MongoDB Atlas Vector Search now includes
extended capabilities for querying contextual data and performance
improvements to accelerate building generative AI
applications
New integration with Confluent Cloud and
MongoDB Atlas Vector Search allows developers to access real-time
data streams from a variety of sources to fuel generative AI
applications
Dataworkz, Drivly, ExTrac, Inovaare
Corporation, NWO.ai, One AI, and VISO Trust among organizations
building with MongoDB Atlas Vector Search
LONDON, Sept. 26,
2023 /PRNewswire/ -- MongoDB, Inc. (NASDAQ: MDB)
today at MongoDB.local London
announced new capabilities, performance improvements, and a
data-streaming integration for MongoDB Atlas Vector Search that
make it even faster and easier for developers to build generative
AI applications. Organizations of all sizes have rushed to adopt
MongoDB Atlas Vector Search as part of a unified solution to
process data for generative AI applications since being announced
in preview in June of this year. MongoDB Atlas Vector Search has
made it even easier for developers to aggregate and filter data,
improving semantic information retrieval and reducing
hallucinations in AI-powered applications. With new performance
improvements for MongoDB Atlas Vector Search, the time it takes to
build indexes is now significantly reduced by up to 85 percent to
help accelerate application development. Additionally, MongoDB
Atlas Vector Search is now integrated with fully managed data
streams from Confluent Cloud to make it easier to use real-time
data from a variety of sources to power AI applications. To learn
more about MongoDB Atlas Vector Search, visit
mongodb.com/products/platform/atlas-vector-search.
"It has been really exciting to see the overwhelmingly positive
response to the preview version of MongoDB Atlas Vector Search as
our customers eagerly move to incorporate generative AI
technologies into their applications and transform their
businesses—without the complexity and increased operational burden
of 'bolting on' yet another software product to their technology
stack. Customers are telling us that having the capabilities of a
vector database directly integrated with their operational data
store is a game changer for their developers," said Sahir Azam, Chief Product Officer at MongoDB.
"This customer response has inspired us to iterate quickly with new
features and improvements to MongoDB Atlas Vector Search, helping
to make building application experiences powered by generative AI
even more frictionless and cost effective."
Many organizations today are on a mission to invent new classes
of applications that take advantage of generative AI to meet
end-user expectations. However, the large language models (LLMs)
that power these applications require up-to-date, proprietary data
in the form of vectors—numerical representations of text, images,
audio, video, and other types of data. Working with vector data is
new for many organizations, and single-purpose vector databases
have emerged as a short-term solution for storing and processing
data for LLMs. However, adding a single-purpose database to their
technology stack requires developers to spend valuable time and
effort learning the intricacies of developing with and maintaining
each point solution. For example, developers must synchronize data
across data stores to ensure applications can respond in real time
to end-user requests, which is difficult to implement and can
significantly increase complexity, cost, and potential security
risks. Many single-purpose databases also lack the flexibility to
run as a managed service on any major cloud provider for high
performance and resilience, severely limiting long-term
infrastructure options. Because of these challenges, organizations
from early-stage startups to established enterprises want the
ability to store vectors alongside all of their data in a flexible,
unified, multi-cloud developer data platform to quickly deploy
applications and improve operational efficiency.
MongoDB Atlas Vector Search addresses these challenges by
providing the capabilities needed to build generative AI
applications on any major cloud provider for high availability and
resilience with significantly less time and effort. MongoDB Atlas
Vector Search provides the functionality of a vector database
integrated as part of a unified developer data platform, allowing
teams to store and process vector embeddings alongside virtually
any type of data to more quickly and easily build generative AI
applications. Dataworkz, Drivly, ExTrac, Inovaare Corporation,
NWO.ai, One AI, VISO Trust, and many other organizations are
already using MongoDB Atlas Vector Search in preview to build
AI-powered applications for reducing public safety risk, improving
healthcare compliance, surfacing intelligence from vast amounts of
content in multiple languages, streamlining customer service, and
improving corporate risk assessment. The updated capabilities for
MongoDB Atlas Vector Search further accelerate generative AI
application development:
- Increase the accuracy of information retrieval for
generative AI applications: Whether personalized movie
recommendations, quick responses from chatbots for customer
service, or tailored options for food delivery, application
end-users today expect accurate, up-to-date, and highly engaging
experiences that save them time and effort. Generative AI is
helping developers deliver these capabilities, but the LLMs
powering applications can hallucinate (i.e., generate inaccurate
information that is not useful) because they lack the necessary
context to provide relevant information. By extending MongoDB
Atlas's unified query interface, developers can now create a
dedicated data aggregation stage with MongoDB Atlas Vector Search
to filter results from proprietary data and significantly improve
the accuracy of information retrieval to help reduce LLM
hallucinations in applications.
- Accelerate data indexing for generative AI
applications: Generating vectors is the first step in
preparing data for use with LLMs. Once vectors are created, an
index must be built for the data to be efficiently queried for
information retrieval—and when data changes or new data is
available, the index must then be updated. The unified and flexible
document data model powering MongoDB Atlas Vector Search allows
operational data, metadata, and vector data to be seamlessly
indexed in a fully managed environment to reduce complexity. With
new performance improvements, the time it takes to build an index
with MongoDB Atlas Vector Search is now reduced by up to 85 percent
to help accelerate developing AI-powered applications.
- Use real-time data streams from a variety of sources for
AI-powered applications: Businesses use Confluent Cloud's
fully managed, cloud-native data streaming platform to power highly
engaging, responsive, real-time applications. As part of the
Connect with Confluent partner program, developers can now use
Confluent Cloud data streams within MongoDB Atlas Vector Search as
an additional option to provide generative AI applications
ground-truth data (i.e. accurate information that reflects current
conditions) in real time from a variety of sources across their
entire business. Configured with a fully managed connector for
MongoDB Atlas, developers can make applications more responsive to
changing conditions and provide end user results with greater
accuracy.
Organizations Already Innovating with MongoDB Atlas Vector
Search in Preview
Dataworkz enables enterprises to harness the power of LLMs on
their own proprietary data by combining data, transformations, and
AI into a single experience to produce high-quality, LLM-ready
data. "Our goal is to accelerate the creation of AI applications
with a product offering that unifies data, processing, and machine
learning for business analysts and data engineers," said
Sachin Smotra, CEO and co-founder of
Dataworkz. "Leveraging the power of MongoDB Atlas Vector Search has
allowed us to enable semantic search and contextual information
retrieval, vastly improving our customers' experiences and
providing more accurate results. We look forward to continuing
using Atlas Vector Search to make retrieval-augmented generation
with proprietary data easier for highly relevant results and
driving business impact for our customers."
Drivly provides commerce infrastructure for the automotive
industry to programmatically buy and sell vehicles through simple
APIs. "We are using AI embeddings and Atlas Vector Search to go
beyond full-text search with semantic meaning, giving context and
memory to generative AI car-buying assistants," said Nathan Clevenger, Founder and CTO at Drivly. "We
are very excited that MongoDB has added vector search capabilities
to Atlas, which greatly simplifies our engineering efforts."
ExTrac draws on thousands of data sources identified by domain
experts, using AI-powered analytics to locate, track, and forecast
both digital and physical risks to public safety in real-time. "Our
domain experts find and curate relevant streams of data, and then
we use AI to anonymize and make sense of it at scale. We take a
base model and fine-tune it with our own labeled data to create
domain-specific models capable of identifying and classifying
threats in real-time," said Matt
King, CEO of ExTrac. "Atlas Vector Search is proving to be
incredibly powerful across a range of tasks where we use the
results of the search to augment our LLMs and reduce
hallucinations. We can store vector embeddings right alongside the
source data in a single system, enabling our developers to build
new features way faster than if they had to bolt-on a standalone
vector database—many of which limit the amount of data that can be
returned if it has meta-data attached to it. Because the
flexibility of MongoDB's document data model allows us to land,
index, and analyze data of any shape and structure—no matter how
complex—we are now moving beyond text to vectorize images and
videos from our archives dating back over a decade. Being able to
query and analyze data in any modality will help us to better model
trends, track evolving narratives, and predict risk for our
customers."
Inovaare Corporation is a leading provider of AI-powered
compliance automation solutions for healthcare payers. "At Inovaare
Corporation, we believe that healthcare compliance is not just
about meeting regulations but transforming how healthcare payers
excel in the entire compliance lifecycle. We needed a partner with
the technological prowess and one who shares our vision to pioneer
the future of healthcare compliance," said Mohar Mishra, CTO and Co-Founder at Inovaare
Corporation. "MongoDB's robust data platform, known for its
scalability and agility, perfectly aligns with Inovaare's
commitment to providing healthcare payers with a unified, secure,
and AI-powered compliance operations platform. MongoDB's innovative
Atlas Vector Search powers the reporting capabilities of our
products. It allows us to deliver context-aware compliance guidance
and real-time data-driven insights."
NWO.ai is a premier AI-driven Consumer Intelligence platform
helping Fortune 500 brands bring new products to market. "In
today's rapidly evolving digital age, the power of accurate and
timely information is paramount," said Pulkit Jaiswal, Cofounder of NWO.ai. "At NWO.ai,
our flagship offering, Worldwide Optimal Policy Response (WOPR), is
at the forefront of intelligent diplomacy. WOPR harnesses the
capabilities of AI to navigate the vast oceans of global
narratives, offering real-time insights and tailored communication
strategies. This not only empowers decision-makers but also
provides a vital counterbalance against AI-engineered
disinformation. We're thrilled to integrate Atlas Vector Search
into WOPR, enhancing our ability to instantly search and analyze
embeddings for our dual-use case. It's an exciting synergy, and we
believe it's a testament to the future of diplomacy in the digital
age."
One AI is a platform that offers AI Agents, Language Analytics,
and APIs, enabling seamless integration of accurate,
production-ready language capabilities into products and services.
"Our hero product - OneAgent - facilitates trusted conversations
through AI agents that operate strictly upon company-sourced
content, secured with built-in fact-checking," said Amit Ben, CEO and Founder of One AI. "With
MongoDB Atlas, we're able to take source customer documents,
generate vector embeddings from them that we then index and store
in MongoDB Atlas Vector Search. Then, when a customer has a
question about their business and asks one of our AI agents, Atlas
Vector Search will provide the chatbot with the most relevant data
and supply customers with the most accurate answers. By enabling
semantic search and information retrieval, we're providing our
customers with an improved and more efficient experience."
VISO Trust puts reliable, comprehensive, actionable vendor
security information directly in the hands of decision-makers who
need to make informed risk assessments. "At VISO Trust, we leverage
innovative technologies to continue our growth and expansion in AI
and security. Atlas Vector Search, combined with the efficiency of
AWS and Terraform integrations, has transformed our platform," said
Russell Sherman, Cofounder and CTO
at VISO Trust. "With Atlas Vector Search, we now possess a
battle-tested vector and metadata database, refined over a decade,
effectively addressing our dense retrieval requirements. There's no
need to deploy a new database, as our vectors and artifact metadata
can be seamlessly stored alongside each other."
About MongoDB Atlas
MongoDB Atlas is the leading
multi-cloud developer data platform that accelerates and simplifies
building applications with data. MongoDB Atlas provides an
integrated set of data and application services in a unified
environment that enables development teams to quickly build with
the performance and scale modern applications require. Tens of
thousands of customers and millions of developers worldwide rely on
MongoDB Atlas every day to power their business-critical
applications. To get started with MongoDB Atlas, visit
mongodb.com/atlas.
About MongoDB
Headquartered in New York, MongoDB's mission is to empower
innovators to create, transform, and disrupt industries by
unleashing the power of software and data. Built by developers, for
developers, our developer data platform is a database with an
integrated set of related services that allow development teams to
address the growing requirements for today's wide variety of modern
applications, all in a unified and consistent user experience.
MongoDB has tens of thousands of customers in over 100 countries.
The MongoDB database platform has been downloaded hundreds of
millions of times since 2007, and there have been millions of
builders trained through MongoDB University courses. To learn more,
visit mongodb.com.
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our Quarterly Report on Form 10-Q for the quarter ended
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MongoDB Public Relations
press@mongodb.com
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SOURCE MongoDB, Inc.