831 lines
27 KiB
Python
831 lines
27 KiB
Python
# novel_generator.py
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# -*- coding: utf-8 -*-
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import os
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import logging
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import re
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import time
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import traceback
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from typing import List, Optional
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# langchain 相关
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from langchain_openai import ChatOpenAI, OpenAIEmbeddings
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from langchain_chroma import Chroma
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from chromadb.config import Settings
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from langchain.docstore.document import Document
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# nltk、sentence_transformers 及文本处理相关
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import nltk
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import math
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from sentence_transformers import SentenceTransformer
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from sklearn.metrics.pairwise import cosine_similarity
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# 工具函数
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from utils import (
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read_file, append_text_to_file, clear_file_content,
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save_string_to_txt
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)
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# prompt模板
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from prompt_definitions import (
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# 设定相关
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set_prompt, character_prompt, dark_lines_prompt,
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finalize_setting_prompt, novel_directory_prompt,
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# 写作流程相关
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summary_prompt, update_character_state_prompt,
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chapter_outline_prompt, chapter_write_prompt
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)
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# Ollama嵌入 (如使用Ollama时需要)
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from embedding_ollama import OllamaEmbeddings
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# 用于目录解析章节标题/简介
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from chapter_directory_parser import get_chapter_info_from_directory
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logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s")
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# ============ 帮助函数 ============
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def remove_think_tags(text: str) -> str:
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"""移除 <think>...</think> 包裹的内容"""
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return re.sub(r'<think>.*?</think>', '', text, flags=re.DOTALL)
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def debug_log(prompt: str, response_content: str):
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logging.info(f"\n[######################################### Prompt #########################################]\n {prompt}\n")
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logging.info(f"\n[######################################### Response #########################################]\n {response_content}\n")
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def invoke_with_cleaning(model: ChatOpenAI, prompt: str) -> str:
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"""通用封装：调用模型并移除 <think>...</think> 文本，记录日志后返回"""
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response = model.invoke(prompt)
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if not response:
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logging.warning("No response from model.")
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return ""
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cleaned_text = remove_think_tags(response.content)
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debug_log(prompt, cleaned_text)
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return cleaned_text.strip()
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def ensure_openai_base_url_has_v1(url: str) -> str:
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"""
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若用户输入的 url 不包含 '/v1'，则在末尾追加 '/v1'。
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"""
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import re
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url = url.strip()
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if not url:
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return url
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if not re.search(r'/v\d+$', url):
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if '/v1' not in url:
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url = url.rstrip('/') + '/v1'
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return url
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def is_using_ollama_api(interface_format: str) -> bool:
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return interface_format.lower() == "ollama"
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def is_using_ml_studio_api(interface_format: str) -> bool:
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return interface_format.lower() == "ml studio"
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# ============ 获取 vectorstore 路径 ============
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def get_vectorstore_dir(filepath: str) -> str:
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"""
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返回存储向量库的本地路径：
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在用户指定的 `filepath` 下创建/使用 'vectorstore' 文件夹。
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"""
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return os.path.join(filepath, "vectorstore")
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# ============ 创建 Embeddings 对象 ============
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def create_embeddings_object(
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api_key: str,
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base_url: str,
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interface_format: str,
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embedding_model_name: str
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):
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"""
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根据 embedding_interface_format，选择 Ollama 或 OpenAIEmbeddings 等不同后端。
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"""
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if is_using_ollama_api(interface_format):
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fixed_url = base_url.rstrip("/")
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return OllamaEmbeddings(
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model_name=embedding_model_name,
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base_url=fixed_url
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)
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else:
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# OpenAI 或 ML Studio 均使用 OpenAIEmbeddings，注意 base_url 可能需要 ensure /v1
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fixed_url = ensure_openai_base_url_has_v1(base_url)
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return OpenAIEmbeddings(
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openai_api_key=api_key,
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openai_api_base=fixed_url,
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model=embedding_model_name
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)
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# ============ 向量库相关操作 ============
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def clear_vector_store(filepath: str) -> bool:
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"""
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返回值表示是否成功清空向量库。
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"""
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import shutil
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store_dir = get_vectorstore_dir(filepath)
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if not os.path.exists(store_dir):
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logging.info("No vector store found to clear.")
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return False
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try:
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if os.path.exists(store_dir):
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shutil.rmtree(store_dir)
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logging.info(f"Vector store directory '{store_dir}' removed.")
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return True
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except Exception as e:
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logging.error(f"程序正在运行，无法删除，请在程序关闭后手动前往 {store_dir} 删除目录。\n {str(e)}")
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traceback.print_exc()
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return False
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def init_vector_store(
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api_key: str,
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base_url: str,
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interface_format: str,
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embedding_model_name: str,
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texts: List[str],
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filepath: str
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) -> Chroma:
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"""
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在 filepath 下创建/加载一个 Chroma 向量库并插入 texts。
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"""
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store_dir = get_vectorstore_dir(filepath)
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os.makedirs(store_dir, exist_ok=True)
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embeddings = create_embeddings_object(
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api_key=api_key,
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base_url=base_url,
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interface_format=interface_format,
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embedding_model_name=embedding_model_name
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)
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documents = [Document(page_content=str(t)) for t in texts]
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vectorstore = Chroma.from_documents(
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documents,
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embedding=embeddings,
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persist_directory=store_dir,
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client_settings=Settings(anonymized_telemetry=False),
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collection_name="novel_collection"
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)
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return vectorstore
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def load_vector_store(
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api_key: str,
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base_url: str,
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interface_format: str,
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embedding_model_name: str,
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filepath: str
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) -> Optional[Chroma]:
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"""
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读取已存在的 Chroma 向量库。若不存在则返回 None。
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"""
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store_dir = get_vectorstore_dir(filepath)
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if not os.path.exists(store_dir):
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logging.info("Vector store not found. Will return None.")
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return None
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embeddings = create_embeddings_object(
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api_key=api_key,
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base_url=base_url,
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interface_format=interface_format,
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embedding_model_name=embedding_model_name
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)
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return Chroma(
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persist_directory=store_dir,
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embedding_function=embeddings,
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client_settings=Settings(anonymized_telemetry=False),
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collection_name="novel_collection"
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)
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def split_by_length(text: str, max_length: int = 500) -> List[str]:
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segments = []
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start_idx = 0
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while start_idx < len(text):
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end_idx = min(start_idx + max_length, len(text))
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segment = text[start_idx:end_idx]
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segments.append(segment.strip())
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start_idx = end_idx
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return segments
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def split_text_for_vectorstore(chapter_text: str,
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max_length: int = 500,
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similarity_threshold: float = 0.7) -> List[str]:
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"""
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对新的章节文本进行分段后，再用于存入向量库。
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"""
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if not chapter_text.strip():
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return []
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nltk.download('punkt', quiet=True)
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nltk.download('punkt_tab', quiet=True)
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sentences = nltk.sent_tokenize(chapter_text)
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if not sentences:
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return []
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# 先对相近句子进行合并
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model = SentenceTransformer('paraphrase-MiniLM-L6-v2')
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embeddings = model.encode(sentences)
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merged_paragraphs = []
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current_sentences = [sentences[0]]
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current_embedding = embeddings[0]
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for i in range(1, len(sentences)):
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sim = cosine_similarity([current_embedding], [embeddings[i]])[0][0]
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if sim >= similarity_threshold:
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current_sentences.append(sentences[i])
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current_embedding = (current_embedding + embeddings[i]) / 2.0
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else:
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merged_paragraphs.append(" ".join(current_sentences))
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current_sentences = [sentences[i]]
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current_embedding = embeddings[i]
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if current_sentences:
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merged_paragraphs.append(" ".join(current_sentences))
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# 再对合并好的段落做 max_length 切分
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final_segments = []
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for para in merged_paragraphs:
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if len(para) > max_length:
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sub_segments = split_by_length(para, max_length=max_length)
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final_segments.extend(sub_segments)
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else:
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final_segments.append(para)
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return final_segments
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def update_vector_store(
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api_key: str,
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base_url: str,
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new_chapter: str,
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interface_format: str,
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embedding_model_name: str,
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filepath: str
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):
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"""
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将最新章节文本插入到向量库中。若库不存在则初始化。
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"""
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splitted_texts = split_text_for_vectorstore(new_chapter)
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if not splitted_texts:
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logging.warning("No valid text to insert into vector store. Skipping.")
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return
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store = load_vector_store(
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api_key=api_key,
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base_url=base_url,
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interface_format=interface_format,
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embedding_model_name=embedding_model_name,
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filepath=filepath
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)
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if not store:
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logging.info("Vector store does not exist. Initializing a new one for new chapter...")
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init_vector_store(
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api_key=api_key,
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base_url=base_url,
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interface_format=interface_format,
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embedding_model_name=embedding_model_name,
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texts=splitted_texts,
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filepath=filepath
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)
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return
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docs = [Document(page_content=str(t)) for t in splitted_texts]
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store.add_documents(docs)
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logging.info("Vector store updated with the new chapter splitted segments.")
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def get_relevant_context_from_vector_store(
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api_key: str,
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base_url: str,
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query: str,
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interface_format: str,
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embedding_model_name: str,
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filepath: str,
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k: int = 2
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) -> str:
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"""
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从向量库中检索与 query 最相关的 k 条文本，拼接后返回。
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"""
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store = load_vector_store(
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api_key=api_key,
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base_url=base_url,
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interface_format=interface_format,
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embedding_model_name=embedding_model_name,
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filepath=filepath
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)
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if not store:
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logging.info("No vector store found. Returning empty context.")
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return ""
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docs = store.similarity_search(query, k=k)
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if not docs:
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logging.info(f"No relevant documents found for query '{query}'. Returning empty context.")
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return ""
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combined = "\n".join([d.page_content for d in docs])
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return combined
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# ============ 1. 生成小说“设定” (Novel_setting.txt) ============
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def Novel_setting_generate(
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api_key: str,
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base_url: str,
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llm_model: str,
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topic: str,
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genre: str,
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number_of_chapters: int,
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word_number: int,
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filepath: str,
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temperature: float = 0.7
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) -> None:
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os.makedirs(filepath, exist_ok=True)
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model = ChatOpenAI(
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model=llm_model,
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api_key=api_key,
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base_url=ensure_openai_base_url_has_v1(base_url),
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temperature=temperature
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)
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# Step1: 基础设定
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prompt_base = set_prompt.format(
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topic=topic,
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genre=genre,
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number_of_chapters=number_of_chapters,
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word_number=word_number
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)
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base_setting = invoke_with_cleaning(model, prompt_base)
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# Step2: 角色设定
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prompt_char = character_prompt.format(
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novel_setting=base_setting
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)
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character_setting = invoke_with_cleaning(model, prompt_char)
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# Step3: 暗线/雷点
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prompt_dark = dark_lines_prompt.format(
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character_info=character_setting
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)
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dark_lines = invoke_with_cleaning(model, prompt_dark)
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# Step4: 最终整合
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prompt_final = finalize_setting_prompt.format(
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novel_setting_base=base_setting,
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character_setting=character_setting,
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dark_lines=dark_lines
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)
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final_novel_setting = invoke_with_cleaning(model, prompt_final)
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filename_set = os.path.join(filepath, "Novel_setting.txt")
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clear_file_content(filename_set)
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final_novel_setting_cleaned = final_novel_setting.replace('#', '').replace('*', '')
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save_string_to_txt(final_novel_setting_cleaned, filename_set)
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logging.info("Novel_setting.txt has been generated successfully.")
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# ============ 2. 生成小说目录 (Novel_directory.txt) ============
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def Novel_directory_generate(
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api_key: str,
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base_url: str,
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llm_model: str,
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number_of_chapters: int,
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filepath: str,
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temperature: float = 0.7
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) -> None:
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filename_set = os.path.join(filepath, "Novel_setting.txt")
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final_novel_setting = read_file(filename_set).strip()
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if not final_novel_setting:
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logging.warning("Novel_setting.txt 内容为空，请先生成小说设定。")
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return
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model = ChatOpenAI(
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model=llm_model,
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api_key=api_key,
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base_url=ensure_openai_base_url_has_v1(base_url),
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temperature=temperature
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)
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prompt_dir = novel_directory_prompt.format(
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final_novel_setting=final_novel_setting,
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number_of_chapters=number_of_chapters
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)
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final_novel_directory = invoke_with_cleaning(model, prompt_dir)
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if not final_novel_directory.strip():
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logging.warning("Novel_directory生成结果为空。")
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return
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filename_dir = os.path.join(filepath, "Novel_directory.txt")
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clear_file_content(filename_dir)
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final_novel_directory_cleaned = final_novel_directory.replace('#', '').replace('*', '')
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save_string_to_txt(final_novel_directory_cleaned, filename_dir)
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logging.info("Novel_directory.txt has been generated successfully.")
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# ============ 获取最近 N 章内容，生成短期摘要 ============
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def get_last_n_chapters_text(chapters_dir: str, current_chapter_num: int, n: int = 3) -> List[str]:
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texts = []
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start_chap = max(1, current_chapter_num - n)
|
||
for c in range(start_chap, current_chapter_num):
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chap_file = os.path.join(chapters_dir, f"chapter_{c}.txt")
|
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if os.path.exists(chap_file):
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text = read_file(chap_file).strip()
|
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if text:
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texts.append(text)
|
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if len(texts) < n:
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texts = [''] * (n - len(texts)) + texts
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return texts
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||
|
||
def summarize_recent_chapters(
|
||
llm_model: str,
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||
api_key: str,
|
||
base_url: str,
|
||
temperature: float,
|
||
chapters_text_list: List[str]
|
||
) -> str:
|
||
if not chapters_text_list:
|
||
return ""
|
||
if all(not txt.strip() for txt in chapters_text_list):
|
||
return "暂无摘要。"
|
||
|
||
model = ChatOpenAI(
|
||
model=llm_model,
|
||
api_key=api_key,
|
||
base_url=ensure_openai_base_url_has_v1(base_url),
|
||
temperature=temperature
|
||
)
|
||
|
||
combined_text = "\n".join(chapters_text_list)
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||
prompt = f"""你是一名资深长篇小说写作辅助AI，下面是最近几章的合并文本：
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||
{combined_text}
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||
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||
请用中文输出不超过500字的摘要，只包含主要剧情进展、角色变化、冲突焦点等要点："""
|
||
|
||
summary_text = invoke_with_cleaning(model, prompt)
|
||
if not summary_text:
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||
return (combined_text[:800] + "...") if len(combined_text) > 800 else combined_text
|
||
return summary_text
|
||
|
||
|
||
# ============ 剧情要点/冲突 ============
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||
PLOT_ARCS_PROMPT = """\
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||
下面是新生成的章节内容:
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||
{chapter_text}
|
||
|
||
这里是已记录的剧情要点/未解决冲突(可能为空):
|
||
{old_plot_arcs}
|
||
|
||
请基于新的章节内容，提炼本章引入或延续的悬念、冲突、角色暗线等，将其合并到旧的剧情要点中。
|
||
若有新的冲突则添加，若有已解决/不再重要的冲突可标注或移除。
|
||
最终输出更新后的剧情要点列表，以帮助后续保持故事整体的一致性和悬念延续。
|
||
"""
|
||
|
||
def update_plot_arcs(
|
||
chapter_text: str,
|
||
old_plot_arcs: str,
|
||
api_key: str,
|
||
base_url: str,
|
||
model_name: str,
|
||
temperature: float
|
||
) -> str:
|
||
model = ChatOpenAI(
|
||
model=model_name,
|
||
api_key=api_key,
|
||
base_url=ensure_openai_base_url_has_v1(base_url),
|
||
temperature=temperature
|
||
)
|
||
prompt = PLOT_ARCS_PROMPT.format(
|
||
chapter_text=chapter_text,
|
||
old_plot_arcs=old_plot_arcs
|
||
)
|
||
arcs_text = invoke_with_cleaning(model, prompt)
|
||
if not arcs_text:
|
||
logging.warning("update_plot_arcs: No response or empty result.")
|
||
return old_plot_arcs
|
||
return arcs_text
|
||
|
||
|
||
# ============ 生成章节草稿 ============
|
||
def generate_chapter_draft(
|
||
novel_settings: str,
|
||
global_summary: str,
|
||
character_state: str,
|
||
recent_chapters_summary: str,
|
||
user_guidance: str,
|
||
api_key: str,
|
||
base_url: str,
|
||
model_name: str,
|
||
novel_number: int,
|
||
word_number: int,
|
||
temperature: float,
|
||
novel_novel_directory: str,
|
||
filepath: str,
|
||
interface_format: str,
|
||
embedding_model_name: str,
|
||
embedding_base_url: str,
|
||
embedding_retrieval_k: int = 4
|
||
) -> str:
|
||
# 1) 根据目录解析标题、简介
|
||
chapter_info = get_chapter_info_from_directory(novel_novel_directory, novel_number)
|
||
chapter_title = chapter_info["chapter_title"]
|
||
chapter_brief = chapter_info["chapter_brief"]
|
||
|
||
# 合并要检索的文本（用户指导 + 章节简介 + 最近摘要）
|
||
combined_query_parts = []
|
||
if user_guidance.strip():
|
||
combined_query_parts.append(user_guidance)
|
||
if chapter_brief.strip():
|
||
combined_query_parts.append(chapter_brief)
|
||
if recent_chapters_summary.strip():
|
||
combined_query_parts.append(recent_chapters_summary)
|
||
# 额外加一个关键字
|
||
combined_query_parts.append("回顾剧情")
|
||
|
||
merged_query_str = "\n".join(combined_query_parts)
|
||
|
||
# 2) 从向量库检索上下文
|
||
relevant_context = get_relevant_context_from_vector_store(
|
||
api_key=api_key,
|
||
base_url=embedding_base_url if embedding_base_url else base_url,
|
||
query=merged_query_str,
|
||
interface_format=interface_format,
|
||
embedding_model_name=embedding_model_name,
|
||
filepath=filepath,
|
||
k=embedding_retrieval_k
|
||
)
|
||
if not relevant_context.strip():
|
||
relevant_context = "暂无相关内容。"
|
||
|
||
# 3) 生成本章大纲
|
||
model = ChatOpenAI(
|
||
model=model_name,
|
||
api_key=api_key,
|
||
base_url=ensure_openai_base_url_has_v1(base_url),
|
||
temperature=temperature
|
||
)
|
||
|
||
outline_prompt_text = chapter_outline_prompt.format(
|
||
novel_setting=novel_settings,
|
||
character_state=character_state + "\n\n【检索到的上下文】\n" + relevant_context,
|
||
global_summary=global_summary,
|
||
novel_number=novel_number,
|
||
chapter_title=chapter_title,
|
||
chapter_brief=chapter_brief
|
||
)
|
||
outline_prompt_text += f"\n\n【最近几章摘要】\n{recent_chapters_summary}"
|
||
outline_prompt_text += f"\n\n【用户指导】\n{user_guidance if user_guidance else '（无）'}"
|
||
|
||
chapter_outline = invoke_with_cleaning(model, outline_prompt_text)
|
||
|
||
outlines_dir = os.path.join(filepath, "outlines")
|
||
os.makedirs(outlines_dir, exist_ok=True)
|
||
outline_file = os.path.join(outlines_dir, f"outline_{novel_number}.txt")
|
||
clear_file_content(outline_file)
|
||
save_string_to_txt(chapter_outline, outline_file)
|
||
|
||
# 4) 生成正文草稿
|
||
writing_prompt_text = chapter_write_prompt.format(
|
||
novel_setting=novel_settings,
|
||
character_state=character_state + "\n\n【检索到的上下文】\n" + relevant_context,
|
||
global_summary=global_summary,
|
||
chapter_outline=chapter_outline,
|
||
word_number=word_number,
|
||
novel_number=novel_number,
|
||
chapter_title=chapter_title,
|
||
chapter_brief=chapter_brief
|
||
)
|
||
writing_prompt_text += f"\n\n【最近几章摘要】\n{recent_chapters_summary}"
|
||
writing_prompt_text += f"\n\n【用户指导】\n{user_guidance if user_guidance else '（无）'}"
|
||
|
||
chapter_content = invoke_with_cleaning(model, writing_prompt_text)
|
||
|
||
chapters_dir = os.path.join(filepath, "chapters")
|
||
os.makedirs(chapters_dir, exist_ok=True)
|
||
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)
|
||
|
||
logging.info(f"[Draft] Chapter {novel_number} generated as a draft.")
|
||
return chapter_content
|
||
|
||
|
||
# ============ 定稿章节 ============
|
||
def finalize_chapter(
|
||
novel_number: int,
|
||
word_number: int,
|
||
api_key: str,
|
||
base_url: str,
|
||
interface_format: str,
|
||
embedding_model_name: str,
|
||
model_name: str,
|
||
temperature: float,
|
||
filepath: str,
|
||
embedding_base_url: str,
|
||
embedding_api_key: str
|
||
):
|
||
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()
|
||
if not chapter_text:
|
||
logging.warning(f"Chapter {novel_number} is empty, cannot finalize.")
|
||
return
|
||
|
||
character_state_file = os.path.join(filepath, "character_state.txt")
|
||
global_summary_file = os.path.join(filepath, "global_summary.txt")
|
||
plot_arcs_file = os.path.join(filepath, "plot_arcs.txt")
|
||
|
||
old_char_state = read_file(character_state_file)
|
||
old_global_summary = read_file(global_summary_file)
|
||
old_plot_arcs = read_file(plot_arcs_file)
|
||
|
||
# 篇幅不足，二次扩写
|
||
if len(chapter_text) < 0.8 * word_number:
|
||
logging.info("Chapter text is shorter than 80% of desired length. Enriching...")
|
||
chapter_text = enrich_chapter_text(
|
||
chapter_text=chapter_text,
|
||
word_number=word_number,
|
||
api_key=api_key,
|
||
base_url=base_url,
|
||
model_name=model_name,
|
||
temperature=temperature
|
||
)
|
||
clear_file_content(chapter_file)
|
||
save_string_to_txt(chapter_text, chapter_file)
|
||
|
||
# 更新全局摘要
|
||
model = ChatOpenAI(
|
||
model=model_name,
|
||
api_key=api_key,
|
||
base_url=ensure_openai_base_url_has_v1(base_url),
|
||
temperature=temperature
|
||
)
|
||
|
||
def update_global_summary(chapter_text: str, old_summary: str) -> str:
|
||
prompt = summary_prompt.format(
|
||
chapter_text=chapter_text,
|
||
global_summary=old_summary
|
||
)
|
||
return invoke_with_cleaning(model, prompt) or old_summary
|
||
|
||
new_global_summary = update_global_summary(chapter_text, old_global_summary)
|
||
|
||
# 更新角色状态
|
||
def update_character_state(chapter_text: str, old_state: str) -> str:
|
||
prompt = update_character_state_prompt.format(
|
||
chapter_text=chapter_text,
|
||
old_state=old_state
|
||
)
|
||
return invoke_with_cleaning(model, prompt) or old_state
|
||
|
||
new_char_state = update_character_state(chapter_text, old_char_state)
|
||
|
||
# 更新剧情要点
|
||
new_plot_arcs = update_plot_arcs(
|
||
chapter_text=chapter_text,
|
||
old_plot_arcs=old_plot_arcs,
|
||
api_key=api_key,
|
||
base_url=base_url,
|
||
model_name=model_name,
|
||
temperature=temperature
|
||
)
|
||
|
||
# 写回文件
|
||
clear_file_content(character_state_file)
|
||
save_string_to_txt(new_char_state, character_state_file)
|
||
|
||
clear_file_content(global_summary_file)
|
||
save_string_to_txt(new_global_summary, global_summary_file)
|
||
|
||
clear_file_content(plot_arcs_file)
|
||
save_string_to_txt(new_plot_arcs, plot_arcs_file)
|
||
|
||
# 更新向量库（此时用 embedding_api_key/embedding_base_url）
|
||
update_vector_store(
|
||
api_key=embedding_api_key,
|
||
base_url=embedding_base_url if embedding_base_url else base_url,
|
||
new_chapter=chapter_text,
|
||
interface_format=interface_format,
|
||
embedding_model_name=embedding_model_name,
|
||
filepath=filepath
|
||
)
|
||
|
||
logging.info(f"Chapter {novel_number} has been finalized.")
|
||
|
||
|
||
def enrich_chapter_text(
|
||
chapter_text: str,
|
||
word_number: int,
|
||
api_key: str,
|
||
base_url: str,
|
||
model_name: str,
|
||
temperature: float
|
||
) -> str:
|
||
model = ChatOpenAI(
|
||
model=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)
|
||
return enriched_text if enriched_text else chapter_text
|
||
|
||
|
||
# ============ 导入外部知识文本到向量库 ============
|
||
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:
|
||
return []
|
||
|
||
model = SentenceTransformer('paraphrase-MiniLM-L6-v2')
|
||
embeddings = model.encode(sentences)
|
||
|
||
merged_paragraphs = []
|
||
current_sentences = [sentences[0]]
|
||
current_embedding = embeddings[0]
|
||
|
||
for i in range(1, len(sentences)):
|
||
sim = cosine_similarity([current_embedding], [embeddings[i]])[0][0]
|
||
if sim >= similarity_threshold:
|
||
current_sentences.append(sentences[i])
|
||
current_embedding = (current_embedding + embeddings[i]) / 2.0
|
||
else:
|
||
merged_paragraphs.append(" ".join(current_sentences))
|
||
current_sentences = [sentences[i]]
|
||
current_embedding = embeddings[i]
|
||
|
||
if current_sentences:
|
||
merged_paragraphs.append(" ".join(current_sentences))
|
||
|
||
final_segments = []
|
||
for para in merged_paragraphs:
|
||
if len(para) > max_length:
|
||
sub_segments = split_by_length(para, max_length=max_length)
|
||
final_segments.extend(sub_segments)
|
||
else:
|
||
final_segments.append(para)
|
||
|
||
return final_segments
|
||
|
||
def import_knowledge_file(
|
||
api_key: str,
|
||
base_url: str,
|
||
interface_format: str,
|
||
embedding_model_name: str,
|
||
file_path: str,
|
||
embedding_base_url: str,
|
||
filepath: str
|
||
):
|
||
logging.info(f"开始导入知识库文件: {file_path}, 接口格式: {interface_format}, 模型: {embedding_model_name}")
|
||
if not os.path.exists(file_path):
|
||
logging.warning(f"知识库文件不存在: {file_path}")
|
||
return
|
||
|
||
content = read_file(file_path)
|
||
if not content.strip():
|
||
logging.warning("知识库文件内容为空。")
|
||
return
|
||
|
||
paragraphs = advanced_split_content(content)
|
||
|
||
# 若向量库不存在则初始化，否则追加
|
||
store = load_vector_store(
|
||
api_key=api_key,
|
||
base_url=base_url if base_url else "http://localhost:11434/v1",
|
||
interface_format=interface_format,
|
||
embedding_model_name=embedding_model_name,
|
||
filepath=filepath
|
||
)
|
||
if not store:
|
||
logging.info("Vector store does not exist. Initializing a new one for knowledge import...")
|
||
init_vector_store(
|
||
api_key=api_key,
|
||
base_url=base_url if base_url else "http://localhost:11434/v1",
|
||
interface_format=interface_format,
|
||
embedding_model_name=embedding_model_name,
|
||
texts=paragraphs,
|
||
filepath=filepath
|
||
)
|
||
else:
|
||
docs = [Document(page_content=str(p)) for p in paragraphs]
|
||
store.add_documents(docs)
|
||
logging.info("知识库文件已成功导入至向量库。")
|