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Expand Up @@ -53,6 +53,7 @@ For periodic or longer workflows, you may opt for a batch approach. This diagram

## Related resources

- [Tutorial: Using BigQuery with Workers AI](/workers-ai/tutorials/using-bigquery-with-workers-ai/)
- [Workers AI: Get Started](/workers-ai/get-started/workers-wrangler/)
- [Workers: Secrets](/workers/configuration/secrets/)
- [Workers: Cron Triggers](/workers/runtime-apis/handlers/scheduled/)
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Expand Up @@ -677,7 +677,7 @@ Once you obtained the results, you formatted them to later be passed to generati

## Next Steps

If, instead of displaying the results of ingesting the data to the AI model in a browser, your workflow requires fetching and store data (for example in [R2](/r2/) or [D1](/d1/)) on regular intervals, you may want to consider adding a [scheduled handler](/workers/runtime-apis/handlers/scheduled/) for this Worker. It allows triggering the Worker with a predefined cadence via a [Cron Trigger](/workers/configuration/cron-triggers/).
If, instead of displaying the results of ingesting the data to the AI model in a browser, your workflow requires fetching and store data (for example in [R2](/r2/) or [D1](/d1/)) on regular intervals, you may want to consider adding a [scheduled handler](/workers/runtime-apis/handlers/scheduled/) for this Worker. It allows triggering the Worker with a predefined cadence via a [Cron Trigger](/workers/configuration/cron-triggers/). Consider reviewing the Reference Architecture Diagrams on [Ingesting BigQuery Data into Workers AI](/reference-architecture/diagrams/ai/bigquery-workers-ai/).

A use case to ingest data from other sources, like you did in this tutorial, is to create a RAG system. If this sounds relevant to you, please check out the tutorial [Build a Retrieval Augmented Generation (RAG) AI](/workers-ai/tutorials/build-a-retrieval-augmented-generation-ai/).

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