Enriching adds enhancments to the processed data that Unstructured produces. These enrichments include:
  • Providing a summarized description of the contents of a detected image. Learn more.
  • Providing a summarized description of the contents of a detected table. Learn more.
  • Providing a representation of a detected table in HTML markup format. Learn more.
  • Providing a list of recognized entities and their types, through a process known as named entity recognition (NER). Learn more.
To add an enrichment, in an Enrichment node in a workflow, select one of the following enrichment types:
You can change enrichment settings only through Custom workflow settings.
Unstructured can potentially generate image summary descriptions, table summary descriptions, and table-to-HTML output only for workflows that are configured as follows:
  • With a Partitioner node set to use the Auto or High Res partitioning strategy, and an image summary description node, table summary description node, or table-to-HTML output node is added.
  • With a Partitioner node set to use the VLM partitioning strategy. No image summary description node, table summary description node, or table-to-HTML output node is needed (or allowed).
Even with these configurations, Unstructured actually generates image summary descriptions, table summary descriptions, and table-to-HTML output only for files that contain images or tables and are also eligible for processing with the following partitioning strategies:
  • High Res, when the workflow’s Partitioner node is set to use Auto or High Res.
  • VLM or High Res, when the workflow’s Partitioner node is set to use VLM.
Unstructured never generates image summary descriptions, table summary descriptions, or table-to-HTML output for workflows that are configured as follows:
  • With a Partitioner node set to use the Fast partitioning strategy.
  • With a Partitioner node set to use the Auto, High Res, or VLM partitioning strategy, for all files that Unstructured encounters that do not contain images or tables.
  • Image to provide a summarized description of the contents of each detected image. Learn more.
  • Table to provide a summarized description of the contents of each detected table. Learn more.
  • Table can also provide a representation of each detected table in HTML markup format. Learn more.
  • Text to provide a list of recognized entities and their types by using a technique called named entity recognition (NER). Learn more.
The Text enrichment type also supports custom prompts. In the Details tab, first select the input type Text and choose a provider (and model) combination. Then, under Prompt, click Edit. Then, you can test your custom prompt in the Edit & Test Prompt section by clicking Run Prompt. To add multiple enrichments, create an additional Enrichment node for each enrichment type that you want to add.