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Updated 7 months ago

How can I efficiently read, parse and index a CSV file in a way that the column headers be

@kapa.ai
How can I efficiently read, parse and index a CSV file in a way that the column headers being integrated to the index of each row. hence, I can ask the question related to the content of different columns for each row.
k
3 comments
Please stand by @afshinblue as I search the knowledge base. I normally take about 20 seconds or so. :wave:
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To efficiently read, parse, and index a CSV file with LlamaIndex, you can use the CSVReader class. This class is designed to parse CSV files and create a Document for each row, with the option to concatenate all rows into one document. Here's a basic example of how you can use it:

Plain Text
from llama_index.readers.file import CSVReader
from pathlib import Path

# Initialize the CSVReader
reader = CSVReader(concat_rows=False)

# Load the data from the CSV file
documents = reader.load_data(Path("your_file.csv"))


In this example, concat_rows is set to False, which means a Document will be created for each row in the CSV file. If you set concat_rows to True, all rows will be concatenated into one document.

Each Document created by the CSVReader contains the text of the row and some metadata, such as the filename and extension. If you want to add more metadata, you can pass an extra_info dictionary to the load_data method.

Once you've ingested your data, you can build an index on top of it. LlamaIndex provides several indexing methods, which you can find in the Indexing Guide ().

After indexing, you can ask questions using a Query Engine. The Query Engine allows you to retrieve documents based on your questions. You can find more information about this in the Query Engine Guide ().

Please note that the column headers are not automatically integrated into the index of each row. If you want to include the column headers in your index, you might need to modify the way you create your Document objects to include the column headers in the text or metadata.
Here are the relevant knowledge sources:
  • CSVReader API Reference ()
  • Data Connectors Concept ()
  • Indexing Guide ()
  • Query Engine Guide ()
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