@qduan 

Genie is right regarding the embedding models. When Knowledge Assistant ingests files directly, it creates a managed, internal index using databricks-agent-bricks-embedding-v1. Because that model isn't on the supported list for external AI Search indexes (databricks-gte-large-en, databricks-bge-large-en, or databricks-qwen3-embedding-0-6b), it won't appear as an available source for a new assistant as its a design restriction. There is not a supported way to use that auto generated internal index and repurpose it for new assistants. You may prepare an index and embed the files using one of those three supported models if you plan to use index as an source.

For the load agent error, its likely a platform side issue on the agent's serving endpoint and not due to misconfigurations at your end. You can check - Serving in the platform and verify if the agent's endpoint exists and its status currently. If inference table is enabled, you can run a quick query filtering on status_code to get the exact error details if available. You can inspect the raw metadata using the SDK to see the state the backend agent

Given the volume of 2000+ mixed files and the likelihood of a service side issue during creation, you can reach Databricks support. Share them workspace URL, the creation time and the agent name and they can check it.

Recreating the assistant and reingesting the files is the path forward since that internal index cannot be used.