Elevate Your Java Applications with MongoDB and Spring AI

Taylor Pacelli
August 26, 2024 | Updated: September 19, 2024
#genAI

MongoDB is excited to announce an integration with Spring AI, enhancing MongoDB Atlas Vector Search for Java developers. This collaboration brings Vector Search to Java applications, making it easier to build intelligent, high-performance AI applications.

Why Spring AI?

Spring AI is an AI library designed specifically for Java, applying the familiar principles of the Spring ecosystem to AI development. It enables developers to build, train, and deploy AI models efficiently within their Java applications. Spring AI addresses the gap left by other AI frameworks and integrations that focus on other programming languages, such as Python, providing a streamlined solution for Java developers.

Spring has been a cornerstone for Java developers for decades, offering a consistent and reliable framework for building robust applications. The introduction of Spring AI continues this legacy, providing a straightforward path for Java developers to incorporate AI into their projects. With the MongoDB-Spring integration, developers can leverage their existing Spring knowledge to build next-generation AI applications without the friction associated with learning a new framework.

Key features of Spring AI include:

Familiarity: Leverage the design principles of the Spring ecosystem. Spring AI allows Java developers to use the same familiar tools and patterns they already know from other Spring projects, reducing the learning curve and allowing them to focus on building innovative AI applications. This means you can integrate AI capabilities—including Atlas Vector Search—without having to learn a new language or framework, making the transition smoother and more intuitive.
Portability: Applications built with Spring AI can run anywhere the Spring framework runs. This ensures that AI applications are highly portable and can be deployed across various environments without modification, guaranteeing flexibility and consistency in deployment strategies.
Modular design: Use Plain Old Java Objects (POJOs) as building blocks. Spring AI’s modular design promotes clean code architecture and maintainability. By using POJOs, developers can create modular, reusable components that simplify the development and maintenance of AI applications. This modularity also facilitates easier testing and debugging, leading to more robust applications that efficiently integrate with Atlas Vector Search.
Efficiency: Streamline development with tools and features designed for AI applications in Java. Spring AI provides a range of tools that enhance development efficiency, including pre-built templates, configuration management, and integrated testing tools. These features reduce the time and effort required to develop AI applications, allowing developers to bring their ideas to market faster.

These features streamline AI development by enhancing the integration and performance of Atlas Vector Search within Java applications, making it easier to build and scale AI-driven features.

Enhancing AI development with Spring AI and Atlas Vector Search

MongoDB Atlas Vector Search enhances AI application development by providing advanced search capabilities. The new Spring AI integration enables developers to manage and search vector data within AI models, enabling features like recommendation systems, natural language processing, and predictive analytics.

Atlas Vector Search allows you to store, index, and search high-dimensional vectors, which are crucial for AI and machine learning models. This capability supports a range of AI features:

Recommendation systems: Provide personalized recommendations based on user behavior and preferences.
Natural language processing: Enhance text analysis and understanding for chatbots, sentiment analysis, and more.
Predictive analytics: Improve forecasting and decision-making with advanced data models.

What the integration means for Java developers

Prior to MongoDB-Spring integration, Java developers did not have an easy way to integrate Spring into their AI applications using MongoDB Atlas Vector Search, which led to longer development times and suboptimal application performance.

With this integration, the Java development landscape is transformed, allowing developers to build and deploy AI applications with greater efficiency. The integration simplifies the entire process, enabling developers to concentrate on creating innovative solutions rather than dealing with integration hurdles. This approach not only reduces development time but also accelerates time-to-market.

Additionally, MongoDB offers robust support through comprehensive tutorials and a wealth of community-driven content. Whether you’re just beginning or looking to optimize existing applications, you’ll find the resources and guidance you need at every stage of your development journey.

Get started!

The MongoDB and Spring AI integration is designed to simplify the development of intelligent Java applications. By combining MongoDB's robust data platform with Spring AI's capabilities, you can create high-performance applications more efficiently.

To start using MongoDB with Spring AI, explore our documentation, tutorial, and check out our GitHub repository to build the next generation of AI-driven applications today.

Head over to our quick-start guide to get started with Atlas Vector Search today.

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Better Business Loans with MongoDB and Generative AI

Business loans are a cornerstone of banking operations, providing significant benefits to both financial institutions and broader economies. For example, in 2023 the value of commercial and industrial loans in the United States reached nearly $2.8 trillion . However, these loans can present unique challenges and risks that banks must navigate. Besides credit risk, where the borrower may default, banks also face business risk, in which economic downturns or sector-specific declines can impact borrowers' ability to repay loans. In this post, we dive into the potential of generative AI to generate detailed risk assessments for business loans, and how MongoDB’s multimodal features can be leveraged for comprehensive and multidimensional risk analyses. The critical business plan A business plan is essential for a business loan as it serves as a comprehensive roadmap detailing the borrower's plans, strategies, and financial projections. It helps lenders understand the business's goals, viability, and profitability, demonstrating how the loan will be used for growth and repayment. A detailed business plan includes market analysis, competitive positioning, operational plans, and financial forecasts which build a compelling case for the lender's investment and the business’s ability to manage risks effectively, increasing the likelihood of securing the loan. Reading through borrower credit information and detailed business plans (roughly 15-20 pages long ) poses significant challenges for loan officers due to time constraints, the material’s complexity, and the difficulty of extracting key metrics from detailed financial projections, market analyses, and risk factors. Navigating technical details and industry-specific jargon can also be challenging and require specialized knowledge. Identifying critical risk factors and mitigation strategies only adds further complexity along with ensuring accuracy and consistency among loan officers and approval committees. To overcome these challenges, gen AI can assist loan officers by efficiently analyzing business plans, extracting essential information, identifying key risks, and providing consistent interpretations, thereby facilitating informed decision-making. Assessing loans with gen AI Interactive risk analysis with gen AI-powered chatbots Gen AI can help analyze business plans when built on a flexible developer data platform like MongoDB Atlas . One approach is implementing a gen AI-powered chatbot that allows loan officers to "discuss" the business plan. The chatbot can analyze the input and provide insights on the various risks associated with lending to the borrower for the proposed business. MongoDB sits at the heart of many customer support applications due to its flexible data model that makes it easy to build a single, 360-degree view of data from a myriad of siloed backend source systems. Figure 1 below shows an example of how ChatGPT-4o responds when asked to assess the risk of a business loan. Although the input of the loan purpose and business description is simplistic, gen AI can offer a detailed analysis. Figure 1: Example of how ChatGPT-4o could respond when asked to assess the risk of a business loan Hallucinations or ignorance? By applying gen AI to risk assessments, lenders can explore additional risk factors that gen AI can evaluate. One factor could be the risk of natural disasters or broader climate risks. In Figure 2 below, we added flood risk specifically as a factor to the previous question to see what the ChatGPT4-o comes back with. Figure 2: Example of how ChatGPT-4o responded to flood risk as a factor Based on the above, there is a low risk of flooding. To validate this, we asked ChatGPT-4o the question differently, focusing on its knowledge of flood data. It suggested reviewing FEMA flood maps and local flood history, indicating it might not have the latest information. Figure 3: Asking location-specific flood questions In the query shown in Figure 3 above, ChatGPT gave an opposite answer and indicated there is “significant flooding” providing references to flood evidence after having performed an internet search across 4 sites which it did not perform previously. From this example, we can see that when ChatGPT does not have the relevant data, it starts to make false claims, which can be considered hallucinations. Initially, it indicated a low flood risk due to a lack of information. However, when specifically asked about flood risk in the second query, it suggested reviewing external sources like FEMA flood maps, recognizing its limitations and need for external validation. Gen AI-powered chatbots can recognize and intelligently seek additional data sources to fill their knowledge gaps. However, a causal web search won’t provide the level of detail required. Retrieval-augmented generation-assisted risk analysis The promising example above demonstrates the experience of how gen AI can augment loan officers to analyze business loans. However, interacting with a gen AI chatbot relies on loan officers repeatedly prompting and augmenting the context with relevant information. This can be time-consuming and impractical due to the lack of prompt engineering skills or the lack of data needed. Below is a simplified solution of how gen AI can be used to augment the risk analysis process to fill the knowledge gap of the LLM. This demo uses MongoDB as an operational data store leveraging geospatial queries to find out the floods within 5km of the proposed business location. The prompting for this risk analysis highlights the analysis of the flood risk assessment rather than the financial projections. A similar test was performed on Llama 3 , hosted by our MAAP partner Fireworks.AI . It tested the model’s knowledge of flood data showing a similar knowledge gap as ChatGPT-4o. Interestingly, rather than providing misleading answers, LLama 3 provided a “hallucinated list of flood data,” but highlighted that “this data is fictional and for demonstration purposes only. In reality, you would need to access reliable sources such as FEMA's flood data or other government agencies' reports to obtain accurate information.” Figure 4: LLM’s response with Fictional flood locations With this consistent demonstration of the knowledge gap in the LLMs in specialized areas, it reinforces the need to explore how RAG (retrieval-augmented generation) with a multimodal data platform can help. In this simplified demo, you select a business location, a business purpose, and a description of a business plan. To make inputs easier, an “Example” button has been added to leverage gen AI to generate a sample brief business description to avoid the need to key in the description template from scratch. Figure 5: Choosing a location on the map and writing a brief plan description Upon submission, it will provide an analysis using RAG with the appropriate prompt engineering to provide a simplified analysis of the business with the consideration of the location and also the flood risk earlier downloaded from external flood data sources. Figure 6: Loan risk response using RAG In the Flood Risk Assessment section, gen AI-powered geospatial analytics enable loan officers to quickly understand historical flood occurrences and identify the data sources. You can also reveal all the sample flood locations within the vicinity of the business location selected by clicking on the “Pin” icon. The geolocation pins include the flood location and the blue circle indicates the 5km radius in which flood data is queried, using a simple geospatial command $geoNear . Figure 7: Flood locations displayed with pins The following diagram provides a logical architecture overview of the RAG data process implemented in this solution highlighting the different technologies used including MongoDB, Meta Llama 3, and Fireworks.AI. Figure 8: RAG data flow architecture diagram With MongoDB's multimodal capabilities, developers can enhance the RAG process by utilizing features such as network graphs, time series, and vector search. This enriches the context for the gen AI agent, enabling it to provide more comprehensive and multidimensional risk analysis through multimodal analytics. Building risk assessments with MongoDB When combined with RAG and a multimodal developer data platform like MongoDB Atlas , gen AI applications can provide more accurate and context-aware insights to reduce hallucination and offer profound insights to augment a complex business loan risk assessment process. Due to the iterative nature of the RAG process, the gen AI model will continually learn and improve from new data and feedback, leading to increasingly accurate risk assessments and minimizing hallucinations. A multimodal data platform would allow you to fully maximize the capabilities of the multimodal AI models. Head over to our quick-start guide to get started with Atlas Vector Search today. If you would like to discover how MongoDB can help you on this multimodal gen AI application journey, we encourage you to apply for an exclusive innovation workshop with MongoDB's industry experts to explore bespoke modern app development and tailored solutions to your organization. Additionally, you can enjoy these resources: Solution GitHub: Loan Risk Assessor How Leading Industries are Transforming with AI and MongoDB Atlas Accelerate Your AI Journey with MongoDB’s AI Applications Program The MongoDB Solutions Library is curated with tailored solutions to help developers kick-start their projects

August 22, 2024

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MongoDB’s 2024 Year in Review

It’s hard to believe that another year is almost over! 2024 was a transformative year for MongoDB, and it was marked by both innovation and releases that further our commitment to empowering customers, developers, and partners worldwide. So without further ado, let’s dive into MongoDB’s 2024 highlights. We’ll also share our executive team’s predictions of what 2025 might have in store. A look back at 2024 MongoDB 8.0: The most performant version of MongoDB ever In October we released MongoDB 8.0 , the fastest, most resilient, secure, and reliable version of MongoDB yet. Architectural optimizations in MongoDB 8.0 have significantly improved the database’s performance, with 36% faster reads and 59% higher throughput for updates. Our new architecture also makes horizontal scaling cheaper and faster. Finally, working with encrypted data is easier than ever, thanks to the addition of range queries in Queryable Encryption (which allows customers to encrypt, store, and perform queries directly on data). Whether you’re a startup building your first app, or you’re a global enterprise managing mission-critical workloads, MongoDB 8.0 offers unmatched power and flexibility, solidifying MongoDB’s place as the world’s most popular document database. Learn more about what makes 8.0 the best version of MongoDB ever on the MongoDB 8.0 page . Delivering customer value with the MongoDB AI Applications Program AI applications have become a cornerstone of modern software, and MongoDB is committed to equipping customers with the technology, tools, and support they need to succeed on their AI journey. That’s why we launched the MongoDB AI Applications Program (MAAP) in 2024, a comprehensive program designed to accelerate the development of AI applications. By offering customers resources like access to AI specialists, an ecosystem of leading AI and tech companies, and AI architectural best practices supported by integrated services, MAAP helps solve customers’ most pressing business challenges, unlocks competitive advantages, and accelerates time to value for AI investments. Overall, MAAP’s aim is to set customers on the path to AI success. Visit the MongoDB AI Applications Program page or watch our session from AWS re:Invent to learn more! Advancing AI with MongoDB Atlas Vector Search In 2024, MongoDB further cemented its role in the AI space with enhancements to MongoDB Atlas Vector Search . Recognized in 2024 (for the second consecutive year!) as one of the most loved vector databases , MongoDB continues to provide a scalable, unified, and secure platform for building cutting-edge AI use cases. Recent advancements like vector quantization in Atlas Vector Search help deliver even more value to our customers, enabling them to scale applications to billions of vectors at a lower cost. Head over to our Atlas Vector Search quick start guide to get started with Atlas Vector Search today, or visit our AI resources hub to learn more about how MongoDB can power AI applications. Search Nodes: Performance at scale Search functionality is indispensable in modern applications, and with Atlas Search Nodes, organizations can now optimize their search workloads like never before. By providing dedicated infrastructure for Atlas Search and Vector Search workloads, Search Nodes ensure high performance (e.g., a 40–60% decrease in query times), scalability, and reliability, even for the most demanding use cases. As of this year , Search Nodes are generally available across AWS, Google Cloud, and Microsoft Azure. This milestone underscores MongoDB’s commitment to delivering powerful solutions that scale alongside our customers’ needs. To learn more about Search Nodes, check out our documentation or watch our tutorial . Looking ahead: MongoDB’s 2025 predictions After the excitement of the past few years, 2025 will be defined by ensuring that technology investments deliver tangible value. Organizations remain excited about the potential AI and emerging technologies hold to solve real business challenges, but are increasingly focused on maintaining a return on investment. “Enterprises need to innovate faster than ever, but speed is no longer the only measure of success. Increasingly, organizations are laser-focused on ensuring that their technology investments directly address critical business challenges and provide clear ROI and competitive advantage—whether it’s optimizing supply chains, delivering hyper-personalized customer experiences, or scaling operations efficiently,” said Sahir Azam, Chief Product Officer at MongoDB. “In 2025, I expect to see organizations make significant strides in driving this innovation and efficiency by applying AI to more production use cases and by maturing the way they leverage their data to build compelling and differentiated customer experiences.” Indeed, we expect to see organizations make more strategic investments in emerging technologies like gen AI—innovating with a sharp focus on solving business challenges. “In 2025, we can expect the focus to shift from ‘what AI can do’ to ‘what AI should do,’ moving beyond the hype to a clearer understanding of where AI can provide real value and where human judgment is still irreplaceable,” said Tara Hernandez, VP of Developer Productivity at MongoDB. “As we advance, I think we’ll see organizations begin to adopt more selective, careful applications of AI, particularly in areas where stakes are high, such as healthcare, finance, and public safety. A refined approach to AI development will be essential—not only for producing quality results but also to build trust, ensuring these tools genuinely support human goals rather than undermining them.” With more capable, accessible application development tools and customer-focused programs like MAAP at developers’ fingertips, 2025 is an opportunity to make a data-driven impact faster than ever before. "Right now, organizations have an opportunity to leverage their data to reimagine how they do business, to more effectively adapt to a changing world, and to revolutionize our quality of life,” said Andrew Davidson, SVP of Products at MongoDB. “By harnessing our latest technologies, developers can build a foundation for a transformative future." Head over to our updates page to learn more about the new releases and updates from MongoDB in 2024. Keep an eye on our events page to learn what's to come from MongoDB in 2025!

December 19, 2024