Rerank search results by natural-language criteria

01 · Role in the system

What Jev does here

Pinecone full-text search retrieves 200 candidate articles; a TypeSafe judgment reranks the top candidates by criteria you specify in natural language - the niche where standard rerankers are too coarse and you need per-criterion quality. The example ships self-contained with its own deploy button, an empty-index bootstrap flow (ingest 2,155 seed articles once, then the deployed app returns results), and an optional Claude baseline column that stays disabled without an Anthropic key. It is one of an official collection from Pinecone demonstrating the retrieval-plus-judgment pairing, each example independently deployable with shared root environment keys.

02 · Control boundary

Where Jev sits

Vector or full-text retrieval to a shortlist; one typed judgment pass reranks by natural-language criteria against a retrieval service's candidates.

Code owns the loop, permissions, thresholds, validation, and side effects. Jev owns only the bounded judgments described above.

03 · Known limits

What this evidence does not prove

• Retrieval quality still bounds the ceiling: judgment reranks what retrieval surfaced, it cannot recall what search missed.
• One shipped example at capture time (nli-reranking), with the collection positioned as growing.
• Fresh deploys start with an empty index and need a one-time ingest before results appear.

04 · Attribution

Public sources

This is a Community record: the project was published by a third-party community author.

• pinecone-io ↗Community · github · public · checked 2026-09-24
• pinecone-io ↗Community · demo · public · checked 2026-09-24