Weaviate Podcast

Join the 89th Weaviate Podcast on Matryoshka Embeddings with Aditya Kusupati, Zach Nussbaum, and Zain Hasan. Learn about challenges in training Matryoshka embeddings, experiences building embeddings API, Aditya's research on differentiable ANN indexes, and more!

Jason Liu, creator of Instructor, a leading LLM framework, discusses the benefits of structured output parsing with language models and the future of function calling. The podcast also explores selecting tools, organizing prompts, using Go library for reranking, and the capabilities of modal API for serverless GPU compute. The importance of fine-tuning embedding models for improved performance is emphasized.

Karel D'Oosterlinck, creator of IReRa, discusses Extreme Multi-Label Classification, DSPy compilation, language models for large-scale classification, optimization process in DSPY, parsing techniques, model fine-tuning with bootstrapping examples, designing Infour retrieve rank, and using DS pi syntax for generating diverse outputs.

Vinod Valloppillil and Bob van Luijt discuss open-source AI, the business of AI models, the potential of retrieval augmented generation (RAG) in open-source AI, the future of knowledge bases and weight manipulation, advancements in knowledge graphs and vector databases, and the convenience of AI integration in devices.

Omar Khattab, leading scientist on AI and NLP, discusses the concept of LLM programs and program optimization with DSPy. He explores the components of query writer, retrieve, rerank, and answer, and the potential of DSPy in optimizing prompts. The podcast also delves into exploring language models and DSPY modules, compilers for program synthesis, and the power of ColBERT in contextual awareness and document scoring.

Hey everyone! Thank you so much for watching the fourth and final episode of the AI-Native Database series with Dan Shipper! This was another epic one! Dan has had an absolutely remarkable career creating and selling a company and now co-founding and working as the CEO of Every! Every is an incredibly future-looking business focused on content online, both with an amazing newsletter, community of writers and thinkers, an AI-note taking app, and more! I think Dan brings a very unique perspective to the series, as well as the Weaviate podcast broadly, because of his experience with writers and understanding how writers are going to use these new technologies! We heavily discussed the role of personality or subjectivity in AI, amongst many other topics! I really hope you enjoy the podcast, as always we are more than happy to answer any questions or discuss any ideas you have about the content in the podcast! Read writings from Dan Shipper on Every: https://every.to/@danshipper Chapters 0:00 AI-Native Databases 0:58 Welcome Dan Shipper! 1:37 GPT-4 is a Reasoning Engine 8:40 Subjectivity in LLMs 12:14 AI in Note Taking 16:38 The opinions of LLMs 25:50 Cookbooks for you 31:16 Overdrive in LLMs 34:50 Tweaking the voice of AI 40:45 Multi-Agent Personalities

Hey everyone! Thank you so much for watching the 3rd episode of the AI-Native Database series featuring John Maeda and Bob van Luijt! This one dives into how humans perceive AI, from Anthroaormorphization to Doomsday scenario thinking and how important understanding how AI actually work is to the engineering of these systems. Bob and John discuss the evolution of the design in tech report, 3 categories of design, and many others! I hope you enjoy the podcast! As always, we are more than happy to answer any questions or discuss any ideas you have about the content in the podcast! Links: Design in Tech Report: https://designintech.report/ 3 Kinds of Design: https://qz.com/1585165/john-maeda-on-the-importance-of-computational-design Microsoft Semantic Kernel: https://github.com/microsoft/semantic-kernel Chapters 0:00 AI-Native Databases 0:58 Welcome John Maeda! 1:35 Design in Tech Report 4:07 Anthropomorphizing AI 15:30 3 Types of Design 19:30 The ChatGPT Shift 22:58 Explaining Technology 32:54 Impact of AI on the Creative Industries 39:00 Semantic Kernel

Hey everyone! Thank you so much for watching the second episode of AI-Native Databases with Paul Groth! This was another epic one, diving deep into the role of structure in our data! Beginning with Knowledge Graphs and LLMs, there are two perspectives: LLMs for Knowledge Graphs (using LLMs to extract relationships or predict missing links) and then Knowledge Graph for LLMs (to provide factual information in RAG). There is another intersection that sits in the middle of both LLMs for KGs and KGs for LLMs, which is using LLMs to query Knowledge Graphs, e.g. Text-to-Cypher/SPARQL/... From there I think the conversation evolves in a really fascinating way exploring the ability to structure data on-the-fly. Paul says "Unstructured data is now becoming a peer to structured data"! I think in addition to RAG, Generative Search is another underrated use case -- where we use LLMs to summarize search results or parse out the structure. Super interesting ideas, I hope you enjoy the podcast -- as always more than happy to answer any questions or discuss any ideas you have about the content in the podcast! Learn more about Professor Groth's research here: https://scholar.google.com/citations?... Knowledge Engineering using Large Language Models: https://arxiv.org/pdf/2310.00637.pdf How Much Knowledge Can You Pack into the Parameters of a Language Model? https://arxiv.org/abs/2002.08910 Chapters 0:00 AI-Native Databases! 0:58 Welcome Paul! 1:25 Bob’s overview of the series 2:30 How do we build great datasets? 4:28 Defining Knowledge Graphs 7:15 LLM as a Knowledge Graph 15:18 Adding CRUD Support to Models 28:10 Database of Model Weights 32:50 Structuring Data On-the-Fly

Hey everyone! Thank you so much for watching the first episode of AI-Native Databases with Andy Pavlo! This was an epic one! We began by explaining the "Self-Driving Database" and all the opportunities to optimize DBs with AI and ML at both the low-level, as well as how we query and interact with them. We also discussed new opportunities with DBs + LLMs, such as bringing the data to the model (such as ROME, MEMIT, GRACE), in addition to bringing the model to the data (such as RAG). We also discuss the subjective "opinion" of these models and many more! I hope you enjoy the podcast! As always we are more than happy to answer any questions or discuss any ideas you have about the content in the podcast! This one means a lot to me. Andy Pavlo's CMU DB course was one of the most impactful resources in my personal education, and I love the vision for the future outlined by OtterTune! It was amazing to see Etienne Dilocker featured in the ML for DBs, DBs for ML series at CMU. I am so grateful to Andy for joining the Weaviate Podcast! Links: CMU Database Group on YouTube: https://www.youtube.com/@CMUDatabaseGroup/videos Self-Driving Database Management Systems - Pavlo et al. - https://db.cs.cmu.edu/papers/2017/p42-pavlo-cidr17.pdf Database of Databases: https://dbdb.io/ Generative Feedback Loops: https://weaviate.io/blog/generative-feedback-loops-with-llms Weaviate Gorilla: https://weaviate.io/blog/weaviate-gorilla-part-1 Chapters 0:00 AI-Native Databases 0:58 Welcome Andy 1:58 Bob’s overview of the series 3:20 Self-Driving Databases 8:18 Why isn’t there just 1 Database? 12:46 Collaboration of Models and Databases 20:05 LLM Schema Tuning 23:44 The Opinion of the System 28:20 PyTorchDB - Moving the Data to the Model 33:30 Database APIs 38:15 Learning to operate Databases 42:54 Vector DBs and the DB Hype Cycle 51:38 SQL in Weaviate? 1:07:40 The Future of DBs 1:14:00 Thank you Andy!

Hey everyone! Thank you so much for watching the Weaviate 1.23 Release Podcast with Weaviate Co-Founder and CTO Etienne Dilocker! Weaviate 1.23 is a massive step forward for managing multi-tenancy with vector databases. For most RAG and Vector DB applications, you will have an uneven distribution in the # of vectors per user. Some users have 10k docs, others 10M+! Weaviate now offers a flat index with binary quantization to efficiently balance when you need an HNSW graph for the 10M doc users and when brute force is all you need for the 10k doc users! Weaviate also comes with some other "self-driving database" features like lazy shard loading for faster startup times with multi-tenancy and automatic resource limiting with the GOMEMLIMIT and other details Etienne shares in the podcast! I am also beyond excited to present our new integration with Anyscale (@anyscalecompute)! Anyscale has amazing pricing for serving and fine-tuning popular open-source LLMs. At the time of this release we are now integrating the Llama 70B/13B/7B, Mistral 7B, and Code Llama 34B into Weaviate -- but we expect much further development with adding support for fine-tuned models, the super cool new function calling models Anyscale announced yesterday. and other model such as Diffusion and multimodal models! Chapters 0:00 Weaviate 1.23 1:08 Lazy Shard Loading 8:20 Flat Index + BQ 33:15 Default Segments for PQ 38:55 AutoPQ 42:20 Auto Resource Limiting 46:04 Node Endpoint Update 47:25 Generative Anyscale Links: Etienne Dilocker on Native Multi-Tenancy at the AI Conference in SF: https://www.youtube.com/watch?v=KT2RFMTJKGs Etienne Dilocker in the CMU DB Series: https://www.youtube.com/watch?v=4sLJapXEPd4 Self-Driving Databases by Andy Pavlo: https://www.cs.cmu.edu/~pavlo/blog/2018/04/what-is-a-self-driving-database-management-system.html