Building AI with MongoDB: Supercharging Three Communication Paradigms

Mat Keep and Elliott Gluck
October 16, 2023 | Updated: January 10, 2024
#genAI #Vector Search

Communication mediums are core to who we are as humans, from understanding each other to creating bonds and a shared purpose. The methods of communication have evolved over thousands of years, from cave drawings and scriptures to now being able to connect with anyone at any time via internet-enabled devices. The latest paradigm shift to supercharge communication is through the use and application of natural language processing and artificial intelligence.

In our latest roundup of AI innovators building with MongoDB, we’re going to focus on three companies building the future across three mediums of communication: data, language, and video. Our blog begins by featuring SuperDuperDB. The company provides tools for developers to apply AI and machine learning on top of their existing data stores for generative AI applications such as chatbots, Question-Answering (Q-A), and summarization. We then cover Algomo, who uses generative AI to help companies offer their best and most personalized service to customers and employees across more than 100 languages. Finally, Source Digital is a monetization platform delivering a new era of customer engagement through video and the metaverse.

Let’s dive in to learn more about each company and use case.

Check out our AI resource page to learn more about building AI-powered apps with MongoDB.

Bringing AI to your database

SuperDuperDB is an open-source Python package providing tools for developers to apply AI and machine learning on top of their existing data stores. Developers and data scientists continue to use their preferred tools, avoiding both data migration and duplication to specialized data stores. They also have the freedom to run SuperDuperDB anywhere, avoiding lock-in to any one AI ecosystem.

With SuperDuperDB developers can:

Deploy their chosen AI models to automatically compute outputs (inference) in their database in a single environment with simple Python commands.
Train models on their data simply by querying without additional ingestion and pre-processing.
Integrate AI APIs (such as OpenAI) to work together with other models on their data effortlessly.
Search data with vector search, including model management and serving.

Today SuperDuperDB supports MongoDB alongside select relational databases, cloud data warehouses, data lake houses, and object stores.

Graphic displaying the workflow from engineers and the Developer API to SuperDuperDB. The users, in this case Platform engineers, data scientists, ML Ops engineers, and BI users, interact with the platforms in the developer API (Python, jupyter, and upcoming SDKs). The developer API flows into SuperDuperDB which interacts with the Data Layers through model outputs, vector search embeddings, and documents. SuperDuperDB then interacts with the application layer through Rest API and displays data to the users.

SuperDuperDB provides an array of sample use cases and notebooks that developers can use to get started including vector search with MongoDB, multimodal search, retrieval augmented generation (RAG), transfer learning, and many more.

The team has also built an AI chatbot app that allows users to ask questions about technical documentation. The app is built on top of MongoDB and OpenAI with FastAPI and React (FARM stack) + SuperDuperDB. It showcases how easily developers can build next-generation AI applications on top of their existing data stores with SuperDuperDB. You can try the app and read more about how it is built at SuperDuperDB's documentation.

“We integrate MongoDB as one of the key backend databases for our platform, the PyMongo driver for the app connectivity and Atlas Vector Search for storing and querying vector embeddings”, said Duncan Blythe, co-founder of SuperDuperDB. “It therefore made sense for us to partner more closely with the company through MongoDB Ventures. We get direct access to the MongoDB engineering team to help optimize our product, along with visibility within MongoDB’s vast ecosystem of developers.”

Here are some useful links to learn more:

Conversational support, powered by generative AI

Algomo uses generative AI to help companies offer their best service to both their customers and employees across more than 100 languages. The company’s name is a portmanteau of the words Algorithm (originating from Arabic) and Homo, (human in Latin). It reflects the two core design principles underlying Algomo’s products:

Human-centered AI that amplifies and augments rather than displaces human abilities.
Inclusive AI that is accessible to all, and that is non-discriminatory and unbiased in its outputs.

With Algomo, customers can get a ChatGPT-powered bot up on their site in less than 3 minutes. More than just a bot, Algomo also provides a complete conversational platform. This includes Question-Answering text generators and autonomous agents that triage and orchestrate support processes, escalating to human support staff for live chat as needed. It works across any communication channel from web and Google Chat to Intercom, Slack, WhatsApp, and more.

Customers can instantly turn their support articles, past conversations, slack channels, Notion pages, Google Docs, and content on their public website into personalized answers. Algomo vectorizes customer content, using that alongside OpenAI’s ChatGPT. The company uses RAG (Retrieval Augmented Generation) prompting to inject relevant context to LLM prompts and Chain-Of-Thought prompting to increase answer accuracy. A fine-tuned implementation of BERT is also used to classify user intent and retrieve custom FAQs.

Taking advantage of its flexible document data model, Algomo uses MongoDB Atlas to store customer data alongside conversation history and messages, providing long-term memory for context and continuity in support interactions. As a fully managed cloud service, Algomo’s team can leave all of the operational heavy lifting to MongoDB, freeing its team up to focus on building great conversational experiences.

The team considers using MongoDB as a “no-brainer,” allowing them to iterate quickly while removing the support burden via the simplicity and reliability of the Atlas platform. The company’s engineers are now evaluating Atlas Vector Search as a replacement for its current standalone vector database, further reducing costs and simplifying their codebase. Being able to store source data, chunks, and metadata alongside vector embeddings eliminates the overhead and duplication of synchronizing data across two separate systems. The team is also looking forward to using Atlas Vector Search for their upcoming Agent Assist feature that will provide suggested answers, alongside relevant documentation snippets, to customer service agents who are responding to live customer queries.

Being part of the AI Innovators program provides Algomo with direct access to MongoDB technical expertise and best practices to accelerate its evaluation of Atlas Vector Search. Free Atlas credits in addition to those provided by the AWS and Azure start-up program help Algomo reduce its development costs.

Creating a new media currency with video detection and monetization

Source Digital, Inc. is a monetization platform that delivers a new era of customer engagement through video and the metaverse. The company provides tools for content creators and advertisers to display real-time advertisements and content recommendations directly to users on websites or in video streams hosted on platforms like Netflix, YouTube, Meta, and Vimeo.

Source Digital engineers built it’s own in-house machine learning and vector embedding models using Google Vision AI and TensorFlow. These models provide computer vision across video streams, detecting elements that automatically trigger the display of relevant ads and recommendations. An SDK is also provided to customers so that they can integrate the video detection models onto their own websites.

The company started out using PostgreSQL to store video metadata and model features, alongside the pgvector extension for video vector embeddings. This initial setup worked well at a small scale, but as Source Digital grew, PostgreSQL began to creak with costs rapidly escalating. PostgreSQL can only be scaled vertically, and so the company encountered step changes in costs as they moved to progressively larger cloud instance sizes. Scaling limitations were compounded by the need for queries to execute resource-intensive JOIN operations. These were needed to bring together data in all of the different database tables hosting video metadata, model features, and vector embeddings.

With prior MongoDB experience from an earlier audio streaming project, the company’s engineers were confident they could tame their cost challenges. Horizontal scale-out allows MongoDB to grow at much more granular levels, aligning costs with application usage. Expensive JOIN operations are eliminated because of the flexibility of MongoDB’s document data model. Now developers store the metadata, model features, and vector embeddings together in a single record. The company estimates that the migration from PostgreSQL to MongoDB Atlas and Vector Search will reduce monthly costs by 7x. These are savings that can be reinvested into accelerating delivery against the feature backlog.

Being part of the MongoDB AI Innovators Program provides Source Digital with access to expert technical advice on scaling its platform, along with co-marketing opportunities to further fuel its growth.

What's next?

If you are getting started with building AI-enabled apps on MongoDB, sign up for our AI Innovators Program. Successful applicants get access to expert technical advice, free MongoDB Atlas credits, co-marketing opportunities, and – for eligible startups, introductions to potential venture investors.

We’ve seen a whole host of interesting use cases and different companies building the future with AI, so you can refer back to some of our earlier blog posts below:

Building AI with MongoDB: first qualifiers include AI at the network edge for computer vision and augmented reality; risk modeling for public safety; and predictive maintenance paired with Question-Answering generation for maritime operators.
Building AI with MongoDB: compliance to copilots features AI in healthcare along with intelligent assistants that help product managers specify better products and help sales teams compose emails that convert 2x higher.
Building AI with MongoDB: unlocking value from multimodal data showcases open source libraries that transform unstructured data into a usable JSON format; entity extraction for contracts management; and making sense of “dark data” to build customer service apps.
Building AI with MongoDB: Cultivating Trust with Data covers three key customer use cases improving model explainability, securing generative AI outputs, and transforming cyber intelligence with the power of MongoDB.

And please take a look at the MongoDB for Artificial Intelligence resources page for the latest best practices that get you started in turning your idea into an AI-driven reality.

Consider joining our AI Innovators Program to build the next big thing in AI with us!

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How to Avoid GenAI Sprawl and Complexity

There's no doubt that generative AI and large language models (LLMs) are disruptive forces that will continue to transform our industry and economy in profound ways. But there's also something very familiar about the path organizations are taking to tap into GenAI capabilities. It's the same journey that happens anytime there's a need for data that serves a very specific and narrow purpose. We've seen it with search where bolt-on full-text search engines have proliferated, resulting in search-specific domains and expertise required to deploy and maintain them. We've also seen it with time-series data where the need to deliver real-time experiences while solving for intermittent connectivity has resulted in a proliferation of edge-specific solutions for handling time-stamped data. And now we're seeing it with GenAI and LLMs, where niche solutions are emerging for handling the volume and velocity of all the new data that organizations are creating. The challenge for IT decision-makers is finding a way to capitalize on innovative new ways of using and working with data while minimizing the extra expertise, storage, and computing resources required for deploying and maintaining purpose-built solutions. Check out our AI resource page to learn more about building AI-powered apps with MongoDB. Purpose-built cost and complexity The process of onboarding search databases illustrates the downstream effects that adding a purpose-built database has on developers. In order to leverage advanced search features like fuzzy search and synonyms, organizations will typically onboard a search-specific solution such as Solr, Elasticsearch, Algolia, and OpenSearch. A dedicated search database is yet another system that requires already scarce IT resources to deploy, manage, and maintain. Niche or purpose-built solutions like these often require technology veterans who can expertly deploy and optimize them. More often than not, it's the responsibility of one person or a small team to figure out how to stand up, configure, and optimize the new search environment as they go along. Time-series data is another example. The effort it takes to write sync code that resolves conflicts between the mobile device and the back end consumes a significant amount of developer time. On top of that, the work is non-differentiating since users expect to see up-to-date information and not lose data as a result of poorly written conflict-resolution code. So developers are spending precious time on work that is not of strategic importance to the business, nor does it differentiate their product or service from your competition. The arrival and proliferation of GenAI and LLMs is likely to accelerate new IT investments in order to capitalize on this powerful, game-changing technology. Many of these investments will take the form of dedicated technology resources and developer talent to operationalize. But the last thing tech buyers and developers need is another niche solution that pulls resources away from other strategically important initiatives. Documents to the rescue Leveraging GenAI and LLMs to gain new insights, create new user experiences, and drive new sources of revenue can entail something other than additional architectural sprawl and complexity. Drawing on the powerful document data model and an intuitive API, the MongoDB Atlas developer data platform allows developers to move swiftly and take advantage of fast-paced breakthroughs in GenAI without having to learn new tools or proprietary services. Documents are the perfect vehicle for GenAI feature development because they provide an intuitive and easy-to-understand mapping of data into code objects. Plus, the flexibility they provide enables developers to adapt to ever-changing application requirements, whether it's the addition of new types of data or the implementation of new features. The huge diversity of your typical application data and even vector embeddings of thousands of dimensions can all be handled with documents. The MongoDB Query API makes developers' lives easier, allowing them to use one unified and consistent system to perform CRUD operations while also taking advantage of more sophisticated features such as keyword and vector search , analytics, and stream processing — all without having to switch between different query languages and drivers, helping to keep your tech stack agile and streamlined. Making the most out of GenAI AI-driven innovation is pushing the envelope of what is possible in terms of the user experience — but to find real transformative business value, it must be seamlessly integrated as part of a comprehensive, feature-rich application that moves the needle for companies in meaningful ways. MongoDB Atlas takes the complexity out of AI-driven projects. Our intuitive developer data platform streamlines the process of bringing new experiences to market quickly and cost-effectively. With Atlas, you can reduce the risk and complexity associated with operational and security models, data wrangling, integration work, and data duplication. To find out more about how Atlas helps organizations integrate and operationalize GenAI and LLM data, download our white paper, Embedding Generative AI and Advanced Search into your Apps with MongoDB . If you're interested in leveraging generative AI at your organization, reach out to us today and find out how we can help your digital transformation.

October 12, 2023

Next →

MongoDB Database Observability: Integrating with Monitoring Tools

This post is the final in a three-part series on leveraging database observability. Welcome back to our series on Leveraging Database Observability! Our previous post showcased a real-world use case highlighting how MongoDB Atlas’s observability tools effectively tackle database performance challenges. Whether you’re a developer, DBA, or DevOps engineer, our mission is to empower you to harness the full potential of your data through our observability suite . Integrating Atlas metrics with your central enterprise observability tools can simplify your operations. By seamlessly working with popular observability tools, our approach helps teams streamline workflows and enhance visibility across systems. Integrating MongoDB Atlas with third-party monitoring tools MongoDB’s developer data platform combines all essential data services for building modern applications within a unified experience. Our purpose-built observability tools for Atlas environments offer automatic monitoring and optimization, guiding diagnostics tailored specifically for MongoDB. Additionally, we extend Atlas metrics into your existing enterprise observability stack, enabling seamless integration without replacing your current tools. This creates a consolidated, single-pane view that unifies Atlas telemetry with other tech and application metrics, ensuring comprehensive visibility into both database and full-stack performance. This integration empowers you to monitor, receive alerts, and make data-driven decisions within your existing workflows, driving greater efficiency. Below is a quick guide to modifying integration settings through the Atlas UI and the popular integrations we support: Navigate to the Project Integrations page in Atlas. Choose the organization and project you want to configure from the navigation bar. On the Project Integrations page, select the third-party services you’d like to integrate. Configure the chosen services with the required API keys and regions. Critical integrations for your observability platform With Atlas’s Datadog and Prometheus integrations, you can send critical MongoDB metrics to these platforms, empowering detailed, real-time monitoring. Through Datadog , you can track database operation counts, query efficiency, and resource usage, ideal for pinpointing bottlenecks and managing resources. Similarly, Prometheus enables you to monitor essential metrics like query times, connection rates, and memory usage, supporting flexible tracking of database health and performance. Both integrations facilitate proactive detection of issues, alert configuration for resource thresholds, and a cohesive view of Atlas data when visualized in Grafana. Atlas’s integration with PagerDuty streamlines incident management by sending metrics like performance alerts, billing anomalies, and security events directly to PagerDuty. This integration records incidents automatically, notifies teams upon alerts, and supports two-way syncing, ensuring resolved alerts in Atlas are reflected in PagerDuty. It enables efficient incident response and resource allocation to maintain system stability. With Atlas integrations for Microsoft Teams and Slack, you can route key metrics—such as query latency, disk usage, and throughput—to these channels for timely updates. Teams can use these insights for real-time performance monitoring, incident response, and collaboration. Notifications through these platforms ensure your team stays informed on database performance, storage health, and user activity changes as they occur. Use case: Centralized observability with MongoDB Atlas, Datadog, and Slack Let’s walk through a hypothetical scenario for ShopSmart, an e-commerce company that leverages MongoDB Atlas to manage its product catalog and customer data. As traffic surges, the DevOps team faces challenges in monitoring application performance and database health effectively. To tackle these challenges, the team leverages MongoDB Atlas’ integration with Datadog and Slack, creating a powerful observability ecosystem. Integrating MongoDB Atlas with Datadog: The team pushes key MongoDB Atlas metrics into Datadog, such as query performance, connection counts, and Atlas Vector Search metrics. With Datadog, they can visualize these metrics and correlate overall MongoDB performance with their other applications. Out-of-the-box monitors and dedicated dashboards allow the team to track metrics like throughput, average read/write latency, and current connections. This visibility helps pinpoint bottlenecks in real time, ensuring optimal database performance and improving overall application responsiveness. Setting up alerts in Datadog: The team configures alerts for critical metrics like high query latency and increased error rates. When thresholds are breached, Datadog instantly notifies the team. This proactive approach allows the team to address potential performance issues before they impact customers. Integrating Datadog with Slack: To ensure fast communication, alerts are sent directly to the dedicated Slack channel, “ShopSmart-Alerts.” This integration fosters seamless collaboration, enabling the team to discuss and resolve issues in real-time. With these integrations, ShopSmart’s engineering team can monitor performance quickly and address issues efficiently. The unified observability approach enhances operational efficiency, improves the customer experience, and supports ShopSmart’s competitive edge in the e-commerce industry. By leveraging MongoDB Atlas, Datadog, and Slack, the team ensures scalable performance and drives continuous innovation. Conclusion MongoDB Atlas empowers developers and organizations to achieve unparalleled observability and control over their database environments. By seamlessly integrating with central enterprise observability tools, Atlas enhances your ability to monitor performance metrics and ensures you can do so within your existing workflows. This means you can focus on building modern applications confidently, knowing you have the insights and alerts necessary to maintain optimal performance. Embrace the power of MongoDB Atlas and transform your approach to database management—because your applications can thrive when your data is observable. And that wraps up our Leveraging Database Observability series! We hope you learned something new and found value in these discussions. Sign up for MongoDB Atlas , our cloud database service, to see database observability in action. To dive deeper and expand your knowledge, check out this learning byte for more insights on the MongoDB observability suite and how it can enhance your database performance.

November 14, 2024