Sparse Vector Search — SPLADE & Learned Sparse Retrieval

I will implement SPLADE or a similar learned sparse retrieval model — generating sparse vectors that capture keyword importance with semantic expansion, producing a sparse representation that outperforms BM25 by understanding which related terms to weight alongside the exact query terms. SPLADE-based sparse retrieval outperforms BM25 in hybrid search because it applies semantic understanding to sparse vector generation — where BM25 weights exact term matches, SPLADE weights semantically related terms that frequently co-occur with the query terms, providing a richer sparse signal that improves hybrid fusion quality.

The implementation covers SPLADE model deployment, sparse vector generation for your corpus, sparse index configuration in your vector store, integration with dense retrieval for hybrid fusion, and quality comparison against BM25-based hybrid search.