Qdrant Launches Groundbreaking Pure Vector-Based Hybrid Search, Setting Higher Standards for RAG and AI Applications
July 02 2024 - 8:00AM
Business Wire
Qdrant, the leading high-performance open-source vector
database, today announced the launch of BM42, a pure vector-based
hybrid search approach that delivers more accurate and efficient
retrieval for modern retrieval-augmented generation (RAG)
applications. The BM42 search algorithm marks a significant step
forward beyond traditional text-based search for RAG and AI
applications.
BM42 provides enterprises 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.
Shifting from Keyword to Vector-First Search
Traditional keyword-based search engines, using algorithms like
BM25 that have been around for over 50 years, are not optimized for
the precise retrieval needed in modern applications and so struggle
with specific RAG demands, particularly with short text segments
that require further context to inform successful search and
retrieval.
“By moving away from keyword-based search to a fully
vector-based approach, Qdrant sets a new industry standard,” said
Andrey Vasnetsov, Qdrant CTO & 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.
Boosting Accuracy, Efficiency, and Scalability
Developers often face critical decisions about choosing between
sparse or dense vectors or a hybrid approach. Many existing hybrid
solutions struggle with scalability and accuracy or are
prohibitively expensive. Qdrant's new hybrid search system
addresses these challenges, providing an efficient, and
cost-effective solution for both new and existing users. Most
importantly, BM42 will enable users to quickly jump from prototype
to production quickly, and then scale the solution globally.
Learn more about the announcement here:
qdrant.tech/articles/bm42
About Qdrant
Qdrant is the leading, high-performance, scalable, open-source
vector database and search engine, essential for building the next
generation of AI/ML applications. Qdrant is able to handle billions
of vectors, supports the matching of semantically complex objects,
and is implemented in Rust for performance, memory safety, and
scale. Recently, the company was recognized among the top 10
startups on Sifted’s 2024 B2B SaaS Rising 100, which annually ranks
Europe's most promising B2B SaaS startups valued under $1bn.
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For more information, please visit Qdrant's website or contact:
press@qdrant.com