Vector database company Qdrant wants RAG to be more cost-effective

by | Jul 2, 2024 | Technology

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More companies are looking to include retrieval augmented generation (RAG) systems in their technology stack, and new methods to improve it are now coming to light. 

Vector database company Qdrant believes its new search algorithm, BM42, will make RAG more efficient and cost-effective. 

Qdrant, founded in 2021, developed BM42 to provide vectors to companies working on new search methods. The company wants to offer more hybrid search—which combines semantic and keyword search—to customers. 

Andrey Vasnetsov, co-founder and chief technology officer of Qdrant, said in an interview with VentureBeat that BM42 is an update to the algorithm BM25, which “traditional” search platforms use to rank the relevance of documents in search queries. RAG often uses vector databases or databases that store data as mathematical metrics that make it easy to match data.

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“When we apply traditional keyword matching algorithms, the most commonly used one is BM25, which assumes documents have enough size to calculate statistics,” Vasnetsov said. “But we’re working with chunks of information now with RAG, so it doesn’t make sense to use BM25 anymore.”

Vasnetsov added that BM42 uses a language model, but instead of creating embeddings or representations of information, the model extracts the information from the documents. This information becomes tokens, which the algorithm then scores or weights in order to rank its re …

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Don’t miss OpenAI, Chevron, Nvidia, Kaiser Permanente, and Capital One leaders only at VentureBeat Transform 2024. Gain essential insights about GenAI and expand your network at this exclusive three day event. Learn More

More companies are looking to include retrieval augmented generation (RAG) systems in their technology stack, and new methods to improve it are now coming to light. 

Vector database company Qdrant believes its new search algorithm, BM42, will make RAG more efficient and cost-effective. 

Qdrant, founded in 2021, developed BM42 to provide vectors to companies working on new search methods. The company wants to offer more hybrid search—which combines semantic and keyword search—to customers. 

Andrey Vasnetsov, co-founder and chief technology officer of Qdrant, said in an interview with VentureBeat that BM42 is an update to the algorithm BM25, which “traditional” search platforms use to rank the relevance of documents in search queries. RAG often uses vector databases or databases that store data as mathematical metrics that make it easy to match data.

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Join enterprise leaders in San Francisco from July 9 to 11 for our flagship AI event. Connect with peers, explore the opportunities and challenges of Generative AI, and learn how to integrate AI applications into your industry. Register Now

“When we apply traditional keyword matching algorithms, the most commonly used one is BM25, which assumes documents have enough size to calculate statistics,” Vasnetsov said. “But we’re working with chunks of information now with RAG, so it doesn’t make sense to use BM25 anymore.”

Vasnetsov added that BM42 uses a language model, but instead of creating embeddings or representations of information, the model extracts the information from the documents. This information becomes tokens, which the algorithm then scores or weights in order to rank its re …nnDiscussion:nn” ai_name=”RocketNews AI: ” start_sentence=”Can I tell you more about this article?” text_input_placeholder=”Type ‘Yes'”]

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