Weaviate Podcast

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

Hey everyone! Thank you so much for watching the 78th episode of the Weaviate podcast featuring Rudy Lai, the founder and CEO of Tactic Generate! Tactic Generate has developed a user experience around applying LLMs in parallel to multiple documents, or even folders / collections / databases. Rudy discussed the user research that lead the company to this direction and how he sees the opportunities in building AI products with new LLM and Vector Database technologies! 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 Tactic Generate here: https://tactic.fyi/generative-insights/ Weaviate Podcast #69 with Charles Pierse: https://www.youtube.com/watch?v=L_nyz1xs9AU Chapters 0:00 Welcome Rudy! 0:48 Story of Tactic Generate 7:45 Finding Common Workflows 19:30 Multiple Document RAG UIs 26:14 Parallel LLM Execution 32:40 Aggregating Parallel LLM Analysis 38:25 Pretty Reports 44:28 Research Agents

Join Jithin James and Shahul ES, co-founders of RAGAS, a pioneering framework for evaluating retrieval-augmented generation, along with Erika Cardenas, a developer advocate at Weaviate. They delve into the innovative RAGAS score, which uses LLMs to evaluate generation and retrieval metrics, streamlining the evaluation process. The trio discusses optimizing RAG applications through various tuning strategies and the exciting potential of future technologies like fine-tuning smaller models and enhancing automated systems for smarter, efficient retrieval.

Hey everyone, I am SUPER excited to present our 76th Weaviate Podcast featuring Patrick Lewis, an NLP Research Scientist at Cohere! Patrick has had an absolutely massive impact on Natural Language Processing with AI and Deep Learning! Especially notable for the current climate in AI and Weaviate is that Patrick is the lead author of the original "Retrieval-Augmented Generation" paper!! Patrick has contributed to many other profoundly impactful papers in the space as well such as DPR, Atlas, Task-Aware Retrieval with Instruction, and many many others! This was such an illuminating conversation, here is a quick overview of the chapters in the podcast! 1. Origin of RAG - Patrick explains the build-up that lead to the RAG paper, AskJeeves, IBM Watson, conceptual shift to retrieve-read in mainstream connectionist approaches to AI. 2. Atlas - Atlas shows that a much smaller LLM when paired with Retrieval-Augmentation can still achieve competitive few-shot and zero-shot task performance. This is super impactful because this few-shot and zero-shot capability has been a massive evangelist for AI broadly, and the fact that smaller Retrieval-Augmented models can do this is massive for the economically unlocking these applications. Teasing apart some architectural details of RAG: 3. Fusion In-Decoder - Interesting encoder-decoder transformer design in which each document + the query is encoded separately, then concatenated and passed to the LM. 4. End-to-End RAG - How to think about jointly training an embedding model and an LLM augmented with retrieval? 5. Query Routers - How to route queries from say SQL or Vector DBs? (More nuance on this later with Multi-Index Retrieval) 6. ConcurrentQA - Super interesting work on the privacy of multi-index routers. For example, if you ask "Who is the father of our new CEO" - this may reveal the private information of the new CEO with the public query of their father. 7. Multi-Index Retrieval 8. New APIs for LLMs 9. Self-Instructed Gorillas 10. Task-Aware Retrieval with Instructions 11. Editing Text, EditEval and PEER 12. What future direction excites you the most? Links: Learn more about Patrick Lewis: https://www.patricklewis.io/ Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks: https://arxiv.org/abs/2005.11401 Atlas: https://arxiv.org/pdf/2208.03299.pdf Fusion In-Decoder: https://arxiv.org/pdf/2007.01282.pdf Chapters 0:00 Welcome Patrick Lewis! 0:36 Origin of RAG 5:20 Atlas 10:43 Fusion In-Decoder 17:50 End-to-End RAG 27:05 Query Routers 32:05 ConcurrentQA 37:30 Multi-Index Retrieval 40:05 New APIs for LLMs 41:50 Self-Instructed Gorillas 44:35 Task-Aware Retrieval with Instructions 52:00 Editing Text, EditEval and PEER 55:35 What future direction excites you the most?

Hey everyone! Thank you so much for watching the 75th Weaviate Podcast featuring Tanmay Chopra! The podcast details Tanmay's incredible career in Machine Learning from Tik Tok to Neeva and now building his own startup, Emissary! Tanmay shared some amazing insights into Search AI such as how to process Temporal Queries, how to think about diversity in Retrieval, and Query Recommendation products! We then dove into the opportunity Tanmay sees in fine-tuning LLMs and knowledge distillation that motivated Tanmay to build Emissary! I thought Tanmay's analogy of GPT-4 to 3D printers was really interesting, tons of great nuggets in here! I really hope you enjoy the podcast, as always more than happy to answer any questions or discuss any ideas with you related to the content in the podcast! Chapters 0:00 Welcome Tanmay! 0:23 Early Career Story 2:02 Tik Tok 4:10 Neeva 8:45 Temporal Queries 11:40 Retrieval Diversity 17:22 Query Recommendation 23:20 Emissary, starting a company! 30:20 A Simple API for Custom Models 35:42 GPT-4 = 3D Printer?

Hey everyone! Thank you so much for watching the 74th Weaviate Podcast feature Simba Khadder, the CEO and Co-Founder of FeatureForm! To begin, "features" broadly describe the inputs to machine learning models that they use to produce outputs, or predictions. Feature stores orchestrate the construction of features, whether that be transformations for tabular machine learning models such as XGBoost, to chunking for vector embedding inference, and now features for LLM inference in RAG. Right out of the gate, Simba really opened my eyes to the role that feature engineering plays in RAG. Further touching on this at the very end under the "Exciting future for RAG with Features" chapter, Simba further describes how we can use more advanced features to provide better context to LLMs. In addition to these insights on RAG, there are so many nuggets in the podcast, Simba is a world class professional when it comes to building distributed systems, production scale recommendation systems, and more! I learned so much from chatting with Simba, I hope you enjoy listening to 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! FeatureForm: https://www.featureform.com/ Highly Recommend!! Simba Khadder at the CMU DB Seminar series: https://www.youtube.com/watch?v=ZsWa6XiBc-U FeatureForm and Weaviate demo! https://docs.featureform.com/providers/weaviate Chapters 0:00 Simba Khadder 0:35 RAG and Feature Stores 4:30 Experience building Recommendation Systems 9:47 The End-to-End Feature Lifecycle 15:08 Virtual Feature Store Orchestration 26:45 RAG Evaluation 31:27 Feature Engineering 34:15 LLM Tuning and Features 39:55 Streaming Features 51:15 Data Drift Detection 54:20 Exciting future for RAG with Features

Charles Packer, lead author of MemGPT at UC Berkeley, discusses the concept of explicit memory management in GPT models, the use of prompts to handle memory limitations, interrupts in retrieval augmented generation (RAG), achieving ideal running speed in high parameter models, fine-tuning MemGBT for long conversations, search actions pagination, role-playing language models, and the future integration of memory in chatbot platforms.

Hey everyone! Thank you so much for watching the 72nd episode of the Weaviate Podcast with Madelon Hulsebos!! Madelon is one of the world's experts on Machine Learning with Tables and Tabular-Structured Data, this was such an eye-opening conversation! We discussed all sorts of topics from the relationship of tabular data and embeddings, to searching through tables, semantic joins, more complex Text-to-SQL, using machine learning for query execution, using tabular data in search and recommendation reranking, and many more! This was easily one of the most knowledge packed episodes of the Weaviate podcast so far, please don't hesitate to leave any questions or ideas you have related to the content discussed! You can learn more about Madelon's incredible research career and publications / talks here: https://www.madelonhulsebos.com/! Papers such as GitTables are listed here! Another nice nugget form the podcast - Madelon introduced me to the BIRD-SQL benchmark which really expanded my understanding of Text-to-SQL (https://arxiv.org/pdf/2305.03111.pdf. Chapters 0:00 Welcome Madelon! 0:58 Tabular Data and Embeddings 3:10 Tabular Representation Learning 5:48 Semantic Type Detection 9:50 Pandas as an LLM Tool 11:52 Table-Based Question Answering and Text-to-SQL 19:35 Joins with Machine Learning 21:38 Query Execution with Machine Learning 22:45 Graph Neural Networks 24:07 XGBoost 28:28 Merging Tables 32:10 Fact Representation 35:50 GPT-4V and Tables 39:00 Metadata in Embeddings 42:45 Table Retrieval in Weaviate 46:25 Exciting future directions!!