Data Source Configuration
This section discusses operations you can perform on data source objects or fields, such as anonymization and creating custom recognizers.
To Ingest data with an Aisera application:
Select Settings > Data Sources command to navigate to the Data Source Details page.
Choose an application that is integrated with the data source that you want to ingest, or click + New Data Source (that includes documents with the newly-supported file types) and associate it with an existing Aisera application.
Select the arrow/triangle in the upper-right section of the Data Source Details page to start the data ingestion job.
The Data Source Details window displays the metrics for the data as it is ingested. You will see the functions that you selected while creating the data source (User Learning in this example) and details of all the integration runs.
The Data Ingestion function supports .txt, .html, .md, .pdf, and .ppt file types.
Then, when you create your application/bot, you can choose this Data Source from the list of available sources, after you select + Add Data Source on your application's Detail (summary) page. For a detailed diagram, see Integrations and Data Sources.
Auto-Commit Setting Now Runs Index Jobs
Currently, you can schedule data source ingestion updates to your tenant. If the auto-commit flag is set to true in your Data Source Configuration, then the content is automatically approved and ingested.
In releases prior to 5/7/2025, the ingested data was not used in Knowledge Base Article responses until you ran the Neural search and RAG indexing jobs after the data ingestion.
Now when the data source is updated, the Aisera Gen AI platform determines all applications/bots using this data source and automatically triggers search indexing jobs for those applications/bots. After the jobs are completed, the content is published and appears in live results.
Post-Ingestion Indexing Tasks
There are post-ingestion tasks that you need complete before your ingested data is ready for use. Post-Ingestion tasks may include: Neural Search RAG indexing, Knowledge Article indexing, running Access Attribute Extraction jobs for User data, or running Discovery Ontology Indexing for Ticket data.
After your data is ingested, you need to run an Indexer job before you can use the AI Learning or Content Generation features on your application or bot.
See Post-Ingestion Tasks for more details.
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