The correct option is Create a Vertex AI Search data store and run an index build on the knowledge base then attach the data store to the application. This is necessary because Vertex AI Search retrieves content from an indexed data store that has been associated with the application that serves the chat experience.

With Vertex AI Search you first ingest or connect your documents into a data store and then you build the index so the service can efficiently retrieve relevant passages. You then connect that store to your chat app or call the service with the data store so the assistant can ground answers on your documentation during live conversations.

Upload the documents to Cloud Storage and reference the bucket from the app is not sufficient because files in Cloud Storage are not searchable by the service until a data store is created that points to the bucket and an index build has completed. Simply referencing a bucket from an application does not enable retrieval.

Load the documents into a BigQuery table and query them from the service does not meet the requirement because the service does not ground on arbitrary SQL tables. Retrieval for chat uses a data store index rather than ad hoc BigQuery queries.

Configure a Pub/Sub topic to trigger a Cloud Function on file updates is an optional ingestion workflow and it does not provide retrieval at query time. Event driven processing can keep content fresh but the assistant still needs an indexed data store that is attached to the app.

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