Repo · Search & RAG

Milvus Model

Use a Python reranker interface to score candidate documents with Jev and return their original indices in score order.

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From the repository

A library integrating embedding and reranker models from OpenAI, SentenceTransformers etc for semantic search in vector database.

aiembeddingnlpragreranker

How builders describe it

Python reranker adapter that sends candidate documents as Jev Noul questions in one request, then sorts the returned scores and preserves original document indices.

The model-integration layer for the Milvus vector database, shipping adapters for embedding and reranker models from hosted providers, Jev among them, plus local backends.

The decision Jev makes

Rank candidates against a query, or answer whether a passage is relevant.

Where it fits

Search pipelines retrieve too much. These projects use Jev as a reranker or relevance check after the first search, so only useful results reach the user or the model. All 108 search & rag projects →

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