Mastering Vector Databases: Product & Binary Quantization, Multi-Vector Search

Ever wondered how AI systems handle images and videos, or how they make lightning-fast recommendations? Tune in as Nicolay chats with Zain Hassan, an expert in vector databases from Weaviate. They break down complex topics like quantization, multi-vector search, and the potential of multimodal search, making them accessible for all listeners. Zain even shares a sneak peek into the future, where vector databases might connect our brains with computers!

Zain Hasan:

• LinkedIn
• X (Twitter)
• Weaviate

Nicolay Gerold:

• ⁠LinkedIn⁠
• ⁠X (Twitter)

Key Insights:

• Vector databases can handle not just text, but also image, audio, and video data
• Quantization is a powerful technique to significantly reduce costs and enable in-memory search
• Binary quantization allows efficient brute force search for smaller datasets

• Multi-vector search enables retrieval of heterogeneous data types within the same index
• The future lies in multimodal search and recommendations across different senses
• Brain-computer interfaces and EEG foundation models are exciting areas to watch

Key Quotes:

• "Vector databases are pretty much the commercialization and the productization of representation learning."
• "I think quantization, it builds on the assumption that there is still noise in the embeddings. And if I'm looking, it's pretty similar as well to the thought of Matryoshka embeddings that I can reduce the dimensionality."
• "Going from text to multimedia in vector databases is really simple."
• "Vector databases allow you to take all the advances that are happening in machine learning and now just simply turn a switch and use them for your application."

Chapters

00:00 - 01:24 Introduction

01:24 - 03:48 Underappreciated aspects of vector databases

03:48 - 06:06 Quantization trade-offs and techniques

• Various quantization techniques: binary quantization, product quantization, scalar quantization

06:06 - 08:24 Binary quantization

• Reducing vectors from 32-bits per dimension down to 1-bit
• Enables efficient in-memory brute force search for smaller datasets
• Requires normally distributed data between negative and positive values

08:24 - 10:44 Product quantization and other techniques

• Alternative to binary quantization, segments vectors and clusters each segment
• Scalar quantization reduces vectors to 8-bits per dimension

10:44 - 13:08 Quantization as a "superpower" to reduce costs

13:08 - 15:34 Comparing quantization approaches

15:34 - 17:51 Placing vector databases in the database landscape

17:51 - 20:12 Pruning unused vectors and nodes

20:12 - 22:37 Improving precision beyond similarity thresholds

22:37 - 25:03 Multi-vector search

25:03 - 27:11 Impact of vector databases on data interaction

27:11 - 29:35 Interesting and weird use cases

29:35 - 32:00 Future of multimodal search and recommendations

32:00 - 34:22 Extending recommendations to user data

34:22 - 36:39 What's next for Weaviate

36:39 - 38:57 Exciting technologies beyond vector databases and LLMs

vector databases, quantization, hybrid search, multi-vector support, representation learning, cost reduction, memory optimization, multimodal recommender systems, brain-computer interfaces, weather prediction models, AI applications