Hey everyone! Thank you so much for watching the 57th Weaviate podcast with Charles Frye! Charles is an educator at Full Stack Deep Learning, one of the world's top courses on Deep Learning with lectures available on YouTube (link below)! This was one of the most thorough Weaviate podcasts published so far, covering all sorts of topics around the evolution of Deep Learning! Particularly we discussed the Retrieval-Augmented Generation stack with Vector Databases and Zero-Shot Large Language Models and how that compares to more conventional machine learning workflows and the MLOPs stack! I really enjoyed chatting with Charles and am more than happy to answer any questions or discuss any ideas you have about the content in the podcast! Thank you so much for listening!

Chapters

Hey everyone! Thank you so much for watching the 55th episode of the Weaviate Podcast with Aleksa Gordcic! This episodes dives into Aleksa's incredible story from Deep Learning YouTube to DeepMind and now creating Ortus! We dived into all sorts of topics, I loved hearing about the latest updates on Ortus and how Aleksa is sees the current state of AI development! We are more than happy to answer any questions or discuss any ideas you might have about the content in the podcast! Thanks so much for watching!

Chapters

Hey everyone, thank you so much for watching the 52nd episode of the Weaviate Podcast with Yana Welinder! Yana is the Founder and CEO of Kratful (https://www.kraftful.com/). Kratful is an incredibly interesting "ChatGPT but for Product Research" -- curating specific skills for Product Managers into a collection of prompts. We discussed all sorts of things from the latest innovations in LLMs to the ChatGPT marketplace and product management, I really hope you enjoy the podcast!

Hey everyone, thank you so much for watching the 51st episode of the Weaviate Podcast with Greg Kamradt and Colin Harmon! Greg and Colin are both entrepreneurs in the space of new AI tools powered by LLMs! This podcast is about keeping up with the evolution of LLM Agents from AutoGPT to connecting LLMs with Vector Databases or Wolfram Alpha, as well as the ChatGPT Marketplace, Personalized LLMs, Private LLMs, and many more! I think there are so many interesting nuggets from this podcast, thank you so much to Greg and Colin for joining, really enjoyed this one!

This video explores a new paper exploring the use of summarization chains to represent long texts and use (original text, summary) pairs for optimizing text embeddings models! Here are 3 main takeaways I think everyone working with Weaviate may get value from:

Hey everyone, thank you so much for watching the 50th (!!!) Weaviate Podcast with Emil Sorensen and Finn Bauer from Kapa AI! Are you curious about taking either your, or your company's, specific information and putting into a Vector DB + LLM system? Emil and Finn are doing this at the highest level, taking the documentation of software companies like Weaviate and building these LLM-augmetnted assistant systems for them. This podcast takes a complete tour from Data Ingestion to Cleaning, Chunking, LLM latency, and emerging trends in LLMs such as cheap fine-tuning with LoRA or Long Context Windows such as GPT-4 32K, MPT-7B 65K, or Anthropic Claude's 100k. I learned so much from speaking with Emil and Finn! Please let us know any questions you have or ideas you would like to discuss!

Hey everyone, thank you so much for watching the 49th episode of the Weaviate Podcast!! This podcast features Professor Laura Dietz from the University of New Hampshire! I came across Dr. Dietz's tutorial at ECIR on Neuro-Symbolic Approaches for Information Retrieval and am so grateful that she was interested in joining the Weaviate Podcast! I learned so much about Neurosymbolic Search, especially around the role of Entity Linking and Entity Re-Ranking -- as well as the topic of Knowledge Graphs and Vector Search. We also discussed Prof. Dietz and collaborators latest perspectives paper on Large Language Models for Relevance Judgment. TLDR this describes the idea of using LLMs to either generate synthetic queries for documents or say annotate the relevance for query, document pairs. We discussed this kind of idea with Leo Boytsov and his work on InPars, and have presented Promptagator on past episodes of the Weaviate Air show. Although this idea comes with a lot of potential, Dr. Dietz explains the potentials for bias and poor judgements, as well as generally diving more into the details of this kind of idea! I really hope you enjoy the podcast, we are more than happy to answer any questions you might have about these ideas, or discuss any of your ideas! Thanks so much for watching!

Hey everyone, thank you so much for watching the 48th episode of the Weaviate Podcast!! This is a SUPER exciting one, welcoming Brian Raymond the CEO / Founder of Unstructured! Unstructured is a perfect complimenting technology for Weaviate, helping people get their Unstructured data into Weaviate! The podcast dives into the nuances of this task, but it generally revolves around Unstructured's abstraction of Partitioning, Cleaning, and Staging! Unstructured is making groundbreaking innovations on using Visual Document Layout models for Partitioning, for example saying that this part of the PDF is the header, body, image caption, and so on. Cleaning then describes removing pesky details like whitespaces or odd characters. Staging then describes the transformations of say formatting a text chunk with it's metadata into the JSON for a Weaviate object upload! I really hope you find this podcast interesting! We are publishing a blog post as well showing an example of how to use Unstructured to get PDF data into Weaviate, please please check that out and let us know if it works for your data and how we can improve it! This blog post can be found on weaviate.io and we will be managing discussions around it both in the Weaviate slack, as well as Unstructured! Thank you so much for listening!