Weaviate Podcast

Latest Episodes:

ChatArena with Yuxiang Wu - Weaviate Podcast #47! May 17, 2023

Show notes

Hey everyone, thank you so much for watching the Weaviate podcast! I am so excited about this episode! ChatArena is a software framework for multi-agent chat games. There are quite a few interesting applications of this, firstly we can use this kind of system to evaluate the intelligence of an LLM based on how intelligent it sounds in conversation with another LLM! Another interesting idea is to have the LLM impersonate people such as Lex Fridman or Sam Altman and simulate conversations between these people -- retrieving from their digital content to facilitate the impersonation. I thought there was so many interesting ideas in this podcast, please let us know what you think! Links: ChatArena on GitHub (please give it a star!) - https://github.com/chatarena/chatarena Twitter thread from Yuxiang describing the launch of ChatArena - https://twitter.com/YuxiangJWu/status/1643633046208249856 Chapters 0:00 Welcome Yuxiang! 0:38 What is ChatArena? 2:38 Impersonating People with LLMs 4:58 Weaviate and ChatArena 8:14 Generative Feedback Loops 11:10 Chat Games 16:30 Scientific Peer Review Discussions 20:05 Code Repos and Multi-Agent LLMs 23:05 Scaling Multi-Agent LLMs 25:16 Role Evolution in Startups 26:00 Evolution of Multi-Agent RL Research 29:22 AlphaGo and MCTS Text Generation 36:55 Hallucination in Role Maintenance 41:15 Evaluating LLMs with ChatArena 45:40 ChatGPT Marketplace and Tool Use 50:30 Upcoming work from Yuxiang and ChatArena!

HyperDB with John Dagdelen, Bob van Luijt, and Etienne Dilocker - Weaviate Podcast #46! May 10, 2023

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Hey everyone! Thank you so much for watching the Weaviate Podcast! This is pretty novel episode featuring both Weaviate Co-Founders Bob van Luijt and Etienne Dilocker! This is also extremely novel because we are featuring a competitor vector database, HyperDB! John Dagdelen is the founder of HyperDB which is a hyper-fast local vector database for use with LLM Agents. Now accepting SAFEs at $135M cap. HyperDB: https://github.com/jdagdelen/hyperDB More seriously, John has produced an incredible body of research - https://scholar.google.com/citations?user=TiCS5FEAAAAJ&hl=en&oi=ao. John's work on Scientific Literature Mining for Materials Science literature has played an enormous role in my personal education of this technology and what it is capable. Please also follow John on twitter @jmdagdelen. Chapters 0:00 Introduction 0:26 HyperDB! 3:58 Initial Discovery of Vector Dos 15:00 Search Engine versus Databases 18:40 Scientific Literature Mining 21:42 Structured Information Extraction 27:47 Generative Feedback Loops

Generative Feedback Loops with Bob van Luijt - Weaviate Podcast #45! May 05, 2023

Show notes

Hey everyone! Thank you so much for watching the Generative Feedback Loops Podcast! We have also created a blog post and GitHub repository for more information! Chapters 0:00 Bob the Podcast Host 1:20 Retrieval-Augmented Generation 4:10 Hallucination in LLMs 6:15 Solving Hallucination with RLHF 7:44 LLM Monster - Reasoning and Knowledge 10:12 Feedback Loops 11:00 Hands-on Code Demo 26:00 Demo Analysis from Bob and Connor 30:35 Star Wars Wes Anderson Generated Video 34:12 Multimodal Vector Databases 36:00 Speculative Design Theory Links: John Schulman - Reinforcement Learning from Human Feedback: Progress and Challenges: https://www.youtube.com/watch?v=hhiLw5Q_UFg Colin Nesh (HaystackUS 2023 slide deck) - Ground is NOT all you need, Stop hallucinations & defects in generative search: https://docs.google.com/presentation/d/1uycLEUeRuF8A85Uso_A3OU6EF-qq4aYswWVxkPWHBKI/edit#slide=id.p Generative Starwars video source - https://twitter.com/CuriousRefuge/status/1652412004626497536 Speculative Design Theory - https://readings.design/PDF/speculative-everything.pdf Aggregation Theory - https://stratechery.com/aggregation-theory/

Weaviate 1.19 Release with Etienne Dilocker - Weaviate Podcast #44! May 04, 2023

Show notes

Hey everyone! Thank you so much for watching the Weaviate 1.19 release podcast! We have all sorts of cool new features, in addition to the database and module features, I really want to encourage readers to see the `groupBy` search discussed at 14:32, quite an interesting idea for improving search performance! Chapters 0:00 Welcome Etienne! 0:38 gRPC API 9:50 Generative Cohere 14:32 groupBy search 19:33 Bitmap or BM25 index tuning 22:20 Additional Tokenization Options 24:05 Tunable Consistency

Erika Cardenas, Roman Grebennikov, and Vsevolod Goloviznin on Recommendation and Metarank - Pod #43! Apr 12, 2023

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Thank you so much for watching the 43rd episode of the Weaviate Podcast with Roman Grebennikov and Vesvolod Goloviznin from Metarank, as well as Erika Cardenas from Weaviate! This podcast is a masterclass on Ranking models, additionally touching on the connection between Search and Recommendation. Learning-to-rank is an exciting idea where we use models that produce more fine-grained relevance scores than the offline indexing techniques of vector search and bm25, however with the tradeoff of the speed of these inferences. Romand and Vsevolod touched on another extremely interesting part of these ranking models which is the estimation of features such as Click-through-Rates and how they use streaming technology to do this. I learned so much from this podcast about the directions in ranking, I hope you enjoy it as well! As always, we are more than happy to answer any questions or discuss any ideas with you! In reflecting on this podcast, Erika and I wrote up our latest thoughts on Ranking Models in a Weaviate blogpost, check it out here if interested: https://weaviate.io/blog/ranking-models-for-better-search. Chapters 0:00 Welcome Everyone! 0:40 Recommendation with Weaviate 4:20 Metarank - Founding Story 8:20 Ranking MLOps 9:52 User Friendliness Perspective 15:10 Retrieval vs. Ranking 17:45 Ranking Optimization 25:20 Multi-Vector Object Representations 27:55 Click-through-Rate Feature Streaming 33:06 Weaviate Properties vs. Feature Stores 40:06 Cold-Start Recommendation Problem 46:04 Ranklens Demo - RecSys Datasets 52:02 Cross Encoders

Ethan Steininger on Mixpeek and the AI Landscape - Weaviate Podcast #42! Apr 05, 2023

Show notes

Thank you so much for watching the 42nd episode of the Weaviate Podcast! Ethan Steininger is the founder of Mixpeek, an intelligence layer that sits on top of your S3 bucket, so you can search and analyze unstructured data at scale. Ethan has also created Collie with the headline of "Enter your website and Collie will fetch every asset, then give you an embedded search bar that wows your users". Ethan began the podcast by describing his background at MongoDB and integrating the database with full text search functionality. Ethan then presented the founding vision of Mixpeek and some of the most outstanding problems with adapting the latest AI technologies to solve business problems. This lead us to discuss a massive range of topics around the AI landscape from the Llama / Alpaca models to ChatGPT Plugins, the paradigm shift in coding and serverless GPUs. I really enjoyed speaking with Ethan about all these things, I hope you enjoy listening! We would more than happy to discuss any ideas you have with you or answer any questions, thanks again for watching! Chapters 0:00 Welcome Ethan Steininger! 0:50 Entry into Search from MongoDB 6:45 Founding Vision of Mixpeek 10:15 Data Ingestion 13:45 ChatGPT Plugins 16:25 Paradigm shift in Coding with GPT-4 18:54 Alpaca Models 22:42 Tuning LLMs with Retrieval 31:45 Adding Structure to Code Repo Search 35:06 Re-Ranking / Learning-to-Rank 43:30 AGI Monopoly 49:10 Hybrid Search! Zero-Shot + BM25 54:20 Open-Source Business 59:35 Serverless GPUs 1:11:18 Ethan’s Advice for Stress Management 1:13:00 Existential AI Fear Links: Mixpeek - https://mixpeek.com/ Collie - https://collie.ai/ An Open-Source, Personalized Generative Model Framework - https://esteininger.medium.com/an-open-source-personalized-generative-model-framework-6df865de51bf Teaching GPT-4 to write code from research papers - https://esteininger.medium.com/teaching-gpt-4-to-write-code-from-research-papers-889a880fb4f0 The Need for an AI Content Verification Layer - https://esteininger.medium.com/the-need-for-an-ai-content-verification-layer-10be9379b354 Building the ML Stack of the Future - https://esteininger.medium.com/building-the-ml-stack-of-the-future-d66c8a8b566a Vertical Integration is Key to Winning the AI Race - https://esteininger.medium.com/vertical-integration-is-key-to-winning-the-ai-race-44c8e4bd3b30

Dennis Xu on Mem and LLMs! - Weaviate Podcast #41 Mar 29, 2023

Show notes

Chapters 0:00 Welcome Dennis Xu! 0:30 Founding Vision of Mem 4:18 Personalized Embeddings 6:02 GPT-4, How will this change everything? 11:00 Writing code with LLMs 13:18 Embeddings at Mem 17:10 Structure in Vector Search 19:10 Zero-Shot vs. Fine-Tuned Models 25:05 Ranking Models and LLM Distillation

Weaviate 1.18 Release Podcast - Weaviate Podcast #40! Mar 07, 2023

Show notes

Chapters
0:00 Weaviate 1.18!!!
0:32 Bitmap Indexing!
11:40 HNSW PQ
25:33 Cursor API
30:03 Filters in Hybrid Search
32:55 WAND Scoring
40:35 Replication
49:10 Building a Database in Golang
1:00:55 Thank you!

Floris Hoogenboom on OpenVerkiezingenNL - Weaviate Podcast #39 Mar 06, 2023

Show notes

Check out the website here! https://openverkiezingen.nl/

Leo Boystov on Information Retrieval Science - Weaviate Podcast #38 Mar 01, 2023

Show notes

Hey everyone! Thank you so much for watching the 38th episode of the Weaviate podcast! This episode features Leo Boystov, an expert in Information Retrieval technology! We discussed a very wide range of topics from an overview of IR methods such as BM25, Neural Bi-Encoder and Cross-Encoder rankers, and a super exciting new work Leo has co-authored on using Large Language Models to generate training data for Neural Ranking models titled "InPars-Light: Cost-Effective Unsupervised Training of Efficient Rankers." We also discussed Leo's work on Non-Metric Space Search, the challenge of long document ranking, Robustness in Generalization Testing, and ended with some thoughts on Hybrid Rank Fusion. I really hope you enjoy the podcast, more than happy to answer any questions you have or clarify anything!
In-Pars Light: Cost-Effective Unsupervised Training of Efficient Rankers - https://arxiv.org/abs/2301.02998
Google Scholar Leo Boystov - https://scholar.google.com/citations?...
Chapters
0:00 Introduction
1:08 Information Retrieval Research
25:20 Ranker Inference Requirements
40:40 Non Metric Space Search
52:38 Code Libraries for IR Research
59:40 Long Document Ranking
1:07:00 Robustness Generalization
1:15:40 Hybrid Rank Fusion

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