Mayo Oshin

One of the major challenges with building AI chat apps and agents powered by RAG is the complexity of creating, storing, and syncing embeddings with real-time source data.

If this isn't managed effectively, your AI app will likely generate outdated, inaccurate information leading users to lose trust and abandon use of the app. Fortunately, there's a simple solution that eliminates this complexity.

pgai Vectorizer by @TimescaleDB helps to ensure that your AI app always utilises relevant, up-to-date data via efficient RAG and vector search processes.

With just one line of SQL (as per image below), you can automate embedding creation and synchronization directly within PostgreSQL. Once you're set up, pgai Vectorizer will automatically sync embeddings with inserts, updates, deletes to source data. No need for you to build external pipelines or synchronization services.

pgai Vectorizer also enables A/B testing of different embedding models and data chunking methods without reprocessing data yourself.

In a nutsell, pgai benefits include:

• Vector embeddings in PostgreSQL: Create, store, and search embeddings all in PostgreSQL.
• No more stale embeddings: Keeps embeddings synced as your underlying data changes.
• All in SQL: Everything is configured in SQL, no extra tools needed.
• Familiar postgreSQL ecosystem: pgai Vectorizer works with other popular tools for AI in PostgreSQL, like the pgvector, pgvectorscale, and pgai extensions.

If you’re looking to simplify your AI infra for your RAG, search or AI agent app, check out the pgai Vectorizer Github repo here: https://t.co/0szormuVFE