DataStax looks to help enterprises escape RAG ‘Hell’ with AI tools update 

by | Jun 24, 2024 | Technology

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Retrieval Augmented Generation (RAG) is key to enterprise usage of generative AI, but it’s not as easy as just simply connecting a Large Language Model (LLM) to a database.

DataStax is looking to help solve the challenge of enabling RAG for enterprise production deployments, with series of technologies announced today. DataStax is perhaps best known for its commercially supported version of the Apache Cassandra database, known as a DataStax Astra DB. In the last year, DataStax has increasingly focussed on enabling gen AI and specifically RAG, adding vector database search support alongside a data API to build gen AI RAG apps. 

Now DataStax is pushing further into enterprise RAG, with the release of Langflow 1.0 for building RAG and AI agent workflows. The company is also out with a new release of Vectorize which provides different vector embedding models. On top of it all is RAGStack 1.0 which combines a series of tools and technologies to help enterprise production deployments.

According to DataStax CPO Ed Anuff, the basics of RAG architecture is deceptively simple, but getting actual enterprise grade efficiency is a challenge many organizations now face.

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“A lot of companies right now are in RAG Hell,” Anuff told VentureBeat.

Anuff explained that RAG Hell refers to the challenges companies face when they start importing full, live datasets into a RAG application after an initial proof of concept. Initially the results are good, but then 2 out of 5 times the results become terrible. Anuf emphasized that the goal …

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Don’t miss OpenAI, Chevron, Nvidia, Kaiser Permanente, and Capital One leaders only at VentureBeat Transform 2024. Gain essential insights about GenAI and expand your network at this exclusive three day event. Learn More

Retrieval Augmented Generation (RAG) is key to enterprise usage of generative AI, but it’s not as easy as just simply connecting a Large Language Model (LLM) to a database.

DataStax is looking to help solve the challenge of enabling RAG for enterprise production deployments, with series of technologies announced today. DataStax is perhaps best known for its commercially supported version of the Apache Cassandra database, known as a DataStax Astra DB. In the last year, DataStax has increasingly focussed on enabling gen AI and specifically RAG, adding vector database search support alongside a data API to build gen AI RAG apps. 

Now DataStax is pushing further into enterprise RAG, with the release of Langflow 1.0 for building RAG and AI agent workflows. The company is also out with a new release of Vectorize which provides different vector embedding models. On top of it all is RAGStack 1.0 which combines a series of tools and technologies to help enterprise production deployments.

According to DataStax CPO Ed Anuff, the basics of RAG architecture is deceptively simple, but getting actual enterprise grade efficiency is a challenge many organizations now face.

Countdown to VB Transform 2024

Join enterprise leaders in San Francisco from July 9 to 11 for our flagship AI event. Connect with peers, explore the opportunities and challenges of Generative AI, and learn how to integrate AI applications into your industry. Register Now

“A lot of companies right now are in RAG Hell,” Anuff told VentureBeat.

Anuff explained that RAG Hell refers to the challenges companies face when they start importing full, live datasets into a RAG application after an initial proof of concept. Initially the results are good, but then 2 out of 5 times the results become terrible. Anuf emphasized that the goal …nnDiscussion:nn” ai_name=”RocketNews AI: ” start_sentence=”Can I tell you more about this article?” text_input_placeholder=”Type ‘Yes'”]

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