Hybrid Search With Apache Solr Reciprocal Rank Fusion | Alessandro Benedetti скачать в хорошем качестве

Hybrid Search With Apache Solr Reciprocal Rank Fusion | Alessandro Benedetti

Welcome to the twenty-first London Information Retrieval & AI Meetup, a free evening event aimed at Information Retrieval and AI enthusiasts and professionals who are curious to explore and discuss the latest trends in the field. The second talk is from Alessandro Benedetti, Director at Sease. Title: "Hybrid Search With Apache Solr Reciprocal Rank Fusion" Abstract: "Vector-based search gained incredible popularity in the last few years: Large Language Models fine-tuned for sentence similarity proved to be quite effective in encoding text to vectors and representing some of the semantics of sentences in a numerical form. These vectors can be used to run a K-nearest neighbour search and look for documents/paragraphs close to the query in a n-dimensional vector space, effectively mimicking a similarity search in the semantic space (Apache Solr KNN Query Parser). Although exciting, vector-based search nowadays still presents some limitations: – it’s very difficult to explain (e.g. why is document A returned and why at position K?) – it doesn’t care about exact keyword matching (and users still rely on keyword searches a lot) Hybrid search comes to the rescue, combining lexical (traditional keyword-based) search with neural (vector-based) search. So, what does it mean to combine these two worlds? It starts with the retrieval of two sets of candidates: – one set of results coming from lexical matches with the query keywords – a set of results coming from the K-Nearest Neighbours search with the query vector The result sets are merged and a single ranked list of documents is returned to the user. Reciprocal Rank Fusion (RRF) is one of the most popular algorithms for such a task. This talk introduces the foundation algorithms involved with RRF and walks you through the work done to implement them in Apache Solr, with a focus on the difficulties of the process, the distributed support(SolrCloud), the main components affected and the limitations faced, all updated to the latest days. The audience is expected to learn more about this interesting approach, the challenges in it and how the contribution process works for an Open Source search project as complex as Apache Solr." If you are willing to attend the next London Information Retrieval & AI Meetup, don't forget to join our group: https://bit.ly/2IjSBeX We are also accepting talks for the next meetups. If you have any talk you would like to propose, feel free to send us an abstract at talk@sease.io. ********************************** Hosted by Sease: https://sease.io