to load a taxonomy from a given non-default location text file into a variable, the order of steps should be as follows:

Create a new variable of type ' DocumentTaxonomy ' called ' documentTaxonomy ' , then create a new string variable called ' taxonomyPath ' .

This step involves setting up the necessary variables that will be used in the process. The ' documentTaxonomy ' variable will hold the deserialized taxonomy object, and ' taxonomyPath ' will store the path to the taxonomy file.

Using an Assign activity, assign the given non-default taxonomy text file path to the most recent variable created.

Here you will assign the path of the taxonomy file to the ' taxonomyPath ' variable.

Use the Read Text File activity on the ' taxonomyPath ' variable created. Next, create a new variable ' taxonomyText ' , then save the output of the Read Text File activity to ' taxonomyText ' .

This step is where you read the contents of the taxonomy file using the ' Read Text File ' activity. The contents are stored in the ' taxonomyText ' variable.

Inside the last Assign activity ' Value to Save ' property add the following expression: " Newtonsoft.Json.JsonConvert.DeserializeObject(Of DocumentTaxonomy)(taxonomyText) " where ' taxonomyText ' is the text read from the file and ' documentTaxonomy ' (the most recent variable created) is the variable you created.

In this final step, you will deserialize the JSON content from the ' taxonomyText ' into a ' DocumentTaxonomy ' object using the ' JsonConvert.DeserializeObject ' method and assign it to the ' documentTaxonomy ' variable.

Following these steps in this order will load the taxonomy from a text file into the ' documentTaxonomy ' variable in UiPath.

Model-based data extraction, often involving machine learning models, is particularly effective for handling complex data structures such as variable-length data or data that spans multiple pages. This approach adapts better to varying formats and can extract information more accurately in such scenarios compared to rule-based or manual methods.

In the Document Understanding framework, the dispatcher is responsible for ensuring that one job is created for each input file. It works by submitting files to be processed individually, ensuring that each document or group of documents is handled as a separate transaction. This allows for more efficient processing and better tracking of each file, especially in high-volume workflows where managing each file as a separate job is critical for performance and error handling.

(Source: UiPath Documentation on Document Understanding)

The Explore phase is the second phase of model training in UiPath Communications Mining, which is a solution that enables the analysis of large volumes of text-based communications using natural language processing and machine learning. The Explore phase builds on the foundations of the taxonomy that was created in the Discover phase by reviewing clusters and searching for different terms and phrases. The objective of the Explore phase is to provide each of the labels or entities that are important for the use case with enough varied and consistent training examples, so that the platform has sufficient training data from which to make accurate predictions across the entire dataset.  The Explore phase is the core phase of model training, and requires the most time and effort, but also leads to better model performance and accuracy 1 .

References :  1 :  Communications Mining - Explore

The correct pipeline configuration for creating a new model version that includes both the initial 1000 pages and the newly labeled 800 pages is to run a Full pipeline. This ensures that the model is fully retrained with all available data (1800 pages in total). The evaluation dataset should remain the 100 pages to gauge the model ' s performance after retraining. This approach maximizes the model ' s learning by incorporating all labeled data while allowing for evaluation against a known dataset.

The Configure Extractors Wizard is a tool that enables you to select and customize the extractors that are used for data extraction from documents. It is accessed via the Data Extraction Scope activity, which is a container for extractor activities. The wizard allows you to map the fields defined in your taxonomy with the fields supported by each extractor, and to set the minimum confidence level and the framework alias for each extractor.  The wizard is mandatory for the extractor configuration, as it ensures that the extractors are applied correctly to each document type and field 1 .

References:  Configure Extractors Wizard of Data Extraction Scope

In UiPath AI Center , ML Skills can be managed in various ways, allowing users to customize and control how these skills are deployed and used. The management options include:

Creating a new ML skill.

Stopping a deployed skill.

Redeploying an ML skill.

Updating to a new package version .

Rolling back to a previous version if needed.

Modifying GPU usage .

Modifying the use of AI units .

Making the skill public or private .

Deleting an ML skill when no longer needed.

This provides flexibility for both managing the ML infrastructure and optimizing resources in real-time.

For more details, refer to:

UiPath AI Center Documentation : Managing ML Skills

ML Skill Management Options : Managing Machine Learning Skills in AI Center