Vector Podcast

00:00 Intro

01:31 Leo's story

09:59 SPLADE: single model to solve both dense and sparse?

21:06 DeepImpact

29:58 NMSLIB: what are non-metric spaces

34:21 How HNSW and NMSLIB joined forces

41:11 Why FAISS did not choose NMSLIB's algorithm

43:36 Serendipity of discovery and the creation of industries

47:06 Vector Search: intellectually rewarding, professionally undervalued

52:37 Why RDBMS Still Struggles with Scalable Vector and Free-Text Search

1:00:16 Leo's recent favorite papers

• Leo Boytsov on LinkedIn: https://www.linkedin.com/in/leonidboytsov/ and X: https://x.com/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

This episode on YouTube: https://www.youtube.com/watch?v=PNB70TbQUBE

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

This episode on YouTube: https://youtu.be/dVIPBxHJ1kQ

00:00 Intro

00:15 Greets for Sonam

01:02 Importance of metric learning

3:37 Sonam's background: Rasa, Qdrant

4:31 What's EmbedAnything

5:52 What a user gets

8:48 Do I need to know Rust?

10:18 Call-out to the community

10:35 Multimodality

12:32 How to evaluate quality of LLM-based systems

16:38 QA for multimodal use cases

18:17 Place for a human in the LLM craze

19:00 Use cases for EmbedAnything

20:54 Closing theme (a longer one - enjoy!)

Show notes:

- GitHub: https://github.com/StarlightSearch/EmbedAnything

- HuggingFace Candle: https://github.com/huggingface/candle

- Sonam's talk on Berlin Buzzwords 2024: https://www.youtube.com/watch?v=YfR3kuSo-XQ

- Removing GIL from Python: https://peps.python.org/pep-0703

- Blind pairs in CLIP: https://arxiv.org/abs/2401.06209

- Dark matter of intelligence: https://ai.meta.com/blog/self-supervised-learning-the-dark-matter-of-intelligence/

- Rasa chatbots: https://github.com/RasaHQ/rasa

- Prometheus: https://github.com/prometheus-eval/prometheus-eval

- Dino: https://github.com/facebookresearch/dino

This episode on YouTube: https://www.youtube.com/watch?v=fIPC_xzqJ0o

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/

This episode on YouTube: https://www.youtube.com/watch?v=1L7UjjPz5wM

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

This episode on YouTube: https://www.youtube.com/watch?v=5fafSkzKpfw

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

This episode on YouTube: https://www.youtube.com/watch?v=TiwqVlDpsl8

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

This episode on YouTube: https://www.youtube.com/watch?v=nx6BH9Z_gBA

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

Topics:

00:00 Intro

00:22 Quick demo of SWIRL on the summary transcript of this episode

01:29 Sid’s background

08:50 Enterprise vs Federated search

17:48 How vector search covers for missing folksonomy in enterprise data

26:07 Relevancy from vector search standpoint

31:58 How ChatGPT improves programmer’s productivity

32:57 Demo!

45:23 Google PSE

53:10 Ideal user of SWIRL

57:22 Where SWIRL sits architecturally

1:01:46 How to evolve SWIRL with domain expertise

1:04:59 Reasons to go open source

1:10:54 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/

Topics:

00:00 Intro

02:20 Atita’s path into search engineering

09:00 When it’s time to contribute to open source

12:08 Taking management role vs software development

14:36 Knowing what you like (and coming up with a Solr course)

19:16 Read the source code (and cook)

23:32 Open Bistro Innovations Lab and moving to Germany

26:04 Affinity to Search world and working as a Search Relevance Consultant

28:39 Bringing vector search to Chorus and Querqy

34:09 What Atita learnt from Eric Pugh’s approach 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