Building Modern Search Pipelines with Haystack, Large Language Models and Hybrid Retrieval

Abstract: 

In this talk we navigate through the latest buzz around semantic search and separate the noise from the meaningful advancements. Is dense retrieval better than BM25’s keyword search? Do large language models outperform smaller transformers? How well do the models generalize to industry corpora? How can we leverage Question Answering?
We will benchmark different methods, share best practices from industry use cases and show how you can use the open source framework Haystack to build, test and deploy stellar search pipelines easily yourself.

Bio: 

Malte Pietsch is CTO & Co-Founder at deepset. His current focus is on building deepset Cloud - a SaaS platform for developers to build, deploy and operate modern NLP pipelines. He holds a M.Sc. with honors from TU Munich and conducted research at Carnegie Mellon University. Before founding deepset he worked as a data scientist for multiple startups. He is an active open-source contributor and author of the NLP framework Haystack.

Open Data Science

 

 

 

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