140 lines
4.6 KiB
Python
140 lines
4.6 KiB
Python
import os
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import time
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from pathlib import Path
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from langchain_text_splitters import MarkdownTextSplitter
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from src import config
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from src.utils import hashstr, logger
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def split_text_into_chunks(text: str, file_id: str, filename: str, params: dict = {}) -> list[dict]:
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"""
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将文本分割成块，使用 LangChain 的 MarkdownTextSplitter 进行智能分割
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"""
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chunks = []
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chunk_size = params.get("chunk_size", 1000)
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chunk_overlap = params.get("chunk_overlap", 200)
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# 使用 MarkdownTextSplitter 进行智能分割
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# MarkdownTextSplitter 会尝试沿着 Markdown 格式的标题进行分割
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text_splitter = MarkdownTextSplitter(
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chunk_size=chunk_size,
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chunk_overlap=chunk_overlap,
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)
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text_chunks = text_splitter.split_text(text)
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# 转换为标准格式
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for chunk_index, chunk_content in enumerate(text_chunks):
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if chunk_content.strip(): # 跳过空块
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chunks.append(
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{
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"id": f"{file_id}_chunk_{chunk_index}",
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"content": chunk_content.strip(),
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"file_id": file_id,
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"filename": filename,
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"chunk_index": chunk_index,
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"source": filename,
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"chunk_id": f"{file_id}_chunk_{chunk_index}",
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}
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)
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logger.debug(f"Successfully split text into {len(chunks)} chunks using MarkdownTextSplitter")
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return chunks
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def prepare_item_metadata(item: str, content_type: str, db_id: str) -> dict:
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"""
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准备文件或URL的元数据
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"""
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if content_type == "file":
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file_path = Path(item)
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file_id = f"file_{hashstr(str(file_path) + str(time.time()), 6)}"
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file_type = file_path.suffix.lower().replace(".", "")
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filename = file_path.name
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item_path = os.path.relpath(file_path, Path.cwd())
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else: # URL
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file_id = f"url_{hashstr(item + str(time.time()), 6)}"
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file_type = "url"
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filename = f"webpage_{hashstr(item, 6)}.md"
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item_path = item
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return {
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"database_id": db_id,
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"filename": filename,
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"path": item_path,
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"file_type": file_type,
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"status": "processing",
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"created_at": time.time(),
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"file_id": file_id,
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}
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def split_text_into_qa_chunks(
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text: str, file_id: str, filename: str, qa_separator: None | str = None, params: dict = {}
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) -> list[dict]:
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"""
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将文本按QA对分割成块，使用 LangChain 的 CharacterTextSplitter 进行分割"""
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qa_separator = qa_separator or "\n\n"
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text_chunks = text.split(qa_separator)
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# 转换为标准格式
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chunks = []
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for chunk_index, chunk_content in enumerate(text_chunks):
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if chunk_content.strip(): # 跳过空块
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chunk_content = chunk_content.strip()[:4096]
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chunks.append(
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{
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"id": f"{file_id}_qa_chunk_{chunk_index}",
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"content": chunk_content.strip(),
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"file_id": file_id,
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"filename": filename,
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"chunk_index": chunk_index,
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"source": filename,
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"chunk_id": f"{file_id}_qa_chunk_{chunk_index}",
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"chunk_type": "qa", # 标识为QA类型的chunk
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}
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)
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logger.debug(f"QA chunks: {chunks[0]}")
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logger.debug(
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f"Successfully split QA text into {len(chunks)} chunks using CharacterTextSplitter with `{qa_separator=}`"
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)
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return chunks
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def get_embedding_config(embed_info: dict) -> dict:
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"""
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获取嵌入模型配置
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Args:
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embed_info: 嵌入信息字典
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Returns:
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dict: 标准化的嵌入配置
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"""
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config_dict = {}
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try:
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if embed_info:
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config_dict["model"] = embed_info["name"]
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config_dict["api_key"] = os.getenv(embed_info["api_key"], embed_info["api_key"])
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config_dict["base_url"] = embed_info["base_url"]
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config_dict["dimension"] = embed_info.get("dimension", 1024)
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else:
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from src.models import select_embedding_model
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default_model = select_embedding_model(config.embed_model)
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config_dict["model"] = default_model.model
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config_dict["api_key"] = default_model.api_key
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config_dict["base_url"] = default_model.base_url
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config_dict["dimension"] = getattr(default_model, "dimension", 1024)
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except Exception as e:
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logger.error(f"Error in get_embedding_config: {e}, {embed_info}")
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raise ValueError(f"Error in get_embedding_config: {e}")
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logger.debug(f"Embedding config: {config_dict}")
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return config_dict
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