AI Factory terminology#
AI Factory terminology defines key concepts and technologies used across EDB Postgres® AI (EDB PG AI), Hybrid Manager AI Factory, and related components.
This page complements these conceptual explanations:
Understanding these terms helps you build trusted, governed, and scalable AI solutions with EDB PG AI.
---
Core AI concepts#
Machine learning (ML)#
Machine learning uses algorithms that improve through data exposure to perform tasks without explicit programming. It powers predictive analytics, automation, and decision making in AI Factory through Model Serving Reference Manual , Pipelines, and Assistants.
NOTE THIS IS NOT AN INTENDED USE CASE SUPPORTED BY HYBRID MANAGER CURRENTLY *
Deep learning (DL)#
Deep learning leverages multi-layer neural networks to model complex data patterns. It supports advanced applications such as language understanding and image recognition within AI Factory, enabled through Model Serving Reference Manual with GPU acceleration.
Natural language processing (NLP)#
Natural language processing (NLP) enables computers to understand and generate human language. It underpins semantic search and conversational AI in AI Factory via Knowledge Bases Reference Manual , Retrievers Reference Manual , and Assistants Reference Manual .
Large language models (LLMs)#
LLMs are large deep learning models trained on massive text corpora. In AI Factory, they drive Assistants Reference Manual , Retrieval-Augmented Generation (RAG), and various model pipelines, deployed using Model Serving Reference Manual .
Embeddings#
Embeddings are vector representations of data that capture semantic meaning. AI Factory Pipelines create embeddings used in Knowledge Bases Reference Manual and served through the Vector Engine (PGvector) to enable semantic search and RAG.
Vector databases#
Vector databases store embeddings and enable fast similarity search. AI Factory provides this through the Vector Engine (PGvector) , built on the open-source pgvector extension, integrated directly with Postgres.
Retrieval-augmented generation (RAG)#
RAG combines vector search with LLM generation to ground model responses in relevant documents. In AI Factory, it is implemented through Knowledge Bases Reference Manual , Retrievers Reference Manual , and Model Serving Reference Manual .
---
AI for databases#
Intelligent database management#
Intelligent database management applies AI to optimize Postgres performance and operations. AI Factory extends this with intelligent retrieval and search using Vector Engine (PGvector) and Pipelines.
Vector search in Postgres#
Vector search allows you to query embeddings directly within Postgres. AI Factory uses pgvector to power this capability through the Vector Engine (PGvector) , supporting Knowledge Bases and RAG.
AIDB#
AIDB (AI-in-Database) brings vector search, embedding pipelines, and future capabilities to HM-managed Postgres clusters. It is the foundation for AI Factory Pipelines and data preparation and Knowledge Bases. ======== Intelligent database management applies AI to optimize Postgres performance and operations. AI Factory extends this with intelligent retrieval and search using Vector Engine and Pipelines.
In-database machine learning (In-DB ML)#
In-DB ML enables running vector search and ML pipelines inside Postgres, reducing data movement and latency. AI Factory implements this through Vector Engine and Pipelines .
Vector search in Postgres#
Vector search allows you to query embeddings directly within Postgres. AI Factory uses pgvector to power this capability through the Vector Engine (PGvector) , supporting Knowledge Bases and RAG.
AIDB#
AIDB brings vector search, embedding pipelines, and other capabilities to HM-managed Postgres clusters. It is the foundation for AI Factory Pipelines and Knowledge Bases.
Natural language interfaces to databases#
Natural language interfaces enable users to query Postgres using natural language rather than SQL. AI Factory Assistants leverage this pattern through Gen AI Builder , combining LLMs with structured and unstructured retrieval.
---
AI infrastructure#
AI-accelerated hardware#
AI Factory uses GPU-accelerated Kubernetes clusters to serve deep learning models and high-throughput inference. Model workloads in Model Serving run on GPU-enabled nodes.
KServe#
KServe is the open-source Kubernetes-native framework AI Factory uses to deploy and manage models. It provides InferenceServices, autoscaling, and observability for AI Factory Model Serving Reference Manual .
Model Serving#
Model Serving deploys AI models as production-grade inference services, using KServe under the hood. It supports LLMs, embedding models, vision models, and custom AI workloads.
See: Model Serving Reference Manual , Model Serving Explained
---
Gen AI Builder#
Gen AI Builder#
Gen AI Builder is the application layer of AI Factory for building AI-powered apps and agents. It integrates:
Assistants Reference Manual for conversational AI
Knowledge Bases Reference Manual for RAG and semantic search
Retrievers Reference Manual for advanced search patterns
Tools Reference Manual to perform external actions
Rulesets Manual to control Assistant behavior
Threads Reference Manual to persist conversation history
Structures Reference Manual to define AI workflows and pipelines
It is deployed on Hybrid Manager AI Factory , enabling full-stack Sovereign AI.
---
Hybrid Manager AI Factory#
Hybrid Manager#
Hybrid Manager provides the Kubernetes-based control plane for AI Factory workloads, managing GPU resources, Pipelines, Model Serving, and Knowledge Bases. It provides end-to-end governance, security, and observability.
Pipelines#
Pipelines automate the creation of embeddings and Knowledge Bases from data sources in your control, ensuring transparency and auditability.
Knowledge Bases#
Knowledge Bases index content for semantic search and RAG. They support multi-source, multi-modal search and power AI Factory Assistants.
Model Serving#
Model Serving deploys models using Kubernetes-native KServe and integrates with the Model Library Reference Manual . It powers Assistants, Knowledge Bases, and custom AI applications.
See: Model Serving Reference Manual
---
Additional concepts#
Image and Model Library#
The Image and Model Library in Hybrid Manager manages container images for both Postgres and AI model deployments. The Model Library Reference Manual provides an AI-focused view, supporting Model Serving and governed image workflows.
NVIDIA NIM#
NVIDIA NIM Microservices are optimized AI model images from NVIDIA for high-performance inference. AI Factory uses NIM containers for LLMs, embeddings, and vision models, served through Model Serving.
See: How-To Deploy an NVIDIA NIM Microservices available on build.nvidia.comcontainer with KServe
---
Summary#
Understanding these terms will help you build with:
Together, these capabilities enable you to deliver trusted, governed, Sovereign AI — with your data, in your databases, under your control.
---