The News: Oracle introduces several enhancements to Oracle Autonomous Database integrating AI and advanced machine learning into applications including Autonomous Database Select AI, spatial enhancement in Oracle Machine Learning, “no-code” model monitoring interface, and property graph views on Resource Description Framework (RDF) knowledge graphs. Read the full blog post on the Oracle website.
Oracle Autonomous Database Select AI Fuels AI-Infused Innovation
Analyst Take: Oracle unveils its Autonomous Database Select AI with the strategic goal of enabling developers to create AI-enabled applications that can unlock private enterprise data through the capabilities of Generative AI and machine learning. To achieve such a major objective, Oracle is bringing AI to an organization’s data, which is consistent with Oracle’s converged database approach. Essentially, with Autonomous Database Select AI, workforces converse with data using natural language – there is no need for them to write SQL queries or understand the intricacies of how or where their data is stored to gain insights.
Autonomous Database Select AI is designed to simplify and streamline how organizations can get answers about their business with a particular focus on the data layer. Select AI translates the language of the organization and its workforce into Oracle SQL queries by processing questions through an AI large language model (LLM). Select AI is built directly into Autonomous Database, making it easy for any organization to immediately take advantage of these new capabilities. No new downloads, coding, proprietary interfaces, cutting and pasting from one form to another, or installations are required.
Select AI now makes that chat history available to the LLM so that it can interpret the context of follow-up questions. Users can now have a “conversation” with their database to explore and narrow down the answers they need, just like a real-life discussion. In addition, to be completely transparent and validate results, users can now ask Select AI to produce the generated SQL and a description of the query processing. Moreover, Select AI leverages the data security and user authentication protections of the database along with giving people their choice of LLMs, which are easily adaptable to the latest technology or proprietary extensions. As a result, Oracle Autonomous Database Select AI provides new conversational AI benefits including the understanding of the users’ natural language questions in a conversational thread.
Autonomous Database Select AI supports a broad set of available LLMs such as OpenAI, Cohere, and Azure OpenAI in addition to new OCI Generative AI Cohere and Llama2 models. Essentially, Select AI is available to every database developer, accessible from any client or development language and framework.
Of key importance, Select AI is only the inaugural step of AI integration as Oracle Database 23c provides vector database functionality that augments Generative AI with the organization’s business data, including support for retrieval augmented generation (RAG). Specifically, Oracle AI vector search in 23c is designed for ease-of-use and understanding. New features, such as a new VECTOR data type for storing vector embeddings and SQL syntax and functions, express similarity search with ease. Similar capabilities will be available in Oracle MySQL HeatWave with Vector Store.
The Vital Role of Vector Databases with LLMs
Fundamentally, LLMs are frozen on a past snapshot of the Internet and have no access to private enterprise data, limiting their ability to provide high-quality responses to business questions. However, vector databases provide enterprises content to enhance their LLM interactions through RAG techniques. Plus, they are essential to avoid having to train LLMs on sensitive enterprise data, which can prove costly, not to mention an unacceptable security risk.
Vector databases cache previous LLM prompts and responses that are integral to improving performance and reducing costs, thereby bolstering business outcomes. For instance, enterprises can immediately engage the OCI Generative AI Agents RAG service to act on call transcripts, troves of PDF documents, internal knowledge sources and extremely large corpuses of proprietary corporate data—while a ReRanker adjusts outputs to best match individual user queries.
OCI Generative AI Agents RAG service is now available in beta with OpenSearch. Once it becomes available on Oracle Database 23c with AI Vector Search and MySQL HeatWave Vector Store, I find that the competitive advantages that customers obtain can prove vastly superior to competitive offerings.
OCI Generative AI: Advancing the Frontier of Enterprise Innovation
As background, OCI Generative AI is a fully managed service that integrates LLMs from Cohere and Meta Llama 2 to address a wide range of business use cases. The OCI Generative AI service now includes multilingual capabilities that support over 100 languages, an improved GPU cluster management experience, and flexible fine-tuning options. Customers can use the OCI Generative AI service in Oracle Cloud Infrastructure and on-premises through OCI Dedicated Region.
Currently, I see that many enterprises are finding that the pre-training as well as the fine-tuning or continuous training of LLMs are proving expensive and time-consuming. With Oracle Generate AI, Oracle is directly addressing the main generative AI challenges that customers are facing. OCI Generative AI brings new features such as flexible fine tuning of both Cohere Command 52/6B models, Meta Llama 2, as well as improved cluster UX including multi-endpoint support in hosting clusters and scaling clusters by adding/removing units to handle more model requests.
Oracle Autonomous Database Unleashing AI/ML Innovations
Continuing its path of AI innovations, I find that Oracle Autonomous Database is providing the data layer foundation to spur developer and organization AI/ML innovations. Select AI uses LLMs to generate SQL queries based on natural language conversations, enabling developers to build applications that integrate private data with GenAI to improve workforce productivity.
For ML, Oracle is enhancing Oracle Machine Learning for Python to incorporate location relationships while leveraging Oracle Spatial’s native support for spatial data types, index, and analytics, improving overall model performance alongside spatial ML algorithms such as regression, classification, clustering, and anomaly detection.
In addition, the support of the model monitoring user interface (UI) takes advantage of new “no-code” UI capabilities in Oracle Machine Learning that simplifies the detection of concept and quality drift as well as burnishes the support of MLOps by consolidating model and data monitoring into one streamlined UI. Moreover, the new “no-code” UI in Graph Studio facilitates graph analytics such as pathfinding on knowledge graphs without data duplication and reveals insights by examining connections within a knowledge graph.
Key Takeaway: Oracle Autonomous Database Ready to Accelerate Development of AI-enabled Applications
These enhancements to Autonomous Database reflect Oracle’s ongoing commitment to provide the tools customers need to integrate AI and ML into their applications, allowing workforces to swiftly gain insights from data and make timely business decisions. However, it’s the new contextual conversation capabilities that captured my attention. When it comes to having a conversation with data, workforces just want to have a real-world type of dialogue—not dealing with coding, complex interfaces or manually copying and pasting from one form to another.
With Select AI, I believe that Oracle is breaking AI ground with a generally available capability for organizations to have a contextual dialogue with their private, proprietary data—intuitively. It’s so simple that organizations of all sizes can use it immediately, placing Autonomous Database with OCI Generative AI at the forefront of data platform innovations.
Disclosure: The Futurum Group is a research and advisory firm that engages or has engaged in research, analysis, and advisory services with many technology companies, including those mentioned in this article. The author does not hold any equity positions with any company mentioned in this article.
Analysis and opinions expressed herein are specific to the analyst individually and data and other information that might have been provided for validation, not those of The Futurum Group as a whole.
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Author Information
Ron is an experienced, customer-focused research expert and analyst, with over 20 years of experience in the digital and IT transformation markets, working with businesses to drive consistent revenue and sales growth.
Ron holds a Master of Arts in Public Policy from University of Nevada — Las Vegas and a Bachelor of Arts in political science/government from William and Mary.
