94 lines
3.4 KiB
Python
94 lines
3.4 KiB
Python
#novel_generator/knowledge.py
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# -*- coding: utf-8 -*-
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"""
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知识文件导入至向量库（advanced_split_content、import_knowledge_file）
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"""
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import os
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import logging
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import re
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import traceback
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import nltk
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import warnings
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from utils import read_file
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from novel_generator.vectorstore_utils import load_vector_store, init_vector_store
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from langchain.docstore.document import Document
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# 禁用特定的Torch警告
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warnings.filterwarnings('ignore', message='.*Torch was not compiled with flash attention.*')
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os.environ["TOKENIZERS_PARALLELISM"] = "false"
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logging.basicConfig(
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filename='app.log', # 日志文件名
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filemode='a', # 追加模式（'w' 会覆盖）
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level=logging.INFO, # 记录 INFO 及以上级别的日志
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format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
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datefmt='%Y-%m-%d %H:%M:%S'
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)
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def advanced_split_content(content: str, similarity_threshold: float = 0.7, max_length: int = 500) -> list:
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"""使用基本分段策略"""
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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(content)
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if not sentences:
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return []
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final_segments = []
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current_segment = []
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current_length = 0
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for sentence in sentences:
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sentence_length = len(sentence)
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if current_length + sentence_length > max_length:
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if current_segment:
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final_segments.append(" ".join(current_segment))
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current_segment = [sentence]
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current_length = sentence_length
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else:
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current_segment.append(sentence)
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current_length += sentence_length
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if current_segment:
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final_segments.append(" ".join(current_segment))
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return final_segments
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def import_knowledge_file(
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embedding_api_key: str,
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embedding_url: str,
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embedding_interface_format: str,
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embedding_model_name: str,
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file_path: str,
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filepath: str
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):
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logging.info(f"开始导入知识库文件: {file_path}, 接口格式: {embedding_interface_format}, 模型: {embedding_model_name}")
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if not os.path.exists(file_path):
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logging.warning(f"知识库文件不存在: {file_path}")
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return
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content = read_file(file_path)
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if not content.strip():
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logging.warning("知识库文件内容为空。")
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return
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paragraphs = advanced_split_content(content)
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from embedding_adapters import create_embedding_adapter
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embedding_adapter = create_embedding_adapter(
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embedding_interface_format,
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embedding_api_key,
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embedding_url if embedding_url else "http://localhost:11434/api",
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embedding_model_name
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)
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store = load_vector_store(embedding_adapter, filepath)
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if not store:
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logging.info("Vector store does not exist or load failed. Initializing a new one for knowledge import...")
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store = init_vector_store(embedding_adapter, paragraphs, filepath)
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if store:
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logging.info("知识库文件已成功导入至向量库(新初始化)。")
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else:
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logging.warning("知识库导入失败，跳过。")
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else:
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try:
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docs = [Document(page_content=str(p)) for p in paragraphs]
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store.add_documents(docs)
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logging.info("知识库文件已成功导入至向量库(追加模式)。")
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except Exception as e:
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logging.warning(f"知识库导入失败: {e}")
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traceback.print_exc()
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