- Getting started
- Balance
- Clusters
- Concept drift
- Coverage
- Datasets
- General fields (previously Entities)
- Labels (predictions, confidence levels, hierarchy, etc.)
- Models
- Streams
- Model Rating
- Projects
- Precision
- Recall
- Reviewed and unreviewed messages
- Sources
- Taxonomies
- Training
- True and false positive and negative predictions
- Validation
- Messages
- Administration
- Manage sources and datasets
- Understanding the data structure and permissions
- Create a data source in the GUI
- Uploading a CSV file into a source
- Create a new dataset
- Multilingual sources and datasets
- Enabling sentiment on a dataset
- Amend a dataset's settings
- Delete messages via the UI
- Delete a dataset
- Export a dataset
- Using Exchange Integrations
- Preparing data for .CSV upload
- Model training and maintenance
- Understanding labels, general fields and metadata
- Label hierarchy and best practice
- Defining your taxonomy objectives
- Analytics vs. automation use cases
- Turning your objectives into labels
- Building your taxonomy structure
- Taxonomy design best practice
- Importing your taxonomy
- Overview of the model training process
- Generative Annotation (NEW)
- Understanding the status of your dataset
- Model training and annotating best practice
- Training with label sentiment analysis enabled
- Train
- Introduction to Refine
- Precision and recall explained
- Precision and recall
- How does Validation work?
- Understanding and improving model performance
- Why might a label have low average precision?
- Training using Check label and Missed label
- Training using Teach label (Refine)
- Training using Search (Refine)
- Understanding and increasing coverage
- Improving Balance and using Rebalance
- When to stop training your model
- Defining and setting up your general fields
- Understanding general fields
- Which pre-trained general fields are available?
- Enabling, disabling, updating and creating general fields
- General field filtering
- Reviewing and applying general fields
- Validation for general fields
- Improving general field performance
- Building custom regex general fields
- Generative extraction
- Using analytics and monitoring
- Automations and Communications Mining
- FAQs and more
Overview
Generative Extraction (GenEx) is an innovative new feature for UiPath Communications Mining that leverages Generative AI to understand the complex relationships between multiple requests and the data points required to process them.
An email can contain multiple requests, with each one requiring multiple fields extracted to enable automation. Automating this end-to-end requires more than just correctly extracting the field itself, but also an understanding of how each of these elements are related to one another. GenEx significantly advances the scope of what’s possible for communications-based automation.
Generative Extraction leverages the very latest in NLP capabilities, and also provides the necessary guardrails required by enterprises to implement complex communication-based automations for business processes.
More complex processes and communications containing multiple different requests can now also be prime candidates for automation.
For some use cases, extractions can be generated with no training, and can further be fine-tuned with little training data.
The following steps describe the end-to-end process of validating extractions. Each step is covered in more details in the subsequent sections.
- Define your extraction schema.
- Identify what processes (i.e., labels) you’re looking to automate, and the data points (i.e. fields) that need to be captured to enable automation.
- Create the corresponding extraction schema.
- Generate extractions. Generating extractions enables you to significantly speed up the process of finding and relating data. For some use cases, the platform does not require training examples to generate
its extractions.
- Use the platform’s generative capabilities to create your initial extractions.
- Validate and correct extractions.
- Review the platform’s extractions and accept them if they’re accurate or correct them if they are not.
- The platform is flexible and easy, and you can add new extraction schemas at any point in the training process.
- Review the validation for extractions.
- Verify how well your extractions are performing (via Validation).
- Determine if your extractions are at a performance level suitable for your use case.
The following diagram illustrates how Generative Extraction works at a high-level. You can check the relation between labels, extractions, and the corresponding fields required to automate a process end-to-end.
- When setting up your extraction schema, you need to decide what processes (i.e. labels) you want to automate.
- For the platform to understand the relationship between the process, and what data points need to be extracted, the platform prompts you to provide the appropriate data points. The Configuring Fields section goes into more detail on best practices, and how this specifically works.
In the example below, the requestor is asking about two different topics in the same message, with each request calling for different data points for extraction and action.
- If you have previously worked with entities in Communications Mining, as of 2024.4, all your existing entities automatically transition to general fields.
- All the existing settings on your entities migrate over to the respective settings for the corresponding general field.
- If you have any general fields that you want to switch over to extraction fields, you need to re-create those fields as extraction fields, and apply the appropriate amount of training examples (if applicable).
- Field types have the same names and configurations as the old entities previously set up. These are mapped over using the api-name.
- If you have any existing automations that use previous entities, these automations will not be impacted.
- Automating processes using Generative Extraction is slightly different from how processes were previously automated using entities. Check the Automating with GenEx section of this guide for more details.
