DB-GPT/docs/modules/knowledge/url/url_embedding.md

URL Embedding

url embedding can import PDF text into a vector knowledge base. The entire embedding process includes the read (loading data), data_process (data processing), and index_to_store (embedding to the vector database) methods.

inheriting the SourceEmbedding

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

    def __init__(self, file_path, vector_store_config):
        """Initialize with url path."""
        super().__init__(file_path, vector_store_config)
        self.file_path = file_path
        self.vector_store_config = vector_store_config

implement read() and data_process() read() method allows you to read data and split data into chunk

@register
    def read(self):
        """Load from url path."""
        loader = WebBaseLoader(web_path=self.file_path)
        if CFG.LANGUAGE == "en":
            text_splitter = CharacterTextSplitter(
                chunk_size=CFG.KNOWLEDGE_CHUNK_SIZE,
                chunk_overlap=20,
                length_function=len,
            )
        else:
            text_splitter = CHNDocumentSplitter(pdf=True, sentence_size=1000)
        return loader.load_and_split(text_splitter)

data_process() method allows you to pre processing your ways

@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