Vector database company Qdrant wants RAG to be more cost-effective
How does BM42 improve retrieval augmented generation (RAG)?
BM42, a new search algorithm developed by Qdrant, enhances Retrieval Augmented Generation (RAG) by offering a cost-efficient and faster alternative to other options like Splade2. It updates the traditional BM25 algorithm, using a language model to extract information from documents and convert it into tokens. These tokens are then scored and weighted to determine relevance to the search query, allowing Qdrant to precisely identify necessary information to answer queries. This improvement in the search algorithm aids in making RAG systems more efficient and reliable.
What is the foundational concept of Qdrant's BM42 algorithm?
Qdrant's BM42 algorithm is a pure vector-based hybrid search approach designed for modern retrieval-augmented generation (RAG) applications. It combines the best of traditional text-based search and vector-based search to deliver more accurate and efficient retrieval. The algorithm uses a transformer AI model, which extracts information from documents and scores it for relevance, rather than relying on statistics over a collection of documents.
How does BM42 differ from its predecessor, BM25?
BM42 is a new search algorithm developed by Qdrant, designed for retrieval augmented generation (RAG) systems1. It is an update to the BM25 algorithm, which is commonly used by traditional search platforms to rank the relevance of documents in search queries. Unlike BM25, BM42 is optimized for working with chunks of information, rather than full documents, making it more suitable for RAG applications1.