Supercarregue aplicativos autogerenciados com capacidades de pesquisa e pesquisa vetorial

Gyana Guity, Scott Amerman, and Sam Wickline
September 17, 2025 | Updated: September 30, 2025

O MongoDB tem o prazer de anunciar a prévia pública das funcionalidades de pesquisa e pesquisa vetorial para uso com o MongoDB Community Edition e o MongoDB Enterprise Server. Essas novas capacidades permitem que os desenvolvedores prototipem, iterem e construam aplicações sofisticadas com tecnologia de IA diretamente em ambientes autogerenciados com funcionalidades de pesquisa robustas.

A versatilidade é uma das razões pelas quais os desenvolvedores adoram o MongoDB. O MongoDB pode ser executado em qualquer lugar.1 Isso inclui configurações locais onde muitos desenvolvedores iniciam sua jornada no MongoDB, até os maiores data centers corporativos quando é hora de dimensionar, e o serviço de cloud totalmente gerenciado do MongoDB, o MongoDB Atlas. Independentemente de onde o desenvolvimento ocorra, o MongoDB se integra sem esforço ao fluxo de trabalho de qualquer desenvolvedor.

MongoDB Community Edition é a versão gratuita e com código disponível do MongoDB que milhões de desenvolvedores usam para aprender, testar e aprimorar suas habilidades. O MongoDB Enterprise Server é a versão comercial do banco de dados principal do MongoDB. Ele oferece recursos adicionais de nível empresarial para empresas que preferem autogerenciar suas implantações no local ou em ambientes de nuvem pública, privada ou híbrida.

Com funcionalidades nativas de pesquisa e pesquisa vetorial agora disponíveis para uso com a Community Edition e o Enterprise Server, o MongoDB visa oferecer uma experiência mais simples e consistente para a criação de ótimas aplicações onde quer que sejam implantadas.

O que é pesquisa e pesquisa vetorial?

Semelhante às ofertas do MongoDB Atlas, o MongoDB Community Edition e o MongoDB Enterprise Server agora oferecem suporte a duas capacidades de pesquisa distintas, mas complementares:

  • A pesquisa de texto completo é uma capacidade incorporada que oferece uma experiência contínua e dimensionável para a criação de funcionalidades de aplicativos baseados em relevância.
  • A pesquisa vetorial permite que os desenvolvedores criem aplicativos inteligentes com tecnologia de pesquisa semântica e IA generativa usando recursos nativos e completos de banco de dados vetorial.

Não existem limitações funcionais nos estágios principais de agregação de pesquisa nesta prévia pública. Portanto, $search, $searchMeta e $vectorSearch são todos suportados com paridade funcional ao que está disponível no Atlas, exceto por recursos em estado de visualização. Para obter mais informações, consulte as páginas de documentação de pesquisa e pesquisa vetorial.

Resolvendo desafios de desenvolvedores com pesquisa integrada

Historicamente, integrar recursos avançados de pesquisa em aplicações autogerenciadas frequentemente exigia acoplar mecanismos de pesquisa externos ou bancos de dados vetoriais ao MongoDB. Essa abordagem criou atrito em todas as etapas para desenvolvedores e organizações, resultando em:

  • Complexidade arquitetônica: Gerenciar e sincronizar dados em múltiplos sistemas díspares adicionou camadas de complexidade, exigiu habilidades adicionais e complicou os fluxos de trabalho de desenvolvimento.
  • Sobrecarga operacional: o gerenciamento separado de provisionamento, segurança, upgrades e monitoramento para cada sistema impôs uma carga pesada nas equipes de DevOps.
  • Redução da produtividade dos desenvolvedores: os desenvolvedores são obrigados a aprender e utilizar diferentes APIs e linguagens de query tanto para o banco de dados quanto para o mecanismo de pesquisa. Isso resultou em mudanças frequentes de contexto, curvas de aprendizado mais íngremes e ciclos de lançamento mais lentos.
  • Desafios de consistência: alinhar o banco de dados primário com índices de pesquisa ou vetoriais separados corre o risco de produzir resultados fora de sincronização. Apesar das promoções de garantias transacionais e consistência dos dados, esses índices eram apenas eventualmente consistentes. Isso resultou em resultados incompletos em ambientes de rápida mudança.
    •

Com a pesquisa e a pesquisa vetorial agora integradas ao MongoDB Community Edition e ao MongoDB Enterprise Server, esses trade-offs desaparecem. Os desenvolvedores agora podem criar poderosas funcionalidades de pesquisa usando o query framework familiar do MongoDB, removendo a carga de sincronização e a necessidade de gerenciar múltiplos sistemas de propósito único. Esta versão simplifica a arquitetura de dados, reduz a sobrecarga operacional e acelera o desenvolvimento de aplicativo.

Com essas capacidades, os desenvolvedores podem aproveitar sofisticadas capacidades prontas para uso para criar uma variedade de aplicativos poderosos. Os possíveis casos de uso incluem:

O MongoDB oferece integrações nativas com frameworks como o LangChain, o LangGraph e o LlamaIndex. Isso agiliza os fluxos de trabalho, acelera o desenvolvimento e incorpora recursos RAG ou agênticos diretamente nos aplicativos. Para aprender mais sobre outros frameworks de IA suportados pelo MongoDB, consulte esta documentação.

Os parceiros e campeões do MongoDB já estão experimentando os benefícios de utilizar a pesquisa e a pesquisa vetorial em uma faixa mais ampla de ambientes:

"Estamos entusiasmados com o fato de que a pesquisa do MongoDB e a pesquisa vetorial agora estão acessíveis na já popular MongoDB Community Edition. "Agora nossos clientes podem aproveitar o MongoDB e o LangChain em qualquer modo de implantação e no ambiente de sua preferência para criar aplicações de LLM de ponta." — Harrison Chase, CEO da LangChain.

"O MongoDB ajudou a Clarifresh a desenvolver um software incrível, e sempre fiquei impressionado com suas bases sólidas Com as capacidades de pesquisa e pesquisa vetorial agora disponíveis no MongoDB Community Edition, ganhamos a confiança de um código-fonte acessível, a flexibilidade para implantar em qualquer lugar e a promessa de extensibilidade impulsionada pela comunidade. "É um marco empolgante que reafirma o compromisso do MongoDB com os desenvolvedores." — Luke Jackson, MongoDB Campeões, Clarofresh.

“Estamos animados com a próxima iteração das experiências de pesquisa no MongoDB Community Edition "Nossos clientes desejam a máxima flexibilidade para executar suas aplicações de pesquisa e IA generativa, e trazer essa funcionalidade para a Community desbloqueia uma nova maneira de construir e testar em qualquer lugar." —Jerry Liu, CEO da LlamaIndex.

Participar da prévia privada de Full-text e pesquisa vetorial para a MongoDB Community foi uma oportunidade emocionante Ter $search, $searchMeta e $vectorSearch diretamente no Community Edition traz as mesmas capacidades poderosas que usamos no Atlas—sem sistemas ou integrações adicionais. "Mesmo na pré-visualização inicial, ele já está simplificando os fluxos de trabalho e produzindo resultados mais rápidos e relevantes." —Michael Höller, MongoDB Champion, akazia consultoria.

Acessando a pré-visualização pública

A prévia pública está disponível gratuitamente e destina-se apenas a fins de teste, avaliação e feedback.

Pesquisa e pesquisa vetorial com o MongoDB Community Edition. As novas funcionalidades são compatíveis com a versão 8.2+ do MongoDB e operam em um binário separado, o mongot, que faz a interação com o binário padrão do banco de dados mongodb.

Para começar, certifique-se de que:

  • Um cluster do MongoDB Community Server está em execução usando um dos três métodos a seguir:
    • Baixe o MongoDB Community Server versão 8.2 na página de baixar do MongoDB. A partir da pré-visualização pública, este recurso está disponível para implantações autogerenciadas em distribuições e arquiteturas Linux suportadas para o MongoDB Community Edition versão 8.2+.
    • Baixe o binário do mongot na página de baixar do MongoDB.
    • Baixe a imagem do container do Community Server 8.2 de um repositório público do Docker Hub.
    • Em breve: implantar usando os controladores MongoDB para o operador Kubernetes (o suporte de pesquisa para o Community Server está planejado para a versão 1.5+).

Pesquisa e Vector Search para uso com o MongoDB Enterprise Server. As novas funcionalidades são implantadas como nós de pesquisa autogerenciados no ambiente Kubernetes de um cliente. Isso se conectará perfeitamente a qualquer cluster do MongoDB Enterprise Server, que resida dentro ou fora do próprio Kubernetes.

Para começar, certifique-se de que:

  • Um cluster do Servidor MongoDB Enterprise está em execução.
    • versão 8.0.10+ (para Operador de Kubernetes do MongoDB 1.4).
    • versão 8.2+ (para Operador de Kubernetes do MongoDB 1.5+).
  • Um ambiente Kubernetes.
  • Os Controladores MongoDB para o Operador Kubernetes são instalados no cluster Kubernetes. Encontre as instruções de instalação aqui.
    •

A documentação abrangente para configurar o MongoDB Community Edition e o MongoDB Enterprise Server também está disponível.

O que vem a seguir

Durante a visualização pública, o MongoDB fornecerá atualizações adicionais e recursos do roteiro com base no feedback dos clientes. Após a prévia pública, espera-se que essas funcionalidades de pesquisa e pesquisa vetorial estejam geralmente disponíveis para uso com implantações no local. Para a Community Edition, essas funcionalidades estarão disponíveis sem custo adicional como parte da Server Side Public License (SSPL).

Para o MongoDB Enterprise Server, essas funcionalidades serão incluídas em uma nova oferta de assinatura paga que será lançada no futuro. Os detalhes de preços e pacotes da assinatura estarão disponíveis mais próximo do lançamento. Para desenvolvedores que buscam uma experiência totalmente gerenciada na nuvem, o MongoDB Atlas oferece uma versão pronta para produção dessas capacidades hoje.

O MongoDB adoraria receber feedback! Sugira novos recursos ou vote em ideias existentes em feedback.mongodb.com. A entrada é crítica para moldar o futuro deste produto. Os usuários podem entrar em contato com a equipe de contas do MongoDB para fornecer um feedback mais abrangente.

Consulte a documentação do MongoDB para aprender como iniciar com a pesquisa e a Vector Search no MongoDB Community Edition e no MongoDB Enterprise Server.

1 O MongoDB pode ser implantado como um serviço multinuvem totalmente gerenciado em todos os principais provedores de nuvem pública, em nuvens privadas, localmente, no local e em ambientes híbridos.

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MongoDB AMP: An AI-Driven Approach to Modernization

Why should a database company be your modernization partner? It’s a fair question. From over a decade of experience with database migrations, we've learned that the database is often the single biggest blocker preventing digital transformation. It's where decades of business logic have been embedded, where critical dependencies multiply, and where the complexity that blocks innovation actually lives. But by working with MongoDB, customers have found that transforming their data layer removed the barriers that had stalled previous modernization attempts. This post is also available in: Deutsch , Français , Español , Português , Italiano , 한국어 , 简体中文 . Now, with today’s launch of the MongoDB Application Modernization Platform (AMP), we're providing customers a proven approach to full-stack modernization. MongoDB AMP is an AI-powered solution that rapidly and safely transforms legacy applications into modern, scalable services. MongoDB AMP integrates agentic AI workflows into our modernization methodology, alongside reusable, battle-tested tooling, and the expertise we've developed through customer engagements over the past decade—a powerful combination of tools, technique, and talent. By combining AMP tooling with MongoDB’s proven, repeatable framework, customers have seen tasks like code transformation sped up by 10x or more—with overall modernization projects implemented 2–3 times faster on average. Figure 1. The MongoDB Application Modernization Platform. The common challenges Many of our customers are facing the same impossible choice: accept growing technical debt that slows every business initiative, or risk disruption with a full system rewrite. Their teams are stuck maintaining legacy code instead of building new capabilities. These legacy systems have evolved into interconnected webs (“spaghetti messes”) where even simple changes require coordination across multiple systems and teams. Database changes require corresponding updates to middleware integrations, application business logic, and user interface components. Teams struggle to update systems because any change brings risks breaking something else they don't fully understand. Innovation initiatives often get blocked because new capabilities struggle to integrate within the constraints of legacy systems. Technical debt accumulates with every workaround, making each subsequent change more complex and risky than the last. Before working with MongoDB, Intellect Design's Wealth Management platform exemplified this challenge perfectly . Key business logic was locked in hundreds of SQL stored procedures, leading to batch processing delays of up to eight hours and limiting scalability as transaction volumes grew. The platform’s rigid architecture hindered innovation and blocked integration with other systems, such as treasury and insurance platforms, preventing the delivery of unified financial services that their enterprise clients demanded. In cases like this, the result is stagnation disguised as stability. Systems "work" but can't evolve. Applications can handle today's requirements, but can't adapt to tomorrow's opportunities. Legacy architectures have become the foundation on which everything else depends—and the constraint that prevents everything else from changing. Battle-tested solutions By working through challenges with customers, we've built a comprehensive methodology for modernization, backed by sophisticated tools that address the messy reality of legacy applications. Our approach empowers application teams with proven processes and purpose-built technology to systematically address key challenges. Central to our methodology is a test-first philosophy that has proven essential for safe, reliable modernization. Before any transformation begins, we develop comprehensive test coverage for existing applications, creating a baseline that captures how legacy systems actually behave in production. This upfront investment in testing becomes the foundation for everything that follows, providing guardrails that ensure modernized code performs identically to the original while giving teams the confidence to make changes without fear of breaking critical business processes. Our test-driven approach ensures modernization is a methodical, validated process where every change is verified. Before we make any code changes, we establish a complete picture of the legacy system. We've built sophisticated analysis tools that comprehensively map legacy application architectures. These tools uncover the complex interdependencies and embedded logic that make legacy applications far more intricate than they appear on the surface. This deep analysis isn't just about cataloging complexity; it's about understanding the true scope, informing execution of the transformation, and identifying potential risks before they derail projects. Analysis is just the start. By working with customers, we've learned that successful modernization requires careful sequencing and planning. Our dependency analysis capabilities help teams understand not just what needs to be migrated, but the critical order of operations and what safeguards need to be in place at each step. It's critical to avoid the temptation to migrate everything at once. MongoDB’s approach is designed to make complex modernizations successful by transforming applications incrementally with robust validation. Instead of crossing your fingers and hoping everything works after months of development, our methodology decomposes large modernization efforts into manageable components where every component is iteratively tested and verified. Issues are caught early when they're easy to fix, not after months of development when rollback becomes costly and complex. Each successful iteration reduces risk rather than accumulating it. The agentic AI acceleration MongoDB AMP represents over two years of dedicated effort to integrate AI-powered automation into our battle-tested processes, dramatically accelerating modernization while maintaining the reliability our customers depend on. AI powerfully expands our validation processes by generating additional test cases to validate modernized applications against their legacy counterparts. This dramatically improves confidence in migration results while reducing the time teams spend manually creating test cases for the complex business logic they are trying to preserve. Our existing analysis tools, which decompose embedded logic into smaller segments, now feed directly into AI systems that can automatically transform the code components they discover. What once required weeks of manual code conversion can now happen in hours, with testing frameworks providing the same rigorous validation we've always insisted on. For example, Bendigo and Adelaide Bank reduced the development time to migrate a banking application by up to 90% . The difference is speed and scale, without sacrificing quality or safety. Figure 2. The AMP process. Years of customer engagement and refined processes provide the foundation and guardrails that make AI-powered modernization effective and safe. With MongoDB AMP, AI becomes a force multiplier that transforms our proven approach into something that can tackle modernization challenges at unprecedented speed and scale. Migrating simple code is now 50 to 60 times quicker, and we can migrate small applications 20 times faster to MongoDB. Regression testing also went from three days to three hours with automated test generation. Fabrice Bidard, Head of Technical Architecture, Lombard Odier Ready to begin your modernization journey? Legacy application modernization doesn't have to be a leap of faith. With MongoDB as your partner, you gain access to proven methodologies, battle-tested tools, and the accelerated capabilities that agentic AI brings to our existing expertise. Contact our team to discuss your specific challenges and learn how our proven methodology can be applied to your environment.

September 16, 2025
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Innovating with MongoDB | Customer Successes, October 2025

It’s officially fall! The start of every new season is a perfect time to consider change and new beginnings. While fall might make you think about pumpkin spice and newly chilly evenings, I’m thinking about the latest round of transformations that MongoDB’s customers are embracing to thrive in an AI-powered world. In all seriousness, legacy systems and technical debt are huge challenges: the cost of tech debt has been estimated at almost $4 trillion dollars. That’s trillion with a T! Legacy systems can slow down innovation, create bottlenecks, and make it tough to deliver the seamless, real-time experiences customers increasingly expect. But companies are finding that modernizing their applications isn't just about fixing what's broken—modernization enables them to move faster and innovate for end-users. That’s why I'm incredibly excited to share the recent launch of MongoDB’s Application Modernization Platform (AMP). This AI-powered program is designed to help enterprises move beyond outdated infrastructures to embrace a flexible, data-driven future. AMP is a comprehensive approach to modernization that combines smart AI tooling with proven methodologies, enabling businesses to transform their applications from the ground up, moving from legacy monoliths to a more flexible, microservices-based architecture. In this roundup, we're spotlighting customers who understand the strategic importance of modernization. You'll see how Wells Fargo is using MongoDB to power a new credit card platform, how CSX is ensuring business continuity during a critical migration, how Intellect Design is modernizing its wealth management platform, and how Deutsche Telekom is transforming its B2C digital channels. With MongoDB, customers are showing how integral a modern database is to powering the next generation of applications—and succeeding in the AI era. Wells Fargo Wells Fargo sought to modernize its mainframe-dependent credit card platform to provide a faster, more seamless customer experience and handle an exponential increase in transaction data. The company's legacy system was costly to manage and lacked the scalability needed for its "Cards 2.0" initiative. To solve this, Wells Fargo built an operational data store (ODS) using MongoDB. This new platform allowed them to adopt reusable APIs, streamline integrations, and move from a monolithic architecture to flexible microservices. The ODS now serves 40% of traffic from external vendors, handling more than 7 million transactions with sub-second service. By leveraging MongoDB, Wells Fargo was able to jumpstart its mainframe modernization and create curated data products to serve real-time, personalized financial services. CSX CSX , a major U.S. railroad company, sought to modernize its critical operations platform, RTOP, by migrating it to the cloud. The challenge was to maintain the platform's 24/7 availability with minimal disruption to its mission-critical, near real-time operations during the transition. To solve this, CSX selected MongoDB Atlas on Azure and partnered with MongoDB Professional Services . Leveraging the Cluster-to-Cluster Sync feature, the team was able to facilitate continuous data synchronization and complete the entire migration in just a few hours. The move to MongoDB Atlas has equipped CSX with a more scalable and resilient platform. This modernization effort established a blueprint for migrating other critical applications and helped CSX continue its digital transformation journey toward becoming America’s best-run railroad. Intellect Design Intellect Design , a global fintech company, sought to modernize its wealth management platform to overcome legacy system bottlenecks and multihour batch processing delays. The company's rigid relational database architecture limited its ability to scale and innovate. To solve this, the company partnered with MongoDB, using our AMP methodology and generative AI tools. This transformation reengineered the platform's core components, resulting in an 85% reduction in onboarding workflow times, allowing clients to access critical portfolio insights faster than ever. This initiative is the first step in Intellect Design's long-term vision to integrate its entire application suite into a unified, AI-driven service. By leveraging MongoDB Atlas's flexible schema and powerful native tools, the company is now better positioned to deliver smarter analytics and advanced AI capabilities to its customers. Watch Intellect AI’s MongoDB.local Bengaluru keynote presentation to learn how AMP helped them transform outdated systems into scalable, modern solutions. Deutsche Telekom Deutsche Telekom , a leading telecommunications company, sought to modernize its B2C digital channels, which were fragmented by outdated legacy systems. The company needed to create a unified digital experience for its 30 million customers while improving developer productivity. By leveraging MongoDB Atlas as part of its Internal Developer Platform, Deutsche Telekom built a robust data infrastructure to unify customer data and power its new digital services. This approach allowed the company to retire legacy systems and reduce its reliance on physical shops and call centers. The transition to MongoDB Atlas led to a massive surge in digital engagement, with daily customer interactions rising from under 50,000 to approximately 1.5 million. The company's customer data platform now handles up to 15 times the load of legacy systems, supporting large-scale loyalty programs and transforming the customer experience. Video spotlight: Bendigo Bank Before you go, watch how Bendigo and Adelaide Bank modernized their core banking technology using MongoDB Atlas and generative AI. Bendigo and Adelaide Bank reduced the migration time for legacy applications from 80 hours to just five minutes. This innovative approach allowed them to quickly modernize their systems and better serve their 2.5 million customers. Want to get inspired by your peers and discover all the ways we empower businesses to innovate for the future? Visit MongoDB’s Customer Success Stories hub to see why these customers, and so many more, build modern applications with MongoDB.

October 2, 2025