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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:
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.
