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https://github.com/Mintplex-Labs/langchain-python.git
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Leonid Ganeline
1feac83323
updated docstring for the `document_loaders` Maintainer responsibilities: - DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
101 lines
3.5 KiB
Python
101 lines
3.5 KiB
Python
import csv
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from typing import Any, Dict, List, Optional
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from langchain.docstore.document import Document
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from langchain.document_loaders.base import BaseLoader
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from langchain.document_loaders.unstructured import (
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UnstructuredFileLoader,
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validate_unstructured_version,
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)
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class CSVLoader(BaseLoader):
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"""Loads a CSV file into a list of documents.
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Each document represents one row of the CSV file. Every row is converted into a
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key/value pair and outputted to a new line in the document's page_content.
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The source for each document loaded from csv is set to the value of the
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`file_path` argument for all doucments by default.
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You can override this by setting the `source_column` argument to the
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name of a column in the CSV file.
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The source of each document will then be set to the value of the column
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with the name specified in `source_column`.
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Output Example:
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.. code-block:: txt
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column1: value1
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column2: value2
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column3: value3
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"""
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def __init__(
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self,
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file_path: str,
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source_column: Optional[str] = None,
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csv_args: Optional[Dict] = None,
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encoding: Optional[str] = None,
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):
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"""
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Args:
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file_path: The path to the CSV file.
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source_column: The name of the column in the CSV file to use as the source.
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Optional. Defaults to None.
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csv_args: A dictionary of arguments to pass to the csv.DictReader.
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Optional. Defaults to None.
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encoding: The encoding of the CSV file. Optional. Defaults to None.
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"""
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self.file_path = file_path
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self.source_column = source_column
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self.encoding = encoding
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self.csv_args = csv_args or {}
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def load(self) -> List[Document]:
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"""Load data into document objects."""
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docs = []
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with open(self.file_path, newline="", encoding=self.encoding) as csvfile:
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csv_reader = csv.DictReader(csvfile, **self.csv_args) # type: ignore
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for i, row in enumerate(csv_reader):
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content = "\n".join(f"{k.strip()}: {v.strip()}" for k, v in row.items())
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try:
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source = (
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row[self.source_column]
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if self.source_column is not None
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else self.file_path
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)
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except KeyError:
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raise ValueError(
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f"Source column '{self.source_column}' not found in CSV file."
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)
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metadata = {"source": source, "row": i}
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doc = Document(page_content=content, metadata=metadata)
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docs.append(doc)
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return docs
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class UnstructuredCSVLoader(UnstructuredFileLoader):
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"""Loader that uses unstructured to load CSV files."""
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def __init__(
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self, file_path: str, mode: str = "single", **unstructured_kwargs: Any
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):
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"""
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Args:
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file_path: The path to the CSV file.
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mode: The mode to use when loading the CSV file.
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Optional. Defaults to "single".
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**unstructured_kwargs: Keyword arguments to pass to unstructured.
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"""
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validate_unstructured_version(min_unstructured_version="0.6.8")
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super().__init__(file_path=file_path, mode=mode, **unstructured_kwargs)
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def _get_elements(self) -> List:
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from unstructured.partition.csv import partition_csv
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return partition_csv(filename=self.file_path, **self.unstructured_kwargs)
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