t cht lk based responses . This is now a reality , thanks to vector search , a core feature of Astra DB . This means users no longer need to sit through lengthy manuals ; instead , it enables teams to respond swiftly .
t cht lk based responses . This is now a reality , thanks to vector search , a core feature of Astra DB . This means users no longer need to sit through lengthy manuals ; instead , it enables teams to respond swiftly .
The impact goes beyond just answering questions . Vector search enables semantic or similarity search to capture meaning and context within the embedding . This feature allows users to find what they want based on meaning and context , not just exact keywords . What ’ s more , it applies to text data ( documents ), images and audio , enabling users to quickly and easily find the products or information related to their query .
After the introduction of vector support in DataStax ' s Astra DB in July 2023 , nearly half of new users have used vector capabilities to develop generative AI applications .
Why ?
Vector search enables the storage of vector embeddings – which are crucial for building AI applications like those powered by GPT-4 .
Plus , it ’ s available on Microsoft Azure , Amazon Web Services ( AWS ) and Google Cloud platforms , making Astra DB the go-to vector database for businesses to drive their AI projects on any cloud infrastructure . In essence , Astra DB delivers best-in-class performance for AI initiatives by leveraging Cassandra ’ s speed and limitless scalability .
The evolving needs of real-world vector search
But how does Astra DB stay ahead in a competitive landscape ? DataStax takes a multi-pronged approach . Their newly launched Data API offers comprehensive data management and retrievalaugmented generation capabilities . This translates to highly relevant , low-latency applications that can power the future of AI interactions . Pair that with updated developer experience and users will now find it easier to harness the power of vector search — offering up to 20 % higher relevancy , 9x higher throughput , and 74x faster response times .
Traditional database paradigms face challenges in real-world vector search applications . Here ,
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