DB-GPT/pilot/embedding_engine/word_embedding.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
from typing import List

from langchain.document_loaders import UnstructuredWordDocumentLoader
from langchain.schema import Document
from langchain.text_splitter import CharacterTextSplitter, SpacyTextSplitter

from pilot.configs.config import Config
from pilot.embedding_engine import SourceEmbedding, register

CFG = Config()


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

    @register
    def read(self):
        """Load from word path."""
        loader = UnstructuredWordDocumentLoader(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 = SpacyTextSplitter(
                pipeline="zh_core_web_sm",
                chunk_size=CFG.KNOWLEDGE_CHUNK_SIZE,
                chunk_overlap=100,
            )
        return loader.load_and_split(text_splitter)

    @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