DEVOPSdigest

DataStax announced the general availability of its Data API, a one-stop API for GenAI, that provides all the data and a complete stack for production GenAI and retrieval augmented generation (RAG) applications with high relevancy and low latency.

DataStax is also debuting a completely updated developer experience for DataStax Astra DB, the best vector database for building production-level AI applications.

The new vector Data API and experience makes the proven, petabyte-scale power of Apache Cassandra® available to JavaScript, Python, or full-stack application developers in a more intuitive experience for AI development. It is specifically designed for ease of use, while offering up to 20% higher relevancy, 9x higher throughput, and up to 74x faster response times than Pinecone, another vector database, by using the JVector search engine. It introduces an intuitive dashboard, efficient data loading and exploration tools, and seamless integration with leading AI and machine learning (ML) frameworks.

Developers can use the Data API for an out-of-the-box AI ecosystem that simplifies integrations with major GenAI ecosystem leaders like LangChain, LLamaIndex, OpenAI, Vercel, Google Vertex AI, Amazon Bedrock, GitHub Copilot, Azure, and all major platforms while supporting the breadth of security and compliance standards. Any developer can now support advanced RAG techniques such as FLARE and ReAct that must synthesize multiple responses, while still hitting latency SLAs.

“Astra DB is ideal for JavaScript and Python developers, simplifying vector search and large-scale data management, putting the power of Apache Cassandra behind a user-friendly but powerful API,” said Ed Anuff, chief product officer, DataStax. “This release redefines how software engineers build GenAI applications, offering a streamlined interface that simplifies and accelerates the development process for AI engineers.”