langchain.document_loaders.csv_loader.UnstructuredCSVLoader

class langchain.document_loaders.csv_loader.UnstructuredCSVLoader(file_path: str, mode: str = 'single', **unstructured_kwargs: Any)[source]

Load CSV files using Unstructured.

Like other Unstructured loaders, UnstructuredCSVLoader can be used in both “single” and “elements” mode. If you use the loader in “elements” mode, the CSV file will be a single Unstructured Table element. If you use the loader in “elements” mode, an HTML representation of the table will be available in the “text_as_html” key in the document metadata.

Examples

from langchain.document_loaders.csv_loader import UnstructuredCSVLoader

loader = UnstructuredCSVLoader(“stanley-cups.csv”, mode=”elements”) docs = loader.load()

Parameters
  • file_path – The path to the CSV file.

  • mode – The mode to use when loading the CSV file. Optional. Defaults to “single”.

  • **unstructured_kwargs – Keyword arguments to pass to unstructured.

Methods

__init__(file_path[, mode])

param file_path

The path to the CSV file.

lazy_load()

A lazy loader for Documents.

load()

Load file.

load_and_split([text_splitter])

Load Documents and split into chunks.

__init__(file_path: str, mode: str = 'single', **unstructured_kwargs: Any)[source]
Parameters
  • file_path – The path to the CSV file.

  • mode – The mode to use when loading the CSV file. Optional. Defaults to “single”.

  • **unstructured_kwargs – Keyword arguments to pass to unstructured.

lazy_load() Iterator[Document]

A lazy loader for Documents.

load() List[Document]

Load file.

load_and_split(text_splitter: Optional[TextSplitter] = None) List[Document]

Load Documents and split into chunks. Chunks are returned as Documents.

Parameters

text_splitter – TextSplitter instance to use for splitting documents. Defaults to RecursiveCharacterTextSplitter.

Returns

List of Documents.

Examples using UnstructuredCSVLoader