Gen AI Builder

Gen AI Builder provides comprehensive tooling for developing intelligent applications that leverage organizational knowledge through retrieval‑augmented generation patterns. The platform operates within controlled infrastructure environments, ensuring complete organizational sovereignty over data, models, and inference operations.

New here? Start with Getting started, review Concepts and the Architecture, then use the How‑to guides or jump into the Quickstart UI.

Platform Architecture

Gen AI Builder integrates multiple components to deliver end-to-end AI application development capabilities within the EDB Postgres AI ecosystem. The platform emphasizes data sovereignty by maintaining all operations within organizational boundaries while providing enterprise-grade governance and auditability. Explore the assistants, knowledge bases, retrievers, rulesets, structures, tools, threads, data sources, data lake, and libraries manuals.

Core Infrastructure Integration

Hybrid Manager Foundation Gen AI Builder operates within Hybrid Manager's Kubernetes orchestration layer, leveraging distributed computing capabilities while maintaining centralized governance. This architecture ensures consistent operational procedures across development and production environments.

Data Estate Integration Knowledge bases connect directly to Postgres databases and object storage systems (see Data Lake) within organizational infrastructure, eliminating external data dependencies while supporting diverse data source types.

Model Serving Integration Applications access language models and embedding models through the AI Factory Models infrastructure, ensuring consistent governance policies across all AI capabilities.

Architectural Components

Knowledge Base Framework

Knowledge bases serve as the foundation for retrieval‑augmented generation applications by combining structured database content with unstructured document repositories. The framework provides semantic search capabilities through vector embeddings (see Vector Engine) while maintaining complete data lineage.

Data Source Integration

• Postgres database tables with semantic indexing
• Document repositories from object storage systems
• Real-time data feeds through API integrations
• Web content through controlled scraping operations

Embedding Management

• Private embedding models for sensitive content processing
• Vector storage optimization within Postgres infrastructure
• Semantic similarity algorithms for retrieval accuracy
• Hybrid search combining vector and keyword approaches

Retrieval Systems

Retrieval components orchestrate search operations across diverse data sources, providing unified interfaces for knowledge discovery while maintaining source attribution and access controls.

Retrieval Strategies

• Vector similarity search for semantic content matching
• Keyword-based search for exact term identification
• Hybrid approaches combining multiple retrieval methods
• Contextual filtering based on user permissions and data classification

Assistant Framework

Intelligent assistants combine knowledge retrieval with language model capabilities to provide conversational interfaces for organizational knowledge. The framework supports complex reasoning workflows while maintaining audit trails for all interactions (see threads).

Assistant Capabilities

• Multi-turn conversational interfaces with context preservation
• Tool integration for external system interactions
• Reasoning workflows for complex query resolution
• Audit logging for compliance and operational transparency

Implementation Patterns

Enterprise Knowledge Systems

Organizations implement comprehensive knowledge management systems that unify structured and unstructured information sources through semantic search capabilities.

Typical Architecture

1. Data Integration Layer: Connects multiple organizational data sources including databases, document repositories, and knowledge management systems
2. Processing Pipeline: Transforms content into searchable formats with appropriate metadata and access control annotations
3. Retrieval Interface: Provides unified search across all integrated content with relevance ranking and source attribution
4. Application Layer: Delivers knowledge access through conversational interfaces and API integrations

Implementation Considerations

• Data governance policies for content classification and access control
• Performance optimization for large document collections
• Integration with existing authentication and authorization systems
• Compliance requirements for audit trails and data lineage

Retrieval-Augmented Generation Applications

Applications combine retrieval capabilities with language model inference to provide context-aware responses based on organizational knowledge.

Development Workflow

1. Knowledge Base Construction: Establish comprehensive document indexing with semantic embeddings (see create a Knowledge Base and manage a Knowledge Base)
2. Retrieval Configuration: Implement search strategies optimized for application requirements
3. Generation Integration: Connect language models for response generation based on retrieved context
4. Response Orchestration: Coordinate retrieval and generation workflows with appropriate caching and optimization

Technical Requirements

• Low-latency retrieval for interactive applications
• Context window management for large document processing
• Response quality evaluation and improvement workflows
• Integration testing across knowledge base updates

Conversational AI Platforms

Interactive applications provide natural language interfaces for complex organizational workflows and information discovery.

Platform Components

• Multi-modal input processing for text, voice, and document interactions
• Context management for extended conversational sessions
• Tool integration for external system operations
• Response personalization based on user roles and preferences

Operational Features

• Session management with conversation state persistence
• Performance monitoring for response quality and latency
• Usage analytics for application optimization and capacity planning
• Security controls for sensitive information handling

Development Framework

Knowledge Base Development

Knowledge base creation involves data source integration, content processing, and semantic indexing to support efficient retrieval operations.

Development Process

1. Source Assessment: Evaluate data sources for content quality, update frequency, and access requirements
2. Processing Pipeline Design: Configure content transformation workflows including cleaning, chunking, and metadata extraction (see configure Data Lake)
3. Indexing Strategy: Implement embedding generation and vector storage optimization
4. Retrieval Testing: Validate search quality through systematic evaluation procedures

Quality Assurance

• Content accuracy verification through systematic sampling
• Retrieval relevance evaluation using standard metrics
• Performance benchmarking under expected load conditions
• Security testing for access control and data isolation

Assistant Configuration

Assistant development requires careful orchestration of retrieval, reasoning, and response generation capabilities to deliver reliable user experiences (see create an Assistant).

Configuration Areas

• Behavior Definition: Establish assistant personality, capabilities, and operational boundaries
• Knowledge Integration: Configure retrieval strategies and knowledge source prioritization (see retrievers)
• Tool Integration: Connect external systems and APIs for expanded functionality (see tools)
• Response Optimization: Tune generation parameters for appropriate response characteristics

Testing Strategies

• Conversational flow validation through systematic dialog testing
• Knowledge accuracy verification across diverse query types
• Integration testing for external tool interactions
• Load testing for concurrent user scenarios

API Development

Applications expose functionality through standardized APIs that support diverse client applications while maintaining consistent security and operational characteristics (see SDK reference).

API Design Principles

• RESTful interfaces with clear resource modeling
• Authentication and authorization integration with organizational systems
• Rate limiting and quota management for resource protection
• Comprehensive documentation and SDK support

Integration Architecture

EDB Postgres AI Ecosystem

Gen AI Builder integrates seamlessly with other EDB Postgres AI components to provide comprehensive AI application development capabilities.

Component Interactions

• AI Factory Models: Governed access to language models and embedding models
• AI Accelerator Pipelines: Automated content processing and knowledge base updates
• Vector Engine: High‑performance semantic search within Postgres infrastructure
• Hybrid Manager: Orchestration and resource management

External System Integration

Applications connect with existing organizational systems through standardized interfaces while maintaining security boundaries and audit capabilities.

Integration Patterns

• API-based connections for real-time data access
• Batch processing for large data source synchronization
• Event-driven updates for dynamic content management
• Secure authentication delegation for user access control

Operational Considerations

Performance Characteristics

Gen AI Builder applications require careful performance optimization to deliver responsive user experiences while managing computational resources efficiently.

Performance Factors

• Knowledge base size affects retrieval latency and resource requirements
• Model complexity influences response generation time and GPU utilization
• Concurrent user load impacts system scalability and resource allocation
• Network connectivity affects distributed component communication

Security Framework

Comprehensive security controls protect sensitive organizational knowledge while enabling appropriate access for authorized users and applications.

Security Components

• Multi-tenant isolation for different organizational units
• Role-based access control aligned with existing authorization systems
• Data encryption for knowledge base content and communication channels
• Audit logging for all knowledge access and assistant interactions

Governance Integration

Built-in governance capabilities ensure applications comply with organizational policies and regulatory requirements throughout their operational lifecycle.

Governance Features

• Content approval workflows for knowledge base updates
• Model usage tracking and compliance reporting
• Data lineage maintenance for audit and troubleshooting
• Performance monitoring aligned with service level objectives

Getting Started

Prerequisites

Gen AI Builder requires foundational infrastructure components including Hybrid Manager installation and appropriate compute resources for AI workloads.

Infrastructure Requirements

• Kubernetes cluster with sufficient compute and storage capacity
• GPU resources for model serving and embedding generation
• Network connectivity to organizational data sources
• Integration with identity and access management systems

Initial Implementation

Organizations should begin with focused use cases that demonstrate value while establishing operational procedures for broader deployment.

Implementation Sequence

1. Pilot Knowledge Base: Create initial knowledge base with well-defined content scope
2. Basic Retrieval: Implement simple search functionality with performance validation
3. Assistant Prototype: Develop conversational interface for pilot knowledge domain
4. Production Deployment: Scale successful prototypes with comprehensive operational controls

Learning Resources

Conceptual Foundation

• Getting Started Guide - Platform overview and initial setup procedures
• How-to Documentation - Practical implementation guidance for common scenarios

Component Documentation

• Knowledge Base Management - Comprehensive knowledge base development guidance
• Assistant Development - Intelligent assistant creation and configuration
• API Reference - Detailed technical documentation for development teams

Advanced Topics

• Data Lake Integration - Large-scale data processing and management
• SDK Documentation - Programmatic interfaces for custom development

Gen AI Builder enables organizations to develop sophisticated AI applications while maintaining complete control over data, models, and inference operations within their existing infrastructure environments.

Getting started

Manual

Explainers

How-to (Prepare Data & KB)

How-to (Build Assistants)

How-to (Integrate with Your Apps)

How-to (Operate & Observe)