}
)
documents = loader.load()
Validators
validate_blob_loader » blob_loader
validate_environment » all fields
field blob_loader: Optional[langchain.document_loaders.embaas.EmbaasBlobLoader] = None#
The blob loader to use. If not provided, a default one will be created.
field file_path: str [Required]#
The path to the file to load.
lazy_load() → Iterator[langchain.schema.Document][source]#
Load the documents from the file path lazily.
load() → List[langchain.schema.Document][source]#
Load data into document objects.
load_and_split(text_splitter: Optional[langchain.text_splitter.TextSplitter] = None) → List[langchain.schema.Document][source]#
Load documents and split into chunks.
class langchain.document_loaders.EverNoteLoader(file_path: str, load_single_document: bool = True)[source]#
EverNote Loader.
Loads an EverNote notebook export file e.g. my_notebook.enex into Documents.
Instructions on producing this file can be found at
https://help.evernote.com/hc/en-us/articles/209005557-Export-notes-and-notebooks-as-ENEX-or-HTML
Currently only the plain text in the note is extracted and stored as the contents
of the Document, any non content metadata (e.g. ‘author’, ‘created’, ‘updated’ etc.
but not ‘content-raw’ or ‘resource’) tags on the note will be extracted and stored
as metadata on the Document.
Parameters
file_path (str) – The path to the notebook export with a .enex extension
load_single_document (bool) – Whether or not to concatenate the content of all
notes into a single long Document.
rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/document_loaders.html