Building Gen AI with MongoDB & AI Partners | June 2024

Gregory Maxson
July 9, 2024 | Updated: July 10, 2024
#Partners #genAI

Even for those of us who work in AI, keeping up with the latest news in the AI space can be head-spinning. In just the last few weeks, OpenAI introduced their newest model (GPT-4o), Anthropic continued to develop Claude with the launch of Claude 3.5 Sonnet, and Mistral launched Mixtral 8x22B, their most efficient open model to date. And those are only a handful of recent releases!

In such an ever-changing space, partnerships are critical to combining the strengths of organizations to create solutions that would be challenging to develop independently. Also, it can be overwhelming for any one business to keep track of so much change. So there’s a lot of value in partnering with industry leaders and new players alike to bring the latest innovations to customers.

I’ve been at MongoDB for less than a year, but in that time our team has already built dozens of strategic partnerships that are helping companies and developers build AI applications faster and safer. I love to see these collaborations take off!

A compelling example is MongoDB’s recent work with Vercel. Our team developed an exciting sample application that allows users to deploy a retrieval-augmented generation (RAG) application on Vercel in just a few minutes. By leveraging a MongoDB URI and an OpenAI key, users can one-click deploy this application on Vercel.

Another recent collaboration was with Netlify. Our team also developed a starter template that implements a RAG chatbot on top of their platform using LangChain and MongoDB Atlas Vector Search capabilities for storing and searching the knowledge base that powers the chatbot's responses.

These examples demonstrate the power of combining MongoDB's robust database capabilities with other deployment platforms. They also show how quickly and efficiently users can set up fully functional RAG applications, and highlight the significant advantages that partnerships bring to the AI ecosystem.

And the best part? We’re just getting started! Stay tuned for more information about the MongoDB AI Applications Program later this month.

Welcoming new AI partners

Speaking of partnerships, in June we welcomed seven AI partners that offer product integrations with MongoDB. Read on to learn more about each great new partner.

AppMap is an open source personal observability platform to help developers keep their software secure, clear, and aligned.

Elizabeth Lawler, CEO of AppMap, commented on our joint value for developers. “AppMap is thrilled to join forces with MongoDB to help developers improve and optimize their code. MongoDB is the go-to data store for web and mobile applications, and AppMap makes it easier than ever for developers to migrate their code from other data stores to MongoDB and to keep their code optimized as their applications grow and evolve.”

Read more about our partnership and how to use AppMapp to improve the quality of code running with MongoDB.

Mendable is a platform that automates customer services providing quick and accurate answers to questions without human intervention.

Eric Ciarla, co-founder of Mendable, highlighted the importance of our partnership. "Our partnership with MongoDB is unlocking massive potential in AI applications, from go to market copilots to countless other innovative use cases,” he said. “We're excited to see teams at MongoDB and beyond harnessing our combined technologies to create transformative AI solutions across all kinds of industries and functions."

Learn how Mendable and MongoDB Atlas Vector Search power customer service applications.

OneAI is an API-first platform built for developers to create and manage trusted GPT chatbots.

Amit Ben, CEO of One AI, shared his excitement about the partnership. "We're thrilled to partner with MongoDB to help customers bring trusted GenAI to production. OneAI's platform, with RAG pipelines, LLM-based chatbots, goal-based AI, anti-hallucination guardrails, and language analytics, empowers customers to leverage their language data and engage users even more effectively on top of MongoDB Atlas."

Check out some One AI’s GPT agents & advanced RAG pipelines built on MongoDB.

Prequel allows companies to sync data to and from their customers' data warehouses, databases, or object storage so they get better data access with less engineering effort.

"Sharing MongoDB data just got easier with our partnership,” celebrated Charles Chretien, co-founder of Prequel. “Software companies running on MongoDB can use Prequel to instantly share billions of records with customers on every major data warehouse, database, and object storage service.”

Learn how you can share MongoDB data using Prequel.

Qarbine complements summary data visualization tools allowing for better informed decision-making across teams.

Bill Reynolds, CTO of Qarbine, mentioned the impact of our integration to distill better insights from data: “We’re excited to extend the many MongoDB Atlas benefits upward in the modern application stack to deliver actionable insights from publication quality drill-down analysis. The native integrations enhance in-app real-time decisions, business productivity and operational data ROI, fueling modern application innovation.”

Want to power up your insights with MongoDB Atlas and Qarbine? Read more.

Temporal is a durable execution platform for building and scaling invincible applications faster.

"Organizations of all sizes have built AI applications that are ‘durable by design’ using MongoDB and Temporal. The burden of managing data and agent task orchestration is effortlessly abstracted away by Temporal's development primitives and MongoDB's Atlas Developer Data Platform”, says Jay Sivachelvan, VP of Partnerships at Temporal. He also highlighted the benefits of this partnership. “These two solutions, together, provide compounding benefits by increasing product velocity while also seamlessly automating the complexities of scalability and enterprise-grade resilience."

Learn how to build microservices in a more efficient way with MongoDB and Temporal.

Unstructured is a platform that connects any type of enterprise data for use with vector databases and any LLM framework. Read more about enhancing your gen AI application accuracy using MongoDB and Unstructured.

But wait, there's more!

To learn more about building AI-powered apps with MongoDB, check out our AI Resources Hub, and stop by our Partner Ecosystem Catalog to read about our integrations with MongoDB’s ever-evolving AI partner ecosystem.

← Previous

Elevate Your Python AI Projects with MongoDB and Haystack

MongoDB is excited to announce an integration with Haystack, enhancing MongoDB Atlas Vector Search for Python developers. This integration amplifies our commitment to providing developers with cutting-edge tools for building AI applications centered around semantic search and Large Language Models (LLMs). We’re excited to partner with MongoDB to help developers build top-tier LLM applications. The new Haystack and MongoDB Atlas integration lets developers seamlessly use MongoDB data in Haystack, a reliable framework for creating quality LLM pipelines for use cases like RAG, QA, and agentic pipelines. Whether you're an experienced developer or just starting, your gen AI projects can quickly progress from prototype to adoption, accelerating value for your business and end-users. Malte Pietsch, co-founder and CTO, deepset Simplifying AI app development with Haystack Haystack is an open-source Python framework that simplifies AI application development. It enables developers to start their projects quickly, experiment with different AI models, and to efficiently scale their applications. Indeed, Haystack is particularly effective for building applications requiring semantic understanding and natural language processing (NLP), such as chatbots and question-answering systems. Haystack’s core features include: Components: Haystack breaks down complex NLP tasks into manageable components, such as document retrieval or text summarization. With the new MongoDB-Haystack integration, MongoDB becomes the place where all your data lives, ready for Haystack to use. Pipelines: Haystack lets you link components together into pipelines for more complex tasks. With this integration, your MongoDB data flows through these pipelines. Agents: Haystack Agents use LLMs to resolve complex queries. They can decide which tools (or components) to use for a given question, leveraging MongoDB data to deliver smarter answers. Atlas Vector Search: Enhance AI development with Haystack At the heart of the new integration is MongoDB Atlas Vector Search , transforming how applications search and retrieve data. By leveraging vector embeddings, Atlas Vector Search goes beyond mere keyword matching: it interprets the intent behind queries, enabling applications to provide highly relevant, context-aware responses. This is a breakthrough for Python developers who aim to build applications that think and understand like humans. Building on this foundation, the Atlas Vector Search and Haystack integration gives Python developers a powerful toolkit for navigating the complexities of AI application development. MongoDB becomes a dynamic document store within Haystack's framework, optimizing data storage, processing, and retrieval. Additionally, the integration eases the use of advanced AI models from leading providers such as OpenAI and Cohere into your applications. Developers can thus create applications that do more than just answer queries—they grasp and act on the underlying intent, ensuring responses are both accurate and contextually relevant. What this means for Python developers For Python developers, this integration means: Faster development: Developers can focus on building and innovating rather than spending time configuring and managing infrastructure. MongoDB's integration with Haystack means you can get up and running quickly, leveraging the best of both technologies to accelerate your development cycles. Smarter applications: By utilizing Haystack's powerful Natural Language Processing tooling in combination with MongoDB Atlas Vector Search’s efficient data handling, developers can create applications that understand and process natural language more effectively. This results in applications that can provide more accurate and contextually relevant responses that resonate with user intent. Access to pre-trained AI models: With seamless integration of leading generative AI models from providers like OpenAI, Anthropic, Cohere, Hugging Face, and AWS Bedrock, Python developers can easily incorporate advanced AI functionalities into their projects. This means developers can quickly adopt state-of-the-art models without the need for extensive training or fine-tuning, saving time and resources. Flexible and scalable pipelines: Haystack's modular approach to building AI applications, through its use of components and pipelines, allows developers to create flexible and scalable solutions. With MongoDB data seamlessly flowing through these pipelines, you can easily adapt and expand your applications to meet growing demands and new challenges. Robust search capabilities: Atlas Vector Search transforms the way applications retrieve and interpret data, going beyond simple keyword searches. It enables applications to perform high-precision searches that return more relevant and semantically rich results. This advanced search capability is crucial for developing applications that require high levels of semantic understanding and accuracy. By integrating MongoDB with Haystack, Python developers are equipped with a powerful toolkit that not only simplifies the AI development process but also significantly enhances the intelligence and functionality of their applications. Whether you are building chatbots, search engines, or other AI-driven applications, this integration provides the tools you need to create innovative and impactful solutions. Get started now Start leveraging the MongoDB and Haystack integration for your AI development. Explore our tutorial , documentation , or check out our github repository to begin building smarter, more intuitive Python projects today!

July 8, 2024

Next →

The Converged AI and Application Datastore for Insurance

In the inherently information-driven insurance industry, companies ingest, analyze, and process massive amounts of data, requiring extensive decision-making. To manage this, they rely on a myriad of technologies and IT support staff to keep operations running smoothly but often lack effectiveness due to their outdated nature. Artificial intelligence (AI) holds great promise for insurers by streamlining processes, enhancing decision-making, and improving customer experiences with significantly less time, resources, and staff compared with traditional IT systems. The convergence of AI and innovative application datastores is transforming how insurers work with data. In this post, we’ll look at how these elements are reshaping the insurance industry and offering greater potential for AI-powered applications, with MongoDB at the heart of the converged AI and application datastore. Scenario planning and flexible data layers One of the primary concerns for IT leaders and decision-makers in the insurance industry is making smart technology investments. The goal is to consolidate existing technology portfolios, which often include a variety of systems like SQL Server, Oracle, and IBM IMS. Consolidation helps reduce inventory and prepare for the future. But what does future-proofing really look like? Scenario planning is an effective strategy for future-proofing. This involves imagining different plausible futures and investing in the common elements that remain beneficial across all scenarios. For insurance companies, a crucial common thread is the data layer. By making data easier to work with, companies can ensure that their technology investments remain valuable regardless of how future scenarios unfold. MongoDB’s flexible developer data platform offers a distinct architectural advantage by making data easier to work with, regardless of the cloud vendor or AI application in use. This flexibility is vital for preparing for disruptive future scenarios, whether they involve regulatory changes, market shifts, or technological advancements. Watch now: The Converged AI and Application Datastore: How API's, AI & Data are Reshaping Insurance The role of AI and data in insurance Generative AI is revolutionizing the insurance sector, offering new ways to manage and utilize data. According to Celent's 2023 Technology Insight and Strategy Survey, 33% of companies across different industries have AI projects in planning, 29% in development, and 19% in production (shown in Figure 1 below). This indicates a significant shift towards AI-driven solutions by insurers actively experimenting with gen AI. Figure 1: Celent Technology Insight and Strategy Survey 2023 However, there's tension between maintaining existing enterprise systems and innovating with AI. Insurance companies must balance keeping the lights on with investing in AI to meet the expectations of boards and stakeholders. The solution lies in integrating AI in a way that enhances operational efficiency without overwhelming existing systems. However, data challenges need to be addressed to achieve this, specifically around access to data. According to a Workday Global Survey , only 4% of respondents said their data is fully accessible, and 59% say their enterprise data is somewhat or completely siloed. Without a solid data foundation, insurers will struggle to achieve the benefits they are looking for from AI. Data architectures and unstructured data When adopting advanced technologies like AI and ML, which require data as the foundation, organizations often grapple with the challenge of integrating these innovations into legacy systems due to their inflexibility and resistance to modification. A robust data architecture is essential for future-proofing and consolidating technology investments. Insurance companies often deal with a vast amount of unstructured data, such as claim images and videos, which can be challenging to manage. By leveraging AI, specifically through vector search and large language models, companies can efficiently process and analyze this data. MongoDB is ideal for managing unstructured data due to its flexible, JSON-like document model, which accommodates a wide variety of data types and structures without requiring a predefined schema. Additionally, MongoDB’s flexibility enables insurers to integrate seamlessly with various technologies, making it a versatile and powerful solution for unstructured data management. For example, consider an insurance adjuster assessing damage from claim photos. Traditionally, this would require manually reviewing each image. With AI, the photos can be converted into vector embeddings and matched against a database of similar claims, drastically speeding up the process. This not only improves efficiency but also enhances the accuracy of assessments. The converged AI and application datastore with MongoDB Building a single view of data across various systems is a game-changer for the insurance industry. Data warehouses and data lakes have long provided single views of customer and claim data, but they often rely on historical data, which may be outdated. The next step is integrating real-time data with these views to make them more dynamic and actionable. A versatile database platform plays a crucial role in this integration. By consolidating data into a single, easily accessible view, insurance companies can ensure that various personas, from underwriters to data scientists, can interact with the data effectively. This integration allows for more responsive and informed decision-making, which is crucial for staying competitive in a rapidly evolving market. This can be achieved with a converged AI and application datastore, as shown in Figure 2 below. This is where operational data, analytics insights, and unstructured data become operationally ready for the applications that leverage AI. Figure 2: Converged AI and application datastore reference architecture The convergence of AI, data, and application datastores is reshaping the insurance industry. By making smart technology investments, leveraging AI to manage unstructured data, and building robust data architectures, insurance companies can future-proof their operations and embrace innovation. A versatile and flexible data platform provides the foundation for these advancements, enabling companies to make their data more accessible, actionable, and valuable. The MongoDB Atlas developer data platform puts powerful AI and analytics capabilities directly in the hands of developers and offers the capabilities to enrich applications by consolidating, ingesting, and acting on any data type instantly. Because MongoDB serves as the operational data store (ODS)—with its flexible document model—insurers can efficiently handle large volumes of data in real-time. By integrating MongoDB with AI/ML platforms, insurers can develop models trained on the most accurate and up-to-date data, thereby addressing the critical need for adaptability and agility in the face of evolving technologies. With built-in security controls across all data, whether managed in a customer environment or through MongoDB Atlas, a fully managed cloud service, MongoDB ensures robust security with features such as authentication (single sign-on and multi-factor authentication), role-based access controls, and comprehensive data encryption. These security measures act as a safeguard for sensitive data, mitigating the risk of unauthorized access from external parties and providing organizations with the confidence to embrace AI and ML technologies. If you would like to learn more about the convergence of AI and application datastores, visit the following resources: Video: The Converged AI and Application Datastore: How API's, AI & Data are Reshaping Insurance Paper: Innovation in Insurance with Artificial Intelligence The MongoDB Solutions Library is curated with tailored solutions to help developers kick-start their projects

July 18, 2024