Vector Podcast

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

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

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/

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

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

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

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

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

This episode on YouTube: https://youtu.be/dVIPBxHJ1kQ00:00 Intro00:15 Greets for Sonam01:02 Importance of metric learning3:37 Sonam's background: Rasa, Qdrant4:31 What's EmbedAnything5:52 What a user gets8:48 Do I need to know Rust?10:18 Call-out to the community10:35 Multimodality12:32 How to evaluate quality of LLM-based systems16:38 QA for multimodal use cases18:17 Place for a human in the LLM craze19:00 Use cases for EmbedAnything20:54 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

This episode on YouTube: https://www.youtube.com/watch?v=fIPC_xzqJ0o00:00 Intro00:30 Greets for Doug01:46 Apache Solr and stuff03:08 Hello LTR project04:42 Secret sauce of Doug's continuous blogging08:50 SearchArray13:22 Running complex ML experiments17:29 Efficient search orgs22:58 Writing a book on search and AIShow 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/

This episode on YouTube: https://www.youtube.com/watch?v=1L7UjjPz5wM00:00 Intro00:21 Guest Introduction: Eric Pugh03:00 Eric's story in search and the evolution of search technology7:27 Quepid: Improving Search Relevancy10:08 When to use Quepid14:53 Flash back to Apache Solr 1.4 and the book (of which Eric is one author)17:49 Quepid Demo and Future Enhancements23:57 Real-Time Query Doc Pairs with WebSockets24:16 Integrating Quepid with Search Engines25:57 Introducing LLM-Based Judgments28:05 Scaling Up Judgments with AI28:48 Data Science Notebooks in Quepid33:23 Custom Scoring in Quepid39:23 API and Developer Tools42:17 The Future of Search and Personal ReflectionsShow notes:- Hosted Quepid: https://app.quepid.com/- Ragas: Evaluation framework for your Retrieval Augmented Generation (RAG) pipelines https://github.com/explodinggradients...- Why Quepid: https://quepid.com/why-quepid/- Quepid on Github: https://github.com/o19s/quepid

This episode on YouTube: https://www.youtube.com/watch?v=5fafSkzKpfw00:00 Intro01:54 Reflection on the past year in AI08:08 Reader LLM (and RAG)12:36 Does it need fine-tuning to a domain?14:20 How LLMs can lie17:32 What if data isn't perfect21:21 SWIRL's secret sauce with Reader LLM23:55 Feedback loop26:14 Some surprising client perspective31:17 How Gen AI can change communication interfaces34:11 Call-out to the Community

This episode on YouTube: https://www.youtube.com/watch?v=TiwqVlDpsl800:00 Intro00:42 Louis's background05:39 From Facebook to Rockset07:41 Embeddings prior to deep learning / LLM era12:35 What's Rockset as a product15:27 Use cases18:04 RocksDB as part of Rockset20:33 AI capabilities: ANN index, hybrid search25:11 Types of hybrid search28:05 Can one learn the alpha?30:03 Louis's prediction of the future of vector search33:55 RAG and other AI capabilities41:46 Call out to the Vector Search community46:16 Vector Databases vs Databases49:16 Question of WHY

This episode on YouTube: https://www.youtube.com/watch?v=nx6BH9Z_gBATopics:00:00 Intro - how do you like our new design?00:52 Greets01:55 Saurabh's background03:04 Resume Matcher: 4.5K stars, 800 community members, 1.5K forks04:11 How did you grow the project?05:42 Target audience and how to use Resume Matcher09:00 How did you attract so many contributors?12:47 Architecture aspects15:10 Cloud or not16:12 Challenges in maintaining OS projects17:56 Developer marketing with Swirl AI Connect21:13 What you (listener) can help with22:52 What drives you?Show notes:- Resume Matcher: https://github.com/srbhr/Resume-Matcherwebsite: https://resumematcher.fyi/- Ultimate CV by Martin John Yate: https://www.amazon.com/Ultimate-CV-Cr...- fastembed: https://github.com/qdrant/fastembed- Swirl: https://github.com/swirlai/swirl-search

Topics:00:00 Intro00:22 Quick demo of SWIRL on the summary transcript of this episode01:29 Sid’s background08:50 Enterprise vs Federated search17:48 How vector search covers for missing folksonomy in enterprise data26:07 Relevancy from vector search standpoint31:58 How ChatGPT improves programmer’s productivity32:57 Demo!45:23 Google PSE53:10 Ideal user of SWIRL57:22 Where SWIRL sits architecturally1:01:46 How to evolve SWIRL with domain expertise1:04:59 Reasons to go open source1:10:54 How SWIRL and Sid interact with ChatGPT1:23:22 The magical question of WHY1:27:58 Sid’s announcements to the communityYouTube version: https://www.youtube.com/watch?v=vhQ5LM5pK_YDesign by Saurabh Rai: https://twitter.com/_srbhr_ Check out his Resume Matcher project: https://www.resumematcher.fyi/

Topics:00:00 Intro02:20 Atita’s path into search engineering09:00 When it’s time to contribute to open source12:08 Taking management role vs software development14:36 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 Germany26:04 Affinity to Search world and working as a Search Relevance Consultant28:39 Bringing vector search to Chorus and Querqy34:09 What Atita learnt from Eric Pugh’s approach to improving Quepid36:53 Making vector search with Solr & Elasticsearch accessible through tooling and documentation41:09 Demystifying data embedding for clients (and for Java based search engines)43:10 Shifting away from generic to domain-specific in search+vector saga46:06 Hybrid search: where it will be useful to combine keyword with semantic search50:53 Choosing between new vector DBs and “old” keyword engines58:35 Women of Search1:14:03 Important (and friendly) People of Open Source1:22:38 Reinforcement learning applied to our careers1:26:57 The magical question of WHY1:29:26 AnnouncementsSee show notes on YouTube: https://www.youtube.com/watch?v=BVM6TUSfn3E

Topics:00:00 Intro01:54 Things Connor learnt in the past year that changed his perception of Vector Search02:42 Is search becoming conversational?05:46 Connor asks Dmitry: How Large Language Models will change Search?08:39 Vector Search Pyramid09:53 Large models, data, Form vs Meaning and octopus underneath the ocean13:25 Examples of getting help from ChatGPT and how it compares to web search today18:32 Classical search engines with URLs for verification vs ChatGPT-style answers20:15 Hybrid search: keywords + semantic retrieval23:12 Connor asks Dmitry about his experience with sparse retrieval28:08 SPLADE vectors34:10 OOD-DiskANN: handling the out-of-distribution queries, and nuances of sparse vs dense indexing and search39:54 Ways to debug a query case in dense retrieval (spoiler: it is a challenge!)44:47 Intricacies of teaching ML models to understand your data and re-vectorization49:23 Local IDF vs global IDF and how dense search can approach this issue54:00 Realtime index59:01 Natural language to SQL1:04:47 Turning text into a causal DAG1:10:41 Engineering and Research as two highly intelligent disciplines1:18:34 Podcast search1:25:24 Ref2Vec for recommender systems1:29:48 AnnouncementsFor Show Notes, please check out the YouTube episode below.This episode on YouTube: https://www.youtube.com/watch?v=2Q-7taLZ374Podcast design: Saurabh Rai: https://twitter.com/srvbhr

Toloka’s support for Academia: grants and educator partnershipshttps://toloka.ai/collaboration-with-educators-formhttps://toloka.ai/research-grants-formThese are pages leading to them:https://toloka.ai/academy/education-partnershipshttps://toloka.ai/grantsTopics:00:00 Intro01:25 Jenny’s path from graduating in ML to a Data Advocate role07:50 What goes into the labeling process with Toloka11:27 How to prepare data for labeling and design tasks16:01 Jenny’s take on why Relevancy needs more data in addition to clicks in Search18:23 Dmitry plays the Devil’s Advocate for a moment22:41 Implicit signals vs user behavior and offline A/B testing26:54 Dmitry goes back to advocating for good search practices27:42 Flower search as a concrete example of labeling for relevancy39:12 NDCG, ERR as ranking quality metrics44:27 Cross-annotator agreement, perfect list for NDCG and Aggregations47:17 On measuring and ensuring the quality of annotators with honeypots54:48 Deep-dive into aggregations59:55 Bias in data, SERP, labeling and A/B tests1:16:10 Is unbiased data attainable?1:23:20 AnnouncementsThis episode on YouTube: https://youtu.be/Xsw9vPFqGf4Podcast design: Saurabh Rai: https://twitter.com/srvbhr

00:00 Introduction01:11 Yaniv’s background and intro to Searchium & GSI04:12 Ways to consume the APU acceleration for vector search05:39 Power consumption dimension in vector search 7:40 Place of the platform in terms of applications, use cases and developer experience12:06 Advantages of APU Vector Search Plugins for Elasticsearch and OpenSearch compared to their own implementations17:54 Everyone needs to save: the economic profile of the APU solution20:51 Features and ANN algorithms in the solution24:23 Consumers most interested in dedicated hardware for vector search vs SaaS27:08 Vector Database or a relevance oriented application?33:51 Where to go with vector search?42:38 How Vector Search fits into Search48:58 Role of the human in the AI loop58:05 The missing bit in the AI/ML/Search space1:06:37 Magical WHY question1:09:54 Announcements- Searchium vector search: https://searchium.ai/- Dr. Avidan Akerib, founder behind the APU technology: https://www.linkedin.com/in/avidan-akerib-phd-bbb35b12/- OpenSearch benchmark for performance tuning: https://betterprogramming.pub/tired-of-troubleshooting-idle-search-resources-use-opensearch-benchmark-for-performance-tuning-d4277c9f724- APU KNN plugin for OpenSearch: https://towardsdatascience.com/bolster-opensearch-performance-with-5-simple-steps-ca7d21234f6b- Multilingual and Multimodal Search with Hardware Acceleration: https://blog.muves.io/multilingual-and-multimodal-vector-search-with-hardware-acceleration-2091a825de78- Muves talk at Berlin Buzzwords, where we have utilized GSI APU: https://blog.muves.io/muves-at-berlin-buzzwords-2022-3150eef01c4- 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-9c65a3bd0696Episode on YouTube: https://youtu.be/EerdWRPuqd4Podcast design: Saurabh Rai: https://twitter.com/srvbhr

This episode on YouTube: https://www.youtube.com/watch?v=Kpua1Euc-B8Topics:00:00 Intro01:30 Doug’s story in Search04:55 How Quepid came about10:57 Relevance as product at Shopify: challenge, process, tools, evaluation15:36 Search abandonment in Ecommerce21:30 Rigor in A/B testing23:53 Turn user intent and content meaning into tokens, not words into tokens32:11 Use case for vector search in Maps. What about search in other domains?38:05 Expanding on dense approaches40:52 Sparse, dense, hybrid anyone?48:18 Role of HNSW, scalability and new vector databases vs Elasticsearch / Solr dense search52:12 Doug’s advice to vector database makers58:19 Learning to Rank: how to start, how to collect data with active learning, what are the ML methods and a mindset1:12:10 Blending search and recommendation1:16:08 Search engineer role and key ingredients of managing search projects today1:20:34 What does a Product Manager do on a Search team?1:26:50 The magical question of WHY1:29:08 Doug’s announcementsShow notes:Doug’s course: https://www.getsphere.com/ml-engineering/ml-powered-search?source=Instructor-Other-070922-vector-podUpcoming book: https://www.manning.com/books/ai-powered-search?aaid=1&abid=e47ada24&chan=aipsDoug’s post in Shopify’s blog “Search at Shopify—Range in Data and Engineering is the Future”: https://shopify.engineering/search-at-shopifyDoug’s own blog: https://softwaredoug.com/Using Bayesian optimization for Elasticsearch relevance: https://www.youtube.com/watch?v=yDcYi-ANJwE&t=1sHello LTR: https://github.com/o19s/hello-ltrVector Databases: https://towardsdatascience.com/milvus-pinecone-vespa-weaviate-vald-gsi-what-unites-these-buzz-words-and-what-makes-each-9c65a3bd0696Research: Search abandonment has a lasting impact on brand loyalty: https://cloud.google.com/blog/topics/retail/search-abandonment-impacts-retail-sales-brand-loyaltyQuepid: https://quepid.com/Podcast design: Saurabh Rai [https://twitter.com/srvbhr]

YouTube: https://www.youtube.com/watch?v=N5Brb7Rzc2cTopics:00:00 Introduction01:12 Malte’s background07:58 NLP crossing paths with Search11:20 Product discovery: early stage repetitive use cases pre-dating Haystack16:25 Acyclic directed graph for modeling a complex search pipeline18:22 Early integrations with Vector Databases20:09 Aha!-use case in Haystack23:23 Capabilities of Haystack today30:11 Deepset Cloud: end-to-end deployment, experiment tracking, observability, evaluation, debugging and communicating with stakeholders39:00 Examples of value for the end-users of Deepset Cloud46:00 Success metrics50:35 Where Haystack is taking us beyond MLOps for search experimentation57:13 Haystack as a smart assistant to guide experiments1:02:49 Multimodality1:05:53 Future of the Vector Search / NLP field: large language models1:15:13 Incorporating knowledge into Language Models & an Open NLP Meetup on this topic1:16:25 The magical question of WHY1:23:47 Announcements from MalteShow notes:- Haystack: https://github.com/deepset-ai/haystack/- Deepset Cloud: https://www.deepset.ai/deepset-cloud- Tutorial: Build Your First QA System: https://haystack.deepset.ai/tutorials/v0.5.0/first-qa-system- Open NLP Meetup on Sep 29th (Nils Reimers talking about “Incorporating New Knowledge Into LMs”): https://www.meetup.com/open-nlp-meetup/events/287159377/- Atlas Paper (Few shot learning with retrieval augmented large language models): https://arxiv.org/abs/2208.03299- Zero click search: https://www.searchmetrics.com/glossary/zero-click-searches/Very large LMs:- 540B PaLM by Google: https://lnkd.in/eajsjCMr- 11B Atlas by Meta: https://lnkd.in/eENzNkrG- 20B AlexaTM by Amazon: https://lnkd.in/eyBaZDTy- Players in Vector Search: https://www.youtube.com/watch?v=8IOpgmXf5r8 https://dmitry-kan.medium.com/players-in-vector-search-video-2fd390d00d6- Click Residual: A Query Success Metric: https://observer.wunderwood.org/2022/08/08/click-residual-a-query-success-metric/- Tutorials and papers around incorporating Knowledge into Language Models: https://cs.stanford.edu/people/cgzhu/

00:00 Introduction01:10 Max's deep experience in search and how he transitioned from structured data08:28 Query-term dependence problem and Max's perception of the Vector Search field12:46 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 model33:02 Web-server Mighty and its philosophy35:33 How Mighty compares to in-DB embedding layer, like Weavite or Vespa39:40 The importance of fault-tolerance in search backends43:31 Unit economics of Mighty50:18 Mighty distribution and supported operating systems54:57 The secret sauce behind Mighty's insane fast-ness59:48 What a customer is paying for when buying Mighty1:01:45 How will Max track the usage of Mighty: is it commercial or research use?1:04:39 Role of Open Source Community to grow business1:10:58 Max's vision for Mighty connectors to popular vector databases1:18:09 What tooling is missing beyond Mighty in vector search pipelines1:22:34 Fine-tuning models, metric learning and Max's call for partnerships1:26:37 MLOps perspective of neural pipelines and Mighty's role in it1:30:04 Mighty vs AWS Inferentia vs Hugging Face Infinity1:35:50 What's left in ML for those who are not into Python1:40:50 The philosophical (and magical) question of WHY1:48:15 Announcements from Max25% discount for the first year of using Mighty in your great product / project with promo code VECTOR:https://bit.ly/3QekTWEShow notes:- Max's blog about BERT and search relevance: https://opensourceconnections.com/blog/2019/11/05/understanding-bert-and-search-relevance/- Case study and unit economics of Mighty: https://max.io/blog/encoding-the-federal-register.html- 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-9c65a3bd0696Watch on YouTube: https://youtu.be/LnF4hbl1cE4

YouTube: https://www.youtube.com/watch?v=r4HEpyur-OEVector Podcast LiveTopics:00:00 Kick-off introducing co:rise study platform03:03 Grant’s background04:58 Principle of 3 C’s in the life of a CTO: Code, Conferences and Customers07:16 Principle of 3 C’s in the Search Engine development: Content, Collaboration and Context11:51 Balance between manual tuning in pursuit to learn and Machine Learning15:42 How to nurture intuition in building search engine algorithms18:51 How to change the approach of organizations to true experimentation23:17 Where should one start in approaching the data (like click logs) for developing a search engine29:36 How to measure the success of your search engine33:50 The role of manual query rating to improve search result relevancy36:56 What are the available datasets, tools and algorithms, that allow us to build a search engine?41:56 Vector search and its role in broad search engine development and how the profession is shaping up49:01 The magical question of WHY: what motivates Grant to stay in the space52:09 Announcement from Grant: course discount code DGSEARCH1054:55 Questions from the audienceShow notes:- Grant’s interview at Berlin Buzzwords 2016: https://www.youtube.com/watch?v=Y13gZM5EGdc- “BM25 is so Yesterday: Modern Techniques for Better Search”: https://www.youtube.com/watch?v=CRZfc9lj7Po- “Taming text” - book co-authored by Grant: https://www.manning.com/books/taming-text- Search Fundamentals course - https://corise.com/course/search-fundamentals- Search with ML course - https://corise.com/course/search-with-machine-learning- Click Models for Web Search: https://github.com/markovi/PyClick- Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing, book by Ron Kohavi et al: https://www.amazon.com/Trustworthy-Online-Controlled-Experiments-Practical-ebook/dp/B0845Y3DJV- Quepid, open source tool and free service for query rating and relevancy tuning: https://quepid.com/- Grant’s talk in 2013 where he discussed the need of a vector field in Lucene and Solr: https://www.youtube.com/watch?v=dCCqauwMWFE- Demo of multimodal search with CLIP: https://blog.muves.io/multilingual-and-multimodal-vector-search-with-hardware-acceleration-2091a825de78- Learning to Boost: https://www.youtube.com/watch?v=af1dyamySCs

YouTube: https://www.youtube.com/watch?v=GyXggc4LNKITopics:00:00 Kick-off by Judy Zhu01:33 Introduction by Dmitry Kan and his bio!03:03 Daniel’s background04:46 “Science is the difference between instinct and strategy”07:41 Search as a personal learning experience11:53 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 principle29:05 How content understanding can significantly improve your search engine experience32:02 Available datasets, tools and algorithms to train models for content understanding38:20 Daniel’s take on the role of vector search in modern search engine design as the path to language of users45:17 Mystical question of WHY: what drives Daniel in the search space today49:50 Announcements from Daniel51:15 Questions from the audienceShow notes:[What is Content Understanding?. Content understanding is the foundation… | by Daniel Tunkelang | Content Understanding | Medium](https://medium.com/content-understanding/what-is-content-understanding-4da20e925974)Query Understanding: An Introduction https://queryunderstanding.com/introduction-c98740502103)Science as Strategy [YouTube](https://www.youtube.com/watch?v=dftt6Yqgnuw)Search Fundamentals course - https://corise.com/course/search-fundamentalsSearch with ML course - https://corise.com/course/search-with-machine-learningBooks:Faceted Search, by Daniel Tunkelang: https://www.amazon.com/Synthesis-Lectures-Information-Concepts-Retrieval/dp/1598299999Modern Information Retrieval: The Concepts and Technology Behind Search, by Ricardo Baeza-Yates: https://www.amazon.com/Modern-Information-Retrieval-Concepts-Technology/dp/0321416910/ref=sr11?qid=1653144684&refinements=p_27%3ARicardo+Baeza-Yates&s=books&sr=1-1Introduction to Information Retrieval, by Chris Manning: https://www.amazon.com/Introduction-Information-Retrieval-Christopher-Manning/dp/0521865719/ref=sr1fkmr0_1?crid=2GIR19OTZ8QFJ&keywords=chris+manning+information+retrieval&qid=1653144967&s=books&sprefix=chris+manning+information+retrieval%2Cstripbooks-intl-ship%2C141&sr=1-1-fkmr0Query Understanding for Search Engines, by Yi Chang and Hongbo Deng: https://www.amazon.com/Understanding-Search-Engines-Information-Retrieval/dp/3030583333

YouTube: https://www.youtube.com/watch?v=AU0O_6-EY6sTopics:00:00 Intro01:03 Yusuf’s background03:00 Multimodal search in tech and humans08:53 CLIP: discovering hidden semantics13:02 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 images32:05 Increasing efficiency: vector compression and quantization aspects34:25 Yusuf gives a practical use-case with Conversational AI of where metric learning can prove to be useful. And tools!40:59 A few words on how the podcast is made :) Yusuf’s explanation of how Gmail smart reply feature works internally51:19 Metric learning helps us learn the best vector representation for the given task52:16 Metric learning shines in data scarce regimes. Positive impact on the planet58:30 Yusuf’s motivation to work in the space of vector search, Qdrant, deep learning and metric learning — the question of Why1:05:02 Announcements from Yusuf- Join discussions at Discord: https://discord.qdrant.tech- Yusuf's Medium: https://medium.com/@yusufsarigoz and LinkedIn: https://www.linkedin.com/in/yusufsarigoz/- GSOC 2022: TensorFlow Similarity - project led by Yusuf: https://docs.google.com/document/d/1fLDLwIhnwDUz3uUV8RyUZiOlmTN9Uzy5ZuvI8iDDFf8/edit#heading=h.zftd93u5hfnp- Dmitry's Twitter: https://twitter.com/DmitryKanFull Show Notes: https://www.youtube.com/watch?v=AU0O_6-EY6s

Topics:00:00 Introduction01:21 Jo Kristian’s background in Search / Recommendations since 2001 in Fast Search & Transfer (FAST)03:16 Nice words about Trondheim04:37 Role of NTNU in supplying search talent and having roots in FAST 05:33 History of Vespa from keyword search09:00 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 and ONNX support17:04 Tensor data structure in Vespa and its use cases22:23 Multi-stage ranking pipeline use cases with Vespa24:37 Optimizing your ranker for top 1. Bonus: cool search course mentioned!30:18 Fascination of Query Understanding, ways to implement and its role in search UX33:34 You need to have investment to get great results in search35:30 Game-changing vector search in Vespa and impact of MS Marco Passage Ranking38:44 User aspect of vector search algorithms43:19 Approximate vs exact nearest neighbor search tradeoffs47:58 Misconceptions in neural search52:06 Ranking competitions, idea generation and BERT bi-encoder dream56:19 Helping wider community through improving search over CORD-19 dataset58:13 Multimodal search is where vector search shines1:01:14 Power of building fully-fledged demos1:04:47 How to combine vector search with sparse search: Reciprocal Rank Fusion1:10:37 The philosophical WHY question: Jo Kristian’s drive in the search field1:21:43 Announcement on the coming features from Vespa- Jo Kristian’s Twitter: https://twitter.com/jobergum- Dmitry’s Twitter: https://twitter.com/DmitryKanFor the Show Notes check: https://www.youtube.com/watch?v=UxEdoXtA9oM

YouTube: https://www.youtube.com/watch?v=e2tZ6HD4I44Update: ZIR.AI has relaunched as Vectara: https://vectara.com/Topics:00:00 Intro00:54 Amin’s background at Google Research and affinity to NLP and vector search field05:28 Main focus areas of ZIR.AI in neural search07:26 Does the company offer neural network training to clients? Other support provided with ranking and document format conversions08:51 Usage of open source vs developing own tech10:17 The core of ZIR.AI product14:36 API support, communication protocols and P95/P99 SLAs, dedicated pools of encoders17:13 Speeding up single node / single customer throughput and challenge of productionizing off the shelf models, like BERT23:01 Distilling transformer models and why it can be out of reach of smaller companies25:07 Techniques for data augmentation from Amin’s and Dmitry’s practice (key search team: margin loss)30:03 Vector search algorithms used in ZIR.AI and the need for boolean logic in company’s client base33:51 Dynamics of open source in vector search space and cloud players: Google, Amazon, Microsoft36:03 Implementing a multilingual search with BM25 vs neural search and impact on business38:56 Is vector search a hype similar to big data few years ago? Prediction for vector search algorithms influence relations databases43:09 Is there a need to combine BM25 with neural search? Ideas from Amin and features offered in ZIR.AI product51:31 Increasing the robustness of search — or simply making it to work55:10 How will Search Engineer profession change with neural search in the game?Get a $100 discount (first month free) for a 50mb plan, using the code VectorPodcast (no lock-in, you can cancel any time): https://zir-ai.com/signup/user

YouTube: https://www.youtube.com/watch?v=gvgD98jWrJMTopics:00:00 Introduction01:04 Yury’s background in laser physics, computer vision and startups05:14 How Yury entered the field of nearest neighbor search and his impression of it09:03 “Not all Small Worlds are Navigable”10:10 Gentle introduction into the theory of Small World Navigable Graphs and related concepts13:55 Further clarification on the input constraints for the NN search algorithm design15:03 What did not work in NSW algorithm and how did Yury set up to invent new algorithm called HNSW24:06 Collaboration with Leo Boytsov on integrating HNSW in nmslib26:01 Differences between HNSW and NSW27:55 Does algorithm always converge?31:56 How FAISS’s implementation is different from the original HNSW33:13 Could Yury predict that his algorithm would be implemented in so many frameworks and vector databases in languages like Go and Rust?36:51 How our perception of high-dimensional spaces change compared to 3D?38:30 ANN Benchmarks41:33 Feeling proud of the invention and publication process during 2,5 years!48:10 Yury’s effort to maintain HNSW and its GitHub community and the algorithm’s design principles53:29 Dmitry’s ANN algorithm KANNDI, which uses HNSW as a building block1:02:16 Java / Python Virtual Machines, profiling and benchmarking. “Your analysis of performance contradicts the profiler”1:05:36 What are Yury’s hopes and goals for HNSW and role of symbolic filtering in ANN in general1:13:05 The future of ANN field: search inside a neural network, graph ANN1:15:14 Multistage ranking with graph based nearest neighbor search1:18:18 Do we have the “best” ANN algorithm? How ANN algorithms influence each other1:21:27 Yury’s plans on publishing his ideas1:23:42 The intriguing question of WhyShow notes:- HNSW library: https://github.com/nmslib/hnswlib/- HNSW paper Malkov, Y. A., & Yashunin, D. A. (2018). Efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs. TPAMI, 42(4), 824-836. (arxiv:1603.09320)- NSW paper Malkov, Y., Ponomarenko, A., Logvinov, A., & Krylov, V. (2014). Approximate nearest neighbor algorithm based on navigable small world graphs. Information Systems, 45, 61-68.- Yury Lifshits’s paper: https://yury.name/papers/lifshits2009combinatorial.pdf- Sergey Brin’s work in nearest neighbour search: GNAT - Geometric Near-neighbour Access Tree: [CiteSeerX — Near neighbor search in large metric spaces](http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.173.8156)- Podcast with Leo Boytsov: https://rare-technologies.com/rrp-4-leo-boytsov-knn-search/- FALCONN algorithm: https://github.com/falconn-lib/falconn- Mentioned navigable small world papers:Kleinberg, J. M. (2000). Navigation in a small world. Nature, 406(6798), 845-845.;Boguna, M., Krioukov, D., & Claffy, K. C. (2009). Navigability of complex networks. Nature Physics, 5(1), 74-80.

Topics:00:00 Intro00:42 Joan's background01:46 What attracted Joan's attention in Jina as a company and product?04:39 Main area of focus for Joan in the product05:46 How Open Source model works for Jina?08:38 Deeper dive into Jina.AI as a product and technology stack11:57 Does Jina fit the use cases of smaller / mid-size players with smaller amount of data?13:45 KNN/ANN algorithms available in Jina16:05 BigANN competition and BuddyPQ, increasing 12% in recall over FAISS17:07 Does Jina support customers in model training? Finetuner20:46 How does Jina framework compare to Vector Databases?26:46 Jina's investment in user-friendly APIs31:04 Applications of Jina beyond search engines, like question answering systems33:20 How to bring bits of neural search into traditional keyword retrieval? Connection to model interpretability41:14 Does Jina allow going multimodal, including images / audio etc?46:03 The magical question of Why55:20 Product announcement from JoanOrder your Jina swag https://docs.google.com/forms/d/e/1FAIpQLSedYVfqiwvdzWPX-blCpVu-tQoiFiUJQz2QnIHU1ggy1oyg/ Use this promo code: vectorPodcastxJinaAIShow notes:- Jina.AI: https://jina.ai/- HNSW + PostgreSQL Indexer: [GitHub - jina-ai/executor-hnsw-postgres: A production-ready, scalable Indexer for the Jina neural search framework, based on HNSW and PSQL](https://github.com/jina-ai/executor-h...)- pqlite: [GitHub - jina-ai/pqlite: A fast embedded library for Approximate Nearest Neighbor Search integrated with the Jina ecosystem](https://github.com/jina-ai/pqlite)- BuddyPQ: [Billion-Scale Vector Search: Team Sisu and BuddyPQ | by Dmitry Kan | Big-ANN-Benchmarks | Nov, 2021 | Medium](https://medium.com/big-ann-benchmarks...)- PaddlePaddle: [GitHub - PaddlePaddle/Paddle: PArallel Distributed Deep LEarning: Machine Learning Framework from Industrial Practice （『飞桨』核心框架，深度学习&机器学习高性能单机、分布式训练和跨平台部署）](https://github.com/PaddlePaddle/Paddle)- Jina Finetuner: [Finetuner 0.3.1 documentation](https://finetuner.jina.ai/)- [Not All Vector Databases Are Made Equal | by Dmitry Kan | Towards Data Science](https://towardsdatascience.com/milvus...)- Fluent interface (method chaining): [Fluent interfaces in Python | Florian Einfalt – Developer](https://florianeinfalt.de/posts/fluen...)- Sujit Pal’s blog: [Salmon Run](http://sujitpal.blogspot.com/)- ByT5: Towards a token-free future with pre-trained byte-to-byte models https://arxiv.org/abs/2105.13626Special thanks to Saurabh Rai for the Podcast Thumbnail: https://twitter.com/srbhr_ https://www.linkedin.com/in/srbh077/

YouTube: https://www.youtube.com/watch?v=kVCIDTmiZykShow 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: https://github.com/quickwit-inc/tantivy- Papers with code: https://paperswithcode.com/

YouTube: https://www.youtube.com/watch?v=FQAT6E3EX6gShow 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 CheckList: [2005.04118 Beyond Accuracy: Behavioral Testing of NLP models with CheckList](https://arxiv.org/abs/2005.04118)- Symbolic AI vs Deep Learning battle https://www.technologyreview.com/2020...- Dense Passage Retrieval for Open-Domain Question Answering https://arxiv.org/abs/2004.04906- Data Augmentation Can Improve Robustness https://arxiv.org/abs/2111.05328- Contrastive Loss Explained. Contrastive loss has been used recently… | by Brian Williams | Towards Data Science https://towardsdatascience.com/contra...- Keras Code examples https://keras.io/examples/- https://you.com/ -- new web search engine by Richard Socher- The Book of Why: The New Science of Cause and Effect: Pearl, Judea, Mackenzie, Dana: 9780465097609: Amazon.com: Books https://www.amazon.com/Book-Why-Scien...- Chelsea Finn: https://twitter.com/chelseabfinn- Jeff Clune: https://twitter.com/jeffclune- Michael Bronstein (Geometric Deep Learning): https://twitter.com/mmbronstein https://arxiv.org/abs/2104.13478- Connor's Twitter: https://twitter.com/CShorten30- Dmitry's Twitter: https://twitter.com/DmitryKan

YouTube: https://www.youtube.com/watch?v=fHu8b-EzOzUOrder 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 Neural Network: https://arxiv.org/abs/1904.08990- What BERT is not: Lessons from a new suite of psycholinguistic diagnostics for language models https://arxiv.org/abs/1907.13528- NVIDIA Triton Inference Server: https://developer.nvidia.com/nvidia-t...- Towhee -- ML / Embedding pipeline making steps before Milvus easier: https://github.com/towhee-io/towhee- Being at the leading edge: http://paulgraham.com/startupideas.html

YouTube: https://www.youtube.com/watch?v=iHC5oeAN29oShow notes:1. Layering problem: www.edge.org/conversation/sean_…-layers-of-reality2. Podcast with Etienne Dilocker (SeMI Technologies Co-Founder & CTO): www.youtube.com/watch?v=6lkanzOqhDs3. SOC2: linfordco.com/blog/soc-1-vs-soc-2-audit-reports/4. Dmitry's post on 7 Vector Databases: towardsdatascience.com/milvus-pineco…-9c65a3bd06965. Billion-Scale ANN Challenge: big-ann-benchmarks.com/index.html6. Weaviate Introduction: www.semi.technology/developers/weaviate/current/ Newsletter: www.semi.technology/newsletter/7. Use case: Scalable Knowledge Graph Search for 60+ million academic papers with Weaviate: medium.com/keenious/knowledge-…aviate-7964657ec9118. Bob's Twitter: twitter.com/bobvanluijt9. Dmitry's Twitter: twitter.com/DmitryKan10. Dmitry's tech blog: dmitry-kan.medium.com/

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 [email protected]. Billion-Scale Approximate Nearest Neighbour Search Challenge: https://big-ann-benchmarks.com/index.... 4. ANNOY: https://github.com/spotify/annoy5. FAISS: https://github.com/facebookresearch/f... 6. HNSW: https://github.com/nmslib/hnswlib 7. “How Zero Results Are Killing Ecommerce Conversions” https://lucidworks.com/post/how-zero-... 8. Try out Pinecone vector DB: https://app.pinecone.io/ 9. Twitter: https://twitter.com/Pinecone_io 10. LinkedIn: https://www.linkedin.com/company/pine... 11. Greg’s Twitter: https://twitter.com/grigoriy_kogan 12. Dmitry's Twitter: https://twitter.com/DmitryKanWatch on YouTube: https://www.youtube.com/watch?v=jT3i7NLwJ8w