OpenAI Acquired Rockset
The reason OpenAI bought Rockset is to enhance their retrieval infrastructure, and move closer to the implementation side of Generative AI.
Introduction
With more models being open-source, and the focus small language modes which many are open-sourced, it seems like the market value is shifting from Zone 4 to Zone 5.
Only a few months ago, it seemed like OpenAI has captured the LLM market and no-one will ever be able to compete.
- Then Meta AI open-sourced numerous very capable models.
- Followed by stellar work from Microsoft on imbuing Small Language Models (SLMs) with enhanced reasoning capabilities, and by open-sourcing models.
- Organisations moved their focus from gradient approaches (fine-tuning) to adapt models to their environment, to non-gradient approaches, like RAG and prompt engineering techniques.
- These non-gradient frameworks demand vector technology, data centric tooling and RAG frameworks.
- LLMs are becoming a mere utility and innovation is taking place in building applications which are powered by LLMs under the hood.
- At last the market is moving to toward a Data centric approach.
- This acquisition also brings OpenAI closer to the developer community with Rockset’s integration with LangChian & LlamaIndex.
Application Focus
Considering the landscape diagram above, this acquisition extends OpenAI from Zone 4 into Zone 5.
Rockset covers the following Zone 5 functionality: Vector Search, Data Centric Tooling & RAG Frameworks.
RAG is a method for Large Language Models (LLMs) to retrieve highly contextual information through semantic search, enhancing their ability to provide up-to-date answers.
It offers an alternative to fine-tuning models due to its transparency, observability, and model-agnostic nature.
Emergence of Technologies Around RAG; several new technologies have emerged to support the principles of RAG, emphasising its growing importance in the AI field.
With this acquisition, OpenAI has a model agnostic data platform aimed at enhancing its retrieval infrastructure.
This move reflects OpenAI’s vision of LLMs evolving into a mere utility, with the market value in Zone 5.
Rockset addresses key issues such as data governance, storage, and organisation.
In-Context Learning is gaining significance by delivering highly contextual data during inference (when the LLM is queried).
The Rockset acquisition aligns with the need for efficient and accurate data retrieval, supporting the growing trend of In-Context Learning.
The future lies in applications that deliver value and functionality to organisations.
OpenAI’s focus on enhancing its retrieval capabilities with Rockset will play a crucial role in achieving this goal.
Enhancing RAG Infrastructure With Rockset Acquisition
OpenAI says it wants to enhance their Retrieval Infrastructure with the Rockset acquisition. Rockset is described as a leading real-time analytics database, to improve data indexing and querying capabilities.
According to OpenAI, this integration aims to help users, developers, and enterprises better leverage their data and access real-time information with AI products.
Members of Rockset’s team will join OpenAI, enhancing our retrieval infrastructure across products.
Rockset’s infrastructure is said to empowers companies to transform their data into actionable intelligence.
⭐️ Follow me on LinkedIn for updates on Large Language Models ⭐️
I’m currently the Chief Evangelist @ Kore AI. I explore & write about all things at the intersection of AI & language; ranging from LLMs, Chatbots, Voicebots, Development Frameworks, Data-Centric latent spaces & more.
