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-pic...
Mar 02, 2025•20 min•Ep 10•Transcript available on Metacast 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 o...
Feb 10, 2025•1 hr 7 min•Ep 9•Transcript available on Metacast 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 L...
Jan 17, 2025•1 hr 8 min•Ep 8•Transcript available on Metacast 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?...
Nov 07, 2024•38 min•Ep 7•Transcript available on Metacast 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: ...
Sep 19, 2024•23 min•Ep 6•Transcript available on Metacast 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: ...
Jul 18, 2024•27 min•Ep 5•Transcript available on Metacast 00:00 Intro 00:21 Guest Introduction: Eric Pugh 03:00 Eric's story in search and the evolution of search technology 7:27 Quepid: Improving Search Relevancy 10:08 When to use Quepid 14:53 Flash back to Apache Solr 1.4 and the book (of which Eric is one author) 17:49 Quepid Demo and Future Enhancements 23:57 Real-Time Query Doc Pairs with WebSockets 24:16 Integrating Quepid with Search Engines 25:57 Introducing LLM-Based Judgments 28:05 Scaling Up Judgments with AI 28:48 Data Science Notebooks in ...
Jun 26, 2024•48 min•Ep 4•Transcript available on Metacast 00:00 Intro 01:54 Reflection on the past year in AI 08:08 Reader LLM (and RAG) 12:36 Does it need fine-tuning to a domain? 14:20 How LLMs can lie 17:32 What if data isn't perfect 21:21 SWIRL's secret sauce with Reader LLM 23:55 Feedback loop 26:14 Some surprising client perspective 31:17 How Gen AI can change communication interfaces 34:11 Call-out to the Community...
May 15, 2024•38 min•Ep 3•Transcript available on Metacast 00:00 Intro 00:42 Louis's background 05:39 From Facebook to Rockset 07:41 Embeddings prior to deep learning / LLM era 12:35 What's Rockset as a product 15:27 Use cases 18:04 RocksDB as part of Rockset 20:33 AI capabilities: ANN index, hybrid search 25:11 Types of hybrid search 28:05 Can one learn the alpha? 30:03 Louis's prediction of the future of vector search 33:55 RAG and other AI capabilities 41:46 Call out to the Vector Search community 46:16 Vector Databases vs Databases 49:16 Question of...
May 01, 2024•53 min•Ep 2•Transcript available on Metacast Topics: 00:00 Intro - how do you like our new design? 00:52 Greets 01:55 Saurabh's background 03:04 Resume Matcher: 4.5K stars, 800 community members, 1.5K forks 04:11 How did you grow the project? 05:42 Target audience and how to use Resume Matcher 09:00 How did you attract so many contributors? 12:47 Architecture aspects 15:10 Cloud or not 16:12 Challenges in maintaining OS projects 17:56 Developer marketing with Swirl AI Connect 21:13 What you (listener) can help with 22:52 What drives you? S...
Apr 12, 2024•26 min•Ep 1•Transcript available on Metacast Topics: 00:00 Intro 00:22 Quick demo of SWIRL on the summary transcript of this episode 01:29 Sid’s background 08:50 Enterprise vs Federated search 17:48 How vector search covers for missing folksonomy in enterprise data 26:07 Relevancy from vector search standpoint 31:58 How ChatGPT improves programmer’s productivity 32:57 Demo! 45:23 Google PSE 53:10 Ideal user of SWIRL 57:22 Where SWIRL sits architecturally 1:01:46 How to evolve SWIRL with domain expertise 1:04:59 Reasons to go open source 1:...
Jul 22, 2023•2 hr 32 min•Ep 7•Transcript available on Metacast Topics: 00:00 Intro 02:20 Atita’s path into search engineering 09:00 When it’s time to contribute to open source 12:08 Taking management role vs software development 14:36 Knowing what you like (and coming up with a Solr course) 19:16 Read the source code (and cook) 23:32 Open Bistro Innovations Lab and moving to Germany 26:04 Affinity to Search world and working as a Search Relevance Consultant 28:39 Bringing vector search to Chorus and Querqy 34:09 What Atita learnt from Eric Pugh’s approach t...
May 17, 2023•2 hr 32 min•Ep 6•Transcript available on Metacast Topics: 00:00 Intro 01:54 Things Connor learnt in the past year that changed his perception of Vector Search 02:42 Is search becoming conversational? 05:46 Connor asks Dmitry: How Large Language Models will change Search? 08:39 Vector Search Pyramid 09:53 Large models, data, Form vs Meaning and octopus underneath the ocean 13:25 Examples of getting help from ChatGPT and how it compares to web search today 18:32 Classical search engines with URLs for verification vs ChatGPT-style answers 20:15 Hy...
Mar 11, 2023•2 hr 33 min•Ep 5•Transcript available on Metacast Toloka’s support for Academia: grants and educator partnerships https://toloka.ai/collaboration-with-educators-form https://toloka.ai/research-grants-form These are pages leading to them: https://toloka.ai/academy/education-partnerships https://toloka.ai/grants Topics: 00:00 Intro 01:25 Jenny’s path from graduating in ML to a Data Advocate role 07:50 What goes into the labeling process with Toloka 11:27 How to prepare data for labeling and design tasks 16:01 Jenny’s take on why Relevancy needs m...
Jan 28, 2023•1 hr 27 min•Ep 4•Transcript available on Metacast 00:00 Introduction 01:11 Yaniv’s background and intro to Searchium & GSI 04:12 Ways to consume the APU acceleration for vector search 05:39 Power consumption dimension in vector search 7:40 Place of the platform in terms of applications, use cases and developer experience 12:06 Advantages of APU Vector Search Plugins for Elasticsearch and OpenSearch compared to their own implementations 17:54 Everyone needs to save: the economic profile of the APU solution 20:51 Features and ANN algorithms i...
Dec 21, 2022•1 hr 14 min•Ep 3•Transcript available on Metacast Topics: 00:00 Intro 01:30 Doug’s story in Search 04:55 How Quepid came about 10:57 Relevance as product at Shopify: challenge, process, tools, evaluation 15:36 Search abandonment in Ecommerce 21:30 Rigor in A/B testing 23:53 Turn user intent and content meaning into tokens, not words into tokens 32:11 Use case for vector search in Maps. What about search in other domains? 38:05 Expanding on dense approaches 40:52 Sparse, dense, hybrid anyone? 48:18 Role of HNSW, scalability and new vector databa...
Oct 01, 2022•2 hr 33 min•Ep 2•Transcript available on Metacast Topics: 00:00 Introduction 01:12 Malte’s background 07:58 NLP crossing paths with Search 11:20 Product discovery: early stage repetitive use cases pre-dating Haystack 16:25 Acyclic directed graph for modeling a complex search pipeline 18:22 Early integrations with Vector Databases 20:09 Aha!-use case in Haystack 23:23 Capabilities of Haystack today 30:11 Deepset Cloud: end-to-end deployment, experiment tracking, observability, evaluation, debugging and communicating with stakeholders 39:00 Examp...
Aug 30, 2022•1 hr 26 min•Ep 1•Transcript available on Metacast 00:00 Introduction 01:10 Max's deep experience in search and how he transitioned from structured data 08:28 Query-term dependence problem and Max's perception of the Vector Search field 12:46 Is vector search a solution looking for a problem? 20:16 How to move embeddings computation from GPU to CPU and retain GPU latency? 27:51 Plug-in neural model into Java? Example with a Hugging Face model 33:02 Web-server Mighty and its philosophy 35:33 How Mighty compares to in-DB embedding layer, like Weav...
Jun 16, 2022•2 hr 52 min•Ep 13•Transcript available on Metacast Vector Podcast Live Topics: 00:00 Kick-off introducing co:rise study platform 03:03 Grant’s background 04:58 Principle of 3 C’s in the life of a CTO: Code, Conferences and Customers 07:16 Principle of 3 C’s in the Search Engine development: Content, Collaboration and Context 11:51 Balance between manual tuning in pursuit to learn and Machine Learning 15:42 How to nurture intuition in building search engine algorithms 18:51 How to change the approach of organizations to true experimentation 23:17...
Jun 09, 2022•1 hr 13 min•Ep 12•Transcript available on Metacast Topics: 00:00 Kick-off by Judy Zhu 01:33 Introduction by Dmitry Kan and his bio! 03:03 Daniel’s background 04:46 “Science is the difference between instinct and strategy” 07:41 Search as a personal learning experience 11:53 Why do we need Machine Learning in Search, or can we use manually curated features? 16:47 Swimming up-stream from relevancy: query / content understanding and where to start? 23:49 Rule-based vs Machine Learning approaches to Query Understanding: Pareto principle 29:05 How co...
May 23, 2022•1 hr 3 min•Ep 11•Transcript available on Metacast Topics: 00:00 Intro 01:03 Yusuf’s background 03:00 Multimodal search in tech and humans 08:53 CLIP: discovering hidden semantics 13:02 Where to start to apply metric learning in practice. AutoEncoder architecture included! 19:00 Unpacking it further: what is metric learning and the difference with deep metric learning? 28:50 How Deep Learning allowed us to transition from pixels to meaning in the images 32:05 Increasing efficiency: vector compression and quantization aspects 34:25 Yusuf gives a ...
May 07, 2022•1 hr 10 min•Ep 10•Transcript available on Metacast Topics: 00:00 Introduction 01:21 Jo Kristian’s background in Search / Recommendations since 2001 in Fast Search & Transfer (FAST) 03:16 Nice words about Trondheim 04:37 Role of NTNU in supplying search talent and having roots in FAST 05:33 History of Vespa from keyword search 09:00 Architecture of Vespa and programming language choice: C++ (content layer), Java (HTTP requests and search plugins) and Python (pyvespa) 13:45 How Python API enables evaluation of the latest ML models with Vespa a...
Apr 12, 2022•1 hr 27 min•Ep 9•Transcript available on Metacast Update: ZIR.AI has relaunched as Vectara: https://vectara.com/ Topics: 00:00 Intro 00:54 Amin’s background at Google Research and affinity to NLP and vector search field 05:28 Main focus areas of ZIR.AI in neural search 07:26 Does the company offer neural network training to clients? Other support provided with ranking and document format conversions 08:51 Usage of open source vs developing own tech 10:17 The core of ZIR.AI product 14:36 API support, communication protocols and P95/P99 SLAs, ded...
Feb 16, 2022•1 hr 11 min•Ep 8•Transcript available on Metacast Topics: 00:00 Introduction 01:04 Yury’s background in laser physics, computer vision and startups 05:14 How Yury entered the field of nearest neighbor search and his impression of it 09:03 “Not all Small Worlds are Navigable” 10:10 Gentle introduction into the theory of Small World Navigable Graphs and related concepts 13:55 Further clarification on the input constraints for the NN search algorithm design 15:03 What did not work in NSW algorithm and how did Yury set up to invent new algorithm ca...
Jan 31, 2022•2 hr 30 min•Ep 7•Transcript available on Metacast Topics: 00:00 Intro 00:42 Joan's background 01:46 What attracted Joan's attention in Jina as a company and product? 04:39 Main area of focus for Joan in the product 05:46 How Open Source model works for Jina? 08:38 Deeper dive into Jina.AI as a product and technology stack 11:57 Does Jina fit the use cases of smaller / mid-size players with smaller amount of data? 13:45 KNN/ANN algorithms available in Jina 16:05 BigANN competition and BuddyPQ, increasing 12% in recall over FAISS 17:07 Does Jina ...
Jan 19, 2022•57 min•Ep 6•Transcript available on Metacast Show notes: - The ML Test Score: A Rubric for ML Production Readiness and Technical Debt Reduction https://research.google/pubs/pub46555/ - IEEE MLOps Standard for Ethical AI https://docs.google.com/document/d/1x... - Qdrant: https://qdrant.tech/ - Elixir connector for Qdrant by Tom: https://github.com/tlack/exqdr - Other 6 vector databases: https://towardsdatascience.com/milvus... - ByT5: Towards a token-free future with pre-trained byte-to-byte models https://arxiv.org/abs/2105.13626 - Tantivy...
Dec 23, 2021•47 min•Ep 5•Transcript available on Metacast Show notes: - On the Measure of Intelligence by François Chollet - Part 1: Foundations (Paper Explained) [YouTube]( https://www.youtube.com/watch?v=3_qGr... ) - [2108.07258 On the Opportunities and Risks of Foundation Models]( https://arxiv.org/abs/2108.07258 ) - [2005.11401 Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks]( https://arxiv.org/abs/2005.11401 ) - Negative Data Augmentation: https://arxiv.org/abs/2102.05113 - Beyond Accuracy: Behavioral Testing of NLP models with Ch...
Dec 23, 2021•59 min•Ep 4•Transcript available on Metacast Order your Milvus t-shirt / hoodie! https://milvus.typeform.com/to/IrnLAgui Thanks Filip for arranging. Show notes: - Milvus DB: https://milvus.io/ - Not All Vector Databases Are Made Equal: https://towardsdatascience.com/milvus... - Milvus talk at Haystack: https://www.youtube.com/watch?v=MLSMs... - BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models https://arxiv.org/abs/2104.08663 - End-to-End Environmental Sound Classification using a 1D Convolutional Neur...
Dec 23, 2021•1 hr 13 min•Ep 3•Transcript available on Metacast 1. Layering problem: www.edge.org/conversation/sean_…-layers-of-reality 2. Podcast with Etienne Dilocker (SeMI Technologies Co-Founder & CTO): www.youtube.com/watch?v=6lkanzOqhDs 3. SOC2: linfordco.com/blog/soc-1-vs-soc-2-audit-reports/ 4. Dmitry's post on 7 Vector Databases: towardsdatascience.com/milvus-pineco…-9c65a3bd0696 5. Billion-Scale ANN Challenge: big-ann-benchmarks.com/index.html 6. Weaviate Introduction: www.semi.technology/developers/weaviate/current/ Newsletter: www.semi.techno...
Dec 23, 2021•2 hr 31 min•Ep 2•Transcript available on Metacast Show notes : 1. Pinecone 2.0: https://www.pinecone.io/learn/pinecon... It is GA and free: https://www.pinecone.io/learn/v2-pric... 2. Get your “Love Thy Nearest Neighbour” t-shirt :) shoot an email to greg@pinecone.io 3. Billion-Scale Approximate Nearest Neighbour Search Challenge: https://big-ann-benchmarks.com/index.... 4. ANNOY: https://github.com/spotify/annoy 5. FAISS: https://github.com/facebookresearch/f... 6. HNSW: https://github.com/nmslib/hnswlib 7. “How Zero Results Are Killing Ecomme...
Dec 06, 2021•44 min•Ep 1•Transcript available on Metacast 
