Thomas van Dongen is the head of AI engineering at Springer Nature and the creator of Pyversity! Pyversity is a fast, lightweight open-source Python library for diversifying retrieval results. Retrieval systems often return highly similar items. Pyversity efficiently re-ranks these results to encourage diversity, surfacing items that remain relevant but less redundant. It implements several popular diversification strategies such as MMR, MSD, DPP, and Cover with a clear, unified API.

Matthew Russo is a Ph.D. student at MIT where he is researching the intersection of AI and Database Systems. AI is transforming Database Systems. Perhaps the biggest impact so far has been natural language to query language translations, or Text-to-SQL. However, another massive innovation is brewing. AI presents new Semantic Operators for our query languages. For example, we are all familiar with the WHERE filter. Now we have AI_WHERE, in which an LLM or another AI model computes the filter value without needing it to be already available in the database!

`SELECT * FROM podcasts AI_WHERE “Text-to-SQL” in topics`

Semantic Filters are just the tip of iceberg, the roster of Semantic Operators further includes Semantic Joins, Map, Rank, Classify, Groupby, and Aggregation! And it doesn’t stop there! One of the core ideas for Relational Algebra and how its influenced Database Systems is query planning and finding the optimal order to apply filters. For example, let’s say you have two filters, the car is red and the car is a BMW. Now let’s say the dataset only contains 100 BMW, but 50,000 red cars!! Applying the BMW filter first will limit the size of the set for the next filter!

So many interesting nuggets in this podcast, loved discussing these things with Matthew, and I hope you find it interesting!

Xiaoqiang Lin is a Ph.D. student at the National University of Singapore. During his time at Meta, Xiaoqiang lead the research behind REFRAG: Rethinking RAG-based Decoding. Traditional RAG systems use vectors to retrieve relevant context with semantic search, but then throw away the vectors when passing the context to the LLM. REFRAG instead feeds the LLM these pre-compute vectors, achieving massive gains in long context processing and LLM inference speed! REFRAG makes Time-To-First-Token (TTFT) 31x faster and Time-To-Iterative-Token (TTIT) 3x faster, boosting overall LLM throughput by 7x while also being able to handle much longer contexts!

There are so many interesting aspects to this and I really loved diving into the details with Xiaoqiang! I hope you enjoy the podcast!

This episode dives into Weaviate's partnership with SAS! We are super excited about our recent collaboration on the SAS Retrieval Agent Manager (RAM), featuring a first party integration with Weaviate! The podcast dives into all sorts of aspects of Enterprise AI adoption from what has changed, to what has NOT changed with recent breakthroughs in AI systems!

Charles Pierse is the Director of the Weaviate Labs team, where he has recently lead the GA release of the Weaviate Query Agent. The podcast begins with the journey from alpha to GA release, discussing unexpected lessons and the collaborations between teams at Weaviate. Continuing on the product design, we cover the design of the Python and TypeScript clients and how to think about response models with Agent products. Then diving into the tech, we cover several different aspects of the Query Agent from question answering with citations, to schema introspection and typing for database querying, multi-collection routing, and the newly introduced Search Mode. We also discuss the Weaviate Query Agent's integration with the Cloud Console, a GUI home for the Weaviate Database! We are also super excited to share a case study from one of the Query Agent's power uses, MetaBuddy! The podcast concludes with the MetaBuddy case study and some exciting directions for the future development of the Query Agent.

Lakshya A. Agrawal is a Ph.D. student at U.C. Berkeley! Lakshya has lead the research behind GEPA, one of the newest innovations in DSPy and the use of Large Language Models as Optimizers! GEPA makes three key innovations on how exactly we use LLMs to propose prompts for LLMs, (1) Pareto-Optimal Candidate Selection, (2) Reflective Prompt Mutation, and (3) System-Aware Merging. The podcast discusses all of these details further, as well as topics such as Test-Time Training and the LangProBe benchmarks used in the paper! I hope you find the podcast useful!

Maarten Grootendorst is a psychologist turned AI engineer who has created BERTopic and authored "Hands-On Large Language Models" with Jay Alammar. The rise of LLMs and Agents are transforming many areas of software! This podcast dives deep into their impact on Topic Modeling! Maarten designed BERTopic from the start with modularity in mind -- letting you ablate embedding models, dimensionality reduction, clustering algorithms, and more. This early insight to prioritize modularity makes BERTopic incredibly well structured to become more "Agentic". An "Agentic" Topic Modeling algorithm can use LLMs to generate topics or topic descriptions, as well as contrast them with other topics. It can decide which topics to subdivide, and it can integrate human feedback and evaluate topics in novel ways... I hope you find the podcast interesting!

Hailey Joren is a Ph.D. student at UCSD! Hailey and collaborators at Duke University and Google have recently published Sufficient Context: A New Lens on Retrieval Augmented Generation Systems in ICLR 2025! There are so many interesting nuggets to this work! Firstly, it really helped me understand the difference between *relevant* search results and sufficient context for answering the question. Armed with this lens of looking at retrieved context, Hailey and collaborators make all sorts of interesting observations about the current state of Hallucination. RAG unfortunately makes the models far less likely to hallucinate, and the existing RAG benchmarks unfortunately do not emphasize retrieval adaptation well enough -- indicated by LLMs outputting correct answers despite insufficient context 35-62% of the time! However, reason for optimism! Hailey and team develop an autorater that can detect insufficient context 93% of the time! There are all sorts of interesting ideas around this paper! I really hope you find the podcast useful!

Nandan Thakur is a Ph.D. student at the University of Waterloo! Nandan has worked on many of the most impactful works in Retrieval-Augmented Generation (RAG) and Information Retrieval. His work ranges from benchmarks such as BEIR, MIRACLE, TREC, and FreshStack, to improving the training of embedding models and re-rankings, and more!

Multi-vector retrieval offers richer, more nuanced search, but often comes with a significant cost in storage and computational overhead. How can we harness the power of multi-vector representations without breaking the bank? Rajesh Jayaram, the first author of the groundbreaking MUVERA algorithm from Google, and Roberto Esposito from Weaviate, who spearheaded its implementation, reveal how MUVERA tackles this critical challenge.

Dive deep into MUVERA, a novel compression technique specifically designed for multi-vector retrieval. Rajesh and Roberto explain how it leverages contextualized token embeddings and innovative fixed dimensional encodings to dramatically reduce storage requirements while maintaining high retrieval accuracy. Discover the intricacies of quantization within MUVERA, the interpretability benefits of this approach, and how LSH clustering can play a role in topic modeling with these compressed representations.

This conversation explores the core mechanics of efficient multi-vector retrieval, the challenges of benchmarking these advanced systems, and the evolving landscape of vector database schemas designed to handle such complex data. Rajesh and Roberto also share their insights on the future directions in artificial intelligence where efficient, high-dimensional data representation is paramount.

Whether you're an AI researcher grappling with the scalability of vector search, an engineer building advanced retrieval systems, or fascinated by the cutting edge of information retrieval and AI frameworks, this episode delivers unparalleled insights directly from the source. You'll gain a fundamental understanding of MUVERA, practical considerations for its application in making multi-vector retrieval feasible, and a clear view of future directions in AI.