Embeddings retrieval plugin for InferenceBench Suite (deterministic ranking skeleton; real embedding-model invocation deferred)
Project description
inferencebench-embeddings
Embeddings retrieval plugin for the InferenceBench Suite.
Phase-2-quality skeleton: produces signed envelopes via deterministic hash-based rankings, with placeholders for real embedding-model invocation that future revisions wire to TEI / OpenAI / Cohere.
Suite ID: embeddings.retrieval
Bundled benchmarks:
embeddings.retrieval.beir-mini— 5 queries × 20-doc corpus, recall@5.embeddings.retrieval.long-doc— 3 queries with longer documents, nDCG@10.
The skeleton does NOT actually embed any text. For each query it ranks the
corpus by sha256(query + doc_id), then scores the top-k against the
fixture's relevant set. This produces a real, well-defined retrieval metric
in [0, 1] without external dependencies — future revisions replace the
hash rank with a real vector search.
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