Customers can build generative AI applications
without AI expertise, data movement, or additional cost
HeatWave GenAI is 30X faster than Snowflake,
18X faster than Google BigQuery, and 15X faster than Databricks for
vector processing
AUSTIN,
Texas, June 26, 2024 /PRNewswire/ -- Oracle today
announced the general availability of HeatWave GenAI, which
includes the industry's first in-database large language models
(LLMs), an automated in-database vector store, scale-out vector
processing, and the ability to have contextual conversations in
natural language informed by unstructured content. These new
capabilities enable customers to bring the power of generative AI
to their enterprise data—without requiring AI expertise or having
to move data to a separate vector database. HeatWave GenAI is
available immediately in all Oracle Cloud regions, Oracle Cloud
Infrastructure (OCI) Dedicated Region, and across clouds at no
extra cost to HeatWave customers.
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With HeatWave GenAI, developers can create a vector store for
enterprise unstructured content with a single SQL command, using
built-in embedding models. Users can perform natural language
searches in a single step using either in-database or external
LLMs. Data doesn't leave the database and, due to HeatWave's
extreme scale and performance, there is no need to provision GPUs.
As a result, developers can reduce application complexity, increase
performance, improve data security, and lower costs.
"HeatWave's stunning pace of innovation continues with the
addition of HeatWave GenAI to existing built-in HeatWave
capabilities: HeatWave Lakehouse, HeatWave Autopilot, HeatWave
AutoML, and HeatWave MySQL," said Edward
Screven, chief corporate architect, Oracle. "Today's
integrated and automated AI enhancements allow developers to build
rich generative AI applications faster, without requiring AI
expertise or moving data. Users now have an intuitive way to
interact with their enterprise data and rapidly get the accurate
answers they need for their businesses."
"HeatWave GenAI makes it extremely easy to take advantage of
generative AI," said Vijay Sundhar,
chief executive officer, SmarterD. "The support for in-database
LLMs and in-database vector creation leads to a significant
reduction in application complexity, predictable inference latency,
and most of all, no additional cost to us to use the LLMs or create
the embeddings. This is truly the democratization of generative AI
and we believe it will result in building richer applications with
HeatWave GenAI and significant gains in productivity for our
customers."
New automated and built-in generative AI features include:
- In-database LLMs simplify the development of generative
AI applications at a lower cost. Customers can benefit from
generative AI without the complexity of external LLM selection and
integration, and without worrying about the availability of LLMs in
various cloud providers' data centers. The in-database LLMs enable
customers to search data, generate or summarize content, and
perform retrieval-augmented generation (RAG) with HeatWave Vector
Store. In addition, they can combine generative AI with other
built-in HeatWave capabilities such as AutoML to build richer
applications. HeatWave GenAI is also integrated with the OCI
Generative AI service to access pre-trained, foundational models
from leading LLM providers.
- Automated in-database Vector Store enables
customers to use generative AI with their business documents
without moving data to a separate vector database and without AI
expertise. All the steps to create a vector store and vector
embeddings are automated and executed inside the database,
including discovering the documents in object storage, parsing
them, generating embeddings in a highly parallel and optimized
way, and inserting them into the vector store making
HeatWave Vector Store efficient and easy to use. Using a
vector store for RAG helps solve the hallucination challenge
of LLMs as the models can search proprietary data with appropriate
context to provide more accurate and relevant answers.
- Scale-out vector processing delivers very fast
semantic search results without any loss of accuracy. HeatWave
supports a new, native VECTOR data type and an optimized
implementation of the distance function, enabling customers to
perform semantic queries with standard SQL. In-memory hybrid
columnar representation and the scale-out architecture of
HeatWave enable vector processing to execute at near-memory
bandwidth and parallelize across up to 512 HeatWave nodes. As a
result, customers get their questions answered rapidly. Users can
also combine semantic search with other SQL operators to, for
example, join several tables with different documents and perform
similarity searches across all documents.
- HeatWave Chat is a Visual Code plug-in for MySQL Shell
which provides a graphical interface for HeatWave GenAI and enables
developers to ask questions in natural language or SQL.
The integrated Lakehouse Navigator enables users to select
files from object storage and create a vector store. Users can
search across the entire database or restrict the search to a
folder. HeatWave maintains context with the history of questions
asked, citations of the source documents, and the prompt to
the LLM. This facilitates a contextual conversation and allows
users to verify the source of answers generated by the LLM. This
context is maintained in HeatWave and is available to any
application using HeatWave.
Vector Store Creation and Vector Processing
Benchmarks
Creating a vector store for documents in PDF,
PPT, WORD, and HTML formats is up to 23X faster with HeatWave GenAI
and 1/4th the cost of using Knowledge base for Amazon Bedrock.
As demonstrated by a third-party benchmark using a variety of
similarity search queries on tables ranging from 1.6GB to 300GB in
size, HeatWave GenAI is 30X faster than Snowflake and costs 25
percent less, 15X faster than Databricks and costs 85 percent less,
and 18X faster than Google BigQuery and costs 60 percent less.
A separate benchmark reveals that vector indexes in Amazon
Aurora PostgreSQL with pgvector can have a high degree of
inaccuracy and can yield incorrect results. In contrast, HeatWave
similarity search processing always provides accurate results, has
predictable response time, is performed at near memory speed, and
is up to 10X-80X faster than Aurora using the same number of
cores.
"We are thrilled to continue our strong collaboration with
Oracle to deliver the power and productivity of AI with HeatWave
GenAI for critical enterprise workloads and data sets," said
Dan McNamara, senior vice president
and general manager, Server Business Unit, AMD. "The joint
engineering work undertaken by AMD and Oracle is enabling
developers to design innovative enterprise AI solutions by
leveraging HeatWave GenAI powered by the core density and
outstanding price-performance of AMD EPYC processors."
Additional Customer and Analyst Commentary on HeatWave
GenAI
"We heavily use the in-database HeatWave AutoML for
making various recommendations to our customers," said Safarath
Shafi, chief executive officer, EatEasy. "HeatWave's support for
in-database LLMs and in-database vector store is differentiated and
the ability to integrate generative AI with AutoML provides further
differentiation for HeatWave in the industry, enabling us to offer
new kinds of capabilities to our customers. The synergy with AutoML
also improves the performance and quality of the LLM results."
"HeatWave in-database LLMs, in-database vector store, scale-out
in-memory vector processing, and HeatWave Chat, are very
differentiated capabilities from Oracle that democratize generative
AI and make it very simple, secure, and inexpensive to use," said
Eric Aguilar, founder, Aiwifi.
"Using HeatWave and AutoML for our enterprise needs has already
transformed our business in several ways, and the introduction of
this innovation from Oracle will likely spur growth of a new class
of applications where customers are looking for ways to leverage
generative AI on their enterprise content."
"HeatWave's engineering innovation continues to deliver on the
vision of a universal cloud database," said Holger Mueller, vice president and principal
analyst, Constellation Research. "The latest is generative AI done
'HeatWave style'—which includes the integration of an automated,
in-database vector store and in-database LLMs directly into the
HeatWave core. This enables developers to create new classes of
applications as they combine HeatWave elements. For example, they
can combine HeatWave AutoML and HeatWave GenAI in a
fraud detection application that not only detects
suspicious transactions—but also provides an understandable
explanation. This all runs in the database, so there's no need to
move data to external vector databases, keeping the data more
secure. It also makes HeatWave GenAI highly performant at a
fraction of the cost as demonstrated in competitive
benchmarks."
HeatWave
HeatWave is the only cloud service that
provides automated and integrated generative AI and machine
learning in one offering for transactions and lakehouse-scale
analytics. A core component of Oracle's distributed cloud strategy,
HeatWave is available natively on OCI and Amazon Web Services, on
Microsoft Azure via the Oracle Interconnect for Azure, and in
customers' data centers with OCI Dedicated Region and Oracle
Alloy.
Additional Resources
- Watch Edward Screven
announce new GenAI enhancements to HeatWave
- Read the HeatWave technical blog
- Read what industry analysts are saying about HeatWave
About Oracle
Oracle offers integrated suites of
applications plus secure, autonomous infrastructure in the Oracle
Cloud. For more information about Oracle (NYSE: ORCL), please visit
us at www.oracle.com.
Trademarks
Oracle, Java, MySQL and NetSuite are
registered trademarks of Oracle Corporation. NetSuite was the first
cloud company—ushering in the new era of cloud computing.
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SOURCE Oracle