Weaviate Podcast

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!

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: 1. Understanding of Summary Indexing and the Prompts (as well as Prompt Chains) used to build them. 2. Continued development of LLM-generated data for search -- creating (full text, summary) pairs gives you (1) data to build a summary index with as mentioned, (2) data to compare different embedding models with, and (3) data to train your own embedding model. 3. Tournament style evaluation with human annotators -- the top 5 retrieved texts from one model are concatenated with the top 5 from another model, these 10 are given to human annotators to pick 5 and this is how the authors are reporting the performance of their models rather than traditional benchmarks. This m ay be a more productive evaluation technique for most real world search applications. Thank you so much for watching, here are some links mentioned in the video! Retrieving Texts based on Abstract Descriptions: https://arxiv.org/abs/2305.12517 Weaviate Blog - Combining LangChain and Weaviate: https://weaviate.io/blog/combining-langchain-and-weaviate Weaviate Blog - Generative Feedback Loops: https://weaviate.io/blog/generative-feedback-loops-with-llms Jerry Liu in Llama Index Blog - A New Document Summary Index for LLM-powered QA Systems: https://medium.com/llamaindex-blog/a-new-document-summary-index-for-llm-powered-qa-systems-9a32ece2f9ec Learning to Retrieve Passages without Supervision (Spider): https://arxiv.org/pdf/2112.07708.pdf Weaviate Blog - Analysis of Spider - https://weaviate.io/blog/research-insights-spider Chapters 0:00 Introduction 0:13 Quick Overview 7:30 How to use in Weaviate! 7:50 Background 12:08 Motivation 14:20 Prompts Used 18:14 More Details of training 21:12 Human Evaluation Study 22:40 My Takeaways from the Paper

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! Check out Kapa here! https://www.kapa.ai/ Chapters 0:00 Welcome Emil and Finn! 0:42 Origin Story of Kapa 2:08 Data Ingestion 5:10 Data Cleaning 6:20 Slack / Discord / Forum Ingestion 9:05 Testing Models on Support QA 11:14 Selling Kapa to Weaviate and friends 12:37 Hallucinations in LLMs 14:06 Trends in Open-Source LLMs 15:20 Long Input LLMs (32K, 65K, 100K, …) 16:54 Retrieval-Augmentation for Long Input LLMs 18:08 Fine-Tuning LLMs 23:00 As much or as refined content as possible? 24:40 Adding Docs from Integrations 26:15 Generative Feedback Loops 29:00 What in AI excites you the most?

Professor Laura Dietz discusses Neurosymbolic Search, Entity Linking, Entity Re-Ranking, Knowledge Graphs, and Large Language Models. They explore the potentials of bias in using LLMs for relevance judgments and the complexities of merging neural technologies with symbolic systems in search technology. The conversation delves into enhancing search algorithms, filtered vector search, entity linking with context-specific models, and the nuances of relevance judgments in research papers.

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! Check out Unstructured here! https://www.unstructured.io/ Chapters 0:00 Welcome Brian!! 0:27 What is Unstructured? 5:42 Why now? New Advancements in Unstructured 8:02 Thoughts on Data Connectors Hub 10:55 PDFs to Weaviate with Unstructured 13:53 State-of-the-Art in OCR and Document Parsing 16:10 How to get the data from Weaviate.io? 18:06 Foundation Models from Unstructured 20:45 Evaporate-Code+ 23:15 CSV, Parquet, JSON transformations in Staging 25:08 Cleaning Bricks 28:02 Visual Document Examples 30:45 Text Chunking with Metadata 33:25 Knowledge Graphs with Goldman Sachs example 39:10 LLM Hallucinations 42:10 Announcements from Brian!