DB-GPT/docs/modules/knowledge/word/word_embedding.md

WordEmbedding

word embedding can import word doc/docx 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 WordEmbedding(SourceEmbedding):
    """word embedding for read word document."""

    def __init__(self, file_path, vector_store_config):
        """Initialize with word 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 word path."""
        loader = UnstructuredWordDocumentLoader(self.file_path)
        textsplitter = CHNDocumentSplitter(
            pdf=True, sentence_size=CFG.KNOWLEDGE_CHUNK_SIZE
        )
        return loader.load_and_split(textsplitter)

data_process() method allows you to pre processing your ways

@register
    def data_process(self, documents: List[Document]):
        i = 0
        for d in documents:
            documents[i].page_content = d.page_content.replace("\n", "")
            i += 1
        return documents