Jev Reranker with Pinecone
Pinecone@pinecone
0:07This project uses the Jev model with Pinecone to rerank search results based on natural language criteria by evaluating against distinct binary criteria.
Pattern✦ zero-shot reranking for search
Use @typesafeai's Jev model with Pinecone to rerank results with natural language criteria!
Usually with rerankers, it's hard to cleanly specify what should and shouldn't be returned in results.
Jev resolves this by refactoring the problem into evaluating against distinct binary criteria, which pairs great with Pinecone retrieval!
In this demo, we compare using Jev and Claude to rerank 200 returned candidates from Pinecone. Jev returns a reranked list in about a second — 830 to 1,300 ms across eight test queries.
Claude Opus 5, doing the same job in one long-context call, takes 4.2 to 6.8 seconds.
That's roughly 5x faster, and about 43x cheaper: $0.004 per query against $0.18.
Related builds
Vectorless RAG with Jev
This project introduces a novel approach to Retrieval Augmented Generation (RAG) that replaces traditional embedding methods with a structured, table-of-contents-based navigation system, significantly improving speed and reducing costs by offloading non-generative tasks to Jev.
