What Is Vector Search? Everything You Need to Know

Vector search is a powerful information retrieval technique that uses mathematical representations of data called vectors. Vectors find similar items based on semantic meaning rather than exact matches.

The field of Information Retrieval is as old as the history of computers, and vector search has been used for over 20 years. However, it’s lately been enjoying a huge spike in usage. In the age of generative AI and big data, vector search has become crucial for several applications. These include (but aren't limited to):

• Recommendation systems
• Machine learning models
• Image recognition
• Natural Language Processing (NLP)
• Anomaly detection
• Generative AI

What makes vector search work so well is its inherent capture of context and meaning, and its ability to find approximate matches rather than just exact matches. This allows users to find relevant information even when their query doesn't precisely match the stored data. Another huge advantage is that vector search can be used on many kinds of data, including text, images, audio, structured data, even genomes.

If you're curious how vector search works and how it can help your business, you're in the right place.

How does the vector search engine work? Understanding its inner mechanisms will help you gain the most value from the vector space.

You might remember vectors from your high school algebra class. In computer science, vectors are simply lists of numbers, where each number represents a different characteristic or dimension.

While the vectors you studied in school probably had two or three dimensions, modern vector-based systems often use hundreds or thousands of dimensions. This might sound complex, but you can think of it as an extension of the three-dimensional world we're familiar with. Imagine adding more and more characteristics to describe something, and each of these becomes a new dimension in your vector.

For example, a vector representing the word "cat" might look something like this:

[0.2, -0.5, 0.8, 0.1, -0.3, ...]

While these numbers may seem abstract, they capture various semantic aspects of the concept "cat" that allows for mathematical comparison with other vectors. The word “feline” ends up with a vector very similar to the word “cat,” because the words used near them will be very similar.

Vector search, at its core, is a method of finding similar items in a large dataset by comparing their vector representations. Unlike traditional keyword-based search – which looks for exact matches of words or phrases – vectors seek to understand the underlying meaning or context.

It turns out that in converting text to vectors, the vectors include more of the meaning than other representations, because words used in combination with other words provide the context that can find the meaning behind the words.

Converting data to vectors is the first step in vector search. This usually occurs whenever you add new data to a system. When a user makes a query, that query is also converted into a vector. The search then involves finding the items in the dataset whose vectors are most similar to the query vector.

This approach allows for more nuanced searching. For example, in a text-based vector search:

• A search for "car" might also return results about "automobile" or "vehicle", even if those exact words aren't used.
• A query about "data analysis techniques" might return relevant results about "statistical methods in big data.”

Vector search is a key technology enabling smart data fabric architectures.

Traditional keyword-based search and vector search differ in their approach and capabilities:

1. Matching method: Keyword search looks for exact matches of words or phrases. Vector search looks for similar meanings or concepts by comparing the direction and magnitude of different vectors, and is always approximate rather than exact.
2. Understanding context: Keyword search often struggles with context and synonyms. Vector search can understand context and find semantically related content.
3. Handling ambiguity: Keyword search may return irrelevant results when words have multiple meanings. Vector search can often disambiguate based on the overall context of the query.  
4. Multilingual capabilities: Keyword search typically requires separate indices for different languages. Vector search can often find relevant results across languages if trained on multilingual data.
5. Handling misspellings and variations: Keyword search might miss results due to slight misspellings. Vector search is more robust to variations and can often find relevant results despite minor errors.

Vector generation, also known as embedding, is a crucial step in vector search. Different techniques are used depending on the type of data:

1. Text data: Word embeddings (e.g., Word2Vec, GloVe) convert individual words to vectors, while sentence or document embeddings (e.g., BERT, Universal Sentence Encoder) create vectors for larger pieces of text. These models are typically pre-trained on large volumes of text and can be fine-tuned for specific domains.
2. Image data: Convolutional Neural Networks (CNNs) are often used to generate vector representations of images. These networks learn to extract relevant features from images during training.
3. Audio data: Techniques like Mel-frequency cepstral coefficients (MFCCs) or deep learning models can convert audio into vector representations.
4. Multimodal data: Some advanced models can create vectors that represent combinations of different data types, such as images with captions.

There are also more advanced and specialized types of data that can be represented as vectors. This includes genomic and proteomic information in biology, chemical structures, and graph relationships.

The dimensions of a vector in the context of search represent different features of the data:

1. Semantic features: Each dimension might correspond to a particular semantic concept or attribute of the data.
2. Learned representations: In many cases, especially with deep learning models, the exact meaning of each dimension is not explicitly defined, but is learned by the model during training. The term "latent semantic model" used to be in vogue and is essentially a learned representation.
3. Contextual information: For text data, dimensions often capture contextual usage patterns of words or phrases.
4. Abstract concepts: Some dimensions might represent abstract concepts that are not easily interpretable by humans, but are useful for the model's understanding of the data.  These may not be concepts in the sense you are used to, for example a common set of underlying structure across different images is a concept that a machine will see that humans won’t.

While having more dimensions can typically capture more information and allow finer distinctions, it also increases computational requirements.

Therefore, there's often a balance to be struck between the number of dimensions and practical considerations like search speed or storage requirements.

A vector search engine converts data (such as text, images, or audio) into numerical vectors and finds similar items by measuring the distance between these vectors in high-dimensional space.

Unlike traditional semantic search, which relies on keyword matching and statistical techniques, vector search can capture more nuanced relationships and similarities between items, allowing for more accurate and contextually relevant results, especially for complex queries or multimedia content.

Vector search relies on various algorithms to find similar vectors in high-dimensional spaces. Some of the most common approaches include:

1. Exact Nearest Neighbor (NN) search: This method finds the exact closest vectors to a query vector. While accurate, it can be computationally expensive for large datasets.

2. Approximate Nearest Neighbor (ANN) search: ANN algorithms trade off some accuracy for significant speed improvements. Popular ANN algorithms include:

   • Locality-Sensitive Hashing (LSH)
   • Hierarchical Navigable Small World (HNSW) graphs
   • Product Quantization (PQ)
3. Tree-based methods: Algorithms like KD-trees or Ball trees organize vectors in a tree structure for faster searching. These can be effective for lower-dimensional data but may struggle with high-dimensional vectors.
4. Graph-based methods: These algorithms construct a graph where nodes are vectors and edges connect similar items. Examples include HNSW (mentioned above) and Navigable Small World (NSW) graphs.

Cosine similarity is vital in vector search because it efficiently measures the similarity between vectors based on their orientation rather than magnitude, allowing for accurate comparisons in high-dimensional spaces.

This makes it particularly effective for tasks like semantic search, recommendation systems, and document clustering, where the relationship between items is more important than their absolute values.

Key points about cosine similarity:

1. Range: Cosine similarity values range from -1 to 1, where:

   • 1 indicates vectors pointing in the same direction (most similar)
   • 0 indicates orthogonal (unrelated) vectors
   • -1 indicates vectors pointing in opposite directions (most dissimilar)
2. Magnitude independence: Cosine similarity focuses on the direction of vectors, not their magnitude, making it useful for comparing documents of different lengths.
3. Calculation: The formula for cosine similarity is: cos(θ) = (A · B) / (||A|| * ||B||)Where A · B is the dot product of vectors A and B, and ||A|| and ||B|| are their magnitudes.
4. Efficiency: Cosine similarity can be computed efficiently, especially when vectors are normalized.

Cosine similarity is particularly important because:

• It captures semantic similarity well, especially for text data.
• It's computationally efficient, allowing for fast similarity calculations in high-dimensional spaces.
• It's intuitive to understand and interpret.

Imagine you're a chef in a bustling kitchen, and each recipe is a vector in a vast "flavor space." The dimensions of this space include sweetness, saltiness, spiciness, umami, and so on. Your signature dish is like a particular point in this flavor space, and you want to find similar recipes or create fusion dishes that complement your style.

Cosine similarity is like a special "flavor compass" that measures how closely other recipes align with your signature dish's flavor profile. A recipe very similar to yours would point in nearly the same direction on the flavor compass (high cosine similarity, close to 1).

A somewhat similar dish might point in a related, but not identical direction (moderate cosine similarity, around 0.7). A completely different type of cuisine would point in a perpendicular direction on your flavor compass (cosine similarity of 0, indicating no flavor relation). Importantly, the intensity of flavors (vector magnitude) doesn't matter - a mild and an intense curry could be very similar in terms of their flavor direction.

In this culinary analogy, a vector search engine acts like an incredibly efficient sous chef. It can instantly consult this flavor compass for every recipe in a vast global cookbook, quickly finding dishes that harmonize with your signature flavor profile, regardless of their origin or intensity.

While cosine similarity is widely used, several other distance metrics can be employed in vector search:

1. Euclidean Distance: Measures the straight-line distance between two points in Euclidean space. This is useful when the magnitude of the vectors is important.
2. Manhattan Distance: Also known as L1 distance or city block distance. This calculates the sum of the absolute differences of the coordinates, and is useful in certain grid-like problems or when dealing with sparse data.
3. Dot Product: Simple multiplication of corresponding elements in two vectors often used when vectors are normalized.
4. Jaccard Similarity: Measures similarity between finite sample sets, which is useful for binary or categorical data.
5. Hamming Distance: Measures the number of positions at which corresponding symbols in two vectors are different, often used with binary data or for error detection.

Vector search has become increasingly important across various industries due to its ability to understand context and find relevant information beyond simple keyword matching.

• Medical literature search: Researchers can find relevant studies even when terminology varies.
• Patient record matching: Identifying similar patient cases for personalized treatment plans.
• Drug discovery: Finding chemical compounds with similar properties or effects.

Learn more about Healthcare & Life Sciences

• Product recommendations: Suggesting items based on semantic similarity rather than just category matching.
• Visual search: Allowing customers to find products similar to an uploaded image.
• Fraud detection: Identifying unusual patterns in transaction data.

Learn more about E-commerce and Retail

• Risk assessment: Analyzing financial documents to identify potential risks.
• Market trend analysis: Finding correlations between diverse economic indicators.
• Customer segmentation: Grouping clients based on complex behavioral patterns.

Learn more about Financial Services

• Content recommendation: Suggesting movies, music, or articles based on user preferences.
• Plagiarism detection: Identifying similar content across large databases.
• Audio and video search: Finding specific moments in media based on transcripts or visual features.

• Quality control: Detecting anomalies in production data.
• Inventory management: Optimizing stock levels based on complex demand patterns.
• Predictive maintenance: Identifying equipment likely to fail based on sensor data patterns.

Learn more about Manufacturing and Supply Chain

• Log analysis: Detecting unusual patterns in system logs for security threats.
• Code similarity search: Finding similar code snippets for debugging or optimization.
• Network traffic analysis: Identifying potential security breaches based on traffic patterns.

As vector search gains prominence in various industries, a range of technologies and platforms have emerged to support its implementation.

Vector Databases: A vector database is designed for storing and querying vector data efficiently. Examples include Faiss (Facebook AI Similarity Search) and Annoy (Approximate Nearest Neighbors Oh Yeah).

Machine Learning Frameworks: TensorFlow and PyTorch offer libraries for creating and manipulating vector embeddings. These frameworks can be used to train custom embedding models for specific domains.

NLP Libraries: Libraries like spaCy and Hugging Face's Transformers provide pre-trained models for text embedding. These can be used to generate vector representations of text data for search applications.

Cloud-based Vector Search Services: Major cloud providers offer managed vector search services that can be integrated into applications. These services often provide scalable infrastructure for large-scale vector search operations.

Open-source Search Engines: Some traditional search engines now offer vector search capabilities. These can be useful for organizations looking to add vector search to existing search infrastructure.

Web Search Engines: Major search engines like Google have incorporated vector search techniques to improve semantic understanding of queries. They use neural network models to generate vector representations of both queries and web pages.

E-commerce Search: Online retail platforms use vector search to enhance product discovery, often combining it with traditional keyword search for optimal results.

Enterprise Search Solutions: Many enterprise search platforms now offer vector search capabilities. These solutions often use hybrid approaches, combining vector search with traditional search methods.

Database Management Systems: Some relational database systems have started to incorporate vector search capabilities, allowing for similarity searches alongside traditional SQL queries. This integration enables flexible querying of structured and unstructured data within the same system.

Cloud Data Platforms: Cloud providers are increasingly offering vector search as part of their services. This allows for seamless integration of vector search capabilities into cloud-based applications and data workflows.

Vector search and vector representation have emerged as a game-changing technology in the world of information retrieval and data analysis. By representing data as high-dimensional vectors, vector search enables more intuitive, context-aware, and semantically rich search experiences.

Throughout this piece, we've uncovered the fundamental concepts behind vector search and its applications across various industries. We've seen how vector search excels in understanding context, handling multilingual queries, and finding relevant results even when exact keyword matches aren't present.

However, we've also recognized the computational demands and the complexities of managing high-dimensional data at scale. This is where InterSystems IRIS stands out as a powerful solution. InterSystems IRIS offers a comprehensive, unified platform that seamlessly integrates vector search capabilities with traditional data management features.

Key advantages include:

1. Seamless Integration: Vector search capabilities are fully integrated into the InterSystems IRIS platform, allowing for easy combination with SQL queries and other data processing tasks.
2. Scalability: InterSystems IRIS is designed to handle large-scale vector search operations, supporting distributed computing for enhanced performance.
3. Flexibility: Support for various embedding techniques and distance metrics makes InterSystems IRIS versatile for different vector search applications.
4. Advanced NLP Integration: InterSystems IRIS can be combined with sophisticated natural language processing techniques for improved query understanding and result relevance.
5. Domain-Specific Customization: The platform supports custom embedding models, allowing for tailored solutions in specialized fields like healthcare or finance.
6. Unified Data Management: InterSystems IRIS eliminates the need for multiple separate systems, reducing complexity and potential data inconsistencies.