From a2b86a90dc2615745fc0c9c8b57d21ca736cfef4 Mon Sep 17 00:00:00 2001 From: YILING0013 Date: Thu, 6 Feb 2025 18:35:28 +0800 Subject: [PATCH] =?UTF-8?q?=E4=B8=AD=E9=80=94=E4=BF=9D=E5=AD=98?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- embedding_adapters.py | 122 ++++++++++ llm_adapters.py | 148 ++++++++++++ novel_generator.py | 515 +++++++++++++++++------------------------- tooltips.py | 27 +++ ui.py | 42 ++-- 5 files changed, 523 insertions(+), 331 deletions(-) create mode 100644 embedding_adapters.py create mode 100644 llm_adapters.py create mode 100644 tooltips.py diff --git a/embedding_adapters.py b/embedding_adapters.py new file mode 100644 index 0000000..8c39f51 --- /dev/null +++ b/embedding_adapters.py @@ -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}") diff --git a/llm_adapters.py b/llm_adapters.py new file mode 100644 index 0000000..2fa5e37 --- /dev/null +++ b/llm_adapters.py @@ -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}") diff --git a/novel_generator.py b/novel_generator.py index 2337d5b..9e6206a 100644 --- a/novel_generator.py +++ b/novel_generator.py @@ -7,8 +7,6 @@ import time import traceback from typing import List, Optional, Tuple -# langchain 相关 -from langchain_openai import ChatOpenAI, OpenAIEmbeddings from langchain_chroma import Chroma from chromadb.config import Settings from langchain.docstore.document import Document @@ -37,16 +35,16 @@ from prompt_definitions import ( summarize_recent_chapters_prompt ) -# Ollama嵌入 (如使用Ollama时需要) -from embedding_ollama import OllamaEmbeddings - -# 用于目录解析章节标题/简介 +# 章节目录解析 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") +# ============ 工具函数 ============ -# ============ 基础工具 ============ def remove_think_tags(text: str) -> str: """移除 ... 包裹的内容""" return re.sub(r'.*?', '', text, flags=re.DOTALL) @@ -59,128 +57,79 @@ def debug_log(prompt: str, response_content: str): f"\n[######################################### Response #########################################]\n{response_content}\n" ) -def invoke_with_cleaning(model: ChatOpenAI, prompt: str) -> str: - """通用封装：调用模型并移除 ... 文本，记录日志后返回""" - response = model.invoke(prompt) +def invoke_with_cleaning(llm_adapter, prompt: str) -> str: + """通用封装：调用 LLM，并移除 ... 文本，记录日志后返回""" + response = llm_adapter.invoke(prompt) if not response: logging.warning("No response from model.") return "" - cleaned_text = remove_think_tags(response.content) + cleaned_text = remove_think_tags(response) debug_log(prompt, cleaned_text) 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 ============ + 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") +# ============ 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: - """ - 返回值表示是否成功清空向量库。 - """ import shutil - store_dir = get_vectorstore_dir(filepath) if not os.path.exists(store_dir): logging.info("No vector store found to clear.") return False - try: shutil.rmtree(store_dir) logging.info(f"Vector store directory '{store_dir}' removed.") return True except Exception as e: - logging.error(f"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() return False +# ============ 根据 embedding 接口创建/加载 Chroma ============ + def init_vector_store( - api_key: str, - base_url: str, - interface_format: str, - embedding_model_name: str, + embedding_adapter, texts: List[str], filepath: str ) -> Chroma: """ 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) os.makedirs(store_dir, exist_ok=True) - embeddings = create_embeddings_object( - api_key=api_key, - base_url=base_url, - interface_format=interface_format, - embedding_model_name=embedding_model_name - ) + # 将文本封装为 Document 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( documents, - embedding=embeddings, + embedding=chroma_embedding, 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) diff --git a/tooltips.py b/tooltips.py new file mode 100644 index 0000000..f4e8220 --- /dev/null +++ b/tooltips.py @@ -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。" +} diff --git a/ui.py b/ui.py index 63f6360..3c2a267 100644 --- a/ui.py +++ b/ui.py @@ -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)