DataStax updates tools for building gen AI applications

Posted by on 25 June, 2024

This post was originally published on this site

DataStax is updating its tools for building generative AI-based applications in an effort to ease and accelerate application development for enterprises, databases, and service providers.

One of these tools is Langflow, which DataStax acquired in April. It is an open source, web-based no-code graphical user interface (GUI) that allows developers to visually prototype LangChain flows and iterate them to develop applications faster.

LangChain is a modular framework for Python and JavaScript that simplifies the development of applications that are powered by generative AI language models or LLMs.  

According to the company’s Chief Product Officer Ed Anuff, the update to Langflow is a new version dubbed Langflow 1.0, which is the official open source release that comes after months of community feedback on the preview.

“Langflow 1.0 adds more flexible, modular components and features to support complex AI pipelines required for more advanced retrieval augmented generation (RAG) techniques and multi-agent architectures,” Anuff said, adding that Langflow’s execution engine was now Turing complete.

Turing complete or completeness is a term used in computer science to describe a programmable system that can carry out or solve any computational problem.

Langflow 1.0 also comes with LangSmith integration that will allow enterprise developers to monitor LLM-based applications and perform observability on them, the company said.

A managed version of Langflow is also being made available via DataStax in a public preview.

“Astra DB environment details will be available in Langflow and users will be able to access Langflow via the Astra Portal, and usage will be free,” Anuff explained.

RAGStack 1.0 gets new capabilities

DataStax has also released a new version of RAGStack, its curated stack of open-source software for implementing RAG in generative AI-based applications using Astra DB Serverless or Apache Cassandra as a vector store.

The new version, dubbed RAGStack 1.0, comes with new features such as Langflow, Knowledge Graph RAG, and ColBERT among others.

The Knowledge Graph RAG feature, according to the company, provides an alternative way to retrieve information using a graph-based representation. This alternative method can be more accurate than vector-based similarity search alone with Astra DB, it added.

Other features include the introduction of Text2SQL and Text2CQL (Cassandra Query Language) to bring all kinds of data into the generative AI flow for application development.

While DataStax offers a separate non-managed version of RAGStack 1.0 under the name Luna for RAGStack, Anuff said that the managed version offers more value for enterprises.

“RAGStack is based on open source components, and you could take all of those projects and stitch them together yourself. However, we think there is a huge amount of value for companies in getting their stack tested and integrated for them, so they can trust that it will deliver at scale in the way that they want,” the chief product officer explained.

Other updates related to easing RAG

The company has also partnered with several other companies such as Unstructured to help developers extract and transform data to be stored in AstraDB for building generative AI-based applications.

“The partnership with Unstructured provides DataStax customers with the ability to use the latter’s capabilities to extract and transform data in multiple formats – including HTML, PDF, CSV, PNG, PPTX – and convert it into JSON files for use in AI initiatives,” said Matt Aslett, director at ISG’s Ventana Research.

Other partnerships include collaboration with the top embedding providers, such as OpenAI, Hugging Face, Mistral AI, and Nvidia among others.

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