130 Episodes! 🥳🎉 Massive thank you to everyone who has joined, listened, and supported the Weaviate Podcast! 💚 A recap 🧵 of episodes 126 to 130 👇

How does Maximal Marginal Relevance diversify search results? 🧠 Clip from Weaviate Podcast #132! 💚🎙️

"In many production use cases, you aren't just looking for this single perfect document, you are often exploring this space" 🌌🔍

Last week I had the pleasure of joining the @weaviate_io podcast to talk about Pyversity. If you're interested in a deep-dive into diversification for retrieval, this might be worth a listen. Thanks for having me @CShorten30 !

Weaviate Podcast #132 is live! 🎉 Pyversity with Thomas van Dongen 🔭

I am SUPER EXCITED to publish the 132nd episode of the Weaviate Podcast with Thomas van Dongen (@thomas_v_dongen), head of AI engineering at Springer Nature! 🎉 Thomas is the creator of Pyversity, a fast, lightweight open-source python library for diversifying retrieval results!

[Weaviate Pod #127] GEPA with Lakshya A. Agrawal (@LakshyAAAgrawal) 🧩 This podcast covers: • Natural Language Rewards • Evolution of DSPy • Pareto-Optimal Candidate Selection • Domain Specific Knowledge • Exploration and Diversity • Test-Time Training • LangProBe

❓Do you really need to live in San Francisco to build a generational company? In the first episode of our new @techeurope_ podcast, I sat down with Bob from Weaviate and discussed why he is building in Europe, why San Francisco is like a library for him, and how this influences

YouTube: https://t.co/xfl2LFm4K6 Spotify:

Weaviate Podcast #131 with Matthew Russo (@RussoMatthew) and Connor Shorten (@CShorten30)! This podcast covers: • Semantic Query Processing Engines • Semantic Operators for Database Systems • Relational Algebra and Query Planning • Internal vs. External Query Engines •

This clip explains the evolution of Weaviate's Query Agent into Ask Mode and Search Mode! 🤖🚀

Update or create new properties in your Weaviate Database with the Transformation Agent! ♻️ Just tell us: 1. The name of the new, or updated, property 2. The data type if creating a new property 3. Which other properties the LLM should view to generate the property value 4. The

A Semantic MAP creates a new column in your Database with AI models! 🔥 💭 Imagine you have a dataset of research papers in Weaviate, you can apply a semantic map to compute a `summary` property. 📄♻️

Semantic JOINs in a nutshell 🌰 💭 Imagine a wildlife tracking system that sends data to two separate Database collections; one stores audio recordings and another stores images. 🔊📷 Semantic JOINs describes joining objects in the collections where the audio in one data object

Semantic JOINs in a nutshell 🌰 💭 Imagine a wildlife tracking system that sends data to two separate Database collections; one stores audio recordings and another stores images. 🔊📷 Semantic JOINs describes joining objects in the collections where the audio in one data object

Here is the elevator pitch for Semantic Query Engines ⌛️ We query our data stored in Databases with relational operators such as Filters and Joins. By integrating LLMs into our Database Systems, we have unlocked a new set of operators! AI_WHERE, AI_JOIN, AI_EXTRACT, AI_RANK,

Weaviate Podcast #131 is live! 🚀🚀

Weaviate Podcast #131 is live! 💚🔥 Learn how Semantic Query Processing Engines are transforming Database Systems! 👇

I just watched the latest @weaviatepodcast with @CShorten30 and @RussoMatthew from MIT , where they discussed about semantic query processing. The basic idea: your database has images of cars, and you want to filter for "red cars." Traditional SQL can't do that - it just

📣 We're spreading the word about #SemBench -- a brand new benchmark for semantic query processing over multimodal workloads including text, image, audio, and tabular data! 📜Paper: https://t.co/Vai6Z3aRQK 💻Website: https://t.co/yAPaQwhagF 💾Code: https://t.co/nSBE9vEXbC

I am SUPER EXCITED to publish the 131st episode of the Weaviate Podcast with Matthew Russo (@RussoMatthew), a Ph.D. student at MIT! 🎉 AI is transforming Database Systems. Perhaps the biggest impact so far has been natural language to query language translations, or Text-to-SQL.