Muves: Multimodal and multilingual vector search with Hardware Acceleration
06-14, 16:00–16:40 (Europe/Berlin), Frannz Salon

Bringing multimodal experience into search journey became of high interest lately: searching images with text, or looking inside an audio file, combining that with the rgb frames of a video stream. Today, vector search algorithms (like FAISS, HNSW, BuddyPQ) and databases (Vespa, Weaviate, Milvus and others) make these experiences a reality. But what if you as a user would like to stay with the familiar Elasticsearch / OpenSearch AND leverage the vector search at scale? In this talk we will take a hardware acceleration route to build a vector search experience over products and will show how you can blend the worlds of neural search with symbolic filters.

We will discuss use cases where adding multimodal and multilingual vector search will improve recall and compare results from Elasticsearch/OpenSearch with and without the vector search component using tools like Quepid. We will also investigate different fine-tuning approaches and compare their impact on different quality metrics.

We will demonstrate our findings using our end-to-end search solution Muves which combines traditional symbolic search with multimodal and multilingual vector search and includes an integrated fine-tuner for easy domain adaptation of pre-trained vector models.

The Search track is presented by OpenSource Connections

See also: Slides

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Aarne has more than 16 years of experience in software development, consulting and academic research with specific focus on NLP and search engines.

Aarne is the CEO and co-founder of Basement AI, Lead AI Engineer at Silo AI and a PhD researcher in NLP at University of Helsinki.

Aarne is currently working on a new multilingual and multimodal search engine Muves.

Dmitry has been focusing on search engines since 2010 with Apache Lucene and Solr and since 2020 with Elasticsearch. He was responsible for building a search team and search technology powering AlphaSense product which today is used by thousands of reputed banks, hedge funds and companies in almost any industry vertical around the world. At Silo.AI Dmitry led a team of NLP researchers, search, frontend and QA engineers working on search at web scale, interacting with Product Management, Engineers and Data teams on a daily basis.
Dmitry has worked on open source projects Luke and Quepid and co-founded a few startups: in text analytics, edtech and team engagement space. He is the founder and host of the Vector Podcast (https://www.youtube.com/c/VectorPodcast). Having established himself as an independent researcher in vector search, Dmitry began working on Muves -- multilingual and multimodal search engine, together with his co-founders. In free time he enjoys reading, cycling and blogging about AI and Search. Dmitry holds a PhD in Applied Mathematics and a Master’s in Computer Science.