"""Docx Knowledge.""" from typing import Any, Dict, List, Optional, Union import docx from dbgpt.core import Document from dbgpt.rag.knowledge.base import ( ChunkStrategy, DocumentType, Knowledge, KnowledgeType, ) class DocxKnowledge(Knowledge): """Docx Knowledge.""" def __init__( self, file_path: Optional[str] = None, knowledge_type: Any = KnowledgeType.DOCUMENT, encoding: Optional[str] = "utf-8", loader: Optional[Any] = None, metadata: Optional[Dict[str, Union[str, List[str]]]] = None, **kwargs: Any, ) -> None: """Create Docx Knowledge with Knowledge arguments. Args: file_path(str, optional): file path knowledge_type(KnowledgeType, optional): knowledge type encoding(str, optional): csv encoding loader(Any, optional): loader """ super().__init__( path=file_path, knowledge_type=knowledge_type, data_loader=loader, metadata=metadata, **kwargs, ) self._encoding = encoding def _load(self) -> List[Document]: """Load docx document from loader.""" if self._loader: documents = self._loader.load() else: docs = [] doc = docx.Document(self._path) content = [] for i in range(len(doc.paragraphs)): para = doc.paragraphs[i] text = para.text content.append(text) metadata = {"source": self._path} if self._metadata: metadata.update(self._metadata) # type: ignore docs.append(Document(content="\n".join(content), metadata=metadata)) return docs return [Document.langchain2doc(lc_document) for lc_document in documents] @classmethod def support_chunk_strategy(cls) -> List[ChunkStrategy]: """Return support chunk strategy.""" return [ ChunkStrategy.CHUNK_BY_SIZE, ChunkStrategy.CHUNK_BY_PARAGRAPH, ChunkStrategy.CHUNK_BY_SEPARATOR, ] @classmethod def default_chunk_strategy(cls) -> ChunkStrategy: """Return default chunk strategy.""" return ChunkStrategy.CHUNK_BY_SIZE @classmethod def type(cls) -> KnowledgeType: """Return knowledge type.""" return KnowledgeType.DOCUMENT @classmethod def document_type(cls) -> DocumentType: """Return document type.""" return DocumentType.DOCX