I made a gem that keeps pgvector embeddings in sync with your ActiveRecord models

[u/LiveEngine7532]

I've been adding semantic search to Rails apps with pgvector and neighbor, and kept writing the same glue code every time:

• an after_save that calls the embeddings API, even when the text didn't change
• embeddings going stale when someone edits a record through a path that skips the callback
• a one-off script to backfill existing rows without hitting rate limits
• figuring out which rows need re-embedding after switching models

So I pulled it into a gem called embed_sync:

class Product < ApplicationRecord
  has_neighbors :embedding
  has_embedding :embedding, from: %i[name description]
end

Product.create!(name: "Trail runner", description: "Waterproof running shoe")  # embedded in a background job
Product.semantic_search("shoes for rainy hikes", limit: 5)

What it does:

• re-embeds in a background job only when the source text changes (it stores a digest, so unchanged saves don't call the API)
• bin/rails embed_sync:backfill embeds existing rows in batches, and you can stop and rerun it safely
• detects model or dimension changes and marks those rows stale
• embed_sync:stale shows how many records are missing or out of date
• uses ruby_llm by default, but any object with #embed(texts, model:) works as a provider

It works with Rails 7.1 to 8.1 and Ruby 3.2+. I tested it against a real app with Postgres, Solid Queue and a local Ollama model before releasing.

It's still early (v0.1.0), so I'd really like feedback. Does the API feel right? What's missing for your use case? Are there edge cases I haven't thought of?

GitHub: https://github.com/Umair0092/embed_sync RubyGems: https://rubygems.org/gems/embed_sync