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YILING0013
2025-02-06 18:35:28 +08:00
parent bf55e74ac9
commit a2b86a90dc
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novel_generator.py
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@@ -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 <think>...</think> 包裹的内容"""
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"
)
def invoke_with_cleaning(model: ChatOpenAI, 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"""
response = model.invoke(prompt)
def invoke_with_cleaning(llm_adapter, prompt: str) -> str:
"""通用封装：调用 LLM，并移除 <think>...</think> 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)