DB-GPT/pilot/embedding_engine/url_embedding.py

from typing import List

from bs4 import BeautifulSoup
from langchain.document_loaders import WebBaseLoader
from langchain.schema import Document
from langchain.text_splitter import SpacyTextSplitter, RecursiveCharacterTextSplitter

from pilot.embedding_engine import SourceEmbedding, register



class URLEmbedding(SourceEmbedding):
    """url embedding for read url document."""

    def __init__(self, file_path, vector_store_config, text_splitter=None):
        """Initialize url word path."""
        super().__init__(file_path, vector_store_config, text_splitter=None)
        self.file_path = file_path
        self.vector_store_config = vector_store_config
        self.text_splitter = text_splitter or None

    @register
    def read(self):
        """Load from url path."""
        loader = WebBaseLoader(web_path=self.file_path)
        if self.text_splitter is None:
            try:
                self.text_splitter = SpacyTextSplitter(
                    pipeline="zh_core_web_sm",
                    chunk_size=100,
                    chunk_overlap=100,
                )
            except Exception:
                self.text_splitter = RecursiveCharacterTextSplitter(
                    chunk_size=100, chunk_overlap=50
                )

        return loader.load_and_split(self.text_splitter)

    @register
    def data_process(self, documents: List[Document]):
        i = 0
        for d in documents:
            content = d.page_content.replace("\n", "")
            soup = BeautifulSoup(content, "html.parser")
            for tag in soup(["!doctype", "meta"]):
                tag.extract()
            documents[i].page_content = soup.get_text()
            i += 1
        return documents