loader = EmbaasBlobLoader()
            blob = Blob.from_path(path="example.mp3")
            documents = loader.parse(blob=blob)
            # Custom api parameters (create embeddings automatically)
            from langchain.document_loaders.embaas import EmbaasBlobLoader
            loader = EmbaasBlobLoader(
                params={
                    "should_embed": True,
                    "model": "e5-large-v2",
                    "chunk_size": 256,
                    "chunk_splitter": "CharacterTextSplitter"
                }
            )
            blob = Blob.from_path(path="example.pdf")
            documents = loader.parse(blob=blob)
    """
[docs]    def lazy_parse(self, blob: Blob) -> Iterator[Document]:
        """Parses the blob lazily.
        Args:
            blob: The blob to parse.
        """
        yield from self._get_documents(blob=blob)
    @staticmethod
    def _api_response_to_documents(chunks: List[Dict[str, Any]]) -> List[Document]:
        """Convert the API response to a list of documents."""
        docs = []
        for chunk in chunks:
            metadata = chunk["metadata"]
            if chunk.get("embedding", None) is not None:
                metadata["embedding"] = chunk["embedding"]
            doc = Document(page_content=chunk["text"], metadata=metadata)
            docs.append(doc)
        return docs
    def _generate_payload(self, blob: Blob) -> EmbaasDocumentExtractionPayload:
        """Generates payload for the API request."""
        base64_byte_str = base64.b64encode(blob.as_bytes()).decode()
        payload: EmbaasDocumentExtractionPayload = EmbaasDocumentExtractionPayload(
            bytes=base64_byte_str,