Charlie's site and blog: https://thesearchjuggler.com/

LinkedIn: https://www.linkedin.com/in/charliehullsearch/

Search Relevance Slack (7000 search professionals): https://join.slack.com/t/relevancy/shared_invite/zt-42nhd2z7n-c8QAsDCYU8hFi8c2pRrXKg

Charlie's panel at bbuzz, featuring yours truly among a group of distinguished search and ML people: https://www.youtube.com/watch?v=StaPk0k-52Y

Time codes

00:00 Intro

00:42 Charlie's take on this year's Berlin Buzzwords

01:55 Based on the panel: Charlie's first encounter of AI

04:55 Is preserving search still a question?

5:48 Charlie's "old" search technique to improve quality of search

7:58 Explainability gap

9:03 Did LLMs change Charlie's life

12:07 Query Understanding is still a complex topic

13:14 Vector search or not

17:28 Meetups and places to learn with Charlie

19:25 Charlie's message to the search makers

23:00 What is fundamentally missing in our space

26:03 Where to follow Charlie's work

Show notes:

- Upcoming Vespa conference: http://www.vespaai.live/

- Where find Charlie? On meetups: http://www.searchmeetups.com/

- http://www.thesearchjuggler.com/

- https://searchmeetups.com/

- A neat trick for protecting numbers when using fuzzy search: https://www.youtube.com/watch?v=s34afYwYqyM

Cameraman is Zoom's AI and it goes a little crazy at times zooming on Julien and zooming back out. As usual, audio version will be available. Go to https://www.vectorpodcast.com/

He gave a talk about SPRUCE at the conference: https://2026.berlinbuzzwords.de/session/spruce-it-up-open-source-greenops-at-scale/

Timcodes:

00:00 Intro

00:16 Julien's background

01:36 What is SPRUCE

2:51 Greenops vs FinOps

6:09 Reasons cloud providers won't go deeper on greenops

7:09 Ways to deploy SPRUCE

15:06 Cultural shift needed for Greenops

17:39 How to measure impact of AI agents and LLMs

Shownotes:

- SPRUCE and green ops: https://opensourcegreenops.cloud/latest/

- SPRUCE on GitHub: https://github.com/digitalpebble/spruce

- Julien's company: https://digitalpebble.com/

- Apache StormCrawler: https://stormcrawler.apache.org/ and GitHub: https://github.com/apache/stormcrawler

- SPRUCE is based on Apache Spark: https://spark.apache.org/

- "AI is here - time to throw away our search engines?" panel hosted by Charlie Hull at Berlin Buzzwords 2026: https://www.youtube.com/watch?v=StaPk0k-52Y

- Estimate and track the environmental footprint of GenAI models at inference. Ecologits: https://ecologits.ai/latest/

- Evaluate the environmental impact of digital technologies across organizations. Boavizta: https://boavizta.org/en

YouTube version: https://youtu.be/acOGVynTVpM

The cameraman is pure Zoom's AI ;)

The Course: "AI-Powered Search: Modern Retrieval for Humans & Agents"

aipoweredsearch.com/live-course?promoCode=vector-podcast

Discount Code for course (20% off): "vector-podcast"

AI-Powered Search (Book, Content, Community): https://aipoweredsearch.com/

Timecodes

00:00 Intro

00:30 Doug's and Trey's impression of the conference

01:16 How modern AI changed search (if any)

05:48 How to bring AI techniques into existing search engines on a budget

10:44 What the AI-Powered Search course includes

18:02 Staying hands-on

18:47 Guest's favourite topic that keeps them up at night

27:40 Message to the builders of search tech

32:55 Search continues to be challenging and exciting

Shownotes:

- Upcoming course in detail (grab promo code above to save 20%): https://aipoweredsearch.com/articles/the-frontier-of-ai-search-ai-powered-search-modern-retrieval-for-humans-agents/

- Doug's blog on search, agents, RAG, LLM as a judge and more: https://softwaredoug.com/

- AI-Powered Search: https://aipoweredsearch.com/

- Berlin Buzzwords:

- MICES: https://mices.co/

- Future of Search conference: https://berlinsearchweek.com/future-of-search/

- Women of Search: https://www.women-of-search.org/

- "AI is here - time to throw away our search engines?" panel hosted by Charlie Hull at Berlin Buzzwords 2026: https://www.youtube.com/watch?v=StaPk0k-52Y

- Dmitry's prototype of Wormhole vectors idea with OpenSearch: https://aiven.io/blog/beyond-hybrid-search-traversing-vector-spaces-with-wormhole-vectors

- Dmitry's blog on Medium: https://dmitry-kan.medium.com/

- Dmitry's Tech Stories on Substack: https://substack.com/@dmitrykan

- Follow me on LinkedIn for Search updates: https://www.linkedin.com/in/dmitrykan/

Ohad also shares his thoughts on Agentic memory and what keeps him at night these days.

Podcast design by https://www.linkedin.com/in/srbhr/

Timecodes:

00:00 Intro

01:35 Ohad's background

03:30 How idea was born: what was missing in the search landscape

06:52 Top 3 issues with existing search solutions

10:52 The importance of search latency

13:41 Ohad's solution for latency

19:22 Was Hyperspace up for the challenge?

22:12 New approaches to handling massive scale

26:12 Does latency matter for new agentic AI?

32:12 Agentic AI vs SaaS

35:03 Ohad's learnings from Hyperspace

38:37 Friction points for the hardware-accelerated search

42:40 Product-led growth way

47:43 What keeps Ohad excited about the AI / search field

51:43 Ohad's message to the Search community

Shownotes:

Ohad Levi on LinkedIn: https://www.linkedin.com/in/ohad-levi/

Hyperspace: https://www.hyper-space.io/

Dmitry's blog on Medium: https://dmitry-kan.medium.com/

Dmitry on LinkedIn: https://www.linkedin.com/in/dmitrykan/

Blog post: https://dmitry-kan.medium.com/webinar-building-an-ai-ready-data-backbone-with-aiven-google-cloud-4629f97f69bd

LLM/RAG/AI Agents course: https://dmitry-kan.medium.com/course-large-language-models-and-generative-ai-for-nlp-2025-98e31780de30

Free tier OpenSearch: https://aiven.io/free-opensearch

Time codes:

1:01 Dima's intro + Vector Podcast

4:56 About Aiven

7:06 Why best? - Question from the audience

10:22 Free Tier OpenSearch!

11:57 Aiven's unifed platform

12:58 OpenSearch: What and Why

17:00 Why OpenSearch is AI-Ready?

18:26 What Aiven's OpenSearch gives you

20:44 Lexical vs semantic search

22:51 Technical use cases of OpenSearch

24:17 Reference Architecture with Kafka as event processor, and OpenSearch as storage and search layer

25:37 Aiven's case studies for OpenSearch

26:27 When to choose OpenSearch?

28:21 Demo of OpenSearch query UI

32:12 Is there any advantage in using Qdrant over OpenSearch? - Question from the audience

34:30 What is the vector lenght (in this demo)? - Question from the audience

36:27 What are the main advantages of Aiven's OpenSearch compared to Elasticsearch? - Question from the audience

32:11 Demo of Search Relevancy Workbench: visual way of searching

Show 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

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/VECTOR

Blog post on Medium: https://dmitry-kan.medium.com/novel-idea-in-vector-search-wormhole-vectors-6093910593b8

Session page on maven: https://maven.com/p/8c7de9/beyond-hybrid-search-with-wormhole-vectors?utm_campaign=NzI2NzIx&utm_medium=ll_share_link&utm_source=instructor

To 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=vectorpodcast

Get credits to use Aiven's products (PG, Kafka, Valkey, OpenSearch, ClickHouse): https://aiven.io/startups

Timecodes:

00:00 Intro by Dmitry

01:48 Trey's presentation

03:05 Walk to the AI-Powered Search course by Trey and Doug

07:07 Intro to vector spaces and embeddings

19:03 Disjoint vector spaces and the need of hybrid search

23:11 Different modes of search

24:49 Wormhole vectors

47:49 Q&A

What 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 data

Learn to generate behavioral embeddings to be integrated with dense/semantic and sparse/lexical vector queries.

- Traverse lexical, semantic, & behavioral vectors spaces

Jump 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

Craft decaf & half caf coffee, 25% discount: https://savorista.com/discount/VECTOR

This episode on YouTube: https://youtu.be/I8Ztqajighg

Medium: https://dmitry-kan.medium.com/vector-podcast-simon-eskildsen-turbopuffer-69e456da8df3

Dev: https://dev.to/vectorpodcast/vector-podcast-simon-eskildsen-turbopuffer-cfa

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/

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

YouTube: https://www.youtube.com/watch?v=quY769om1EY

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

YouTube: https://www.youtube.com/watch?v=403rxbWZK9Y

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/

YouTube: https://youtu.be/60HAtHVBYj8

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/srchvrs
• Leo 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

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

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

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/

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 Quepid

33:23 Custom Scoring in Quepid

39:23 API and Developer Tools

42:17 The Future of Search and Personal Reflections

Show 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

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

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 WHY

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?

Show notes:

- Resume Matcher: https://github.com/srbhr/Resume-Matcher

website: 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

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

1:10:54 How SWIRL and Sid interact with ChatGPT

1:23:22 The magical question of WHY

1:27:58 Sid’s announcements to the community

YouTube version: https://www.youtube.com/watch?v=vhQ5LM5pK_Y

Design by Saurabh Rai: https://twitter.com/_srbhr_ Check out his Resume Matcher project: https://www.resumematcher.fyi/

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 to improving Quepid

36:53 Making vector search with Solr & Elasticsearch accessible through tooling and documentation

41:09 Demystifying data embedding for clients (and for Java based search engines)

43:10 Shifting away from generic to domain-specific in search+vector saga

46:06 Hybrid search: where it will be useful to combine keyword with semantic search

50:53 Choosing between new vector DBs and “old” keyword engines

58:35 Women of Search

1:14:03 Important (and friendly) People of Open Source

1:22:38 Reinforcement learning applied to our careers

1:26:57 The magical question of WHY

1:29:26 Announcements

See show notes on YouTube: https://www.youtube.com/watch?v=BVM6TUSfn3E

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 Hybrid search: keywords + semantic retrieval

23:12 Connor asks Dmitry about his experience with sparse retrieval

28:08 SPLADE vectors

34:10 OOD-DiskANN: handling the out-of-distribution queries, and nuances of sparse vs dense indexing and search

39:54 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-vectorization

49:23 Local IDF vs global IDF and how dense search can approach this issue

54:00 Realtime index

59:01 Natural language to SQL

1:04:47 Turning text into a causal DAG

1:10:41 Engineering and Research as two highly intelligent disciplines

1:18:34 Podcast search

1:25:24 Ref2Vec for recommender systems

1:29:48 Announcements

For Show Notes, please check out the YouTube episode below.

This episode on YouTube: https://www.youtube.com/watch?v=2Q-7taLZ374

Podcast design: Saurabh Rai: https://twitter.com/srvbhr

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 more data in addition to clicks in Search

18:23 Dmitry plays the Devil’s Advocate for a moment

22:41 Implicit signals vs user behavior and offline A/B testing

26:54 Dmitry goes back to advocating for good search practices

27:42 Flower search as a concrete example of labeling for relevancy

39:12 NDCG, ERR as ranking quality metrics

44:27 Cross-annotator agreement, perfect list for NDCG and Aggregations

47:17 On measuring and ensuring the quality of annotators with honeypots

54:48 Deep-dive into aggregations

59:55 Bias in data, SERP, labeling and A/B tests

1:16:10 Is unbiased data attainable?

1:23:20 Announcements

This episode on YouTube: https://youtu.be/Xsw9vPFqGf4

Podcast design: Saurabh Rai: https://twitter.com/srvbhr

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 in the solution

24:23 Consumers most interested in dedicated hardware for vector search vs SaaS

27:08 Vector Database or a relevance oriented application?

33:51 Where to go with vector search?

42:38 How Vector Search fits into Search

48:58 Role of the human in the AI loop

58:05 The missing bit in the AI/ML/Search space

1:06:37 Magical WHY question

1:09:54 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-9c65a3bd0696

Episode on YouTube: https://youtu.be/EerdWRPuqd4

Podcast design: Saurabh Rai: https://twitter.com/srvbhr

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 databases vs Elasticsearch / Solr dense search

52:12 Doug’s advice to vector database makers

58:19 Learning to Rank: how to start, how to collect data with active learning, what are the ML methods and a mindset

1:12:10 Blending search and recommendation

1:16:08 Search engineer role and key ingredients of managing search projects today

1:20:34 What does a Product Manager do on a Search team?

1:26:50 The magical question of WHY

1:29:08 Doug’s announcements

Show notes:

Doug’s course: https://www.getsphere.com/ml-engineering/ml-powered-search?source=Instructor-Other-070922-vector-pod

Upcoming book: https://www.manning.com/books/ai-powered-search?aaid=1&abid=e47ada24&chan=aips

Doug’s post in Shopify’s blog “Search at Shopify—Range in Data and Engineering is the Future”: https://shopify.engineering/search-at-shopify

Doug’s own blog: https://softwaredoug.com/

Using Bayesian optimization for Elasticsearch relevance: https://www.youtube.com/watch?v=yDcYi-ANJwE&t=1s

Hello LTR: https://github.com/o19s/hello-ltr

Vector Databases: https://towardsdatascience.com/milvus-pinecone-vespa-weaviate-vald-gsi-what-unites-these-buzz-words-and-what-makes-each-9c65a3bd0696

Research: Search abandonment has a lasting impact on brand loyalty: https://cloud.google.com/blog/topics/retail/search-abandonment-impacts-retail-sales-brand-loyalty

Quepid: https://quepid.com/

Podcast design: Saurabh Rai [https://twitter.com/srvbhr]

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 Examples of value for the end-users of Deepset Cloud

46:00 Success metrics

50:35 Where Haystack is taking us beyond MLOps for search experimentation

57:13 Haystack as a smart assistant to guide experiments

1:02:49 Multimodality

1:05:53 Future of the Vector Search / NLP field: large language models

1:15:13 Incorporating knowledge into Language Models & an Open NLP Meetup on this topic

1:16:25 The magical question of WHY

1:23:47 Announcements from Malte

Show 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/

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 Weavite or Vespa

39:40 The importance of fault-tolerance in search backends

43:31 Unit economics of Mighty

50:18 Mighty distribution and supported operating systems

54:57 The secret sauce behind Mighty's insane fast-ness

59:48 What a customer is paying for when buying Mighty

1:01:45 How will Max track the usage of Mighty: is it commercial or research use?

1:04:39 Role of Open Source Community to grow business

1:10:58 Max's vision for Mighty connectors to popular vector databases

1:18:09 What tooling is missing beyond Mighty in vector search pipelines

1:22:34 Fine-tuning models, metric learning and Max's call for partnerships

1:26:37 MLOps perspective of neural pipelines and Mighty's role in it

1:30:04 Mighty vs AWS Inferentia vs Hugging Face Infinity

1:35:50 What's left in ML for those who are not into Python

1:40:50 The philosophical (and magical) question of WHY

1:48:15 Announcements from Max

25% discount for the first year of using Mighty in your great product / project with promo code VECTOR:

https://bit.ly/3QekTWE

Show 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-9c65a3bd0696

Watch on YouTube: https://youtu.be/LnF4hbl1cE4

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 Where should one start in approaching the data (like click logs) for developing a search engine

29:36 How to measure the success of your search engine

33:50 The role of manual query rating to improve search result relevancy

36:56 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 up

49:01 The magical question of WHY: what motivates Grant to stay in the space

52:09 Announcement from Grant: course discount code DGSEARCH10

54:55 Questions from the audience

Show 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

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 content understanding can significantly improve your search engine experience

32:02 Available datasets, tools and algorithms to train models for content understanding

38:20 Daniel’s take on the role of vector search in modern search engine design as the path to language of users

45:17 Mystical question of WHY: what drives Daniel in the search space today

49:50 Announcements from Daniel

51:15 Questions from the audience

Show 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-fundamentals

Search with ML course - https://corise.com/course/search-with-machine-learning

Books:

Faceted Search, by Daniel Tunkelang: https://www.amazon.com/Synthesis-Lectures-Information-Concepts-Retrieval/dp/1598299999

Modern 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=1-1

Introduction 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-fkmr0

Query Understanding for Search Engines, by Yi Chang and Hongbo Deng: https://www.amazon.com/Understanding-Search-Engines-Information-Retrieval/dp/3030583333

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 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 internally

51:19 Metric learning helps us learn the best vector representation for the given task

52:16 Metric learning shines in data scarce regimes. Positive impact on the planet

58:30 Yusuf’s motivation to work in the space of vector search, Qdrant, deep learning and metric learning — the question of Why

1:05:02 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/DmitryKan

Full Show Notes: https://www.youtube.com/watch?v=AU0O_6-EY6s

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 and ONNX support

17:04 Tensor data structure in Vespa and its use cases

22:23 Multi-stage ranking pipeline use cases with Vespa

24:37 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 UX

33:34 You need to have investment to get great results in search

35:30 Game-changing vector search in Vespa and impact of MS Marco Passage Ranking

38:44 User aspect of vector search algorithms

43:19 Approximate vs exact nearest neighbor search tradeoffs

47:58 Misconceptions in neural search

52:06 Ranking competitions, idea generation and BERT bi-encoder dream

56:19 Helping wider community through improving search over CORD-19 dataset

58:13 Multimodal search is where vector search shines

1:01:14 Power of building fully-fledged demos

1:04:47 How to combine vector search with sparse search: Reciprocal Rank Fusion

1:10:37 The philosophical WHY question: Jo Kristian’s drive in the search field

1:21:43 Announcement on the coming features from Vespa

- Jo Kristian’s Twitter: https://twitter.com/jobergum

- Dmitry’s Twitter: https://twitter.com/DmitryKan

For the Show Notes check: https://www.youtube.com/watch?v=UxEdoXtA9oM

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, dedicated pools of encoders

17:13 Speeding up single node / single customer throughput and challenge of productionizing off the shelf models, like BERT

23:01 Distilling transformer models and why it can be out of reach of smaller companies

25:07 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 base

33:51 Dynamics of open source in vector search space and cloud players: Google, Amazon, Microsoft

36:03 Implementing a multilingual search with BM25 vs neural search and impact on business

38:56 Is vector search a hype similar to big data few years ago? Prediction for vector search algorithms influence relations databases

43:09 Is there a need to combine BM25 with neural search? Ideas from Amin and features offered in ZIR.AI product

51:31 Increasing the robustness of search — or simply making it to work

55:10 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

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 called HNSW

24:06 Collaboration with Leo Boytsov on integrating HNSW in nmslib

26:01 Differences between HNSW and NSW

27:55 Does algorithm always converge?

31:56 How FAISS’s implementation is different from the original HNSW

33:13 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 Benchmarks

41:33 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 principles

53:29 Dmitry’s ANN algorithm KANNDI, which uses HNSW as a building block

1:02:16 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 general

1:13:05 The future of ANN field: search inside a neural network, graph ANN

1:15:14 Multistage ranking with graph based nearest neighbor search

1:18:18 Do we have the “best” ANN algorithm? How ANN algorithms influence each other

1:21:27 Yury’s plans on publishing his ideas

1:23:42 The intriguing question of Why

Show 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.

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 support customers in model training? Finetuner

20:46 How does Jina framework compare to Vector Databases?

26:46 Jina's investment in user-friendly APIs

31:04 Applications of Jina beyond search engines, like question answering systems

33:20 How to bring bits of neural search into traditional keyword retrieval? Connection to model interpretability

41:14 Does Jina allow going multimodal, including images / audio etc?

46:03 The magical question of Why

55:20 Product announcement from Joan

Order your Jina swag https://docs.google.com/forms/d/e/1FAIpQLSedYVfqiwvdzWPX-blCpVu-tQoiFiUJQz2QnIHU1ggy1oyg/ Use this promo code: vectorPodcastxJinaAI

Show 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/2105.13626

Special thanks to Saurabh Rai for the Podcast Thumbnail: https://twitter.com/srbhr_ https://www.linkedin.com/in/srbh077/

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: https://github.com/quickwit-inc/tantivy

- Papers with code: https://paperswithcode.com/

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 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

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 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

Show notes:

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.technology/newsletter/

7. Use case: Scalable Knowledge Graph Search for 60+ million academic papers with Weaviate: medium.com/keenious/knowledge-…aviate-7964657ec911

8. Bob's Twitter: twitter.com/bobvanluijt

9. Dmitry's Twitter: twitter.com/DmitryKan

10. Dmitry's tech blog: dmitry-kan.medium.com/

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 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/DmitryKan

Watch on YouTube: https://www.youtube.com/watch?v=jT3i7NLwJ8w