![]() ![]() ![]() Lexical search looks for patterns and exact word or string matches, while semantic search uses the meaning of your search query or question and puts it into context. Searching text and documents can generally be done in two ways. Let’s look at some common use cases for vector search: 1. The vector database compares the similarity of these objects to find the closest matches, providing accurate results while eliminating irrelevant results that traditional search technology might have returned. In order to find similar matches, you convert the subject item or query into a vector using the same ML embedding model used to create your vector embeddings. Vector search compares the similarity of multiple objects to a search query or subject item. Vector search in production is the most common reason to use a vector database. Vector search can also return results that are similar or near-neighbor matches, providing a more comprehensive list of results that otherwise may have remained hidden. Vector databases excel at similarity search, or “vector search.” Vector search enables users to describe what they want to find without having to know which keywords or metadata classifications are ascribed to the stored objects. The combination of traditional database functionality with the ability to search and compare vectors in an index makes vector databases the powerful tools that they are. Vector databases are also responsible for executing CRUD operations (create, read, update, and delete) and metadata filtering. We will cover algorithms used to index vectors further down. When we say that vector databases index vector embeddings, we mean that they organize them in a way that we can compare any vector to one another or to the vector of a search query. What is a Vector Database?Ī vector database indexes and stores vector embeddings for fast retrieval and similarity search, with capabilities like CRUD operations, metadata filtering, and horizontal scaling.Ī large collection of data organized especially for rapid search and retrieval (as by a computer) This document will describe common uses of vector databases, core components, and how to get started. These solutions range from plugins and open-source projects to fully-managed services that handle security, availability, and performance. There are several solutions available to make it easier to implement. Using a well-constructed vector database gives your applications superior search capability while also meeting performance and cost goals. Unless properly calibrated, they may not provide the performance users require without costing a fortune. Until now, vector databases have been reserved for only a handful of tech giants that have the resources to develop and manage them. ![]() They are, however, difficult to implement. They index vectors for easy search and retrieval by comparing values and finding those that are most similar to one another. Vector databases are purpose-built to handle the unique structure of vector embeddings. These models can be fine-tuned for specific applications and you can also train a new model from scratch, although that is less common. There are several existing public models that are high-performance and easy to use as-is. Many technologies exist for building vectors, ranging from vector representations of words or sentences, to cross-media text, images, audio, and video. Vector embeddings describe complex data objects as numeric values in hundreds or thousands of different dimensions. Classifying complex data with keywords and metadata alone may be insufficient to fully represent all of its various characteristics.įortunately, Machine Learning (ML) techniques can offer a far more helpful representation of complex data by transforming it into vector embeddings. Many organizations would benefit from storing and analyzing complex data, but complex data can be difficult for traditional databases built with structured data in mind. ![]() These are unstructured forms of data that include documents, images, videos, and plain text on the web. IntroductionĬomplex data is growing at break-neck speed. Try it now to see for yourself, or continue reading to learn about vector databases. Pinecone is a vector database that makes it easy for developers to add vector-search features to their applications, using just an API. ![]()
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