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35 lines
1.3 KiB
Python
35 lines
1.3 KiB
Python
"""Load from Dataframe object"""
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from typing import Any, List
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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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class DataFrameLoader(BaseLoader):
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"""Load Pandas DataFrames."""
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def __init__(self, data_frame: Any, page_content_column: str = "text"):
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"""Initialize with dataframe object."""
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import pandas as pd
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if not isinstance(data_frame, pd.DataFrame):
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raise ValueError(
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f"Expected data_frame to be a pd.DataFrame, got {type(data_frame)}"
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)
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self.data_frame = data_frame
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self.page_content_column = page_content_column
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def load(self) -> List[Document]:
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"""Load from the dataframe."""
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result = []
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# For very large dataframes, this needs to yeild instead of building a list
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# but that would require chaging return type to a generator for BaseLoader
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# and all its subclasses, which is a bigger refactor. Marking as future TODO.
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# This change will allow us to extend this to Spark and Dask dataframes.
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for _, row in self.data_frame.iterrows():
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text = row[self.page_content_column]
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metadata = row.to_dict()
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metadata.pop(self.page_content_column)
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result.append(Document(page_content=text, metadata=metadata))
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return result
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