A vector database is an organized collection of vector embeddings that can be created, read, updated, and deleted at any point in time.
There is no universal ‘best’ vector database—the choice depends on your needs. Evaluating scalability, functionality, performance, and compatibility with your use cases is vital.
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.
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...
we were going to move everything to Atlas, it became obvious we should just consolidate everything there, so we ended up migrating to Atlas Vector Search for all of our semantic search needs. This means one query API, one set of dependencies, and build in sync,...
Build relevant capabilities (such as vector databases and data pre- and post-processing pipelines) into the existing data architecture, particularly in support of unstructured data. Focus on key points of the data life cycle to ensure high quality. Develop multiple interventions—both human and autom...
Integrated vector databases What is a vector database Vector database in Azure Cosmos DB NoSQL Vector database in Azure Cosmos DB for MongoDB Related concepts AI Applications Quickstart - build a RAG chatbot AI agent Real-time custom content generation ...
You need to research, identify, and understand your customers and then use that information to build a digital experience that meets your customers’ needs. Understanding will always trump purchase. Information is the key to agility and adaptability. If you know what your customers are doing, who...
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...