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The World's #1 Vector Database for Similarity Search and AI

What is a vector database?

A vector database is a collection of vectors that are organized and stored in a database management system. A vector is a mathematical representation of a quantity that has both magnitude and direction. In a vector database, vectors can represent a wide range of objects, such as images, audio files, text documents, and even physical objects.

How does a vector database work?

Vector databases use algorithms to generate vector representations of data. These algorithms are designed to capture the essential features of the data, such as color, texture, shape, and other characteristics that are relevant to the specific application. Once the vectors are generated, they are stored in the database and can be accessed and searched using various methods.

How are KX customers using kdb for vector-based search?

kdb is used for vector-based search by storing and querying vector data efficiently. kdb is optimized for handling large volumes of data in-memory and can perform complex analytical queries. kdb can be used to store vector representations of data and then perform similarity searches on these vectors in microseconds.

Vector databases have a wide range of applications across many different industries including:

Image and video search

Vector databases can be used to search for images and videos that have similar features. This is useful in applications such as facial recognition, product search, and content filtering.

Natural Language Processing

Vector databases can be used to represent words and phrases in a way that captures their semantic meaning. This is useful in applications such as sentiment analysis, language translation, and text classification.

Recommender Systems

Vector databases can be used to generate recommendations based on user preferences. This is useful in applications such as product recommendations, movie recommendations, and music recommendations.

Data Analysis

Vector databases can be used to analyze large datasets and identify patterns and trends. This is useful in applications such as market research, customer profiling, and predictive analytics.

Vector databases are an essential tool for many applications that require efficient and accurate data representation and retrieval. If you're interested in learning more about vector databases or want to implement one in your organization, please don't hesitate to contact us. Our team of experts is always available to help you.

Why kdb?

With kdb, get an efficient, real-time, and flexible vector-native environment for generating, storing, and running similarity search, including on vector embeddings.

CPU-based similarity search: No GPUs required.
Real-time raw data processing: kdb can seamlessly take raw data, generate vector embeddings, store them, and run similarity searches in real time.
Superior multi-modal performance.
Squish the stack: By integrating streaming, embedding generation, vector database, raw data, timeseries, and analytics, our solution simplifies the technology stack and can be easily embedded into various applications.
Dynamic indexing: Dynamically select vector embeddings for similarity search without index restrictions, offering faster and more flexible search capabilities.
Re-encode and search across datasets: By storing raw data, kdb enables re-encoding and storage with different dimensions, allowing cross-dataset similarity search.
Timeseries similarity search across timeseries data, even without embedding, providing more options for users.

Explore kdb for your next search -
The World’s #1 Vector Database for Similarity Search and AI

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