Weaviate Podcast

Doug Turnbull and Trey Grainger join the Weaviate Podcast to discuss all things AI-Powered Search! The conversation kicks off with designing search experiences, not all search queries are the same! Sometimes the user knows exactly what they want (a product ID, a specific file), other times they're exploring a broad category, and other times they need to compare and contrast options. AI is now making it possible to dynamically construct UIs around search results, moving toward what Trey describes as a "Minority Report"-style future where visualizations adapt on the fly to the query and the data.From there, the discussion dives into query understanding and domain modeling. Doug and Trey break down how LLMs can classify queries against existing taxonomies (like NAICS codes or Google's product taxonomy), while Trey explains a multi-tier RAG approach, using the index itself as grounding for query interpretation before executing the final retrieval. The conversation moves into agentic search, exploring whether iterative LLM-driven search loops reduce the need for ever-better embedding models, or whether simple tools like BM25 and grep are sufficient when paired with strong reasoning.Trey introduces wormhole vectors, a technique for traversing between sparse (lexical) and dense (semantic) vector spaces by treating query results as document sets with shared meaning, enabling exploration across vector spaces rather than treating them as orthogonal. The discussion also covers reflected intelligence, the idea of making search systems self-learning by mining user behavioral signals (clicks, purchases, skipped results) to continuously improve relevance through techniques like signals boosting, collaborative filtering, and learning to rank.The episode wraps with a conversation about how coding agents are changing the way Doug and Trey work, and Trey's philosophy of designing intentional agentic workflows with atomic agents rather than just handing an LLM a bag of tools.AI Powered Search (Discount Code = "weaviate")https://aipoweredsearch.com/live-course?promoCode=weaviate

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!