Weaviate Podcast

Weaviate Podcast

By WeaviateTechnology
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Weaviate Podcast episodes

  • ChatArena with Yuxiang Wu - Weaviate Podcast #47!

    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!

    52 min
  • HyperDB with John Dagdelen, Bob van Luijt, and Etienne Dilocker - Weaviate Podcast #46!

    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

    1 hr 7 min
  • Generative Feedback Loops with Bob van Luijt - Weaviate Podcast #45!

    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/

    55 min
  • Weaviate 1.19 Release with Etienne Dilocker - Weaviate Podcast #44!

    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

    27 min
  • Erika Cardenas, Roman Grebennikov, and Vsevolod Goloviznin on Recommendation and Metarank - Pod #43!

    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

    1 hr 1 min
  • Ethan Steininger on Mixpeek and the AI Landscape - Weaviate Podcast #42!

    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

    1 hr 23 min
  • Dennis Xu on Mem and LLMs! - Weaviate Podcast #41

    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

    45 min
  • Leo Boystov on Information Retrieval Science - Weaviate Podcast #38

    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

    1 hr 29 min

About Weaviate Podcast

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Join Connor Shorten as he interviews machine learning experts and explores Weaviate use cases from users and customers.