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Qdrant introduces pure vector-based hybrid search solution

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Qdrant, a leading high-performance open-source vector database, is releasing BM42, a pure vector-based hybrid search approach that provides accurate and efficient retrieval for modern retrieval-augmented generation (RAG) applications.

According to the company, the BM42 search algorithm marks a significant step forward beyond traditional text-based search for RAG and AI applications.

BM42 provides enterprises with another choice—not just traditional text search or traditional vector search. This pure vector-based hybrid search combines the best of both to achieve better results at lower costs in the realm of RAG. This will help users excel in the unfolding AI-centric world, according to Qdrant.

“By moving away from keyword-based search to a fully vector-based approach, Qdrant sets a new industry standard,” said Andrey Vasnetsov, Qdrant CTO and co-founder. “BM42, for short texts which are more prominent in RAG scenarios, provides the efficiency of traditional text search approaches, plus the context of vectors, so is more flexible, precise and efficient. While Qdrant envisions a future centered on vector-based search, this release helps to make vector search more universally applicable and marks a significant step toward the inevitable shift toward a vector-first approach.”

Qdrant's BM42 introduces a new way of classifying search results and is well suited for RAG applications.

Unlike traditional keyword-based search suited for long-form content, Qdrant’s solution integrates sparse and dense vectors to accurately pinpoint relevant information within a document. A sparse vector handles exact term matching. Dense vectors handle semantic relevance and deep meaning, according to the company.

BM42 will enable users to quickly jump from prototype to production quickly, and then scale the solution globally. 

For more information about this news, visit https://qdrant.tech.

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