Vector Search: Navigating High-Dimensional Spaces

Understanding the building blocks of efficient vector retrieval

Source: Image from zilliz

Introduction

Vector search enables efficient retrieval and analysis of complex data through the use of vector representations. This article aims to explore the building blocks of efficient vector retrieval, exploring the underlying principles, techniques, and applications of vector search. By understanding the core components of vector retrieval, we can gain insights into how AI systems process and interpret data, leading to advancements in natural language processing, image recognition, recommendation systems, and more.

What is Vector Search?

Vector search is a technique used in information retrieval and recommendation systems. It involves finding similar vectors based on their proximity in a high-dimensional space. Vectors represent data points (such as words, images, or documents), and their closeness indicates relatedness. The process includes indexing vectors, converting user queries into vectors, and performing similarity searches.

Source: Image from microsoft.

By mathematically representing content as vectors, we establish a shared foundation for search scenarios. Even if the original content and the query exist in different media or languages, a search can still find matches within the vector space. Real-world applications include image search engines, personalized product recommendations, and content suggestions on streaming platforms.

Vector Embeddings and Databases

Before we dive into vector search, let’s revisit some key concepts:

Vector Embeddings: Vector embedding is a powerful technique in the field of machine learning and data representation, enabling the conversion of complex data points into numerical vectors. This conversion allows for the capture of essential features and attributes of the data, facilitating similarity calculations, clustering, and classification tasks.

For a deeper exploration of this concept, I recommend the article below.

Decoding Vector Embeddings: Empowering AI with Data Representation

Vector Databases: Vector databases play a pivotal role in modern AI applications, enabling efficient storage, retrieval, and contextualization of data in real time. By understanding the complexities of vector databases, we unlock unprecedented possibilities in semantic understanding and information retrieval.

For a deeper exploration of this concept, I recommend the article below.

Vector Databases: From Embeddings to Intelligence

How Does Vector Search Work?

Vector search operates on the nearest neighbor principle, using algorithms to balance precision and computational efficiency.

Source: Image from microsoft

Here’s an overview of the process, including the steps and algorithm choices:

1. Vectorization: The first step is to convert the items in the dataset into vectors. This is done using a model or algorithm that can represent the items as points in a multi-dimensional space. For text, this could involve using word embeddings like Word2Vec, GloVe, or BERT.
2. Indexing: Once all items are vectorized, they are added to an index. The index is a data structure that makes it efficient to search through the high-dimensional space. Some indexing methods include trees, hash tables, or graph-based structures.
3. Query Vectorization: When a search query is received, it is also converted into a vector using the same method that was used for the dataset.
4. Similarity Search: The query vector is then compared to the vectors in the index to find the nearest neighbors. This is typically done using distance metrics like Euclidean distance, cosine similarity, or Manhattan distance.
5. Ranking: The items corresponding to the nearest neighbor vectors are ranked based on their distance or similarity to the query vector. The closest vectors indicate the most similar items.
6. Retrieval: The top-ranked items are retrieved and returned as the search results.

Practical Implementation

Let’s dive into some real-world scenarios where vector search are applied:

Recommendation Systems: Online platforms like Netflix or Amazon use vector search to recommend movies, products, or services. User and item features are vectorized and compared to suggest items similar to a user’s past preferences or items that are similar to each other.

Image Retrieval: In platforms like Google Photos, vector search enables users to find images by content. Images are converted into feature vectors using deep learning models, and searching for similar images involves finding the nearest vectors in the feature space.

Natural Language Processing (NLP): Search engines and chatbots use vector search to understand and respond to user queries. Words, sentences, or documents are embedded into vectors, and semantic search is performed to find the most relevant content or answers.

Fraud Detection: Financial institutions employ vector search to detect anomalous transactions. Each transaction is represented as a vector, and machine learning models search for patterns that deviate significantly from typical customer behavior vectors.

Facial Recognition: Security systems and social media platforms use vector search to identify or tag individuals in images. Facial features are encoded as vectors, and the search algorithm identifies the person by finding the nearest facial feature vectors in a database.

Conclusion

Vector search transforms raw data into actionable intelligence, enabling AI systems to provide pertinent outcomes across various domains, from image retrieval to personalized recommendations. It’s a pivotal tool that enhances the relevance and precision of results in AI-driven services.

References

1. Robertklee. (n.d.). Vector Search — Azure Ai Search. Vector search — Azure AI Search | Microsoft Learn. https://learn.microsoft.com/en-us/azure/search/vector-search-overview
2. McLane, B. (2023, December 18). What is vector search? A comprehensive guide. DataStax. https://www.datastax.com/guides/what-is-vector-search
3. How to implement vector search in Elasticsearch: A practical guide. RSS. (n.d.). https://www.capellasolutions.com/blog/how-to-implement-vector-search-in-elasticsearch-a-practical-guide
4. Announcing the public preview of Integrated Vectorization in azure ai search. TECHCOMMUNITY.MICROSOFT.COM. (2024, February 3). https://techcommunity.microsoft.com/t5/ai-azure-ai-services-blog/announcing-the-public-preview-of-integrated-vectorization-in/ba-p/3960809