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YILING0013
2025-02-06 18:35:28 +08:00
parent bf55e74ac9
commit a2b86a90dc
5 changed files with 523 additions and 331 deletions
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embedding_adapters.py
+122
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@@ -0,0 +1,122 @@
# embedding_adapters.py
# -*- coding: utf-8 -*-
import logging
import requests
import traceback
from typing import List
from langchain_openai import OpenAIEmbeddings
def ensure_openai_base_url_has_v1(url: str) -> str:
"""
若用户输入的 url 不包含 '/v1'，则在末尾追加 '/v1'。
"""
import re
url = url.strip()
if not url:
return url
if not re.search(r'/v\d+$', url):
if '/v1' not in url:
url = url.rstrip('/') + '/v1'
return url
class BaseEmbeddingAdapter:
"""
Embedding 接口统一基类
"""
def embed_documents(self, texts: List[str]) -> List[List[float]]:
raise NotImplementedError
def embed_query(self, query: str) -> List[float]:
raise NotImplementedError
class OpenAIEmbeddingAdapter(BaseEmbeddingAdapter):
"""
基于 OpenAIEmbeddings（或兼容接口）的适配器
"""
def __init__(self, api_key: str, base_url: str, model_name: str):
self._embedding = OpenAIEmbeddings(
openai_api_key=api_key,
openai_api_base=ensure_openai_base_url_has_v1(base_url),
model=model_name
)
def embed_documents(self, texts: List[str]) -> List[List[float]]:
return self._embedding.embed_documents(texts)
def embed_query(self, query: str) -> List[float]:
return self._embedding.embed_query(query)
class OllamaEmbeddingAdapter(BaseEmbeddingAdapter):
"""
Ollama Embedding，示例中和之前的 embedding_ollama.py 类似
其接口路径往往为 /api/embeddings
"""
def __init__(self, model_name: str, base_url: str):
self.model_name = model_name
self.base_url = base_url.rstrip("/")
def embed_documents(self, texts: List[str]) -> List[List[float]]:
embeddings = []
for text in texts:
vec = self._embed_single(text)
embeddings.append(vec)
return embeddings
def embed_query(self, query: str) -> List[float]:
return self._embed_single(query)
def _embed_single(self, text: str) -> List[float]:
"""
调用 Ollama 本地服务 /api/embeddings 接口，获取文本 embedding
"""
# 如果 base_url 中已含 /api/embeddings，可直接用；否则拼上 /api/embeddings
url = self.base_url
if "api/embeddings" not in url:
url = f"{url}/api/embeddings"
data = {
"model": self.model_name,
"prompt": text
}
try:
response = requests.post(url, json=data)
response.raise_for_status()
result = response.json()
if "embedding" not in result:
raise ValueError("No 'embedding' field in Ollama response.")
return result["embedding"]
except requests.exceptions.RequestException as e:
logging.error(f"Ollama embeddings request error: {e}\n{traceback.format_exc()}")
return []
class MLStudioEmbeddingAdapter(BaseEmbeddingAdapter):
def __init__(self, api_key: str, base_url: str, model_name: str):
self._embedding = OpenAIEmbeddings(
openai_api_key=api_key,
openai_api_base=ensure_openai_base_url_has_v1(base_url),
model=model_name
)
def embed_documents(self, texts: List[str]) -> List[List[float]]:
return self._embedding.embed_documents(texts)
def embed_query(self, query: str) -> List[float]:
return self._embedding.embed_query(query)
def create_embedding_adapter(
interface_format: str,
api_key: str,
base_url: str,
model_name: str
) -> BaseEmbeddingAdapter:
"""
工厂函数：根据 interface_format 返回不同的 embedding 适配器实例
"""
if interface_format.lower() == "openai":
return OpenAIEmbeddingAdapter(api_key, base_url, model_name)
elif interface_format.lower() == "ollama":
return OllamaEmbeddingAdapter(model_name, base_url)
elif interface_format.lower() == "ml studio":
return MLStudioEmbeddingAdapter(api_key, base_url, model_name)
else:
raise ValueError(f"Unknown embedding interface_format: {interface_format}")
llm_adapters.py
+148
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@@ -0,0 +1,148 @@
# llm_adapters.py
# -*- coding: utf-8 -*-
import logging
from typing import Optional
from langchain_openai import ChatOpenAI
def ensure_openai_base_url_has_v1(url: str) -> str:
"""
若用户输入的 url 不包含 '/v1'，则在末尾追加 '/v1'。
"""
import re
url = url.strip()
if not url:
return url
if not re.search(r'/v\d+$', url):
if '/v1' not in url:
url = url.rstrip('/') + '/v1'
return url
class BaseLLMAdapter:
"""
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。
"""
def invoke(self, prompt: str) -> str:
raise NotImplementedError("Subclasses must implement .invoke(prompt) method.")
class DeepSeekAdapter(BaseLLMAdapter):
"""
适配官方/OpenAI兼容接口（使用 langchain.ChatOpenAI）
"""
def __init__(self, api_key: str, base_url: str, model_name: str, max_tokens:int, temperature: float = 0.7):
self.base_url = ensure_openai_base_url_has_v1(base_url)
self.api_key = api_key
self.model_name = model_name
self.max_tokens = max_tokens
self.temperature = temperature
self._client = ChatOpenAI(
model=self.model_name,
api_key=self.api_key,
base_url=self.base_url,
max_tokens=self.max_tokens,
temperature=self.temperature
)
def invoke(self, prompt: str) -> str:
response = self._client.invoke(prompt)
if not response:
logging.warning("No response from DeepSeekAdapter.")
return ""
return response.content
class OpenAIAdapter(BaseLLMAdapter):
"""
适配官方/OpenAI兼容接口（使用 langchain.ChatOpenAI）
"""
def __init__(self, api_key: str, base_url: str, model_name: str, max_tokens:int, temperature: float = 0.7):
self.base_url = ensure_openai_base_url_has_v1(base_url)
self.api_key = api_key
self.model_name = model_name
self.max_tokens = max_tokens
self.temperature = temperature
self._client = ChatOpenAI(
model=self.model_name,
api_key=self.api_key,
base_url=self.base_url,
max_tokens=self.max_tokens,
temperature=self.temperature
)
def invoke(self, prompt: str) -> str:
response = self._client.invoke(prompt)
if not response:
logging.warning("No response from OpenAIAdapter.")
return ""
return response.content
class OllamaAdapter(BaseLLMAdapter):
"""
Ollama 同样有一个 OpenAI-like /v1/chat 接口，可直接使用 ChatOpenAI。
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。
"""
def __init__(self, api_key: str, base_url: str, model_name: str, max_tokens:int, temperature: float = 0.7):
self.base_url = ensure_openai_base_url_has_v1(base_url)
self.api_key = api_key
self.model_name = model_name
self.max_tokens = max_tokens
self.temperature = temperature
self._client = ChatOpenAI(
model=self.model_name,
api_key=self.api_key,
base_url=self.base_url,
max_tokens=self.max_tokens,
temperature=self.temperature
)
def invoke(self, prompt: str) -> str:
response = self._client.invoke(prompt)
if not response:
logging.warning("No response from OllamaAdapter.")
return ""
return response.content
class MLStudioAdapter(BaseLLMAdapter):
def __init__(self, api_key: str, base_url: str, model_name: str, max_tokens:int, temperature: float = 0.7):
self.base_url = ensure_openai_base_url_has_v1(base_url)
self.api_key = api_key
self.model_name = model_name
self.max_tokens = max_tokens
self.temperature = temperature
self._client = ChatOpenAI(
model=self.model_name,
api_key=self.api_key,
base_url=self.base_url,
max_tokens=self.max_tokens,
temperature=self.temperature
)
def invoke(self, prompt: str) -> str:
response = self._client.invoke(prompt)
if not response:
logging.warning("No response from MLStudioAdapter.")
return ""
return response.content
def create_llm_adapter(
interface_format: str,
base_url: str,
model_name: str,
api_key: str,
temperature: float
) -> BaseLLMAdapter:
"""
工厂函数：根据 interface_format 返回不同的适配器实例。
"""
if interface_format.lower() == "deepseek":
return DeepSeekAdapter(api_key, base_url, model_name, temperature)
elif interface_format.lower() == "openai":
return OpenAIAdapter(api_key, base_url, model_name, temperature)
elif interface_format.lower() == "ollama":
return OllamaAdapter(api_key, base_url, model_name, temperature)
elif interface_format.lower() == "ml studio":
return MLStudioAdapter(api_key, base_url, model_name, temperature)
else:
raise ValueError(f"Unknown interface_format: {interface_format}")
novel_generator.py
+205 -308
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@@ -7,8 +7,6 @@ import time
import traceback import traceback
from typing import List, Optional, Tuple from typing import List, Optional, Tuple
# langchain 相关
from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from langchain_chroma import Chroma from langchain_chroma import Chroma
from chromadb.config import Settings from chromadb.config import Settings
from langchain.docstore.document import Document from langchain.docstore.document import Document
@@ -37,16 +35,16 @@ from prompt_definitions import (
summarize_recent_chapters_prompt summarize_recent_chapters_prompt
) )
# Ollama嵌入 (如使用Ollama时需要) # 章节目录解析
from embedding_ollama import OllamaEmbeddings
# 用于目录解析章节标题/简介
from chapter_directory_parser import get_chapter_info_from_blueprint from chapter_directory_parser import get_chapter_info_from_blueprint
from llm_adapters import create_llm_adapter
from embedding_adapters import create_embedding_adapter
logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s") logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s")
# ============ 工具函数 ============
# ============ 基础工具 ============
def remove_think_tags(text: str) -> str: def remove_think_tags(text: str) -> str:
"""移除 <think>...</think> 包裹的内容""" """移除 <think>...</think> 包裹的内容"""
return re.sub(r'<think>.*?</think>', '', text, flags=re.DOTALL) return re.sub(r'<think>.*?</think>', '', text, flags=re.DOTALL)
@@ -59,128 +57,79 @@ def debug_log(prompt: str, response_content: str):
f"\n[######################################### Response #########################################]\n{response_content}\n" f"\n[######################################### Response #########################################]\n{response_content}\n"
) )
def invoke_with_cleaning(model: ChatOpenAI, prompt: str) -> str: def invoke_with_cleaning(llm_adapter, prompt: str) -> str:
"""通用封装：调用模型并移除 <think>...</think> 文本，记录日志后返回""" """通用封装：调用 LLM，并移除 <think>...</think> 文本，记录日志后返回"""
response = model.invoke(prompt) response = llm_adapter.invoke(prompt)
if not response: if not response:
logging.warning("No response from model.") logging.warning("No response from model.")
return "" return ""
cleaned_text = remove_think_tags(response.content) cleaned_text = remove_think_tags(response)
debug_log(prompt, cleaned_text) debug_log(prompt, cleaned_text)
return cleaned_text.strip() return cleaned_text.strip()
def ensure_openai_base_url_has_v1(url: str) -> str:
"""
若用户输入的 url 不包含 '/v1'，则在末尾追加 '/v1'。
"""
import re
url = url.strip()
if not url:
return url
if not re.search(r'/v\d+$', url):
if '/v1' not in url:
url = url.rstrip('/') + '/v1'
return url
def is_using_ollama_api(interface_format: str) -> bool:
return interface_format.lower() == "ollama"
def is_using_ml_studio_api(interface_format: str) -> bool:
return interface_format.lower() == "ml studio"
# ============ 获取 vectorstore 路径 ============ # ============ 获取 vectorstore 路径 ============
def get_vectorstore_dir(filepath: str) -> str: def get_vectorstore_dir(filepath: str) -> str:
"""
返回存储向量库的本地路径：
在用户指定的 `filepath` 下创建/使用 'vectorstore' 文件夹。
"""
return os.path.join(filepath, "vectorstore") return os.path.join(filepath, "vectorstore")
# ============ 清空向量库 ============
# ============ 创建 Embeddings 对象 ============
def create_embeddings_object(
api_key: str,
base_url: str,
interface_format: str,
embedding_model_name: str
):
"""
根据 embedding_interface_format，选择 Ollama 或 OpenAIEmbeddings 等不同后端。
"""
if is_using_ollama_api(interface_format):
fixed_url = base_url.rstrip("/")
return OllamaEmbeddings(
model_name=embedding_model_name,
base_url=fixed_url
)
else:
# OpenAI 或 ML Studio 均使用 OpenAIEmbeddings，注意 base_url 可能需要 ensure /v1
fixed_url = ensure_openai_base_url_has_v1(base_url)
return OpenAIEmbeddings(
openai_api_key=api_key,
openai_api_base=fixed_url,
model=embedding_model_name
)
# ============ 向量库相关操作 ============
def clear_vector_store(filepath: str) -> bool: def clear_vector_store(filepath: str) -> bool:
"""
返回值表示是否成功清空向量库。
"""
import shutil import shutil
store_dir = get_vectorstore_dir(filepath) store_dir = get_vectorstore_dir(filepath)
if not os.path.exists(store_dir): if not os.path.exists(store_dir):
logging.info("No vector store found to clear.") logging.info("No vector store found to clear.")
return False return False
try: try:
shutil.rmtree(store_dir) shutil.rmtree(store_dir)
logging.info(f"Vector store directory '{store_dir}' removed.") logging.info(f"Vector store directory '{store_dir}' removed.")
return True return True
except Exception as e: except Exception as e:
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)}") logging.error(f"无法删除向量库文件夹，请关闭程序后手动删除 {store_dir}。\n {str(e)}")
traceback.print_exc() traceback.print_exc()
return False return False
# ============ 根据 embedding 接口创建/加载 Chroma ============
def init_vector_store( def init_vector_store(
api_key: str, embedding_adapter,
base_url: str,
interface_format: str,
embedding_model_name: str,
texts: List[str], texts: List[str],
filepath: str filepath: str
) -> Chroma: ) -> Chroma:
""" """
在 filepath 下创建/加载一个 Chroma 向量库并插入 texts。 在 filepath 下创建/加载一个 Chroma 向量库并插入 texts。
这里 embedding_adapter 是一个实现了 embed_documents(texts) 的对象
""" """
store_dir = get_vectorstore_dir(filepath) store_dir = get_vectorstore_dir(filepath)
os.makedirs(store_dir, exist_ok=True) os.makedirs(store_dir, exist_ok=True)
embeddings = create_embeddings_object( # 将文本封装为 Document
api_key=api_key,
base_url=base_url,
interface_format=interface_format,
embedding_model_name=embedding_model_name
)
documents = [Document(page_content=str(t)) for t in texts] documents = [Document(page_content=str(t)) for t in texts]
# 因为我们是自定义的 embeddings，对接Chroma时需包装一个“langchain兼容对象”
# 这里示例：写一个包装函数
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()
vectorstore = Chroma.from_documents( vectorstore = Chroma.from_documents(
documents, documents,
embedding=embeddings, embedding=chroma_embedding,
persist_directory=store_dir, persist_directory=store_dir,
client_settings=Settings(anonymized_telemetry=False), client_settings=Settings(anonymized_telemetry=False),
collection_name="novel_collection" collection_name="novel_collection"
) )
return vectorstore return vectorstore
def load_vector_store( def load_vector_store(
api_key: str, embedding_adapter,
base_url: str,
interface_format: str,
embedding_model_name: str,
filepath: str filepath: str
) -> Optional[Chroma]: ) -> Optional[Chroma]:
""" """
@@ -191,19 +140,26 @@ def load_vector_store(
logging.info("Vector store not found. Will return None.") logging.info("Vector store not found. Will return None.")
return None return None
embeddings = create_embeddings_object( # 同样要包装embedding_adapter
api_key=api_key, from langchain.embeddings.base import Embeddings as LCEmbeddings
base_url=base_url,
interface_format=interface_format, class LCEmbeddingWrapper(LCEmbeddings):
embedding_model_name=embedding_model_name 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( return Chroma(
persist_directory=store_dir, persist_directory=store_dir,
embedding_function=embeddings, embedding_function=chroma_embedding,
client_settings=Settings(anonymized_telemetry=False), client_settings=Settings(anonymized_telemetry=False),
collection_name="novel_collection" collection_name="novel_collection"
) )
# ============ 文本分段工具 ============
def split_by_length(text: str, max_length: int = 500) -> List[str]: def split_by_length(text: str, max_length: int = 500) -> List[str]:
segments = [] segments = []
@@ -215,12 +171,12 @@ def split_by_length(text: str, max_length: int = 500) -> List[str]:
start_idx = end_idx start_idx = end_idx
return segments return segments
def split_text_for_vectorstore(chapter_text: str, def split_text_for_vectorstore(chapter_text: str,
max_length: int = 500, max_length: int = 500,
similarity_threshold: float = 0.7) -> List[str]: 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。 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(): if not chapter_text.strip():
return [] return []
@@ -230,7 +186,6 @@ def split_text_for_vectorstore(chapter_text: str,
if not sentences: if not sentences:
return [] return []
# 先对相近句子进行合并
model = SentenceTransformer('paraphrase-MiniLM-L6-v2') model = SentenceTransformer('paraphrase-MiniLM-L6-v2')
embeddings = model.encode(sentences) embeddings = model.encode(sentences)
@@ -251,7 +206,6 @@ def split_text_for_vectorstore(chapter_text: str,
if current_sentences: if current_sentences:
merged_paragraphs.append(" ".join(current_sentences)) merged_paragraphs.append(" ".join(current_sentences))
# 再对合并好的段落做 max_length 切分
final_segments = [] final_segments = []
for para in merged_paragraphs: for para in merged_paragraphs:
if len(para) > max_length: if len(para) > max_length:
@@ -262,13 +216,11 @@ def split_text_for_vectorstore(chapter_text: str,
return final_segments return final_segments
# ============ 更新向量库 ============
def update_vector_store( def update_vector_store(
api_key: str, embedding_adapter,
base_url: str,
new_chapter: str, new_chapter: str,
interface_format: str,
embedding_model_name: str,
filepath: str filepath: str
): ):
""" """
@@ -279,49 +231,28 @@ def update_vector_store(
logging.warning("No valid text to insert into vector store. Skipping.") logging.warning("No valid text to insert into vector store. Skipping.")
return return
store = load_vector_store( store = load_vector_store(embedding_adapter, filepath)
api_key=api_key,
base_url=base_url,
interface_format=interface_format,
embedding_model_name=embedding_model_name,
filepath=filepath
)
if not store: if not store:
logging.info("Vector store does not exist. Initializing a new one for new chapter...") logging.info("Vector store does not exist. Initializing a new one for new chapter...")
init_vector_store( init_vector_store(embedding_adapter, splitted_texts, filepath)
api_key=api_key,
base_url=base_url,
interface_format=interface_format,
embedding_model_name=embedding_model_name,
texts=splitted_texts,
filepath=filepath
)
return return
docs = [Document(page_content=str(t)) for t in splitted_texts] docs = [Document(page_content=str(t)) for t in splitted_texts]
store.add_documents(docs) store.add_documents(docs)
logging.info("Vector store updated with the new chapter splitted segments.") 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( def get_relevant_context_from_vector_store(
api_key: str, embedding_adapter,
base_url: str,
query: str, query: str,
interface_format: str,
embedding_model_name: str,
filepath: str, filepath: str,
k: int = 2 k: int = 2
) -> str: ) -> str:
""" """
从向量库中检索与 query 最相关的 k 条文本，拼接后返回。 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( store = load_vector_store(embedding_adapter, filepath)
api_key=api_key,
base_url=base_url,
interface_format=interface_format,
embedding_model_name=embedding_model_name,
filepath=filepath
)
if not store: if not store:
logging.info("No vector store found. Returning empty context.") logging.info("No vector store found. Returning empty context.")
return "" return ""
@@ -334,8 +265,68 @@ def get_relevant_context_from_vector_store(
combined = "\n".join([d.page_content for d in docs]) combined = "\n".join([d.page_content for d in docs])
return combined 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( def Novel_architecture_generate(
api_key: str, api_key: str,
base_url: str, base_url: str,
@@ -348,70 +339,68 @@ def Novel_architecture_generate(
temperature: float = 0.7 temperature: float = 0.7
) -> None: ) -> None:
""" """
依次调用： 依次调用:
1. core_seed_prompt 1. core_seed_prompt
2. character_dynamics_prompt 2. character_dynamics_prompt
3. world_building_prompt 3. world_building_prompt
4. plot_architecture_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) 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, api_key=api_key,
base_url=ensure_openai_base_url_has_v1(base_url),
temperature=temperature temperature=temperature
) )
# 1) 核心种子 # Step1: 核心种子
prompt_core = core_seed_prompt.format( prompt_core = core_seed_prompt.format(
topic=topic, topic=topic,
genre=genre, genre=genre,
number_of_chapters=number_of_chapters, number_of_chapters=number_of_chapters,
word_number=word_number word_number=word_number
) )
core_seed_result = invoke_with_cleaning(model, prompt_core) core_seed_result = invoke_with_cleaning(llm_adapter, prompt_core)
core_seed_text = core_seed_result.strip()
# 2) 角色动力学 # Step2: 角色动力学
prompt_character = character_dynamics_prompt.format(core_seed=core_seed_text) prompt_character = character_dynamics_prompt.format(core_seed=core_seed_result.strip())
character_dynamics_result = invoke_with_cleaning(model, prompt_character) character_dynamics_result = invoke_with_cleaning(llm_adapter, prompt_character)
character_dynamics_text = character_dynamics_result.strip()
# 3) 世界观 # Step3: 世界观
prompt_world = world_building_prompt.format(core_seed=core_seed_text) prompt_world = world_building_prompt.format(core_seed=core_seed_result.strip())
world_building_result = invoke_with_cleaning(model, prompt_world) world_building_result = invoke_with_cleaning(llm_adapter, prompt_world)
world_building_text = world_building_result.strip()
# 4) 三幕式情节架构 # Step4: 三幕式情节
prompt_plot = plot_architecture_prompt.format( prompt_plot = plot_architecture_prompt.format(
core_seed=core_seed_text, core_seed=core_seed_result.strip(),
character_dynamics=character_dynamics_text, character_dynamics=character_dynamics_result.strip(),
world_building=world_building_text world_building=world_building_result.strip()
) )
plot_arch_result = invoke_with_cleaning(model, prompt_plot) plot_arch_result = invoke_with_cleaning(llm_adapter, prompt_plot)
plot_arch_text = plot_arch_result.strip()
# 整合并写入 Novel_architecture.txt # 合并
final_content = ( final_content = (
"#=== 1) 核心种子 ===\n" "#=== 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" "#=== 2) 角色动力学 ===\n"
f"{character_dynamics_text}\n\n" f"{character_dynamics_result}\n\n"
"#=== 3) 世界观 ===\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" "#=== 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") arch_file = os.path.join(filepath, "Novel_architecture.txt")
clear_file_content(arch_file) clear_file_content(arch_file)
save_string_to_txt(final_content, arch_file) save_string_to_txt(final_content, arch_file)
logging.info("Novel_architecture.txt has been generated successfully.") 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( def Chapter_blueprint_generate(
api_key: str, api_key: str,
base_url: str, base_url: str,
@@ -419,10 +408,6 @@ def Chapter_blueprint_generate(
filepath: str, filepath: str,
temperature: float = 0.7 temperature: float = 0.7
) -> None: ) -> 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") arch_file = os.path.join(filepath, "Novel_architecture.txt")
if not os.path.exists(arch_file): if not os.path.exists(arch_file):
logging.warning("Novel_architecture.txt not found. Please generate architecture first.") 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.") logging.warning("Novel_architecture.txt is empty.")
return return
# 从内容中尽量提取 number_of_chapters
match_chaps = re.search(r'约(\d+)章', architecture_text) match_chaps = re.search(r'约(\d+)章', architecture_text)
if match_chaps: if match_chaps:
number_of_chapters = int(match_chaps.group(1)) number_of_chapters = int(match_chaps.group(1))
else: else:
number_of_chapters = 10 # fallback number_of_chapters = 10
# 提取三幕式文本 # 提取三幕式文本
plot_arch_text = "" plot_arch_text = ""
@@ -447,10 +431,11 @@ def Chapter_blueprint_generate(
if m: if m:
plot_arch_text = m.group(1).strip() plot_arch_text = m.group(1).strip()
model = ChatOpenAI( llm_adapter = create_llm_adapter(
model=llm_model, interface_format="openai", # 或实际由UI传入
base_url=base_url,
model_name=llm_model,
api_key=api_key, api_key=api_key,
base_url=ensure_openai_base_url_has_v1(base_url),
temperature=temperature temperature=temperature
) )
@@ -458,7 +443,7 @@ def Chapter_blueprint_generate(
plot_architecture=plot_arch_text, plot_architecture=plot_arch_text,
number_of_chapters=number_of_chapters 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(): if not blueprint_text.strip():
logging.warning("Chapter blueprint generation result is empty.") logging.warning("Chapter blueprint generation result is empty.")
return return
@@ -469,72 +454,7 @@ def Chapter_blueprint_generate(
logging.info("Novel_directory.txt (chapter blueprint) has been generated successfully.") logging.info("Novel_directory.txt (chapter blueprint) has been generated successfully.")
# ============ 3) 生成章节草稿 ============
# ============ 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） ============
def generate_chapter_draft( def generate_chapter_draft(
api_key: str, api_key: str,
@@ -555,16 +475,6 @@ def generate_chapter_draft(
embedding_model_name: str, embedding_model_name: str,
embedding_retrieval_k: int = 2 embedding_retrieval_k: int = 2
) -> str: ) -> 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") arch_file = os.path.join(filepath, "Novel_architecture.txt")
novel_architecture_text = read_file(arch_file) 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_file = os.path.join(filepath, "character_state.txt")
character_state_text = read_file(character_state_file) 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_info = get_chapter_info_from_blueprint(blueprint_text, novel_number)
chapter_title = chapter_info["chapter_title"] chapter_title = chapter_info["chapter_title"]
chapter_role = chapter_info["chapter_role"] chapter_role = chapter_info["chapter_role"]
@@ -590,43 +500,45 @@ def generate_chapter_draft(
chapters_dir = os.path.join(filepath, "chapters") chapters_dir = os.path.join(filepath, "chapters")
os.makedirs(chapters_dir, exist_ok=True) 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) recent_3_texts = get_last_n_chapters_text(chapters_dir, novel_number, n=3)
short_summary, next_chapter_keywords = summarize_recent_chapters( short_summary, next_chapter_keywords = summarize_recent_chapters(
llm_model=model_name, interface_format="openai", # 或由UI传进
api_key=api_key, api_key=api_key,
base_url=base_url, base_url=base_url,
model_name=model_name,
temperature=temperature, temperature=temperature,
chapters_text_list=recent_3_texts 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 = "" previous_chapter_excerpt = ""
for text_block in reversed(recent_3_texts): for text_block in reversed(recent_3_texts):
if text_block.strip(): if text_block.strip():
# 找到最近一个非空章节
if len(text_block) > 1500: if len(text_block) > 1500:
previous_chapter_excerpt = text_block[-1500:] previous_chapter_excerpt = text_block[-1500:]
else: else:
previous_chapter_excerpt = text_block previous_chapter_excerpt = text_block
break 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 retrieval_query = short_summary + " " + next_chapter_keywords
relevant_context = get_relevant_context_from_vector_store( relevant_context = get_relevant_context_from_vector_store(
api_key=embedding_api_key, embedding_adapter=embedding_adapter,
base_url=embedding_url,
query=retrieval_query, query=retrieval_query,
interface_format=embedding_interface_format,
embedding_model_name=embedding_model_name,
filepath=filepath, filepath=filepath,
k=embedding_retrieval_k k=embedding_retrieval_k
) )
if not relevant_context.strip(): if not relevant_context.strip():
relevant_context = "（无检索到的上下文）" relevant_context = "（无检索到的上下文）"
# 6) 组装 Prompt # 组装 Prompt
prompt_text = chapter_draft_prompt.format( prompt_text = chapter_draft_prompt.format(
novel_number=novel_number, novel_number=novel_number,
chapter_title=chapter_title, chapter_title=chapter_title,
@@ -646,24 +558,23 @@ def generate_chapter_draft(
novel_setting=novel_architecture_text, novel_setting=novel_architecture_text,
global_summary=global_summary_text, global_summary=global_summary_text,
character_state=character_state_text, character_state=character_state_text,
previous_chapter_excerpt=previous_chapter_excerpt, previous_chapter_excerpt=previous_chapter_excerpt,
context_excerpt=relevant_context context_excerpt=relevant_context
) )
# 7) 调用 LLM 生成章节正文 # 调用 LLM 生成
model = ChatOpenAI( llm_adapter = create_llm_adapter(
model=model_name, interface_format="openai", # 或由UI传进
base_url=base_url,
model_name=model_name,
api_key=api_key, api_key=api_key,
base_url=ensure_openai_base_url_has_v1(base_url),
temperature=temperature temperature=temperature
) )
chapter_content = invoke_with_cleaning(llm_adapter, prompt_text)
chapter_content = invoke_with_cleaning(model, prompt_text)
if not chapter_content.strip(): if not chapter_content.strip():
logging.warning("Generated chapter draft is empty.") 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") chapter_file = os.path.join(chapters_dir, f"chapter_{novel_number}.txt")
clear_file_content(chapter_file) clear_file_content(chapter_file)
save_string_to_txt(chapter_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.") logging.info(f"[Draft] Chapter {novel_number} generated as a draft.")
return chapter_content return chapter_content
# ============ 4) 定稿章节 ============ # ============ 4) 定稿章节 ============
def finalize_chapter( def finalize_chapter(
novel_number: int, novel_number: int,
word_number: int, word_number: int,
@@ -686,9 +597,6 @@ def finalize_chapter(
embedding_interface_format: str, embedding_interface_format: str,
embedding_model_name: 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") chapters_dir = os.path.join(filepath, "chapters")
chapter_file = os.path.join(chapters_dir, f"chapter_{novel_number}.txt") chapter_file = os.path.join(chapters_dir, f"chapter_{novel_number}.txt")
chapter_text = read_file(chapter_file).strip() chapter_text = read_file(chapter_file).strip()
@@ -696,7 +604,7 @@ def finalize_chapter(
logging.warning(f"Chapter {novel_number} is empty, cannot finalize.") logging.warning(f"Chapter {novel_number} is empty, cannot finalize.")
return 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: if len(chapter_text) < 0.6 * word_number:
chapter_text = enrich_chapter_text(chapter_text, word_number, api_key, base_url, model_name, temperature) chapter_text = enrich_chapter_text(chapter_text, word_number, api_key, base_url, model_name, temperature)
clear_file_content(chapter_file) clear_file_content(chapter_file)
@@ -708,50 +616,49 @@ def finalize_chapter(
character_state_file = os.path.join(filepath, "character_state.txt") character_state_file = os.path.join(filepath, "character_state.txt")
old_character_state = read_file(character_state_file) old_character_state = read_file(character_state_file)
# 1) 更新全局摘要 # 调用 LLM 更新全局摘要
model = ChatOpenAI( llm_adapter = create_llm_adapter(
model=model_name, interface_format="openai",
base_url=base_url,
model_name=model_name,
api_key=api_key, api_key=api_key,
base_url=ensure_openai_base_url_has_v1(base_url),
temperature=temperature temperature=temperature
) )
prompt_summary = summary_prompt.format( prompt_summary = summary_prompt.format(
chapter_text=chapter_text, chapter_text=chapter_text,
global_summary=old_global_summary 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(): if not new_global_summary.strip():
new_global_summary = old_global_summary 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( prompt_char_state = update_character_state_prompt.format(
chapter_text=chapter_text, chapter_text=chapter_text,
old_state=old_character_state 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(): if not new_char_state.strip():
new_char_state = old_character_state new_char_state = old_character_state
# 写回文件 # 写回
clear_file_content(global_summary_file) clear_file_content(global_summary_file)
save_string_to_txt(new_global_summary, global_summary_file) save_string_to_txt(new_global_summary, global_summary_file)
clear_file_content(character_state_file) clear_file_content(character_state_file)
save_string_to_txt(new_char_state, character_state_file) save_string_to_txt(new_char_state, character_state_file)
# 3) 更新向量库 # 更新向量库
update_vector_store( embedding_adapter = create_embedding_adapter(
api_key=embedding_api_key, embedding_interface_format,
base_url=embedding_url, embedding_api_key,
new_chapter=chapter_text, embedding_url,
interface_format=embedding_interface_format, embedding_model_name
embedding_model_name=embedding_model_name,
filepath=filepath
) )
update_vector_store(embedding_adapter, chapter_text, filepath)
logging.info(f"Chapter {novel_number} has been finalized.") logging.info(f"Chapter {novel_number} has been finalized.")
def enrich_chapter_text( def enrich_chapter_text(
chapter_text: str, chapter_text: str,
word_number: int, word_number: int,
@@ -760,28 +667,25 @@ def enrich_chapter_text(
model_name: str, model_name: str,
temperature: float temperature: float
) -> str: ) -> str:
model = ChatOpenAI( llm_adapter = create_llm_adapter(
model=model_name, interface_format="openai",
base_url=base_url,
model_name=model_name,
api_key=api_key, api_key=api_key,
base_url=ensure_openai_base_url_has_v1(base_url),
temperature=temperature 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。 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：
原章节内容： {chapter_text}
{chapter_text}""" """
enriched_text = invoke_with_cleaning(model, prompt) enriched_text = invoke_with_cleaning(llm_adapter, prompt)
return enriched_text if enriched_text else chapter_text return enriched_text if enriched_text else chapter_text
# ============ 导入知识文件到向量库 ============
# ============ 导入外部知识文本到向量库 ============
def advanced_split_content(content: str, def advanced_split_content(content: str,
similarity_threshold: float = 0.7, similarity_threshold: float = 0.7,
max_length: int = 500) -> List[str]: 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) nltk.download('punkt', quiet=True)
sentences = nltk.sent_tokenize(content) sentences = nltk.sent_tokenize(content)
if not sentences: if not sentences:
@@ -837,24 +741,17 @@ def import_knowledge_file(
paragraphs = advanced_split_content(content) paragraphs = advanced_split_content(content)
# 尝试加载已有的向量库 embedding_adapter = create_embedding_adapter(
store = load_vector_store( interface_format=embedding_interface_format,
api_key=embedding_api_key, api_key=embedding_api_key,
base_url=embedding_url if embedding_url else "http://localhost:11434/api", base_url=embedding_url if embedding_url else "http://localhost:11434/api",
interface_format=embedding_interface_format, model_name=embedding_model_name
embedding_model_name=embedding_model_name,
filepath=filepath
) )
store = load_vector_store(embedding_adapter, filepath)
if not store: if not store:
logging.info("Vector store does not exist. Initializing a new one for knowledge import...") logging.info("Vector store does not exist. Initializing a new one for knowledge import...")
init_vector_store( init_vector_store(embedding_adapter, paragraphs, filepath)
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
)
else: else:
docs = [Document(page_content=str(p)) for p in paragraphs] docs = [Document(page_content=str(p)) for p in paragraphs]
store.add_documents(docs) store.add_documents(docs)
tooltips.py
+27
View File
@@ -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。"
}
ui.py
+20 -22
View File
@@ -10,6 +10,7 @@ import traceback
from config_manager import load_config, save_config from config_manager import load_config, save_config
from utils import read_file, save_string_to_txt, clear_file_content from utils import read_file, save_string_to_txt, clear_file_content
from novel_generator import ( from novel_generator import (
Novel_architecture_generate, Novel_architecture_generate,
Chapter_blueprint_generate, Chapter_blueprint_generate,
@@ -17,21 +18,17 @@ from novel_generator import (
finalize_chapter, finalize_chapter,
import_knowledge_file, import_knowledge_file,
clear_vector_store, clear_vector_store,
get_last_n_chapters_text, get_last_n_chapters_text
) )
from consistency_checker import check_consistency from consistency_checker import check_consistency
def log_error(message: str): 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()}") logging.error(f"{message}\n{traceback.format_exc()}")
ctk.set_appearance_mode("System") ctk.set_appearance_mode("System")
ctk.set_default_color_theme("blue") ctk.set_default_color_theme("blue")
class NovelGeneratorGUI: class NovelGeneratorGUI:
def __init__(self, master): def __init__(self, master):
self.master = master self.master = master
@@ -71,14 +68,14 @@ class NovelGeneratorGUI:
self.chapter_num_var = ctk.StringVar(value="1") 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.characters_involved_var = ctk.StringVar(value="")
self.key_items_var = ctk.StringVar(value="") self.key_items_var = ctk.StringVar(value="")
self.scene_location_var = ctk.StringVar(value="") self.scene_location_var = ctk.StringVar(value="")
self.time_constraint_var = ctk.StringVar(value="") self.time_constraint_var = ctk.StringVar(value="")
# UI 布局 # 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.tabview.pack(fill="both", expand=True)
self.main_tab = self.tabview.add("Main Functions") self.main_tab = self.tabview.add("Main Functions")
@@ -197,7 +194,7 @@ class NovelGeneratorGUI:
self.build_optional_buttons_area(start_row=2) self.build_optional_buttons_area(start_row=2)
def build_config_tabview(self): 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.config_tabview.grid(row=0, column=0, sticky="we", padx=5, pady=5)
self.ai_config_tab = self.config_tabview.add("LLM Model settings") self.ai_config_tab = self.config_tabview.add("LLM Model settings")
@@ -256,8 +253,8 @@ class NovelGeneratorGUI:
temp_scale = ctk.CTkSlider( temp_scale = ctk.CTkSlider(
self.ai_config_tab, self.ai_config_tab,
from_=0.0, to=1.0, from_=0.0, to=2.0,
number_of_steps=100, number_of_steps=200,
command=update_temp_label, command=update_temp_label,
variable=self.temperature_var 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 = 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") 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") self.topic_text.grid(row=0, column=1, padx=5, pady=5, sticky="nsew")
if self.topic_default: if self.topic_default:
self.topic_text.insert("0.0", self.topic_default) self.topic_text.insert("0.0", self.topic_default)
@@ -384,7 +381,7 @@ class NovelGeneratorGUI:
# 用户指导 # 用户指导
guide_label = ctk.CTkLabel(self.params_frame, text="本章指导:", font=("Microsoft YaHei", 12)) 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") 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") self.user_guide_text.grid(row=5, column=1, padx=5, pady=5, sticky="nsew")
# 新增：四个可选元素 # 新增：四个可选元素
@@ -528,7 +525,7 @@ class NovelGeneratorGUI:
logging.error(full_message) logging.error(full_message)
self.safe_log(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): def generate_novel_architecture_ui(self):
filepath = self.filepath_var.get().strip() filepath = self.filepath_var.get().strip()
if not filepath: if not filepath:
@@ -568,7 +565,7 @@ class NovelGeneratorGUI:
threading.Thread(target=task, daemon=True).start() 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): def generate_chapter_blueprint_ui(self):
filepath = self.filepath_var.get().strip() filepath = self.filepath_var.get().strip()
if not filepath: if not filepath:
@@ -599,7 +596,7 @@ class NovelGeneratorGUI:
threading.Thread(target=task, daemon=True).start() 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): def generate_chapter_draft_ui(self):
filepath = self.filepath_var.get().strip() filepath = self.filepath_var.get().strip()
if not filepath: if not filepath:
@@ -671,7 +668,7 @@ class NovelGeneratorGUI:
self.chapter_result.insert("0.0", text) self.chapter_result.insert("0.0", text)
self.chapter_result.see("end") self.chapter_result.see("end")
# ------------------ Step4: 定稿章节 ------------------ # ============ Step4: 定稿章节 ============
def finalize_chapter_ui(self): def finalize_chapter_ui(self):
filepath = self.filepath_var.get().strip() filepath = self.filepath_var.get().strip()
if not filepath: if not filepath:
@@ -731,7 +728,7 @@ class NovelGeneratorGUI:
threading.Thread(target=task, daemon=True).start() threading.Thread(target=task, daemon=True).start()
# ------------------ 一致性审校 ------------------ # ============ 一致性审校 (可选) ============
def do_consistency_check(self): def do_consistency_check(self):
filepath = self.filepath_var.get().strip() filepath = self.filepath_var.get().strip()
if not filepath: if not filepath:
@@ -756,7 +753,7 @@ class NovelGeneratorGUI:
self.safe_log("开始一致性审校...") self.safe_log("开始一致性审校...")
result = check_consistency( 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")), character_state=read_file(os.path.join(filepath, "character_state.txt")),
global_summary=read_file(os.path.join(filepath, "global_summary.txt")), global_summary=read_file(os.path.join(filepath, "global_summary.txt")),
chapter_text=chapter_text, chapter_text=chapter_text,
@@ -776,6 +773,7 @@ class NovelGeneratorGUI:
threading.Thread(target=task, daemon=True).start() threading.Thread(target=task, daemon=True).start()
# ============ 导入知识库 ============
def import_knowledge_handler(self): def import_knowledge_handler(self):
selected_file = filedialog.askopenfilename( selected_file = filedialog.askopenfilename(
title="选择要导入的知识库文件", 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.insert("0.0", arcs_text)
text_area.configure(state="disabled") 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): def build_setting_tab(self):
self.setting_tab.rowconfigure(0, weight=0) self.setting_tab.rowconfigure(0, weight=0)
self.setting_tab.rowconfigure(1, weight=1) self.setting_tab.rowconfigure(1, weight=1)
@@ -1032,7 +1031,7 @@ class NovelGeneratorGUI:
save_string_to_txt(content, filename) save_string_to_txt(content, filename)
self.log("已保存对 global_summary.txt 的修改。") 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): def build_chapters_tab(self):
self.chapters_view_tab.rowconfigure(0, weight=0) self.chapters_view_tab.rowconfigure(0, weight=0)
self.chapters_view_tab.rowconfigure(1, weight=1) self.chapters_view_tab.rowconfigure(1, weight=1)
@@ -1165,7 +1164,6 @@ class NovelGeneratorGUI:
else: else:
messagebox.showinfo("提示", "已经是最后一章了。") messagebox.showinfo("提示", "已经是最后一章了。")
if __name__ == "__main__": if __name__ == "__main__":
app = ctk.CTk() app = ctk.CTk()
gui = NovelGeneratorGUI(app) gui = NovelGeneratorGUI(app)