AI Factory Concepts
EDB Postgres® AI (EDB PG AI) brings AI workloads into your Postgres platform and infrastructure — with an architecture designed to power Sovereign AI on hybrid and cloud-native environments.
AI Factory extends Postgres to support:
- Vector search and semantic search
- Data pipelines for embedding generation
- Gen AI application development
- Scalable model serving and inference
- AI-driven governance and observability
AI Factory helps you build and operate trusted, enterprise-grade AI systems using your data, models, and applications.
Before you start
Prerequisites
You’ll get the most out of this section if you have:
- Familiarity with Postgres and database concepts
- Basic understanding of Large Language Models (LLMs) and vector search
- Awareness of Retrieval-Augmented Generation (RAG) techniques
- Experience with AI concepts such as embedding models, pipelines, and inference services
If new to AI Factory, consider starting with:
EDB’s vision for AI with Postgres and Hybrid Manager
EDB’s AI Factory strategy makes Postgres a first-class platform for AI workloads and AI-driven applications.
The architecture combines:
- AI in Postgres: vector search, embedding pipelines, and ML functions within the database
- Gen AI apps: conversational and task-driven AI applications with Gen AI Builder
- Scalable inference: GPU-accelerated model serving using Kubernetes and KServe
- Unified control plane: model lifecycle management and observability in Hybrid Manager
This enables Sovereign AI — AI on your data, on your infrastructure, with full governance.
Core AI patterns
Vector search and semantic search
AI Factory makes it easy to implement semantic search, product recommendations, anomaly detection, and similar patterns using vector search.
You can leverage:
- The Vector Engine, providing pgvector-based capabilities
- AI Factory Pipelines to generate embeddings
- Knowledge Bases Explained for integrated vector search across structured and unstructured data
Retrieval-Augmented Generation (RAG)
RAG pipelines enhance LLM responses by combining:
- Domain-specific embeddings from Knowledge Bases Explained
- Configurable search behavior with Retrievers Explained
- Scalable model inference through Model Serving
- Application orchestration via Gen AI Builder
This enables more accurate, grounded, and compliant AI applications.
AI in Database (In-DB ML and Vector Search)
AI Factory transforms Postgres into an AI data platform with:
- Vector similarity search using the Vector Engine
- Embedding pipelines integrated via Pipelines
- Search and retrieval across Knowledge Bases Explained
Future capabilities will extend in-database ML to include model scoring and advanced AI operations.
Model serving and inference
AI Factory provides robust model serving with:
- Scalable inference services for LLMs, embedding models, and vision models
- GPU-accelerated serving via Kubernetes + KServe
- Flexible deployment options through the Model Serving framework
- Lifecycle management and discoverability with the Model Library Explained
These services power both Gen AI Builder agents and Knowledge Bases.
Gen AI application development
Gen AI Builder enables rapid development of intelligent applications that:
- Orchestrate LLMs with Assistants Explained
- Retrieve content using Knowledge Bases Explained and Retrievers Explained
- Enforce behavior via Rulesets Explained
- Execute actions via Tools Explained
- Maintain conversational context through memory settings in Assistants Explained
Explore Gen AI Builder to start building AI-driven apps.
Pipelines and data preparation
AI Factory Pipelines automate:
- Embedding generation
- Chunking, parsing, summarizing content
- Keeping embeddings fresh with Auto-Processing
Explore Preparers for reusable data preparation components.
Architectural principles
Modular architecture
AI Factory is built for:
- Hybrid environments (on-premises, cloud, multi-cloud)
- Seamless integration with Postgres and cloud-native services
- Composable building blocks for AI applications and workflows
Open standards and interoperability
AI Factory components use:
- Open model formats via KServe
- pgvector for vector search in Postgres
- Griptape for framework-agnostic LLM orchestration
- RAG-friendly patterns with flexible data and model integration
Summary
EDB PG AI enables you to:
- Embed AI capabilities directly into Postgres
- Build scalable, production-ready AI applications
- Operate Sovereign AI on your infrastructure with unified governance
- Integrate open-source tools and standards across the AI stack
Next steps:
Could this page be better? Report a problem or suggest an addition!