field dataset_mapping_function: Callable[[Dict], langchain.schema.Document] [Required]#
A custom function that takes a single dictionary (an Apify dataset item)
and converts it to an instance of the Document class.
load() → List[langchain.schema.Document][source]#
Load documents.
class langchain.document_loaders.ArxivLoader(query: str, load_max_docs: Optional[int] = 100, load_all_available_meta: Optional[bool] = False)[source]#
Loads a query result from arxiv.org into a list of Documents.
Each document represents one Document.
The loader converts the original PDF format into the text.
load() → List[langchain.schema.Document][source]#
Load data into document objects.
class langchain.document_loaders.AzureBlobStorageContainerLoader(conn_str: str, container: str, prefix: str = '')[source]#
Loading logic for loading documents from Azure Blob Storage.
load() → List[langchain.schema.Document][source]#
Load documents.
class langchain.document_loaders.AzureBlobStorageFileLoader(conn_str: str, container: str, blob_name: str)[source]#
Loading logic for loading documents from Azure Blob Storage.
load() → List[langchain.schema.Document][source]#
Load documents.
class langchain.document_loaders.BSHTMLLoader(file_path: str, open_encoding: Optional[str] = None, bs_kwargs: Optional[dict] = None, get_text_separator: str = '')[source]#
Loader that uses beautiful soup to parse HTML files.
load() → List[langchain.schema.Document][source]#
Load data into document objects.
rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/document_loaders.html