Thank you so much for watching the 42nd episode of the Weaviate Podcast! Ethan Steininger is the founder of Mixpeek, an intelligence layer that sits on top of your S3 bucket, so you can search and analyze unstructured data at scale. Ethan has also created Collie with the headline of "Enter your website and Collie will fetch every asset, then give you an embedded search bar that wows your users". Ethan began the podcast by describing his background at MongoDB and integrating the database with full text search functionality. Ethan then presented the founding vision of Mixpeek and some of the most outstanding problems with adapting the latest AI technologies to solve business problems. This lead us to discuss a massive range of topics around the AI landscape from the Llama / Alpaca models to ChatGPT Plugins, the paradigm shift in coding and serverless GPUs. I really enjoyed speaking with Ethan about all these things, I hope you enjoy listening! We would more than happy to discuss any ideas you have with you or answer any questions, thanks again for watching! Chapters 0:00 Welcome Ethan Steininger! 0:50 Entry into Search from MongoDB 6:45 Founding Vision of Mixpeek 10:15 Data Ingestion 13:45 ChatGPT Plugins 16:25 Paradigm shift in Coding with GPT-4 18:54 Alpaca Models 22:42 Tuning LLMs with Retrieval 31:45 Adding Structure to Code Repo Search 35:06 Re-Ranking / Learning-to-Rank 43:30 AGI Monopoly 49:10 Hybrid Search! Zero-Shot + BM25 54:20 Open-Source Business 59:35 Serverless GPUs 1:11:18 Ethan’s Advice for Stress Management 1:13:00 Existential AI Fear Links: Mixpeek - https://mixpeek.com/ Collie - https://collie.ai/ An Open-Source, Personalized Generative Model Framework - https://esteininger.medium.com/an-open-source-personalized-generative-model-framework-6df865de51bf Teaching GPT-4 to write code from research papers - https://esteininger.medium.com/teaching-gpt-4-to-write-code-from-research-papers-889a880fb4f0 The Need for an AI Content Verification Layer - https://esteininger.medium.com/the-need-for-an-ai-content-verification-layer-10be9379b354 Building the ML Stack of the Future - https://esteininger.medium.com/building-the-ml-stack-of-the-future-d66c8a8b566a Vertical Integration is Key to Winning the AI Race - https://esteininger.medium.com/vertical-integration-is-key-to-winning-the-ai-race-44c8e4bd3b30

Chapters 0:00 Welcome Dennis Xu! 0:30 Founding Vision of Mem 4:18 Personalized Embeddings 6:02 GPT-4, How will this change everything? 11:00 Writing code with LLMs 13:18 Embeddings at Mem 17:10 Structure in Vector Search 19:10 Zero-Shot vs. Fine-Tuned Models 25:05 Ranking Models and LLM Distillation

Chapters 0:00 Weaviate 1.18!!! 0:32 Bitmap Indexing! 11:40 HNSW PQ 25:33 Cursor API 30:03 Filters in Hybrid Search 32:55 WAND Scoring 40:35 Replication 49:10 Building a Database in Golang 1:00:55 Thank you!

Check out the website here! https://openverkiezingen.nl/

Hey everyone! Thank you so much for watching the 38th episode of the Weaviate podcast! This episode features Leo Boystov, an expert in Information Retrieval technology! We discussed a very wide range of topics from an overview of IR methods such as BM25, Neural Bi-Encoder and Cross-Encoder rankers, and a super exciting new work Leo has co-authored on using Large Language Models to generate training data for Neural Ranking models titled "InPars-Light: Cost-Effective Unsupervised Training of Efficient Rankers." We also discussed Leo's work on Non-Metric Space Search, the challenge of long document ranking, Robustness in Generalization Testing, and ended with some thoughts on Hybrid Rank Fusion. I really hope you enjoy the podcast, more than happy to answer any questions you have or clarify anything! In-Pars Light: Cost-Effective Unsupervised Training of Efficient Rankers - https://arxiv.org/abs/2301.02998 Google Scholar Leo Boystov - https://scholar.google.com/citations?... Chapters 0:00 Introduction 1:08 Information Retrieval Research 25:20 Ranker Inference Requirements 40:40 Non Metric Space Search 52:38 Code Libraries for IR Research 59:40 Long Document Ranking 1:07:00 Robustness Generalization 1:15:40 Hybrid Rank Fusion

Hey everyone! Thank you so much for watching the 37th episode of the Weaviate podcast! This episode discusses some of the ideas behind GPT Index. GPT Index presents really exciting ideas about how we use LLMs to index our data and then traverse these data structures. We began the podcast by discussing the origins of the tool and the ideas behind the Tree Index. We then discussed generalizing these trees to graphs and whether we are headed to the Knowledge Graph 2.0. Another really interesting topic we covered is the inference cost of building and traversing LLM indices like this! I really hope you enjoy this podcast I think these are some of the most cutting edge ideas in AI and Search! Check out GPT Index (now LlamaIndex here - https://gpt-index.readthedocs.io/en/l...) Chapters 0:00 Introduction 0:18 Origin Story of GPT Index 2:22 GPT Tree Index 5:53 Search Examples - Podcast Clips 11:22 Knowledge Graph 2.0? 16:05 LLM Writing Data to DB 20:18 Weaviate Classes and Index Hierarchy 23:53 Subindices vs. Tool Use 28:50 Inference Requirements for GPT Index 35:53 Design of GPT Index 37:40 Impact of Cheaper LLMs for this 40:02 Name Change for GPT Index? 42:04 Llama Hub 45:07 Relationship in Software Stack 48:15 Extension to Multimodal, e.g. Vision-Language

Hey everyone! Thank you so much for watching the 36th episode of the Weaviate podcast! This episode continues on the marriage between LLMs and Semantic Search, welcoming back Weaviate CEO and Co-Founder Bob van Luijt! Enter LangChain and its creator, Harrison Chase, providing the glue between LLMs and tools, such as semantic search. LangChain provides a set of abstractions around chaining multiple language model calls with different prompts, strategies for overcoming the 4096 token limit, and connecting LLMs with their tools. LangChain Hub is a collection of these chains if you want to check it out yourself! Huge thank you to Harrison and Bob for joining the podcast, this was such an information packed podcast with some great predictions for the future of LLMs + Vector Databases!   Check out LangChain here! https://langchain.readthedocs.io/en/latest/ Chapters 0:00 Welcome 0:14 Origin Story of LangChain 1:27 What are LLM Chains? 4:00 Adding Weaviate Search 7:30 LLM Orchestration and Tool Use 11:24 Extension to Multi-Modal 14:00 Natural Language Interaction with Software 20:36 Will Prompt Engineering Last? 21:00 More on Tool Use 25:47 Favorite Prompts 29:54 Temperature in LLMs 31:00 Reasoning and Knowledge 32:50 LLM as Router 35:50 Model Diversity 39:45 No GPUs before PMF 41:35 Virality of LangChain 43:40 Future of LangChain

This podcast debuts a huge new release from Weaviate... the generate module! The generate module is a new API in Weaviate that facilitates passing YOUR data from the Weaviate database to ChatGPT. This enables ChatGPT to become knowledgeable about your particular business or interests! Here is a great snippet from Bob around the 43 minute mark that describes how this kind of LLM technology is changing the world of database technology, "Yeah so, what I’m really excited about and this is something that it’s just so funny right because if you see it, you have this huge epiphany. I’ve always been thinking of working with these models on input. Right so that they we can solve the problem of not having 100% keyword based search, so that we can have semantic search, image search, and those kind of things. I saw that as this beautiful uniqueness coming from a vector search engine or vector search database. So now what we’re adding is not only the input in the database but the output. So we’re basically saying we’re going to give you relevant information coming from the database, but that’s not per se stored inside the database. That’s new! I mean, just think about the most used databases in the world, Postgres, or MySQL, those kind of databases. It only outputs what’s in there. It makes sense. Because that’s how you use it. But now what we’re saying, is that’s fine you can do that, but also it can give you information, give you data that’s generated based on a task or prompt that you’re giving it. Having databases that make sense of it at input and generate new relevant content if that’s something you want as a user is amazing, and it’s just getting started. We should do this podcast like a half a year from now again and see how it's evolved because this is just too exciting man.". I really hope you enjoy the podcast, we are more than happy to answer any questions or help you get started with Weaviate!

Chapters 1:00 Introduction 1:26 Why does the world need yet another database? 3:57 Memory Allocations 9:40 Delayed Decoding 16:05 SIMD 22:04 Demo Time! 24:38 Mad at Go? 26:00 Audience Questions

I am so excited to host Dmitry Kan on the Weaviate Podcast!! Dmitry is a world class expert on emerging trends in search technology! This podcast reflects on Dmitry's latest characterization of the field, the Neural Search Pyramid. This describes the different components involved with building a Deep Learning-powered Search experience from the Approximate Nearest Neighbor index algorithms, to Database functionality,  LLM orchestration, Vectorization optimization, Data preprocessing, User Interface, and many more! We also concluded the podcast with an interesting debate around renaming "Vector Search" to something else that reaches a broader audience. I really hope you enjoy the podcast, thank you so much for listening! Please see the links below to Dmitry's recent content and the Weaviate Podcast Search App! Links: Dmitry's Keynote at Haystack Europe 2022, Where Vector Search is Taking Us - https://www.youtube.com/watch?v=2o8-dX__EgU Dmitry's latest blog post on Neural Search Frameworks: A Head-to-Head Comparison - https://dmitry-kan.medium.com/neural-search-frameworks-a-head-to-head-comparison-976aa6662d20. Search through this episode of the Weaviate Podcast! - https://github.com/weaviate/weaviate-podcast-search Chapters 0:00 Neural Search Pyramid Visual 0:40 Weaviate Podcast Search! 1:35 Welcome Dmitry!! 2:02 Where is Vector Search taking us? 5:40 Retail and Search 11:02 Neural Search Frameworks 17:10 Data Preprocessing, e.g. PDF to Text / OCR 24:15 Vectorizing Data 31:18 ANN Index and Database Entanglement 37:25 Hardware Accelerators for Vector Search 46:02 Reader Layers, Q&A, Ranking, … 51:20 ChatGPT in Neural Search Frameworks 1:03:40 Search Result Summarization with ChatGPT 1:12:55 User Interfaces for Neural Search 1:26:30 Renaming “Vector Search” 1:46:10 Thank you Dmitry!!