Using models in Gen AI Builder v1.3

Hub quick links: Gen AI how-tosModel ServingModel Library


How models are consumed

In Hybrid Manager, Gen AI Builder lets you build assistants that use model endpoints served from the Model Library.

  • When creating or editing an Assistant, you select which model to use (see Create an Assistant).
  • Models available here are governed by Hybrid Manager: pulled from the Model Library, deployed to your project, and exposed via internal endpoints.
  • This ensures that model calls stay within your environment — no external API calls are made by default.

Knowledge Bases and pipelines

Knowledge Bases in Gen AI Builder can be populated with embeddings generated by Pipelines:

  • Pipelines ingest and prepare documents into vector indexes (see Vector Engine concepts).
  • Knowledge Bases reference these pipelines, making them queryable for Retrieval-Augmented Generation (RAG).
  • Assistants then combine model responses with Knowledge Base retrievals to ground outputs in your organization’s data.

See: Knowledge Bases (hub) and Pipelines (hub).


Environment and service discovery

When Gen AI Builder runs inside Hybrid Manager:

  • Service discovery and endpoints are automatically managed by the platform.
  • You do not need to configure external URLs; models appear directly in the Assistant creation UI.
  • Any required environment variables (for service routing or authentication) are injected by HM.

This means you focus on building assistants — Hybrid Manager takes care of wiring models, data, and observability together.


Common tasks

Use these how-tos from the hub to start building:


Key takeaway

In Hybrid Manager, models and data are co-located:

  • Models: deployed from the Model Library into your HM project.
  • Data: ingested through pipelines, stored in Knowledge Bases.
  • Assistants: combine both, with no traffic leaving your cluster.