Using models in Gen AI Builder v1.3
Hub quick links: Gen AI how-tos — Model Serving — Model 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.