Vector Podcast

Dmitry Kan
  • 5,0 (3)
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  • KUUKAUSITTAIN

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-kan.medium.com/

  1. Economical way of serving vector search workloads with Simon Eskildsen, CEO Turbopuffer

    19.9.

    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. This episode on YouTube: https://youtu.be/I8Ztqajighg If 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=vectorpodcast Time codes: 00:00 Intro 00:15 Napkin Problem 4: Throughput of Redis 01:35 Episode intro 02:45 Simon's background, including implementation of Turbopuffer 09:23 How Cursor became an early client 11:25 How to test pre-launch 14:38 Why a new vector DB deserves to exist? 20:39 Latency aspect 26:27 Implementation language for Turbopuffer 28:11 Impact of LLM coding tools on programmer craft 30:02 Engineer 2 CEO transition 35:10 Architecture of Turbopuffer 43:25 Disk vs S3 latency, NVMe disks, DRAM 48:27 Multitenancy 50:29 Recall@N benchmarking 59:38 filtered ANN and Big-ANN Benchmarks 1:00:54 What users care about more (than Recall@N benchmarking) 1:01:28 Spicy question about benchmarking in competition 1:06:01 Interesting challenges ahead to tackle 1:10:13 Simon's announcement Show notes: - Turbopuffer in Cursor: https://www.youtube.com/watch?v=oFfVt3S51T4&t=5223s transcript: 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/

    1 t 15 min
  2. Adding ML layer to Search: Hybrid Search Optimizer with Daniel Wrigley and Eric Pugh

    21.3.

    Adding ML layer to Search: Hybrid Search Optimizer with Daniel Wrigley and Eric Pugh

    Vector Podcast website: https://vectorpodcast.com Haystack US 2025: https://haystackconf.com/2025/ Federated search, Keyword & Neural Search, ML Optimisation, Pros and Cons of Hybrid search It 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 Intro 00:54 Eric's intro and Daniel's background 02:50 Importance of Hybrid search: Daniel's take 07:26 Eric's take 10:57 Dmitry's take 11:41 Eric's predictions 13:47 Doug's blog on RRF is not enough 16:18 How to not fall short of the blind picking in RRF: score normalization, combinations and weights 25:03 The role of query understanding: feature groups 35:11 Lesson 1 from Daniel: Simple models might be all you need 36:30 Lesson 2: query features might be all you need 38:30 Reasoning capabilities in search 40:02 Question from Eric: how is this different from Learning To Rank? 42:46 Carrying the past in Learning To Rank / any rank 44:21 Demo! 51:52 How to consume this in OpenSearch 55:15 What's next 58:44 Haystack US 2025

    1 t 3 min
  3. Vector Databases: The Rise, Fall and Future - by NotebookLM

    2.3.

    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-6b9fbb43bbbe Time codes: 00:00 Intro to the topic 1:11 Dmitry's knowledge in the space 1:54 Unpacking the Rise & Fall idea 3:14 How attention got back to Vector DBs for a bit 4:18 Getting practical: Dmitry's guide for choosing the right Vector Database 4:39 FAISS 5:34 What if you need fine-grained keyword search? Look at Apache Lucene-based engines 6:41 Exception to the rule: Late-interaction models 8:30 Latency and QPS: GSI APU, Vespa, Hyperspace 9:28 Strategic approach 9:55 Cloud solutions: CosmosDB, Vertex AI, Pinecone, Weaviate Cloud 10:14 Community voice: pgvector 10:48 Picture of the fascinating future of the field 12:23 Question to the audience 12:44 Taking a step back: key points 13:45 Don't get caught up in trendy shiny new tech

    20 min
  4. Code search, Copilot, LLM prompting with empathy and Artifacts with John Berryman

    10.2.

    Code search, Copilot, LLM prompting with empathy and Artifacts with John Berryman

    Vector Podcast website: https://vectorpodcast.com Get 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/4fMj2Ef John 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 Intro 02:19 John's background and story in search and ML 06:03 Is RAG just a prompt engineering technique? 10:15 John's progression from a search engineer to ML researcher 13:40 LLM predictability vs more traditional programming 22:31 Code assist with GitHub Copilot 29:44 Role of keyword search for code at GitHub 35:01 GenAI: existential risk or pure magic? AI Natives 39:40 What are Artifacts 46:59 Demo! 55:13 Typed artifacts, tools, accordion artifacts 56:21 From Web 2.0 to Idea exchange 57:51 Spam will transform into Slop 58:56 John's new book and Acturus Labs intro Show 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/ Watch on https://youtu.be/60HAtHVBYj8

    1 t 7 min
  5. Debunking myths of vector search and LLMs with Leo Boytsov

    17.1.

    Debunking myths of vector search and LLMs with Leo Boytsov

    00:00 Intro 01:31 Leo's story 09:59 SPLADE: single model to solve both dense and sparse? 21:06 DeepImpact 29:58 NMSLIB: what are non-metric spaces 34:21 How HNSW and NMSLIB joined forces 41:11 Why FAISS did not choose NMSLIB's algorithm 43:36 Serendipity of discovery and the creation of industries 47:06 Vector Search: intellectually rewarding, professionally undervalued 52:37 Why RDBMS Still Struggles with Scalable Vector and Free-Text Search 1:00:16 Leo's recent favorite papers Leo 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=pubdate Lots of papers and other material from Leo: https://www.youtube.com/watch?v=gzWErcOXIKk

    1 t 8 min
  6. Berlin Buzzwords 2024 - Alessandro Benedetti - LLMs in Solr

    07.11.2024

    Berlin Buzzwords 2024 - Alessandro Benedetti - LLMs in Solr

    Alessandro's talk on Hybrid Search with Apache Solr Reciprocal Rank Fusion: https://www.youtube.com/watch?v=8x2cbT5CCEM&list=PLq-odUc2x7i8jHpa6PHGzmxfAPEz-c-on&index=5 00:00 Intro 00:50 Alessandro's take on the bbuzz'24 conference 01:25 What and value of hybrid search 04:55 Explainability of vector search results to users 09:27 Explainability of vector search results to search engineers 13:12 State of hybrid search in Apache Solr 14:32 What's in Reciprocal Rank Fusion beyond round-robin? 18:30 Open source for LLMs 22:48 How we should approach this issue in business and research 26:12 How to maintain the status of an open-source LLM / system 30:06 Prompt engineering (hope and determinism) 34:03 DSpy 35:16 What's next in Solr

    38 min
  7. Berlin Buzzwords 2024 - Sonam Pankaj - EmbedAnything

    19.09.2024

    Berlin Buzzwords 2024 - Sonam Pankaj - EmbedAnything

    Video: https://youtu.be/dVIPBxHJ1kQ 00:00 Intro 00:15 Greets for Sonam 01:02 Importance of metric learning 3:37 Sonam's background: Rasa, Qdrant 4:31 What's EmbedAnything 5:52 What a user gets 8:48 Do I need to know Rust? 10:18 Call-out to the community 10:35 Multimodality 12:32 How to evaluate quality of LLM-based systems 16:38 QA for multimodal use cases 18:17 Place for a human in the LLM craze 19:00 Use cases for EmbedAnything 20:54 Closing theme (a longer one - enjoy!) Show notes: - GitHub: https://github.com/StarlightSearch/EmbedAnything - HuggingFace Candle: https://github.com/huggingface/candle - Sonam's talk on Berlin Buzzwords 2024: https://www.youtube.com/watch?v=YfR3kuSo-XQ - Removing GIL from Python: https://peps.python.org/pep-0703 - Blind pairs in CLIP: https://arxiv.org/abs/2401.06209 - Dark matter of intelligence: https://ai.meta.com/blog/self-supervised-learning-the-dark-matter-of-intelligence/ - Rasa chatbots: https://github.com/RasaHQ/rasa - Prometheus: https://github.com/prometheus-eval/prometheus-eval - Dino: https://github.com/facebookresearch/dino

    23 min
  8. Berlin Buzzwords 2024 - Doug Turnbull - Learning in Public

    18.07.2024

    Berlin Buzzwords 2024 - Doug Turnbull - Learning in Public

    00:00 Intro 00:30 Greets for Doug 01:46 Apache Solr and stuff 03:08 Hello LTR project 04:42 Secret sauce of Doug's continuous blogging 08:50 SearchArray 13:22 Running complex ML experiments 17:29 Efficient search orgs 22:58 Writing a book on search and AI Show notes: - Doug's talk on Learning To Rank at Reddit delivered at the Berlin Buzzwords 2024 conference: https://www.youtube.com/watch?v=gUtF1gyHsSM - Hello LTR: https://github.com/o19s/hello-ltr - Lexical search for pandas with SearchArray: https://github.com/softwaredoug/searcharray - https://softwaredoug.com/ - What AI Engineers Should Know about Search: https://softwaredoug.com/blog/2024/06/25/what-ai-engineers-need-to-know-search - AI Powered Search: https://www.manning.com/books/ai-powered-search - Quepid: https://github.com/o19s/quepid - Branching out in your ML / search experiments: https://dvc.org/doc/use-cases - Doug on Twitter: https://x.com/softwaredoug - Doug on LinkedIn: https://www.linkedin.com/in/softwaredoug/

    27 min
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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-kan.medium.com/

Tiedot

  • Tekijä
    Dmitry Kan
  • Julkaisuvuodet
    2021 - 2025
  • Jaksot
    32
  • Luokitus
    Lapsille sopiva
  • Copyright
    © Vector Podcast 2021-2025
  • Ohjelman sivusto
    Vector Podcast

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