Guide
RAG in Ruby on Rails without managing a vector database
Many Rails teams want retrieval-augmented generation without operating embedding infrastructure. Managed knowledge closes that gap for the standard path.
Updated 2026-07-28 · Rails Agent · Tiny Bubble Company
Direct answer
Rails Agent provides managed knowledge embedding and retrieval so teams can ship RAG-style agents without provisioning pgvector or a separate vector database for the standard platform path.
When DIY still makes sense
If you already run pgvector, have strict data residency rules, or need custom indexing pipelines, you can still design around your own store. The platform path optimizes for speed-to-production.
Implementation shape
- Attach knowledge sources to the agent
- Keep policies and FAQs as versioned content
- Cite retrieved context in answers when users need auditability
- Evaluate retrieval failures as first-class test cases
Frequently asked questions
Do I need pgvector?
Not for the standard Rails Agent knowledge path. Teams with existing vector infrastructure can still choose that architecture.
How does this relate to memory?
Knowledge is durable reference material; memory is conversational or user state. Use both when the product needs them.
Ship production Rails AI agents
Install rails-agent-stack, open /agents, and go from scaffold to deploy with Playbooks, memory, guardrails, and monitoring included.
Keep reading
Ruby on Rails AI agents →
Category pillar
Build AI agents in Ruby on Rails →
Implementation guide
Getting started docs →
Install and open /agents
Pricing →
Plans and runtime
RAG on Rails solution →
Solution page
RAG chatbot use case →
Use case
Playbooks →
Scaffold from patterns
Agent types →
Knowledge → Monitoring
Tool calling from Rails models →
Build-time tools
Memory patterns →
Build-time memory
OpenAI on Rails →
BYOK OpenAI
Anthropic on Rails →
Claude on Rails Agent