?? The Rising Star of ML Ops: VectorDB - Why They're Outperforming SQL & NoSQL for Embedding Storage

Why VectorDB?

As part of our journey at Vyrill, we're always learning and exploring new things because of our AI-driven focus. One of the exciting things we've come across is VectorDBs. This interesting technology popped up as we were working on a task, and I thought it would be great to share what we've learned with all of you.

Our goal was to better manage ML model embeddings. We wanted to integrate these with the search results from our dataset. But we found out that typical databases like SQL or NoSQL just weren't the right fit for storing these numerical matrix representations. ????

?? Understanding Embeddings

Before diving into VectorDBs, let's demystify embeddings in ML:

Embeddings in machine learning are like a special type of dictionary that help a computer turn complex data, like words or categories, into numbers it can understand. Embeddings allow a computer to grasp the relationships or similarities between data elements.

?? Word Embeddings: Words are converted into numbers, allowing machines to understand the similarity between words like 'cat' and 'kitten'.

?? Entity Embeddings: Categories are translated into numbers, enabling differentiation of types like movies or foods.

?? Graph Embeddings: Relationships within a network are quantified so a computer can understand social network mappings.

??? Image Embeddings: Images are converted into numbers, enabling machines to perceive the similarity between two images.

Embeddings, particularly word embeddings, play a huge role in applications like Langchain and ChatGPT. They help these AI models understand language by turning words into numbers.

? SQL & NoSQL: Why Not?

Why weren't SQL or NoSQL databases suitable for storing embeddings? Although SQL databases excel with structured data and NoSQL with unstructured data, neither can handle the unique characteristics and volumes that come with embeddings. SQL and NoSQL databases are not designed to perform real-time computations and handle high-dimensional vector data, typical in AI applications. They lack the necessary speed and efficiency to calculate vector similarities on the fly and scale to handle voluminous vector datasets.

?? Enter the Game Changer: VectorDB

VectorDBs are emerging stars in the ML Ops universe, specifically designed to store and query vector data like AI embeddings. They accommodate vast vector data volumes and allow fast approximate vector searches, optimizing the storage and retrieval of vector data.

VectorDBs are harnessed for various use cases:

- ?? Semantic search: finding similar meaning documents

- ??? Product recommendations: identifying similar users/items

- ?? Anomaly detection: pinpointing outliers in data

- ??? Document categorization: classifying documents by topic

- ?? Pattern recognition: matching inputs to trained examples

- ?? Forecasting: predicting future data points based on vectors

?? Peering Under the Hood of VectorDB: A Simplified & Technical Guide

Let's envision VectorDBs as large libraries ?? where books symbolize your data. Librarians (database algorithms) break down books into smaller chapters (subvectors), encoding them compactly ?? while retaining their essence.

When a reader (a query) ?? seeks a chapter, the librarians use an efficient cataloging system (indexing) ??? for quick access, sometimes even employing electronic sorting (GPU optimizations) ??.

In technical lingo, VectorDBs index and query vector data efficiently ??. Vectors are encoded using methods like product quantization. The vectors are split into small subvectors, each assigned to a cluster ??.

These vectors are indexed using advanced data structures, enabling speedy location ?? of similar vectors for a query. Some VectorDBs optimize index building for GPUs to hasten searches ?.

By amalgamating intelligent data encoding, advanced indexing, and computation optimizations, VectorDBs facilitate rapid searches, even amid sizable vector datasets. ????

?? In summary

?? What are VectorDBs?

• Databases for storing and querying vector data like AI embeddings ??
• Allow fast approximate vector searches ??
• Optimize storage and retrieval of vector data

?? Use cases:

• ?? Semantic search - find similar meaning docs ??
• ?? Product recommendations - similar users/items ?
• ?? Anomaly detection - identify outliers in data ??
• ??? Document categorization - classify docs by topic ???
• ?? Pattern recognition - match inputs to trained examples ???
• ?? Forecasting - predict future points based on vectors ??

?? How they differ from SQL & NoSQL:

• ?? Built specifically for vector data
• ? Calculate vector similarities on the fly
• ?? Scale to handle large vector datasets
• ?? Blazing fast response times

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#AI #ML #VectorDB #llm #database