AI Factory Model Library explained

The Model Library in AI Factory is your central interface for discovering, managing, and deploying AI models within EDB PG AI.

It is powered by the core Image and Model Library of Hybrid Control Plane (HCP). The Model Library presents a curated, AI-focused view of model images validated for production use.

Model images deployed through the Model Library power AI Factory features including:

• Gen AI Builder Assistants and pipelines
• Knowledge Base pipelines (embeddings, RAG)
• Other AI Factory applications using Model Serving

Before you start

Prerequisites for understanding Model Library:

• Basic familiarity with Model Serving and KServe concepts
• Understanding of InferenceService and runtime models
• Awareness of Image and Model Library architecture in Hybrid Manager
• Helpful: Awareness of Sovereign AI principles

Suggested path:

• AI Factory Concepts
• Model Serving Concepts

What is the Model Library?

The Model Library:

• Presents a curated AI view of the broader Image and Model Library
• Allows users to deploy validated model images to the Model Serving (KServe) layer
• Provides lifecycle management for models powering AI Factory experiences

Key point: Model Library is not a separate registry. It is a controlled view on top of the governed Image and Model Library.

All model images:

• Flow through the core Asset Library path
• Are subject to platform-wide governance and security policies
• Become available in Model Library only when validated for AI Factory use

How does it work?

Architecture flow:

Container Registry → Image and Model Library → Model Library → Model Serving (KServe) → AI Factory Workloads

Workflow:

1. Images are ingested into Asset Library (private or public registries).
2. Images validated for AI model serving appear in Model Library.
3. Users browse, select, and deploy models to Model Serving via KServe.
4. Deployed models power:

• Knowledge Base ingestion and retrieval
• Gen AI Builder Assistants and pipelines
• Other AI Factory capabilities

Why use the Model Library?

• Unified governance — All images flow through the same governance process.
• Security and auditability — Registry integration, tag selection, and image audit are unified.
• Sovereign AI — You control which models are used, how they are deployed, and where they run.
• Consistency — Database and AI models share the same container image management system.
• Flexibility — You can add your own model images via Asset Library path and control their availability in AI Factory.

What does the Model Library provide?

Users can:

• Browse available AI model images
• View supported tags and versions
• Deploy models to Model Serving infrastructure
• Manage model deployment lifecycle
• Understand which models power current AI Factory workloads, including:
• Knowledge Bases (via AIDB ingestion and retrieval)
• Gen AI Builder (for Assistants and pipelines)

Supported model types

• Support for additional model types is planned (vision, multi-modal, RAG-optimized).
• Custom models can be added via private registry integration.

Patterns of use

Knowledge Bases

• Embedding and retrieval models used in Knowledge Base indexing and RAG pipelines.
• Example: Use arctic-embed-l or nvclip models.

Gen AI Builder Assistants

• Power Assistants with models deployed through Model Library:
• LLMs for text generation (e.g., llama-3)
• OCR or Vision models (e.g., paddleocr)
• Custom tools using Model Serving endpoints

Hybrid + Sovereign AI alignment

• All models run on your infrastructure via Hybrid Manager KServe layer.
• You control:
• Which models are deployed
• Resource allocations (CPU/GPU)
• Deployment topology
• Observability and auditing

Custom model integration

• Use Integrate Private Registry flow to add models from your own registry.
• Define Repository Rules to control image availability.
• Ensure images pass governance checks before appearing in Model Library.

Best practices

• Maintain clear governance over model images.
• Use Repository Rules to manage visibility and compliance.
• Monitor model usage and resource consumption in Hybrid Manager dashboards.
• Version and document models used in production.
• Test custom models thoroughly before enabling for AI Factory workloads.

Model Serving integration

Models deployed from Model Library run on the Model Serving (KServe) layer:

• Guided workflow provided in Model Library UI:

1. Select model image and tag.
2. Configure runtime (replicas, resources, GPUs, etc.).
3. Deploy → creates InferenceService in HCP Kubernetes environment.

• Deployed models power:
• AIDB Knowledge Base ingestion
• Gen AI Builder Assistants and pipelines
• Other AI Factory services

Related topics

• Image and Model Library Explained
• Deploy AI Models
• Integrate Private Registry
• Define Repository Rules
• Model Serving Concepts
• Hybrid Manager: Using Model Serving

Next steps

• Start by browsing available models in your Model Library.
• Deploy your first model for Knowledge Base ingestion or Gen AI Builder Assistants.
• Explore Model Serving How-To Guides to optimize deployment.
• Define governance and compliance policies for model image flow.