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by Dmitry Kan
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- Apr 10, 20261h 18m
AI Webinar - Building an AI-Ready Data Backbone
Webinar I gave with AI Camp and Aiven on AI-ready data backbone, and specifically how OpenSearch unlocks AI-powered search and log analytics: https://www.aicamp.ai/event/eventdetails/W2026032610Blog post: https://dmitry-kan.medium.com/webinar-building-an-ai-ready-data-backbone-with-aiven-google-cloud-4629f97f69bdLLM/RAG/AI Agents course: https://dmitry-kan.medium.com/course-large-language-models-and-generative-ai-for-nlp-2025-98e31780de30Free tier OpenSearch: https://aiven.io/free-opensearchTime codes:1:01 Dima's intro + Vector Podcast4:56 About Aiven7:06 Why best? - Question from the audience10:22 Free Tier OpenSearch!11:57 Aiven's unifed platform12:58 OpenSearch: What and Why17:00 Why OpenSearch is AI-Ready?18:26 What Aiven's OpenSearch gives you20:44 Lexical vs semantic search22:51 Technical use cases of OpenSearch 24:17 Reference Architecture with Kafka as event processor, and OpenSearch as storage and search layer25:37 Aiven's case studies for OpenSearch26:27 When to choose OpenSearch?28:21 Demo of OpenSearch query UI32:12 Is there any advantage in using Qdrant over OpenSearch? - Question from the audience34:30 What is the vector lenght (in this demo)? - Question from the audience36:27 What are the main advantages of Aiven's OpenSearch compared to Elasticsearch? - Question from the audience32:11 Demo of Search Relevancy Workbench: visual way of searchingShow notes:- User Behaviour Insights: https://www.ubisearch.dev/- Webinar's demo code part 1: Episode download / transcribe / index: https://github.com/dimakan-dev/conduit-transcripts/blob/main/DATA_PROCESSING_GUIDE.md- Webinar's demo code part 2: Main UI and quality dashboards: https://github.com/dimakan-dev/preparing-data-for-opensearch-and-rag/blob/main/workshop/STREAMLIT_README.md
- Nov 7, 20251h 19m
Trey Grainger - Wormhole Vectors
This lightning session introduces a new idea in vector search - Wormhole vectors!It has deep roots in physics and allows for transcending spaces of any nature: sparse, vector and behaviour (but could theoretically be any N-dimensional space).Craft decaf & half caf coffee, 25% discount: https://savorista.com/discount/VECTORBlog post on Medium: https://dmitry-kan.medium.com/novel-idea-in-vector-search-wormhole-vectors-6093910593b8Session page on maven: https://maven.com/p/8c7de9/beyond-hybrid-search-with-wormhole-vectors?utm_campaign=NzI2NzIx&utm_medium=ll_share_link&utm_source=instructorTo try the managed OpenSearch (multi-cloud, automatic backups, disaster recovery, vector search and more), go here: https://console.aiven.io/signup?utm_source=youtube&utm_medium&&utm_content=vectorpodcastGet credits to use Aiven's products (PG, Kafka, Valkey, OpenSearch, ClickHouse): https://aiven.io/startupsTimecodes:00:00 Intro by Dmitry01:48 Trey's presentation03:05 Walk to the AI-Powered Search course by Trey and Doug07:07 Intro to vector spaces and embeddings19:03 Disjoint vector spaces and the need of hybrid search23:11 Different modes of search24:49 Wormhole vectors47:49 Q&AWhat you'll learn:- What are "Wormhole Vectors"?Learn how wormhole vectors work & how to use them to traverse between disparate vector spaces for better hybrid search.- Building a behavioral vector space from click stream dataLearn to generate behavioral embeddings to be integrated with dense/semantic and sparse/lexical vector queries.- Traverse lexical, semantic, & behavioral vectors spacesJump back and forth between multiple dense and sparse vector spaces in the same query- Advanced hybrid search techniques (beyond fusion algorithms)Hybrid search is more than mixing lexical + semantic search. See advanced techniques and where wormhole vectors fit in.YouTube: https://www.youtube.com/watch?v=fvDC7nK-_C0
- Sep 19, 20251h 15m
Economical way of serving vector search workloads with Simon Eskildsen, CEO Turbopuffer
Turbopuffer search engine supports such products as Cursor, Notion, Linear, Superhuman and Readwise.Craft decaf & half caf coffee, 25% discount: https://savorista.com/discount/VECTORThis episode on YouTube: https://youtu.be/I8ZtqajighgMedium: https://dmitry-kan.medium.com/vector-podcast-simon-eskildsen-turbopuffer-69e456da8df3Dev: https://dev.to/vectorpodcast/vector-podcast-simon-eskildsen-turbopuffer-cfaIf you are on Lucene / OpenSearch stack, you can go managed by signing up here: https://console.aiven.io/signup?utm_source=youtube&utm_medium=&&utm_content=vectorpodcastTime codes:00:00 Intro00:15 Napkin Problem 4: Throughput of Redis01:35 Episode intro02:45 Simon's background, including implementation of Turbopuffer09:23 How Cursor became an early client11:25 How to test pre-launch14:38 Why a new vector DB deserves to exist?20:39 Latency aspect26:27 Implementation language for Turbopuffer28:11 Impact of LLM coding tools on programmer craft30:02 Engineer 2 CEO transition35:10 Architecture of Turbopuffer43:25 Disk vs S3 latency, NVMe disks, DRAM48:27 Multitenancy50:29 Recall@N benchmarking59:38 filtered ANN and Big-ANN Benchmarks1:00:54 What users care about more (than Recall@N benchmarking)1:01:28 Spicy question about benchmarking in competition1:06:01 Interesting challenges ahead to tackle1:10:13 Simon's announcementShow notes:- Turbopuffer in Cursor: https://www.youtube.com/watch?v=oFfVt3S51T4&t=5223stranscript: https://lexfridman.com/cursor-team-transcript- https://turbopuffer.com/- Napkin Math: https://sirupsen.com/napkin- Follow Simon on X: https://x.com/Sirupsen- Not All Vector Databases Are Made Equal: https://towardsdatascience.com/milvus-pinecone-vespa-weaviate-vald-gsi-what-unites-these-buzz-words-and-what-makes-each-9c65a3bd0696/
- Mar 21, 20251h 3m
Adding ML layer to Search: Hybrid Search Optimizer with Daniel Wrigley and Eric Pugh
Vector Podcast website: https://vectorpodcast.comHaystack US 2025: https://haystackconf.com/2025/Federated search, Keyword & Neural Search, ML Optimisation, Pros and Cons of Hybrid searchIt is fascinating and funny how things develop, but also turn around. In 2022-23 everyone was buzzing about hybrid search. In 2024 the conversation shifted to RAG, RAG, RAG. And now we are in 2025 and back to hybrid search - on a different level: finally there are strides and contributions towards making hybrid search parameters learnt with ML. How cool is that?Design: Saurabh Rai, https://www.linkedin.com/in/srbhr/The design of this episode is inspired by a scene in Blade Runner 2049. There's a clear path leading towards where people want to go to, yet they're searching for something.00:00 Intro00:54 Eric's intro and Daniel's background02:50 Importance of Hybrid search: Daniel's take07:26 Eric's take10:57 Dmitry's take11:41 Eric's predictions13:47 Doug's blog on RRF is not enough16:18 How to not fall short of the blind picking in RRF: score normalization, combinations and weights25:03 The role of query understanding: feature groups35:11 Lesson 1 from Daniel: Simple models might be all you need36:30 Lesson 2: query features might be all you need38:30 Reasoning capabilities in search40:02 Question from Eric: how is this different from Learning To Rank?42:46 Carrying the past in Learning To Rank / any rank44:21 Demo!51:52 How to consume this in OpenSearch55:15 What's next58:44 Haystack US 2025YouTube: https://www.youtube.com/watch?v=quY769om1EY
- Mar 2, 202519 min
Vector Databases: The Rise, Fall and Future - by NotebookLM
https://www.vectorpodcast.com/I had fun interacting with NotebookLM - mostly for self-educational purposes. I think this tool can help by bringing an additional perspective over a textual content. It ties to what RAG (Retrieval Augmented Generation) can do to content generation in another modality. In this case, text is used to augment the generation of a podcast episode.This episode is based on my blog post: https://dmitry-kan.medium.com/the-rise-fall-and-future-of-vector-databases-how-to-pick-the-one-that-lasts-6b9fbb43bbbeTime codes:00:00 Intro to the topic1:11 Dmitry's knowledge in the space1:54 Unpacking the Rise & Fall idea3:14 How attention got back to Vector DBs for a bit4:18 Getting practical: Dmitry's guide for choosing the right Vector Database4:39 FAISS5:34 What if you need fine-grained keyword search? Look at Apache Lucene-based engines6:41 Exception to the rule: Late-interaction models8:30 Latency and QPS: GSI APU, Vespa, Hyperspace9:28 Strategic approach9:55 Cloud solutions: CosmosDB, Vertex AI, Pinecone, Weaviate Cloud10:14 Community voice: pgvector10:48 Picture of the fascinating future of the field12:23 Question to the audience12:44 Taking a step back: key points13:45 Don't get caught up in trendy shiny new techYouTube: https://www.youtube.com/watch?v=403rxbWZK9Y
- Feb 10, 20251h 7m
Code search, Copilot, LLM prompting with empathy and Artifacts with John Berryman
Vector Podcast website: https://vectorpodcast.comGet your copy of John's new book "Prompt Engineering for LLMs: The Art and Science of Building Large Language Model–Based Applications": https://amzn.to/4fMj2EfJohn Berryman is the founder and principal consultant of Arcturus Labs, where he specializes in AI application development (Agency and RAG). As an early engineer on GitHub Copilot, John contributed to the development of its completions and chat functionalities, working at the forefront of AI-assisted coding tools. John is coauthor of "Prompt Engineering for LLMs" (O'Reilly).Before his work on Copilot, John's focus was search technology. His diverse experience includes helping to develop next-generation search system for the US Patent Office, building search and recommendations for Eventbrite, and contributing to GitHub's code search infrastructure. John is also coauthor of "Relevant Search" (Manning), a book that distills his expertise in the field.John's unique background, spanning both cutting-edge AI applications and foundational search technologies, positions him at the forefront of innovation in LLM applications and information retrieval.00:00 Intro02:19 John's background and story in search and ML06:03 Is RAG just a prompt engineering technique?10:15 John's progression from a search engineer to ML researcher13:40 LLM predictability vs more traditional programming22:31 Code assist with GitHub Copilot29:44 Role of keyword search for code at GitHub35:01 GenAI: existential risk or pure magic? AI Natives39:40 What are Artifacts46:59 Demo!55:13 Typed artifacts, tools, accordion artifacts56:21 From Web 2.0 to Idea exchange57:51 Spam will transform into Slop58:56 John's new book and Acturus Labs introShow notes:- John Berryman on X: https://x.com/JnBrymn- Acturus Labs: https://arcturus-labs.com/- John's blog on Artifacts (see demo in the episode): https://arcturus-labs.com/blog/2024/11/11/cut-the-chit-chat-with-artifacts/YouTube: https://youtu.be/60HAtHVBYj8
- Jan 17, 20251h 7m
Debunking myths of vector search and LLMs with Leo Boytsov
00:00 Intro01:31 Leo's story09:59 SPLADE: single model to solve both dense and sparse?21:06 DeepImpact29:58 NMSLIB: what are non-metric spaces34:21 How HNSW and NMSLIB joined forces41:11 Why FAISS did not choose NMSLIB's algorithm43:36 Serendipity of discovery and the creation of industries47:06 Vector Search: intellectually rewarding, professionally undervalued52:37 Why RDBMS Still Struggles with Scalable Vector and Free-Text Search1:00:16 Leo's recent favorite papersLeo Boytsov on LinkedIn: https://www.linkedin.com/in/leonidboytsov/ and X: https://x.com/srchvrsLeo Boytsov’s paper list: https://scholar.google.com/citations?hl=en&user=I79y2i4AAAAJ&view_op=list_works&sortby=pubdateLots of papers and other material from Leo: https://www.youtube.com/watch?v=gzWErcOXIKk
- Nov 7, 202438 min
Berlin Buzzwords 2024 - Alessandro Benedetti - LLMs in Solr
This episode on YouTube: https://www.youtube.com/watch?v=PNB70TbQUBEAlessandro's talk on Hybrid Search with Apache Solr Reciprocal Rank Fusion: https://www.youtube.com/watch?v=8x2cbT5CCEM&list=PLq-odUc2x7i8jHpa6PHGzmxfAPEz-c-on&index=500:00 Intro00:50 Alessandro's take on the bbuzz'24 conference01:25 What and value of hybrid search04:55 Explainability of vector search results to users09:27 Explainability of vector search results to search engineers13:12 State of hybrid search in Apache Solr14:32 What's in Reciprocal Rank Fusion beyond round-robin?18:30 Open source for LLMs22:48 How we should approach this issue in business and research26:12 How to maintain the status of an open-source LLM / system30:06 Prompt engineering (hope and determinism)34:03 DSpy35:16 What's next in Solr
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About Vector Podcast
Vector Podcast is here to bring you the depth and breadth of Search Engine Technology, Product, Marketing, Business. In the podcast we talk with engineers, entrepreneurs, thinkers and tinkerers, who put their soul into search. Depending on your interest, you should find a matching topic for you -- whether it is deep algorithmic aspect of search engines and information retrieval field, or examples of products offering deep tech to its users. "Vector" -- because it aims to cover an emerging field of vector similarity search, giving you the ability to search content beyond text: audio, video, images and more. "Vector" also because it is all about vector in your profession, product, marketing and business.Podcast website: https://www.vectorpodcast.com/Dmitry is blogging on https://dmitry-
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