Chunk documents Transform

This transform is chunking documents. It supports multiple chunker modules (see the chunking_type parameter).

When using documents converted to JSON, the transform leverages the Docling Core HierarchicalChunker to chunk according to the document layout segmentation, i.e. respecting the original document components as paragraphs, tables, enumerations, etc. It relies on documents converted with the Docling library in the pdf2parquet transform using the option contents_type: "application/json", which provides the required JSON structure.

When using documents converted to Markdown, the transform leverages the Llama Index MarkdownNodeParser, which is relying on its internal Markdown splitting logic.

Output format

The output parquet file will contain all the original columns, but the content will be replaced with the individual chunks.

Tracing the origin of the chunks

The transform allows to trace the origin of the chunk with the source_doc_id which is set to the value of the document_id column (if present) in the input table. The actual name of columns can be customized with the parameters described below.

Running

Parameters

The transform can be tuned with the following parameters.

Parameter	Default	Description
`chunking_type`	`dl_json`	Chunking type to apply. Valid options are `li_markdown` for using the LlamaIndex Markdown chunking, `dl_json` for using the Docling JSON chunking, `li_token_text` for using the LlamaIndex Token Text Splitter, which chunks the text into fixed-sized windows of tokens.
`content_column_name`	`contents`	Name of the column containing the text to be chunked.
`doc_id_column_name`	`document_id`	Name of the column containing the doc_id to be propagated in the output.
`chunk_size_tokens`	`128`	Size of the chunk in tokens for the token text chunker.
`chunk_overlap_tokens`	`30`	Number of tokens overlapping between chunks for the token text chunker.
`output_chunk_column_name`	`contents`	Column name to store the chunks in the output table.
`output_source_doc_id_column_name`	`source_document_id`	Column name to store the `doc_id` from the input table.
`output_jsonpath_column_name`	`doc_jsonpath`	Column name to store the document path of the chunk in the output table.
`output_pageno_column_name`	`page_number`	Column name to store the page number of the chunk in the output table.
`output_bbox_column_name`	`bbox`	Column name to store the bbox of the chunk in the output table.

When invoking the CLI, the parameters must be set as --doc_chunk_<name>, e.g. --doc_chunk_column_name_key=myoutput.

Running the samples

To run the samples, use the following make targets

run-cli-sample - runs src/doc_chunk_transform.py using command line args
run-local-sample - runs src/doc_chunk_local.py

These targets will activate the virtual environment and set up any configuration needed. Use the -n option of make to see the detail of what is done to run the sample.

For example,

make run-cli-sample
...

Then

ls output

To see results of the transform.

Transforming data using the transform image

To use the transform image to transform your data, please refer to the running images quickstart, substituting the name of this transform image and runtime as appropriate.