Searching for something you need to get your job done can often feel like digging through an endless mountain of knowledge. Unfortunately, most searches rely on keyword matches, so you need to know exactly what you’re looking for to locate it quickly and efficiently. Without precise terminology, you waste countless hours unearthing irrelevant information. When finding a particular piece of content is required to complete your work, your only option is to keep excavating data with keyword search. That’s where vector search comes in!
What is Vector Search?
Vector search is a technique that leverages the power of vectorization to represent data points in a high-dimensional space. Traditional search doesn’t reliably interpret the context and meaning of words, leading to challenges with factors such as synonyms. Using vector representations allows it to grasp the semantic similarity between words and return more contextually relevant and precise results.
Instead of relying on exact keyword matches, it considers the semantic relationships between words and concepts, enabling it to find documents that might be missed by traditional search engines. Keyword search is also incapable of identifying data contained in a graphic, video file, or audio clip. In the context of enterprise search, videos, images, graphs, audio files, and other data types are transformed into numerical vectors. These vectors capture semantic information about the content, making it possible to compare and retrieve similar items efficiently.
There are typically 4 components:
- Vector embeddings that capture the key characteristics of the original object
- Distance metrics that represent “nearness” between vectors
- Search algorithms that solve problems
- A database that holds the vectors and supports the vector search with indexes
Vector search is multimodal.
Modern enterprises deal with a variety of data types, from text documents to images and videos. Vector search can handle these diverse data types seamlessly, allowing users to find information using different modalities.
Multimodal vector search is made possible through artificial intelligence (AI) and deep learning techniques like neural networks and pre-trained models, which excel at capturing complex patterns. This makes it particularly suitable for tasks such as content recommendation, where user preferences and data nuances require careful and comprehensive consideration.
Examples of vector search use cases:
E-commerce platforms employ vector search to enhance product recommendations. Analyzing user behavior and product data identifies similar items or products that complement users’ preferences, improving the shopping experience and increasing sales.
Content & Media
Content creators leverage vector search to manage vast libraries of images, videos, audio, and textual content. It helps users find relevant multimedia assets quickly, making repurposing and distributing content easier.
It analyzes medical records, images, and research papers to identify similar medical cases, assist in disease diagnosis, and streamline the retrieval of relevant research articles, contributing to better patient care and medical breakthroughs.
Financial institutions utilize it to detect fraudulent activities by identifying unusual patterns and anomalies. It monitors transactions in real time, enabling timely fraud prevention.
Law firms employ it to sift through extensive legal documents and case law. It streamlines the discovery of relevant case precedents, legal briefs, and contracts, expediting the research process.
It helps optimize routes, track inventory, and improve demand forecasting. It enables companies to make data-driven decisions to reduce costs and enhance operational efficiency.
By considering explicit qualifications and implicit skills, it can match job applicants with job descriptions to streamline recruitment.
Social media platforms deploy vector search to moderate content that violates community guidelines.
Scientists and researchers use vector search to discover relevant publications, research papers, and patents.
Vector search enables users to intelligently search across a variety of content types, including audio, video, and more. It can decipher nuance, understand contextual relationships, and comprehend language semantics, allowing it to handle ambiguous queries and provide more relevant results. Upland BA Insight is continuously working to provide the latest and best search experiences to our users. Be on the lookout for vector search capabilities coming soon!