Weaviate Podcast

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

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

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!!

Weaviate podcast #33.

Thank you so much for watching the 33rd Weaviate Podcast! This episode features one of the heroes of Deep Learning for Search, Nils Reimers! Nils' work on SentenceBERT is one of the foundational works for applying Deep Representation Learning to text search. This is the idea that personally inspired me to work in this field. Having seen the successes of Contrastive Representation Learning for Computer Vision, I was mind-blown by the possibility of this for NLP and text search. In addition to the scientific foundation, the software development of the Sentence Transformers library and BEIR benchmarks has been enormously impactful! It was an honor getting to ask Nils the questions I have about these things, from the role of Data Quality to Intent, Sparse Vectors, Long Document Encoding, Distribution Shift, and many more. I really hope you enjoy the podcast! We are so excited about the Cohere Multilingual embedding model and can't wait to see what else comes out of Cohere and their amazing team!

Weaviate Podcast #32.

Weaviate Podcast #31. 

Weaviate 1.17!! This is a massive release for Weaviate, debuting Replication, Hybrid Search, BM25, Faster Startup and Import Times, as well as other fixes! Replication and Hybrid Search are two massive features for Weaviate, and we really hope you enjoy the description of them from the podcast. Please also check out the Weaviate 1.17 release blog post for more information as well - https://weaviate.io/blog/2022/12/Weaviate-release-1-17.html!  

This is also a very special podcast as we welcome Parker Duckworth for the first time to the podcast! Parker gave an excellent explanation of Replication and unpacked some of the questions we are seeing around Ref2Vec!  Thank you so much for listening to the podcast! Please check out the newest version of Weaviate!  

Chapters 

0:00 Weaviate 1.17! Welcome Parker! 

0:28 From Italy to 1.17 

2:04 Replication work in Italy 

3:58 Replication Details 

6:28 Use Cases of Replication 

13:12 Product Engineering 

16:24 Hybrid Search 

21:30 Open Question around Hybrid Search 

23:15 Rank Fusion 24:00 BEIR Benchmarks 

27:28 What is Ref2Vec? 

29:08 Bipartite Graph Ref2Vec Example 

29:30 Graphs in Weaviate 

34:25 Ref2Vec Cascading Updates 

37:45 Custom Aggregation Functions in Ref2Vec 

39:08 Adding Recency Bias in Ref2Vec 

41:18 Startup Time Improvements 

41:50 Batch Latency Improvement

Weaviate Podcast #30. 

Chapters

0:00 The future of search!

Weaviate Podcast #29. Hey everyone, thank you so much for watching another episode of the Weaviate podcast! This episode features Matthijs Douze, one of the most talented and accomplished scientists we've hosted on the Weaviate podcast! Matthijs has pioneered the use of Product Quantization to compress vector representations and enable even faster and more efficient approximate nearest neighbor vector search. Matthijs told an incredible story about the history of this research, from searching from SIFT vectors for Computer Vision Search applications like real-time CD Cover album search to the problems facing modern IVF-PQ systems and the use of PQ in graph-based HNSW search. This is also a very special episode as Abdel Rodriguez makes his debut on the Weaviate podcast to discuss Weaviate's efforts in integrating PQ support and the unique challenges with this algorithm and the incremental updates required for a Vector Database. On this topic, Etienne Dilocker also returned to discuss the topic of Vector Database vs. Library with Matthijs, who is one of the lead developers of the Faiss library. This was a really information-heavy podcast, please don't hesitate to ask us any questions or present any of your ideas! Thanks again for listening!