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DataStax, the AI platform company, announced new features and updates to its AI PaaS, which minimizes hallucinations with up to 20% higher relevance, 74x faster response time, and 9x higher throughput1. DataStax demonstrated all of the new updates, alongside industry experts from Glean, Unstructured.io, and more at its RAG++ NYC event, that held at Pier Sixty at Chelsea Pier.

With DataStax, developers can focus on application development, rather than infrastructure management, powered by multiple, new updates to the DataStax AI PaaS.

Simplifying Data Ingestion to Improve Relevancy with Unstructured.io

Data preparation and ingestion is one of the biggest challenges when building a GenAI application. Developers are faced with converting massive amounts of existing data, in different formats, into a format suitable for use in retrieval-augmented generation (RAG). Often these documents are too large for embedding models to ingest and must be broken up into smaller segments or chunked.

To solve this problem Unstructured is now natively integrated with Langflow and Astra DB, simplifying complex configuration options and bringing the power of Unstructured’s ingestion pipelines to DataStax users. Developers can easily import multiple PDF files of any size, chunk those files, and using DataStax Vectorize, they can generate the vector embeddings for improved query relevancy.

This update adds support for more file types and streamlines data processing by bringing data preparation directly into the data loading process. Users can control chunk sizes to optimize semantic relevance and improve RAG performance. This leads to more relevant query results and better application resource utilization.

Read more about the native integration with Unstructured.

Enabling Seamless Access to Data with New Glean Integrations

DataStax will introduce a new integration that allows users to seamlessly connect their data stored in Astra DB with Glean. With this integration, Glean will be able to directly access and analyze data stored in Astra DB, enabling the platform to answer complex questions and provide relevant, accurate query responses.

Additionally, users will be able to leverage a new Glean Component for DataStax Langflow which enables developers to easily create Glean queries within a Langflow flow. Users can tap into Glean’s indexing capabilities to enrich the context of their operations and make more informed decisions based on real-time data insights.

The Glean integration is another example of the robust GenAI ecosystem being built into DataStax Langflow, which will provide developers the most diverse ecosystem of integration partners via its AI PaaS.

Driving Agility in GenAI Application Development with the Langflow API

DataStax has further enhanced its AI PaaS with the free public preview of the DataStax Langflow API. The Langflow API lets developers build and host their GenAI application anywhere with a simple HTTP call to an API endpoint hosted by DataStax, providing a fast and easy path to production.

This simplifies and speeds up deployment by removing the overhead of self-hosting an application, and integrates with external applications to easily embed GenAI into existing projects. The API is accessible over HTTP, and Langflow includes JavaScript and Python code snippets that can be dropped into a developer’s application.

Read more about the public preview of the DataStax Langflow API.

Supporting Quotes:

·                “As developers move beyond the ideation and experimental phase that has characterized the past year or so, they’re looking to deploy GenAI applications into production with ease,” said Ed Anuff, chief product officer, DataStax. “The DataStax AI PaaS offers users the ability to quickly build, iterate, and deploy applications with speed, at scale. It’s a field-proven platform that enables some of the largest global companies to leverage their data to power production-ready GenAI applications and deliver new internal and customer-facing experiences to the market.”

·       “Data preparation is a common issue for developers as they build their GenAI apps. They need to ingest, process, and chunk more data to ensure applications are delivering accurate, relevant query responses,” said Brian Raymond, CEO, Unstructured. “With our new, native integration with Langflow and Astra DB, we’re allowing AI developers to easily import and process unstructured data like PDFs, emails, and more. This enhanced capability sharpens query results and centralizes unstructured data handling within DataStax’s AI PaaS.”

·       “DataStax Langflow makes developing AI apps easy,” said Arvind Jain, CEO, Glean. “Now with Glean built-in, developers can connect to all their important corporate data sources and build custom AI experiences that helps their company automate work with AI.  DataStax plus Glean will enable both structured and unstructured data to feed AI workflows.”

·       “We rely on Langflow’s underlying infrastructure to provide a robust environment for our customers to build and deploy their own GenAI applications with ease,” said Brendon Geils, Founder, Athena Intelligence. “Our users build custom flows using our UI and we rely on the orchestration and automation provided by Langflow under the hood to make that happen. The Langflow API will provide more flexibility and stability on our platform, providing data analysts with the seamless experience they need to deploy and scale purpose-built AI applications for their day-to-day workflows.”

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