In early October 2026 I posted two X threads (2026‑10‑03 10:22 UTC and 2026‑10‑03 21:58 UTC) showcasing an "insane" use‑case for Jev via the new open‑source Postgres extension pg‑jev. The posts walk through installing the extension, using the four SQL functions (jev(), jev_prob(), jev_choice(), jev_score()) to run semantic filters, classifications, and rankings on a Hacker News dataset, and ending with a summary of session‑level metrics (requests, tokens, cost, cache hits). I also linked the GitHub repo and a hands‑on guide for building a Jev‑style model locally.
These posts reinforce my earlier decision to embed Jev directly in the database rather than route rows through external application code, because it eliminates data movement, keeps the schema simple (no vector columns or embedding indexes), and lets me express rich semantic queries directly in SQL.
I demonstrated pg-jev, an open‑source PostgreSQL extension that exposes the Jev LLM through SQL functions, letting me run semantic filters, classifications, and rankings directly in SQL without any vector column or embedding index.
pg-jev is an open-source Postgres extension that exposes Jev through SQL functions.
jev() works inside WHERE
jev_prob() returns a probability
jev_choice() selects a label
jev_score() ranks rows across ordered levels
I chose pg-jev over building a separate vector store because it eliminates the overhead of maintaining embeddings and an ANN index while still enabling semantic queries directly in SQL. I also batch rows before sending them to Jev to reduce API calls and latency.