DataStax Unveils Glean and AI Integrations at NYC RAG++ Event

DataStax, the AI platform company, today 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 will demonstrate all of the new updates tonight, alongside industry experts from Glean, Unstructured.io, and more at its RAG++ NYC event, to be held at Pier Sixty at Chelsea Pier.

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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.

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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.

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Source – datastax