Vector Search - The New Kid on the Azure AI Search Block

Hello and a fantastic morning to you! We've got something incredibly exciting to share, a recent discovery that has us buzzing with enthusiasm! While it might get a bit theoretical, we assure you it's anything but boring.

Let's dive straight into the heart of the matter—Azure AI Search, formerly known as Azure Cognitive Search, is the ultimate toolkit for crafting search solutions tailored to web, mobile, and enterprise applications. Trust us; we've been in the trenches, and this platform is the real deal. It's not merely a toolkit; it's our secret sauce for mastering the search game. This powerhouse doesn't just index and store data; it executes some serious querying acrobatics, proving its value time and time again.

Now, let's revisit some concepts—embeddings, the unsung heroes in our quest for AI advancement. We've played with these vectors, crafted by the wizards of machine learning models. They're not just about handling text; they are proficient in managing images, audio, and video inputs too! Our language models, especially those exploring the magic of NLP, undergo intense training on massive datasets. It's like a super-smart transformation – any input gets turned into a set of real-number vectors, the intermediary stuff in the retrieval process. Post-training, these models give us those high-dimensional vectors that power our AI adventures.

Behold, this is the spot where the enchantment manifests – the hangout spot for vector queries, Azure's very own embedding space. This zone within our search index is decked out with vector fields sporting embeddings from the same model. We've witnessed the enchantment as machine learning models chart words, phrases, or even entire documents into real-number vectors. It's like giving each piece of data a unique coordinate in this high-dimensional space. It's a nuanced dance where similarities cozy up in clusters, and differences maintain their spatial distance. Sure, the space might seem a bit abstract, but that's the beauty of it – making sense of the dance.

Now, let's talk Vector Search, the detective in our AI playbook. Our search engine gets savvy, deploying fancy algorithms to navigate vectors in the embedding space. It's just like a detective game—finding vectors close to our query vector through nearest neighbor search. Why does this matter? Well, it's the key to understanding how similar different things are. If vectors are closely aligned, it's a clue that the original data shares some serious similarities.

To make this search sleek and efficient, the search engine enlists the help of pals like Hierarchical Navigable Small World (HNSW) and Exhaustive K-nearest neighbors (kNN). HNSW, the rockstar, organizes data points into a sophisticated graph structure with vertices and edges for speedy and scalable searches.

So it starts from the top level, kind of like a treasure hunt. It looks around, trying to find the closest spot to our query vector. When it can't get any closer, it moves down to the next level and keeps going until it hits rock bottom, Level 0. There, it checks out more potential spots, and the size of this list is set by something called efSearch. After all the searching fun, it hands us the top k nearest neighbors it found. Simple, right? HNSW has helped us achieve not only high accuracy and recall, but also low memory and computational costs!

On the other hand, kNN is the detail-oriented one, calculating distances between the query vector and all data points for smaller datasets. It is like the meticulous detective in our crew!

And let's not forget the speedster – Approximate Nearest Neighbor Search (ANN). It is a clever method for swiftly finding nearby items in extensive and high-dimensional data. It doesn't always pick the absolute closest item to the query but focuses on ones that are sufficiently close. They're the adrenaline junkies, sacrificing a tiny bit of accuracy for super-quick retrieval of almost-perfect matches. It's like having a speedy sidekick in our search missions. Guess what? In Azure AI Search, they roll with HNSW as their go-to algorithm for this awesome search adventure.

Azure AI Search is more than a tool for us; it's a game-changer, seamlessly integrating retrieval and generation for a holistic AI experience. This platform has turned our virtual assistant from knowledgeable to intuitively insightful. The magic lies in the blend of sophisticated algorithms and user-friendly design, making it our go-to solution for crafting intelligent, responsive applications.

Stay tuned for next week's insights—XenAIBlog's take on Azure OpenAI's Chat Completion vs the Completion API. Au revoir!