Tonic Textual

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Use Tonic Textual to prepare content for inclusion in an LLM, or to synthesize or redact sensitive information in free-text files.

Guides and references

User guide

Use Textual to redact or synthesize sensitive values in files.

Python SDK reference

Generated documentation for the Textual Python SDK

Release notes

Get information about the latest features and fixes.

Explore key Textual features

Find out more about Textual's main features and tasks.

Pipelines for LLM preparation

Prepare file content to be included in an LLM.

Datasets for file redaction and synthesis

Create sets of files for Textual to detect and replace sensitive values.

Customize the redaction

Determine whether and how to replace detected values.

Customize PDF file redaction

Add manual overrides and create templates.

Snowflake Native App

Use the Snowflake Native App for Textual to redact and parse text from Snowflake.

Other resources

Guide to named entity recognition (NER) models

Provides an overview of NER and explains how it used in Textual.

Understanding model memorization in machine learning

Explains model memorization, its effects on a model, and how to prevent it.