_key: str, base_url: str, model_name: str, max_tokens:int, temperature: float = 0.7):
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self.base_url = ensure_openai_base_url_has_v1(base_url)
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self.api_key = api_key
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self.model_name = model_name
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self.max_tokens = max_tokens
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self.temperature = temperature
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self._client = ChatOpenAI(
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model=self.model_name,
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api_key=self.api_key,
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base_url=self.base_url,
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max_tokens=self.max_tokens,
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temperature=self.temperature
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)
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def invoke(self, prompt: str) -> str:
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response = self._client.invoke(prompt)
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if not response:
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logging.warning("No response from MLStudioAdapter.")
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return ""
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return response.content
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def create_llm_adapter(
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interface_format: str,
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base_url: str,
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model_name: str,
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api_key: str,
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temperature: float
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) -> BaseLLMAdapter:
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"""
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工厂函数：根据 interface_format 返回不同的适配器实例。
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"""
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if interface_format.lower() == "deepseek":
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return DeepSeekAdapter(api_key, base_url, model_name, temperature)
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elif interface_format.lower() == "openai":
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return OpenAIAdapter(api_key, base_url, model_name, temperature)
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elif interface_format.lower() == "ollama":
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return OllamaAdapter(api_key, base_url, model_name, temperature)
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elif interface_format.lower() == "ml studio":
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return MLStudioAdapter(api_key, base_url, model_name, temperature)
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else:
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raise ValueError(f"Unknown interface_format: {interface_format}")
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+205
-308
@@ -7,8 +7,6 @@ import time
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import traceback
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from typing import List, Optional, Tuple
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# langchain 相关
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from langchain_openai import ChatOpenAI, OpenAIEmbeddings
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from langchain_chroma import Chroma
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from chromadb.config import Settings
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from langchain.docstore.document import Document
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@@ -37,16 +35,16 @@ from prompt_definitions import (
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summarize_recent_chapters_prompt
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)
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# Ollama嵌入 (如使用Ollama时需要)
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from embedding_ollama import OllamaEmbeddings
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# 用于目录解析章节标题/简介
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# 章节目录解析
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from chapter_directory_parser import get_chapter_info_from_blueprint
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from llm_adapters import create_llm_adapter
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from embedding_adapters import create_embedding_adapter
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logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s")
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# ============ 工具函数 ============
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# ============ 基础工具 ============
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def remove_think_tags(text: str) -> str:
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"""移除 <think>...</think> 包裹的内容"""
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return re.sub(r'<think>.*?</think>', '', text, flags=re.DOTALL)
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@@ -59,128 +57,79 @@ def debug_log(prompt: str, response_content: str):
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f"\n[######################################### Response #########################################]\n{response_content}\n"
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)
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def invoke_with_cleaning(model: ChatOpenAI, prompt: str) -> str:
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"""通用封装：调用模型并移除 <think>...</think> 文本，记录日志后返回"""
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response = model.invoke(prompt)
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def invoke_with_cleaning(llm_adapter, prompt: str) -> str:
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"""通用封装：调用 LLM，并移除 <think>...</think> 文本，记录日志后返回"""
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response = llm_adapter.invoke(prompt)
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if not response:
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logging.warning("No response from model.")
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return ""
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cleaned_text = remove_think_tags(response.content)
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cleaned_text = remove_think_tags(response)
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debug_log(prompt, cleaned_text)
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return cleaned_text.strip()
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def ensure_openai_base_url_has_v1(url: str) -> str:
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"""
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若用户输入的 url 不包含 '/v1'，则在末尾追加 '/v1'。
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"""
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import re
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url = url.strip()
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if not url:
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return url
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if not re.search(r'/v\d+$', url):
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if '/v1' not in url:
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url = url.rstrip('/') + '/v1'
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return url
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def is_using_ollama_api(interface_format: str) -> bool:
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return interface_format.lower() == "ollama"
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def is_using_ml_studio_api(interface_format: str) -> bool:
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return interface_format.lower() == "ml studio"
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# ============ 获取 vectorstore 路径 ============
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def get_vectorstore_dir(filepath: str) -> str:
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"""
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返回存储向量库的本地路径：
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在用户指定的 `filepath` 下创建/使用 'vectorstore' 文件夹。
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"""
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return os.path.join(filepath, "vectorstore")
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# ============ 清空向量库 ============
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# ============ 创建 Embeddings 对象 ============
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def create_embeddings_object(
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api_key: str,
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base_url: str,
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interface_format: str,
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embedding_model_name: str
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):
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"""
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根据 embedding_interface_format，选择 Ollama 或 OpenAIEmbeddings 等不同后端。
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"""
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if is_using_ollama_api(interface_format):
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fixed_url = base_url.rstrip("/")
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return OllamaEmbeddings(
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model_name=embedding_model_name,
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base_url=fixed_url
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)
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else:
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# OpenAI 或 ML Studio 均使用 OpenAIEmbeddings，注意 base_url 可能需要 ensure /v1
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fixed_url = ensure_openai_base_url_has_v1(base_url)
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return OpenAIEmbeddings(
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openai_api_key=api_key,
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openai_api_base=fixed_url,
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model=embedding_model_name
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)
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# ============ 向量库相关操作 ============
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def clear_vector_store(filepath: str) -> bool:
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"""
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返回值表示是否成功清空向量库。
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"""
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import shutil
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store_dir = get_vectorstore_dir(filepath)
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if not os.path.exists(store_dir):
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logging.info("No vector store found to clear.")
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return False
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try:
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shutil.rmtree(store_dir)
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logging.info(f"Vector store directory '{store_dir}' removed.")
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return True
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except Exception as e:
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logging.error(f"程序正在运行，无法删除，请在程序关闭后手动前往 {store_dir} 删除目录。\n {str(e)}")
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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)}")
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traceback.print_exc()
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return False
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# ============ 根据 embedding 接口创建/加载 Chroma ============
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def init_vector_store(
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api_key: str,
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base_url: str,
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interface_format: str,
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embedding_model_name: str,
|
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embedding_adapter,
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texts: List[str],
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filepath: str
|
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) -> Chroma:
|
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"""
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||||
在 filepath 下创建/加载一个 Chroma 向量库并插入 texts。
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||||
这里 embedding_adapter 是一个实现了 embed_documents(texts) 的对象
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"""
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store_dir = get_vectorstore_dir(filepath)
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||||
os.makedirs(store_dir, exist_ok=True)
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||||
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||||
embeddings = create_embeddings_object(
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api_key=api_key,
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base_url=base_url,
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||||
interface_format=interface_format,
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||||
embedding_model_name=embedding_model_name
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||||
)
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# 将文本封装为 Document
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documents = [Document(page_content=str(t)) for t in texts]
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# 因为我们是自定义的 embeddings，对接Chroma时需包装一个“langchain兼容对象”
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# 这里示例：写一个包装函数
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from langchain.embeddings.base import Embeddings as LCEmbeddings
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||||
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||||
class LCEmbeddingWrapper(LCEmbeddings):
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||||
def embed_documents(self, doc_texts: List[str]) -> List[List[float]]:
|
||||
return embedding_adapter.embed_documents(doc_texts)
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||||
|
||||
def embed_query(self, query_text: str) -> List[float]:
|
||||
return embedding_adapter.embed_query(query_text)
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||||
|
||||
chroma_embedding = LCEmbeddingWrapper()
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||||
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||||
vectorstore = Chroma.from_documents(
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||||
documents,
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embedding=embeddings,
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||||
embedding=chroma_embedding,
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||||
persist_directory=store_dir,
|
||||
client_settings=Settings(anonymized_telemetry=False),
|
||||
collection_name="novel_collection"
|
||||
)
|
||||
return vectorstore
|
||||
|
||||
|
||||
def load_vector_store(
|
||||
api_key: str,
|
||||
base_url: str,
|
||||
interface_format: str,
|
||||
embedding_model_name: str,
|
||||
embedding_adapter,
|
||||
filepath: str
|
||||
) -> Optional[Chroma]:
|
||||
"""
|
||||
@@ -191,19 +140,26 @@ def load_vector_store(
|
||||
logging.info("Vector store not found. Will return None.")
|
||||
return None
|
||||
|
||||
embeddings = create_embeddings_object(
|
||||
api_key=api_key,
|
||||
base_url=base_url,
|
||||
interface_format=interface_format,
|
||||
embedding_model_name=embedding_model_name
|
||||
)
|
||||
# 同样要包装embedding_adapter
|
||||
from langchain.embeddings.base import Embeddings as LCEmbeddings
|
||||
|
||||
class LCEmbeddingWrapper(LCEmbeddings):
|
||||
def embed_documents(self, doc_texts: List[str]) -> List[List[float]]:
|
||||
return embedding_adapter.embed_documents(doc_texts)
|
||||
|
||||
def embed_query(self, query_text: str) -> List[float]:
|
||||
return embedding_adapter.embed_query(query_text)
|
||||
|
||||
chroma_embedding = LCEmbeddingWrapper()
|
||||
|
||||
return Chroma(
|
||||
persist_directory=store_dir,
|
||||
embedding_function=embeddings,
|
||||
embedding_function=chroma_embedding,
|
||||
client_settings=Settings(anonymized_telemetry=False),
|
||||
collection_name="novel_collection"
|
||||
)
|
||||
|
||||
# ============ 文本分段工具 ============
|
||||
|
||||
def split_by_length(text: str, max_length: int = 500) -> List[str]:
|
||||
segments = []
|
||||
@@ -215,12 +171,12 @@ def split_by_length(text: str, max_length: int = 500) -> List[str]:
|
||||
start_idx = end_idx
|
||||
return segments
|
||||
|
||||
|
||||
def split_text_for_vectorstore(chapter_text: str,
|
||||
max_length: int = 500,
|
||||
similarity_threshold: float = 0.7) -> List[str]:
|
||||
"""
|
||||
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 []
|
||||
@@ -230,7 +186,6 @@ def split_text_for_vectorstore(chapter_text: str,
|
||||
if not sentences:
|
||||
return []
|
||||
|
||||
# 先对相近句子进行合并
|
||||
model = SentenceTransformer('paraphrase-MiniLM-L6-v2')
|
||||
embeddings = model.encode(sentences)
|
||||
|
||||
@@ -251,7 +206,6 @@ def split_text_for_vectorstore(chapter_text: str,
|
||||
if current_sentences:
|
||||
merged_paragraphs.append(" ".join(current_sentences))
|
||||
|
||||
# 再对合并好的段落做 max_length 切分
|
||||
final_segments = []
|
||||
for para in merged_paragraphs:
|
||||
if len(para) > max_length:
|
||||
@@ -262,13 +216,11 @@ def split_text_for_vectorstore(chapter_text: str,
|
||||
|
||||
return final_segments
|
||||
|
||||
# ============ 更新向量库 ============
|
||||
|
||||
def update_vector_store(
|
||||
api_key: str,
|
||||
base_url: str,
|
||||
embedding_adapter,
|
||||
new_chapter: str,
|
||||
interface_format: str,
|
||||
embedding_model_name: str,
|
||||
filepath: str
|
||||
):
|
||||
"""
|
||||
@@ -279,49 +231,28 @@ def update_vector_store(
|
||||
logging.warning("No valid text to insert into vector store. Skipping.")
|
||||
return
|
||||
|
||||
store = load_vector_store(
|
||||
api_key=api_key,
|
||||
base_url=base_url,
|
||||
interface_format=interface_format,
|
||||
embedding_model_name=embedding_model_name,
|
||||
filepath=filepath
|
||||
)
|
||||
store = load_vector_store(embedding_adapter, filepath)
|
||||
if not store:
|
||||
logging.info("Vector store does not exist. Initializing a new one for new chapter...")
|
||||
init_vector_store(
|
||||
api_key=api_key,
|
||||
base_url=base_url,
|
||||
interface_format=interface_format,
|
||||
embedding_model_name=embedding_model_name,
|
||||
texts=splitted_texts,
|
||||
filepath=filepath
|
||||
)
|
||||
init_vector_store(embedding_adapter, splitted_texts, filepath)
|
||||
return
|
||||
|
||||
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.")
|
||||
|
||||
# ============ 向量检索上下文 ============
|
||||
|
||||
def get_relevant_context_from_vector_store(
|
||||
api_key: str,
|
||||
base_url: str,
|
||||
embedding_adapter,
|
||||
query: str,
|
||||
interface_format: str,
|
||||
embedding_model_name: 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。
|
||||
"""
|
||||
store = load_vector_store(
|
||||
api_key=api_key,
|
||||
base_url=base_url,
|
||||
interface_format=interface_format,
|
||||
embedding_model_name=embedding_model_name,
|
||||
filepath=filepath
|
||||
)
|
||||
store = load_vector_store(embedding_adapter, filepath)
|
||||
if not store:
|
||||
logging.info("No vector store found. Returning empty context.")
|
||||
return ""
|
||||
@@ -334,8 +265,68 @@ def get_relevant_context_from_vector_store(
|
||||
combined = "\n".join([d.page_content for d in docs])
|
||||
return combined
|
||||
|
||||
# ============ 从目录中获取最近 n 章文本 ============
|
||||
|
||||
def get_last_n_chapters_text(chapters_dir: str, current_chapter_num: int, n: int = 3) -> List[str]:
|
||||
texts = []
|
||||
start_chap = max(1, current_chapter_num - n)
|
||||
for c in range(start_chap, current_chapter_num):
|
||||
chap_file = os.path.join(chapters_dir, f"chapter_{c}.txt")
|
||||
if os.path.exists(chap_file):
|
||||
text = read_file(chap_file).strip()
|
||||
texts.append(text)
|
||||
else:
|
||||
texts.append("")
|
||||
return texts
|
||||
|
||||
# ============ 提炼(短期摘要, 下一章关键字) ============
|
||||
|
||||
def summarize_recent_chapters(
|
||||
interface_format: str,
|
||||
api_key: str,
|
||||
base_url: str,
|
||||
model_name: str,
|
||||
temperature: float,
|
||||
chapters_text_list: List[str]
|
||||
) -> Tuple[str, str]:
|
||||
"""
|
||||
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 ("", "")
|
||||
|
||||
# 1) 构造 llm_adapter
|
||||
llm_adapter = create_llm_adapter(
|
||||
interface_format=interface_format,
|
||||
base_url=base_url,
|
||||
model_name=model_name,
|
||||
api_key=api_key,
|
||||
temperature=temperature
|
||||
)
|
||||
|
||||
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)
|
||||
|
||||
|
||||
# ============ 1) 生成总体架构 ============
|
||||
|
||||
# ============ 1) 生成总体架构 (Novel_architecture.txt) ============
|
||||
def Novel_architecture_generate(
|
||||
api_key: str,
|
||||
base_url: str,
|
||||
@@ -348,70 +339,68 @@ def Novel_architecture_generate(
|
||||
temperature: float = 0.7
|
||||
) -> None:
|
||||
"""
|
||||
依次调用：
|
||||
依次调用:
|
||||
1. core_seed_prompt
|
||||
2. character_dynamics_prompt
|
||||
3. world_building_prompt
|
||||
4. plot_architecture_prompt
|
||||
将结果整合为“Novel_architecture.txt”。
|
||||
最终输出 Novel_architecture.txt
|
||||
"""
|
||||
os.makedirs(filepath, exist_ok=True)
|
||||
model = ChatOpenAI(
|
||||
model=llm_model,
|
||||
|
||||
# 通过工厂函数创建 LLM 适配器
|
||||
llm_adapter = create_llm_adapter(
|
||||
interface_format="openai", # 或根据你的实际：若你在UI中就是 "OpenAI" 就传递过来
|
||||
base_url=base_url,
|
||||
model_name=llm_model,
|
||||
api_key=api_key,
|
||||
base_url=ensure_openai_base_url_has_v1(base_url),
|
||||
temperature=temperature
|
||||
)
|
||||
|
||||
# 1) 核心种子
|
||||
# Step1: 核心种子
|
||||
prompt_core = core_seed_prompt.format(
|
||||
topic=topic,
|
||||
genre=genre,
|
||||
number_of_chapters=number_of_chapters,
|
||||
word_number=word_number
|
||||
)
|
||||
core_seed_result = invoke_with_cleaning(model, prompt_core)
|
||||
core_seed_text = core_seed_result.strip()
|
||||
core_seed_result = invoke_with_cleaning(llm_adapter, prompt_core)
|
||||
|
||||
# 2) 角色动力学
|
||||
prompt_character = character_dynamics_prompt.format(core_seed=core_seed_text)
|
||||
character_dynamics_result = invoke_with_cleaning(model, prompt_character)
|
||||
character_dynamics_text = character_dynamics_result.strip()
|
||||
# Step2: 角色动力学
|
||||
prompt_character = character_dynamics_prompt.format(core_seed=core_seed_result.strip())
|
||||
character_dynamics_result = invoke_with_cleaning(llm_adapter, prompt_character)
|
||||
|
||||
# 3) 世界观
|
||||
prompt_world = world_building_prompt.format(core_seed=core_seed_text)
|
||||
world_building_result = invoke_with_cleaning(model, prompt_world)
|
||||
world_building_text = world_building_result.strip()
|
||||
# Step3: 世界观
|
||||
prompt_world = world_building_prompt.format(core_seed=core_seed_result.strip())
|
||||
world_building_result = invoke_with_cleaning(llm_adapter, prompt_world)
|
||||
|
||||
# 4) 三幕式情节架构
|
||||
# Step4: 三幕式情节
|
||||
prompt_plot = plot_architecture_prompt.format(
|
||||
core_seed=core_seed_text,
|
||||
character_dynamics=character_dynamics_text,
|
||||
world_building=world_building_text
|
||||
core_seed=core_seed_result.strip(),
|
||||
character_dynamics=character_dynamics_result.strip(),
|
||||
world_building=world_building_result.strip()
|
||||
)
|
||||
plot_arch_result = invoke_with_cleaning(model, prompt_plot)
|
||||
plot_arch_text = plot_arch_result.strip()
|
||||
plot_arch_result = invoke_with_cleaning(llm_adapter, prompt_plot)
|
||||
|
||||
# 整合并写入 Novel_architecture.txt
|
||||
# 合并
|
||||
final_content = (
|
||||
"#=== 1) 核心种子 ===\n"
|
||||
f"{core_seed_text}\n\n"
|
||||
f"{core_seed_result}\n\n"
|
||||
"#=== 2) 角色动力学 ===\n"
|
||||
f"{character_dynamics_text}\n\n"
|
||||
f"{character_dynamics_result}\n\n"
|
||||
"#=== 3) 世界观 ===\n"
|
||||
f"{world_building_text}\n\n"
|
||||
f"{world_building_result}\n\n"
|
||||
"#=== 4) 三幕式情节架构 ===\n"
|
||||
f"{plot_arch_text}\n"
|
||||
f"{plot_arch_result}\n"
|
||||
)
|
||||
|
||||
arch_file = os.path.join(filepath, "Novel_architecture.txt")
|
||||
clear_file_content(arch_file)
|
||||
save_string_to_txt(final_content, arch_file)
|
||||
|
||||
logging.info("Novel_architecture.txt has been generated successfully.")
|
||||
|
||||
# ============ 2) 生成章节蓝图 ============
|
||||
|
||||
# ============ 2) 生成章节蓝图 (Novel_directory.txt) ============
|
||||
def Chapter_blueprint_generate(
|
||||
api_key: str,
|
||||
base_url: str,
|
||||
@@ -419,10 +408,6 @@ def Chapter_blueprint_generate(
|
||||
filepath: str,
|
||||
temperature: float = 0.7
|
||||
) -> None:
|
||||
"""
|
||||
基于“Novel_architecture.txt”中的三幕式情节架构，调用 chapter_blueprint_prompt，
|
||||
生成章节蓝图并写入 Novel_directory.txt。
|
||||
"""
|
||||
arch_file = os.path.join(filepath, "Novel_architecture.txt")
|
||||
if not os.path.exists(arch_file):
|
||||
logging.warning("Novel_architecture.txt not found. Please generate architecture first.")
|
||||
@@ -433,12 +418,11 @@ def Chapter_blueprint_generate(
|
||||
logging.warning("Novel_architecture.txt is empty.")
|
||||
return
|
||||
|
||||
# 从内容中尽量提取 number_of_chapters
|
||||
match_chaps = re.search(r'约(\d+)章', architecture_text)
|
||||
if match_chaps:
|
||||
number_of_chapters = int(match_chaps.group(1))
|
||||
else:
|
||||
number_of_chapters = 10 # fallback
|
||||
number_of_chapters = 10
|
||||
|
||||
# 提取三幕式文本
|
||||
plot_arch_text = ""
|
||||
@@ -447,10 +431,11 @@ def Chapter_blueprint_generate(
|
||||
if m:
|
||||
plot_arch_text = m.group(1).strip()
|
||||
|
||||
model = ChatOpenAI(
|
||||
model=llm_model,
|
||||
llm_adapter = create_llm_adapter(
|
||||
interface_format="openai", # 或实际由UI传入
|
||||
base_url=base_url,
|
||||
model_name=llm_model,
|
||||
api_key=api_key,
|
||||
base_url=ensure_openai_base_url_has_v1(base_url),
|
||||
temperature=temperature
|
||||
)
|
||||
|
||||
@@ -458,7 +443,7 @@ def Chapter_blueprint_generate(
|
||||
plot_architecture=plot_arch_text,
|
||||
number_of_chapters=number_of_chapters
|
||||
)
|
||||
blueprint_text = invoke_with_cleaning(model, prompt)
|
||||
blueprint_text = invoke_with_cleaning(llm_adapter, prompt)
|
||||
if not blueprint_text.strip():
|
||||
logging.warning("Chapter blueprint generation result is empty.")
|
||||
return
|
||||
@@ -469,72 +454,7 @@ def Chapter_blueprint_generate(
|
||||
|
||||
logging.info("Novel_directory.txt (chapter blueprint) has been generated successfully.")
|
||||
|
||||
|
||||
# ============ 工具：获取最近N章内容 ============
|
||||
|
||||
def get_last_n_chapters_text(chapters_dir: str, current_chapter_num: int, n: int = 3) -> List[str]:
|
||||
"""
|
||||
返回从 (current_chapter_num - n) 开始到 (current_chapter_num-1) 的章节文本列表。
|
||||
若缺少文件，则对应位置为空字符串。
|
||||
"""
|
||||
texts = []
|
||||
start_chap = max(1, current_chapter_num - n)
|
||||
for c in range(start_chap, current_chapter_num):
|
||||
chap_file = os.path.join(chapters_dir, f"chapter_{c}.txt")
|
||||
if os.path.exists(chap_file):
|
||||
text = read_file(chap_file).strip()
|
||||
texts.append(text)
|
||||
else:
|
||||
texts.append("")
|
||||
return texts
|
||||
|
||||
|
||||
# ============ 新增函数：从合并文本中提炼「当前情节短期摘要」 & 「下一章关键字」 ============
|
||||
def summarize_recent_chapters(
|
||||
llm_model: str,
|
||||
api_key: str,
|
||||
base_url: str,
|
||||
temperature: float,
|
||||
chapters_text_list: List[str]
|
||||
) -> Tuple[str, str]:
|
||||
"""
|
||||
输入若干章节文本，合并后调用 summarize_recent_chapters_prompt，
|
||||
返回 (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 ("", "")
|
||||
|
||||
model = ChatOpenAI(
|
||||
model=llm_model,
|
||||
api_key=api_key,
|
||||
base_url=ensure_openai_base_url_has_v1(base_url),
|
||||
temperature=temperature
|
||||
)
|
||||
|
||||
prompt = summarize_recent_chapters_prompt.format(combined_text=combined_text)
|
||||
response_text = invoke_with_cleaning(model, prompt)
|
||||
|
||||
# 简易解析
|
||||
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()
|
||||
|
||||
# 如果解析失败，就把返回文本当作短期摘要
|
||||
if not short_summary and not next_chapter_keywords:
|
||||
short_summary = response_text
|
||||
|
||||
return (short_summary, next_chapter_keywords)
|
||||
|
||||
|
||||
# ============ 3) 生成章节草稿（新版） ============
|
||||
# ============ 3) 生成章节草稿 ============
|
||||
|
||||
def generate_chapter_draft(
|
||||
api_key: str,
|
||||
@@ -555,16 +475,6 @@ def generate_chapter_draft(
|
||||
embedding_model_name: str,
|
||||
embedding_retrieval_k: int = 2
|
||||
) -> str:
|
||||
"""
|
||||
根据新的 chapter_draft_prompt，生成本章草稿。
|
||||
- 首先获取最近3章文本 => 提炼短期摘要 & 下一章关键字
|
||||
- 使用(短期摘要 + 下一章关键字) 拼成 query => 检索向量库
|
||||
- 同时取上一章(或最后一个非空章节)末尾1500字作为 "前章片段"
|
||||
- 组合所有信息后，调用模型生成章节草稿
|
||||
- 最后保存到 chapters/chapter_{novel_number}.txt
|
||||
"""
|
||||
|
||||
# 1) 读取相关文件
|
||||
arch_file = os.path.join(filepath, "Novel_architecture.txt")
|
||||
novel_architecture_text = read_file(arch_file)
|
||||
|
||||
@@ -577,7 +487,7 @@ def generate_chapter_draft(
|
||||
character_state_file = os.path.join(filepath, "character_state.txt")
|
||||
character_state_text = read_file(character_state_file)
|
||||
|
||||
# 2) 解析 blueprint，得到本章所需的字段
|
||||
# 解析本章信息
|
||||
chapter_info = get_chapter_info_from_blueprint(blueprint_text, novel_number)
|
||||
chapter_title = chapter_info["chapter_title"]
|
||||
chapter_role = chapter_info["chapter_role"]
|
||||
@@ -590,43 +500,45 @@ def generate_chapter_draft(
|
||||
chapters_dir = os.path.join(filepath, "chapters")
|
||||
os.makedirs(chapters_dir, exist_ok=True)
|
||||
|
||||
# 3) 获取最近3章文本 => 提炼 (短期摘要 & 下一章关键字)
|
||||
# 获取最近3章 => (短期摘要, 下一章关键字)
|
||||
recent_3_texts = get_last_n_chapters_text(chapters_dir, novel_number, n=3)
|
||||
short_summary, next_chapter_keywords = summarize_recent_chapters(
|
||||
llm_model=model_name,
|
||||
interface_format="openai", # 或由UI传进
|
||||
api_key=api_key,
|
||||
base_url=base_url,
|
||||
model_name=model_name,
|
||||
temperature=temperature,
|
||||
chapters_text_list=recent_3_texts
|
||||
)
|
||||
|
||||
# 4) 取上一章片段(或最后一个非空章节)的末尾1500字
|
||||
# 上一章片段(末尾1500字)
|
||||
previous_chapter_excerpt = ""
|
||||
for text_block in reversed(recent_3_texts):
|
||||
if text_block.strip():
|
||||
# 找到最近一个非空章节
|
||||
if len(text_block) > 1500:
|
||||
previous_chapter_excerpt = text_block[-1500:]
|
||||
else:
|
||||
previous_chapter_excerpt = text_block
|
||||
break
|
||||
# 如果全为空，则 previous_chapter_excerpt 就是 ""
|
||||
|
||||
# 5) 构造向量检索查询： (短期摘要 + 下一章关键字)
|
||||
# 使用embedding检索上下文
|
||||
embedding_adapter = create_embedding_adapter(
|
||||
embedding_interface_format,
|
||||
embedding_api_key,
|
||||
embedding_url,
|
||||
embedding_model_name
|
||||
)
|
||||
retrieval_query = short_summary + " " + next_chapter_keywords
|
||||
relevant_context = get_relevant_context_from_vector_store(
|
||||
api_key=embedding_api_key,
|
||||
base_url=embedding_url,
|
||||
embedding_adapter=embedding_adapter,
|
||||
query=retrieval_query,
|
||||
interface_format=embedding_interface_format,
|
||||
embedding_model_name=embedding_model_name,
|
||||
filepath=filepath,
|
||||
k=embedding_retrieval_k
|
||||
)
|
||||
if not relevant_context.strip():
|
||||
relevant_context = "（无检索到的上下文）"
|
||||
|
||||
# 6) 组装 Prompt
|
||||
# 组装 Prompt
|
||||
prompt_text = chapter_draft_prompt.format(
|
||||
novel_number=novel_number,
|
||||
chapter_title=chapter_title,
|
||||
@@ -646,24 +558,23 @@ def generate_chapter_draft(
|
||||
novel_setting=novel_architecture_text,
|
||||
global_summary=global_summary_text,
|
||||
character_state=character_state_text,
|
||||
|
||||
previous_chapter_excerpt=previous_chapter_excerpt,
|
||||
context_excerpt=relevant_context
|
||||
)
|
||||
|
||||
# 7) 调用 LLM 生成章节正文
|
||||
model = ChatOpenAI(
|
||||
model=model_name,
|
||||
# 调用 LLM 生成
|
||||
llm_adapter = create_llm_adapter(
|
||||
interface_format="openai", # 或由UI传进
|
||||
base_url=base_url,
|
||||
model_name=model_name,
|
||||
api_key=api_key,
|
||||
base_url=ensure_openai_base_url_has_v1(base_url),
|
||||
temperature=temperature
|
||||
)
|
||||
|
||||
chapter_content = invoke_with_cleaning(model, prompt_text)
|
||||
chapter_content = invoke_with_cleaning(llm_adapter, prompt_text)
|
||||
if not chapter_content.strip():
|
||||
logging.warning("Generated chapter draft is empty.")
|
||||
|
||||
# 8) 写入 chapters
|
||||
# 写入 chapter_X.txt
|
||||
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)
|
||||
@@ -671,8 +582,8 @@ def generate_chapter_draft(
|
||||
logging.info(f"[Draft] Chapter {novel_number} generated as a draft.")
|
||||
return chapter_content
|
||||
|
||||
|
||||
# ============ 4) 定稿章节 ============
|
||||
|
||||
def finalize_chapter(
|
||||
novel_number: int,
|
||||
word_number: int,
|
||||
@@ -686,9 +597,6 @@ def finalize_chapter(
|
||||
embedding_interface_format: str,
|
||||
embedding_model_name: str
|
||||
):
|
||||
"""
|
||||
定稿：更新全局摘要、角色状态，并将本章文本插入向量库。
|
||||
"""
|
||||
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()
|
||||
@@ -696,7 +604,7 @@ def finalize_chapter(
|
||||
logging.warning(f"Chapter {novel_number} is empty, cannot finalize.")
|
||||
return
|
||||
|
||||
# 若篇幅过短，可尝试扩写
|
||||
# 如果篇幅过短，可以扩写
|
||||
if len(chapter_text) < 0.6 * word_number:
|
||||
chapter_text = enrich_chapter_text(chapter_text, word_number, api_key, base_url, model_name, temperature)
|
||||
clear_file_content(chapter_file)
|
||||
@@ -708,50 +616,49 @@ def finalize_chapter(
|
||||
character_state_file = os.path.join(filepath, "character_state.txt")
|
||||
old_character_state = read_file(character_state_file)
|
||||
|
||||
# 1) 更新全局摘要
|
||||
model = ChatOpenAI(
|
||||
model=model_name,
|
||||
# 调用 LLM 更新全局摘要
|
||||
llm_adapter = create_llm_adapter(
|
||||
interface_format="openai",
|
||||
base_url=base_url,
|
||||
model_name=model_name,
|
||||
api_key=api_key,
|
||||
base_url=ensure_openai_base_url_has_v1(base_url),
|
||||
temperature=temperature
|
||||
)
|
||||
prompt_summary = summary_prompt.format(
|
||||
chapter_text=chapter_text,
|
||||
global_summary=old_global_summary
|
||||
)
|
||||
new_global_summary = invoke_with_cleaning(model, prompt_summary)
|
||||
new_global_summary = invoke_with_cleaning(llm_adapter, prompt_summary)
|
||||
if not new_global_summary.strip():
|
||||
new_global_summary = old_global_summary
|
||||
|
||||
# 2) 更新角色状态
|
||||
# 更新角色状态
|
||||
prompt_char_state = update_character_state_prompt.format(
|
||||
chapter_text=chapter_text,
|
||||
old_state=old_character_state
|
||||
)
|
||||
new_char_state = invoke_with_cleaning(model, prompt_char_state)
|
||||
new_char_state = invoke_with_cleaning(llm_adapter, prompt_char_state)
|
||||
if not new_char_state.strip():
|
||||
new_char_state = old_character_state
|
||||
|
||||
# 写回文件
|
||||
# 写回
|
||||
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)
|
||||
|
||||
# 3) 更新向量库
|
||||
update_vector_store(
|
||||
api_key=embedding_api_key,
|
||||
base_url=embedding_url,
|
||||
new_chapter=chapter_text,
|
||||
interface_format=embedding_interface_format,
|
||||
embedding_model_name=embedding_model_name,
|
||||
filepath=filepath
|
||||
# 更新向量库
|
||||
embedding_adapter = create_embedding_adapter(
|
||||
embedding_interface_format,
|
||||
embedding_api_key,
|
||||
embedding_url,
|
||||
embedding_model_name
|
||||
)
|
||||
update_vector_store(embedding_adapter, chapter_text, filepath)
|
||||
|
||||
logging.info(f"Chapter {novel_number} has been finalized.")
|
||||
|
||||
|
||||
def enrich_chapter_text(
|
||||
chapter_text: str,
|
||||
word_number: int,
|
||||
@@ -760,28 +667,25 @@ def enrich_chapter_text(
|
||||
model_name: str,
|
||||
temperature: float
|
||||
) -> str:
|
||||
model = ChatOpenAI(
|
||||
model=model_name,
|
||||
llm_adapter = create_llm_adapter(
|
||||
interface_format="openai",
|
||||
base_url=base_url,
|
||||
model_name=model_name,
|
||||
api_key=api_key,
|
||||
base_url=ensure_openai_base_url_has_v1(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}"""
|
||||
enriched_text = invoke_with_cleaning(model, prompt)
|
||||
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
|
||||
|
||||
|
||||
# ============ 导入外部知识文本到向量库 ============
|
||||
# ============ 导入知识文件到向量库 ============
|
||||
|
||||
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，最后按 max_length 二次切分。
|
||||
"""
|
||||
nltk.download('punkt', quiet=True)
|
||||
sentences = nltk.sent_tokenize(content)
|
||||
if not sentences:
|
||||
@@ -837,24 +741,17 @@ def import_knowledge_file(
|
||||
|
||||
paragraphs = advanced_split_content(content)
|
||||
|
||||
# 尝试加载已有的向量库
|
||||
store = load_vector_store(
|
||||
embedding_adapter = create_embedding_adapter(
|
||||
interface_format=embedding_interface_format,
|
||||
api_key=embedding_api_key,
|
||||
base_url=embedding_url if embedding_url else "http://localhost:11434/api",
|
||||
interface_format=embedding_interface_format,
|
||||
embedding_model_name=embedding_model_name,
|
||||
filepath=filepath
|
||||
model_name=embedding_model_name
|
||||
)
|
||||
|
||||
store = load_vector_store(embedding_adapter, filepath)
|
||||
if not store:
|
||||
logging.info("Vector store does not exist. Initializing a new one for knowledge import...")
|
||||
init_vector_store(
|
||||
api_key=embedding_api_key,
|
||||
base_url=embedding_url if embedding_url else "http://localhost:11434/api",
|
||||
interface_format=embedding_interface_format,
|
||||
embedding_model_name=embedding_model_name,
|
||||
texts=paragraphs,
|
||||
filepath=filepath
|
||||
)
|
||||
init_vector_store(embedding_adapter, paragraphs, filepath)
|
||||
else:
|
||||
docs = [Document(page_content=str(p)) for p in paragraphs]
|
||||
store.add_documents(docs)
|
||||
|
||||
+27
@@ -0,0 +1,27 @@
|
||||
# tooltips.py
|
||||
# -*- coding: utf-8 -*-
|
||||
|
||||
tooltips = {
|
||||
"api_key": "在这里填写你的API Key。如果使用OpenAI官方接口，请在 https://platform.openai.com/account/api-keys 获取。",
|
||||
"base_url": "模型的接口地址。若使用OpenAI官方：https://api.openai.com/v1。若使用Ollama本地部署，则类似 http://localhost:11434/v1。",
|
||||
"interface_format": "指定LLM接口兼容格式，可选OpenAI、Ollama、ML Studio等。",
|
||||
"model_name": "要使用的模型名称，例如gpt-3.5-turbo、llama2等。如果是Ollama，请填写你下载好的本地模型名。",
|
||||
"temperature": "生成文本的随机度。数值越大越具有发散性，越小越严谨。",
|
||||
"max_tokens": "限制单次生成的最大Token数。范围1~100000，请根据模型上下文及需求填写合适值。",
|
||||
"embedding_api_key": "调用Embedding模型时所需的API Key。",
|
||||
"embedding_interface_format": "Embedding模型接口风格，比如OpenAI或Ollama。",
|
||||
"embedding_url": "Embedding模型接口地址。",
|
||||
"embedding_model_name": "Embedding模型名称，如text-embedding-ada-002。",
|
||||
"embedding_retrieval_k": "向量检索时返回的Top-K结果数量。",
|
||||
"topic": "小说的大致主题或主要故事背景描述。",
|
||||
"genre": "小说的题材类型，如玄幻、都市、科幻等。",
|
||||
"num_chapters": "小说期望的章节总数。",
|
||||
"word_number": "每章的目标字数。",
|
||||
"filepath": "生成文件存储的根目录路径。所有txt文件、向量库等放在该目录下。",
|
||||
"chapter_num": "当前正在处理的章节号，用于生成草稿或定稿操作。",
|
||||
"user_guidance": "为本章提供的一些额外指令或写作引导。",
|
||||
"characters_involved": "本章需要重点描写或影响剧情的角色名单。",
|
||||
"key_items": "在本章中出现的重要道具、线索或物品。",
|
||||
"scene_location": "本章主要发生的地点或场景描述。",
|
||||
"time_constraint": "本章剧情中涉及的时间压力或时限设置。"
|
||||
}
|
||||
@@ -10,6 +10,7 @@ import traceback
|
||||
|
||||
from config_manager import load_config, save_config
|
||||
from utils import read_file, save_string_to_txt, clear_file_content
|
||||
|
||||
from novel_generator import (
|
||||
Novel_architecture_generate,
|
||||
Chapter_blueprint_generate,
|
||||
@@ -17,21 +18,17 @@ from novel_generator import (
|
||||
finalize_chapter,
|
||||
import_knowledge_file,
|
||||
clear_vector_store,
|
||||
get_last_n_chapters_text,
|
||||
get_last_n_chapters_text
|
||||
)
|
||||
|
||||
from consistency_checker import check_consistency
|
||||
|
||||
|
||||
def log_error(message: str):
|
||||
"""
|
||||
用于打印详细的错误信息和堆栈信息。
|
||||
"""
|
||||
logging.error(f"{message}\n{traceback.format_exc()}")
|
||||
|
||||
ctk.set_appearance_mode("System")
|
||||
ctk.set_default_color_theme("blue")
|
||||
|
||||
|
||||
class NovelGeneratorGUI:
|
||||
def __init__(self, master):
|
||||
self.master = master
|
||||
@@ -71,14 +68,14 @@ class NovelGeneratorGUI:
|
||||
|
||||
self.chapter_num_var = ctk.StringVar(value="1")
|
||||
|
||||
# 新增四个可选要素
|
||||
# 四个可选要素
|
||||
self.characters_involved_var = ctk.StringVar(value="")
|
||||
self.key_items_var = ctk.StringVar(value="")
|
||||
self.scene_location_var = ctk.StringVar(value="")
|
||||
self.time_constraint_var = ctk.StringVar(value="")
|
||||
|
||||
# UI 布局
|
||||
self.tabview = ctk.CTkTabview(self.master, width=1200, height=800)
|
||||
self.tabview = ctk.CTkTabview(self.master)
|
||||
self.tabview.pack(fill="both", expand=True)
|
||||
|
||||
self.main_tab = self.tabview.add("Main Functions")
|
||||
@@ -197,7 +194,7 @@ class NovelGeneratorGUI:
|
||||
self.build_optional_buttons_area(start_row=2)
|
||||
|
||||
def build_config_tabview(self):
|
||||
self.config_tabview = ctk.CTkTabview(self.config_frame, width=600, height=200)
|
||||
self.config_tabview = ctk.CTkTabview(self.config_frame)
|
||||
self.config_tabview.grid(row=0, column=0, sticky="we", padx=5, pady=5)
|
||||
|
||||
self.ai_config_tab = self.config_tabview.add("LLM Model settings")
|
||||
@@ -256,8 +253,8 @@ class NovelGeneratorGUI:
|
||||
|
||||
temp_scale = ctk.CTkSlider(
|
||||
self.ai_config_tab,
|
||||
from_=0.0, to=1.0,
|
||||
number_of_steps=100,
|
||||
from_=0.0, to=2.0,
|
||||
number_of_steps=200,
|
||||
command=update_temp_label,
|
||||
variable=self.temperature_var
|
||||
)
|
||||
@@ -335,7 +332,7 @@ class NovelGeneratorGUI:
|
||||
|
||||
topic_label = ctk.CTkLabel(self.params_frame, text="主题(Topic):", font=("Microsoft YaHei", 12))
|
||||
topic_label.grid(row=0, column=0, padx=5, pady=5, sticky="e")
|
||||
self.topic_text = ctk.CTkTextbox(self.params_frame, width=200, height=80, wrap="word", font=("Microsoft YaHei", 12))
|
||||
self.topic_text = ctk.CTkTextbox(self.params_frame,height=80, wrap="word", font=("Microsoft YaHei", 12))
|
||||
self.topic_text.grid(row=0, column=1, padx=5, pady=5, sticky="nsew")
|
||||
if self.topic_default:
|
||||
self.topic_text.insert("0.0", self.topic_default)
|
||||
@@ -384,7 +381,7 @@ class NovelGeneratorGUI:
|
||||
# 用户指导
|
||||
guide_label = ctk.CTkLabel(self.params_frame, text="本章指导:", font=("Microsoft YaHei", 12))
|
||||
guide_label.grid(row=5, column=0, padx=5, pady=5, sticky="ne")
|
||||
self.user_guide_text = ctk.CTkTextbox(self.params_frame, width=200, height=80, wrap="word", font=("Microsoft YaHei", 12))
|
||||
self.user_guide_text = ctk.CTkTextbox(self.params_frame,height=80, wrap="word", font=("Microsoft YaHei", 12))
|
||||
self.user_guide_text.grid(row=5, column=1, padx=5, pady=5, sticky="nsew")
|
||||
|
||||
# 新增：四个可选元素
|
||||
@@ -528,7 +525,7 @@ class NovelGeneratorGUI:
|
||||
logging.error(full_message)
|
||||
self.safe_log(full_message)
|
||||
|
||||
# ------------------ Step1: 生成架构 ------------------
|
||||
# ============ Step1: 生成小说架构 ============
|
||||
def generate_novel_architecture_ui(self):
|
||||
filepath = self.filepath_var.get().strip()
|
||||
if not filepath:
|
||||
@@ -568,7 +565,7 @@ class NovelGeneratorGUI:
|
||||
|
||||
threading.Thread(target=task, daemon=True).start()
|
||||
|
||||
# ------------------ Step2: 生成章节蓝图 ------------------
|
||||
# ============ Step2: 生成章节蓝图 ============
|
||||
def generate_chapter_blueprint_ui(self):
|
||||
filepath = self.filepath_var.get().strip()
|
||||
if not filepath:
|
||||
@@ -599,7 +596,7 @@ class NovelGeneratorGUI:
|
||||
|
||||
threading.Thread(target=task, daemon=True).start()
|
||||
|
||||
# ------------------ Step3: 生成草稿 ------------------
|
||||
# ============ Step3: 生成章节草稿 ============
|
||||
def generate_chapter_draft_ui(self):
|
||||
filepath = self.filepath_var.get().strip()
|
||||
if not filepath:
|
||||
@@ -671,7 +668,7 @@ class NovelGeneratorGUI:
|
||||
self.chapter_result.insert("0.0", text)
|
||||
self.chapter_result.see("end")
|
||||
|
||||
# ------------------ Step4: 定稿章节 ------------------
|
||||
# ============ Step4: 定稿章节 ============
|
||||
def finalize_chapter_ui(self):
|
||||
filepath = self.filepath_var.get().strip()
|
||||
if not filepath:
|
||||
@@ -731,7 +728,7 @@ class NovelGeneratorGUI:
|
||||
|
||||
threading.Thread(target=task, daemon=True).start()
|
||||
|
||||
# ------------------ 一致性审校 ------------------
|
||||
# ============ 一致性审校 (可选) ============
|
||||
def do_consistency_check(self):
|
||||
filepath = self.filepath_var.get().strip()
|
||||
if not filepath:
|
||||
@@ -756,7 +753,7 @@ class NovelGeneratorGUI:
|
||||
|
||||
self.safe_log("开始一致性审校...")
|
||||
result = check_consistency(
|
||||
novel_setting="", # 如果需要，可传入最新的 Novel_architecture 内容
|
||||
novel_setting="",
|
||||
character_state=read_file(os.path.join(filepath, "character_state.txt")),
|
||||
global_summary=read_file(os.path.join(filepath, "global_summary.txt")),
|
||||
chapter_text=chapter_text,
|
||||
@@ -776,6 +773,7 @@ class NovelGeneratorGUI:
|
||||
|
||||
threading.Thread(target=task, daemon=True).start()
|
||||
|
||||
# ============ 导入知识库 ============
|
||||
def import_knowledge_handler(self):
|
||||
selected_file = filedialog.askopenfilename(
|
||||
title="选择要导入的知识库文件",
|
||||
@@ -847,7 +845,8 @@ class NovelGeneratorGUI:
|
||||
text_area.insert("0.0", arcs_text)
|
||||
text_area.configure(state="disabled")
|
||||
|
||||
# ------------------ 其他标签页: Novel Architecture, Chapter Blueprint, Character State, Summary ------------------
|
||||
# ============ 其余标签页: Novel Architecture, Chapter Blueprint, Character State, Summary ============
|
||||
|
||||
def build_setting_tab(self):
|
||||
self.setting_tab.rowconfigure(0, weight=0)
|
||||
self.setting_tab.rowconfigure(1, weight=1)
|
||||
@@ -1032,7 +1031,7 @@ class NovelGeneratorGUI:
|
||||
save_string_to_txt(content, filename)
|
||||
self.log("已保存对 global_summary.txt 的修改。")
|
||||
|
||||
# ------------------ 章节管理标签页 ------------------
|
||||
# ============ 章节管理标签页 ============
|
||||
def build_chapters_tab(self):
|
||||
self.chapters_view_tab.rowconfigure(0, weight=0)
|
||||
self.chapters_view_tab.rowconfigure(1, weight=1)
|
||||
@@ -1165,7 +1164,6 @@ class NovelGeneratorGUI:
|
||||
else:
|
||||
messagebox.showinfo("提示", "已经是最后一章了。")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
app = ctk.CTk()
|
||||
gui = NovelGeneratorGUI(app)
|
||||
|
||||
Reference in New Issue
Block a user