A vector database is an organized collection of vector embeddings that can be created, read, updated, and deleted at any point in time.
A guide to selecting the right vector database for your RAG architecture, focusing on scalability, performance, and compatibility with retrieval-augmented ge…
you pass it through an embedding model to create embeddings, and then perform CRUD (Create-Read-Update-Delete) operations whenever the database changes. This complexity compounds as there are several different types of vector embeddings, including word embeddings, document embeddings...
Discover how vector databases power AI, enhance search, and scale data processing. Learn their benefits and applications for your business with InterSystems.
(represented as a vector) and each restaurant location in the database. It then ranks the restaurant locations based on their proximity to the user's location.Then the system returns the nearest restaurant location to the user, along with step-by-step directions on how to get there within ...
The database layer is added to a project as shown below: Select Add Layer from the Layer tab or Map tab. Select Database from the dropdown menu. Select a table. Click the Select button. Below you can check out a short video tutorial, showing you how to upload your vector data to GI...
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Why build a knowledge graph with DataStax? DataStax's RAG stack provides a unique approach to knowledge graphs by eliminating the need for a dedicated graph database. Instead, it leveragesAstra DB's vector capabilitiesto store both thegraph structureand vector embeddings in a single system. This...
Welcome to Azure Cosmos DB FAQ Try Azure Cosmos DB free Choose an API Distributed NoSQL Distributed relational Integrated vector databases What is a vector database Vector database in Azure Cosmos DB NoSQL Vector database in Azure Cosmos DB for MongoDB ...
Vector databases have gained a resurgence due to the widespread availability of pre-trained AI models. Although the concept of a vector database has been around for several decades, it is only now, in the age of large language models (LLMs), that vector databases can be used to their full...