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!

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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!

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...)

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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/

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0:00 Welcome

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!

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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

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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!!