Weaviate Podcast

Hey everyone! Thank you so much for watching the 87th episode of the Weaviate Podcast! I am SUPER excited to welcome Karel D'Oosterlinck! Karel is the creator of IReRa (Infer-Retrieve-Rank)! IReRa is one of the most impressive systems that have been built for Extreme Multi-Label Classification, leveraging the emerging paradigm of DSPy compilation! This podcast dives into all things IReRa, XMC, DSPy compilation, and applications in Biomedical NLP and Recommendation! I hope you find this useful!

Hey everyone! We are super excited to publish this podcast with Vinod Valloppillil and Bob van Luijt on Open-Source AI and future directions for RAG! The podcast begins by discussing Vinod's "Halloween Documents", a series of internal strategy writings at Microsoft related to the open-source software movement! The conversation continues to discuss the current state of Open-Source in AI. One of the major points Bob has been making about the business of AI models is that the models themselves are *stateless*, akin to an MP3 file. Vinod pushes back a bit on this definition and jointly it is then settled that these models neither fall into the pure stateful or stateless bucket, rather a "pre-baked" bucket -- presenting completely new opportunities to build business around software. The conversation then continues to discuss the particular details of how people are building RAG systems and many directions for how that may evolve!

Hey everyone! I am beyond excited to present our interview with Omar Khattab from Stanford University! Omar is one of the world's leading scientists on AI and NLP. I highly recommend you check out Omar's remarkable list of publications linked below! This interview completely transformed my understanding of building RAG and LLM applications! I believe that DSPy will be one of the most impactful software project in LLM development because of the abstractions around *program optimization*. Here is my TLDR of this concept of LLM programs and program optimization with DSPy, I of course encourage you to view the podcast and listen to Omar's explanation haha.

Chapters

0:00 Weaviate at NeurIPS 2023!

0:38 Omar Khattab

0:57 What is the state of AI?

2:35 DSPy

10:37 Pipelines

14:24 Prompt Tuning and Optimization

18:12 Models for Specific Tasks

21:44 LLM Compiler

23:32 Colbert or ColBERT?

24:02 ColBERT

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!

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!

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!

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!

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!

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!

Hey everyone, thank you so much for watching the 77th Weaviate Podcast on RAGAS, featuring Jithin James, Shahul ES, and Erika Cardenas! RAGAS is one of the hottest rising startups in Retrieval-Augmented Generation! RAGAS began it's journey with the RAGAS score, a matrix of evaluations for generation and retrieval. Generation evaluated on Faithfulness (is the response grounded in the context) as well as Relevancy (is the response useful). Retrieval is then evaluated on Precision (How many of the search results are relevant to the question?) and Recall (How many of the relevant search results are captured in the retrieved results?). Now, the super novel thing about this is that an LLM is used to determine these metrics. So we circumvent painstaking manual labeling effort with the RAGAS score! This podcast dives into the development of the RAGAS score as well as how RAG application builders should think about the knobs to tune for optimizing their RAGAS score: embedding models, chunking strategies, hybrid search tuning, rerankers, ... ?!? We also discussed tons of exciting directions for the future such as fine-tuning smaller LLMs for these metrics, agents that use tuning APIs, and long context RAG!