Hey everyone! Thank you so much for watching the 62nd Weaviate Podcast with Atai Barkai! We are stepping into the meta with this one for a podcast about podcasts! Podcasts are one of the biggest opportunities of new technologies, starting with Whisper's ability to transcribe audio to text and advances with speaker diarization, .. the question to be explored is, What Vector Database and LLM applications can we build with this data?! What is the future of podcasting with these new technologies?! I had so much fun discussing all these ideas with Atai! As always we are more than happy to answer any questions or discuss any ideas you have about content discussed in the podcast! Thank you so much for watching! Chapters 0:00 Welcome Atai! 1:04 TawkitAI and PodcastGPT! 2:20 Chat with Podcast PodcastGPT - https://www.podcastgpt.ai/ Tawkit AI - https://twitter.com/tawkitapp Weaviate Podcast Search Demo! https://github.com/weaviate/weaviate-podcast-search

Hey everyone! Thank you so much for watching the 61st episode of the Weaviate Podcast! I am beyond excited to publish this one! I first met Rohit at the Cal Hacks event hosted by UC Berkeley where we had a debate about the impact of Semantic Caching! Rohit taught me a ton about the topic and I think it's going to be one of the most impactful early applications of Generative Feedback Loops! Rohit is building Portkey, a SUPER interesting LLM middleware that does things like load balancing between LLM APIs, and as discussed in the podcast there are all sorts of opportunities for this kind of space whether it be routing to tool-specific LLMs, different cost / accuracy requirements, or multiple models in the HuggingGPT sense. It was amazing chatting with Rohit, this was the best dive into LLMOps I have personally been apart of! As always we are more than happy to answer any questions or discuss any ideas you have about the content in the podcast! Check out portkey here! https://portkey.ai/blog Chapters 0:00 Introduction 0:24 Portkey, Founding Vision 2:20 LLMOps vs. MLOps 4:00 Inference Hosting Options 7:05 3 Layers of LLM Use 8:35 LLM Load Balancers 12:45 Fine-Tuning LLMs 17:08 Retrieval-Aware Tuning 21:16 Portkey Cost Savings 23:08 HuggingGPT 26:28 Semantic Caching 32:40 Frequently Asked Questions 34:00 Embeddings vs. Generative Tasks 35:30 AI Moats, GPT Wrappers 39:56 Unlocks from Cheaper LLM Inference

Hey everyone! Thank you so much for watching the 60th Weaviate podcast with Patrice Bourgougnon! Patrice is the creator of WPSolr, integrating AI search capabilities with Wordpress and Woocommerce. Patrice is one of the most active contributors to Weaviate, filing issues and poking holes in new releases! Patrice shared incredible feedback on Weaviate and how he sees the state of Vector Databases and Search! As always, we are more than happy to answer any questions or ideas you have about the content discussed in the podcast! Thanks for watching! Chapters 0:00 Introduction 0:45 Vector Databases and Wordpress 4:50 Weaviate Client Languages 10:00 Inference and Database Container Management 21:30 Business Opportunities for Search in Production 26:40 Testing Search Performance, “Something to sleep on” 30:50 Zero-Shot Model Ability 36:05 Make LLMs Stateful 43:46 Chatbots and Search Boxes 44:55 Mixing Models in Applications 47:00 BM25 vs. Vector Search in RETRO RAG

Hey everyone! Thank you so much for watching the 58th episode of the Weaviate Podcast! I am SUPER excited to welcome Andriy Muylar! Andriy is the Co-Founder of Nomic AI, a company fresh off a $17M series A raise! Nomic has created some incredible products such as Atlas and GPT4All! I was really impressed by Andriy's vision of the state and forecasted evolution of these topics! 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 discussed in the podcast! Integration Tutorial for Weaviate and Nomic AI Atlas! https://docs.nomic.ai/vector_database.html This example worked for me if you want to clone it with the podcast transcription dataset: https://github.com/weaviate/weaviate-podcast-search/blob/main/atlas-visualizer.py Check out Nomic AI here! https://home.nomic.ai/blog Chapters 0:00 Congrats Nomic and Weaviate Integration! 2:35 Welcome Andriy Mulyar! 3:05 Founding Story of Nomic AI 6:55 Understanding Massive Scale Text Data 10:14 Topic Modeling 16:30 Monitoring Model Training

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! Check out Full Stack Deep Learning! https://fullstackdeeplearning.com/ Full Stack Deep Learning on YouTube! https://www.youtube.com/@The_Full_Stack Chapters 0:00 Welcome Charles Frye! 0:52 Charles’ journey into Deep Learning 3:00 Weights & Biases and MLOps 5:30 Retrieval-Augmented Generation Stack 8:58 Data Engines and AI Products 13:50 Fine-Tuning 16:35 Information Retrieval Techniques 20:10 RAG as Tool Use and RETRO 23:33 Gorilla and Fine-Tuned Tool Use 27:36 Text-to-SQL Tool Use 30:46 Generative Data Augmentation 33:05 LLM generated queries for embeddings 38:04 Long-Tail and Data Imbalance 41:45 LoRA LLM Fine-Tuning 44:50 Eigenvectors and Disentaglement 50:00 LLM for Each User 55:00 Embedding Visualization and ML Observability 58:40 GPU Utilization 1:05:05 Discord Q&A Bot App 1:16:10 Data Schema Design 1:21:25 Graph and Vector Databases 1:28:35 Future Directions in AI

Chapters 0:00 Weaviate 1.20!!! 0:40 Multi-Tenancy 35:36 PQ Rescoring 47:20 Re-Ranking, AutoCut, Rank Fusion 58:58 Cloud Monitoring Metrics

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! Check out Ortus here! - https://www.ortusbuddy.ai/welcome Chapters 0:00 Introduction 1:08 Deep Learning YouTube 5:40 DeepMind 9:40 Ortus 19:50 LangChain and LlamaIndex 23:10 Software 2.0 and Full Stack DL 29:20 Training Embedding Models 32:23 Text Chunking for Vector DBs 34:35 Visual Information in YouTube 38:15 Simulating Conversations 42:46 Aidan Gomez Quote on Synthetic Data 44:40 Tree of Thoughts 47:40 New Ortus Features 49:00 Embedding Marketplace 54:00 Personal Organization

Chapters 0:00 Introduction 0:38 Founding Story of Vody 8:15 Custom Embedding Models 12:42 Movie Genre Vectors 13:42 Classification and Contrastive Learning 15:45 Foundation Model Tuning 21:13 Multimodal Generative Models 25:08 Training Embedding Models 33:20 Tabular Data Ranking Models 36:00 RoomGPT 41:36 Diversity in Recommendations 48:25 Future Directions in Multimodal AI 51:15 Open-Source 55:45 Keeping up with Vody!

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! Data Independent: https://www.youtube.com/@DataIndependent Greg Kamradt on Twitter: https://twitter.com/GregKamradt Nesh: https://hellonesh.io/ Colin Harmon on LinkedIn: https://www.linkedin.com/in/coluha/ Colin Harmon Blog: https://colinharman.substack.com/ Colin Harmon at Haystack US 2023: https://www.youtube.com/watch?v=LO3U5iqnTpk Chapters 0:00 Introduction 0:42 Backgrounds 2:43 Defining “LLM Agents” 6:12 Data-Aware LLMs 13:04 Tool Use 13:38 ChatGPT API vs. Marketplace 17:40 Personalized LLMs, LLM for Greg 19:20 PrivateGPT 25:14 AutoGPT and Chain-of-Thought Prompting 32:30 Few-Shot Examples 35:30 Early AI Signals and Open-Source 43:10 Multi-Agent LLMs 47:14 Fine-Tuning and Long Input Lengths 52:20 Greg’s LLM Wishlist Hierarchy 53:15 Keeping up with Greg and Colin!