Vector Search

29 results

Anti-Money Laundering and Fraud Prevention With MongoDB Vector Search and OpenAI

Fraud and anti-money laundering (AML) are major concerns for both businesses and consumers, affecting sectors like financial services and e-commerce. Traditional methods of tackling these issues, including static, rule-based systems and predictive artificial intelligence (AI) methods, work but have limitations, such as lack of context and feature engineering overheads to keeping the models relevant, which can be time-consuming and costly. Vector search can significantly improve fraud detection and AML efforts by addressing these limitations, representing the next step in the evolution of machine learning for combating fraud. Any organization that is already benefiting from real-time analytics will find that this breakthrough in anomaly detection takes fraud and AML detection accuracy to the next level. In this post, we examine how real-time analytics powered by Atlas Vector Search enables organizations to uncover deeply hidden insights before fraud occurs. The evolution of fraud and risk technology Over the past few decades, fraud and risk technology have evolved in stages, with each stage building upon the strengths of previous approaches while also addressing their weaknesses: Risk 1.0: In the early stages (the late 1990s to 2010), risk management relied heavily on manual processes and human judgment, with decision-making based on intuition, past experiences, and limited data analysis. Rule-based systems emerged during this time, using predefined rules to flag suspicious activities. These rules were often static and lacked adaptability to changing fraud patterns . Risk 2.0: With the evolution of machine learning and advanced analytics (from 2010 onwards), risk management entered a new era with 2.0. Predictive modeling techniques were employed to forecast future risks and detect fraudulent behavior. Systems were trained on historical data and became more integrated, allowing for real-time data processing and the automation of decision-making processes. However, these systems faced limitations such as, Feature engineering overhead: Risk 2.0 systems often require manual feature engineering. Lack of context: Risk 1.0 and Risk 2.0 may not incorporate a wide range of variables and contextual information. Risk 2.0 solutions are often used in combination with rule-based approaches because rules cannot be avoided. Companies have their business- and domain-specific heuristics and other rules that must be applied. Here is an example fraud detection solution based on Risk 1.0 and Risk 2.0 with a rules-based and traditional AI/ML approach. Risk 3.0: The latest stage (2023 and beyond) in fraud and risk technology evolution is driven by vector search. This advancement leverages real-time data feeds and continuous monitoring to detect emerging threats and adapt to changing risk landscapes, addressing the limitations of data imbalance, manual feature engineering, and the need for extensive human oversight while incorporating a wider range of variables and contextual information. Depending on the particular use case, organizations can combine or use these solutions to effectively manage and mitigate risks associated with Fraud and AML. Now, let us look into how MongoDB Atlas Vector Search (Risk 3.0) can help enhance existing fraud detection methods. How Atlas Vector Search can help A vector database is an organized collection of information that makes it easier to find similarities and relationships between different pieces of data. This definition uniquely positions MongoDB as particularly effective, rather than using a standalone or bolt-on vector database. The versatility of MongoDB’s developer data platform empowers users to store their operational data, metadata, and vector embeddings on MongoDB Atlas and seamlessly use Atlas Vector Search to index, retrieve, and build performant gen AI applications. Watch how you can revolutionize fraud detection with MongoDB Atlas Vector Search. The combination of real-time analytics and vector search offers a powerful synergy that enables organizations to discover insights that are otherwise elusive with traditional methods. MongoDB facilitates this through Atlas Vector Search integrated with OpenAI embedding, as illustrated in Figure 1 below. Figure 1: Atlas Vector Search in action for fraud detection and AML Business perspective: Fraud detection vs. AML Understanding the distinct business objectives and operational processes driving fraud detection and AML is crucial before diving into the use of vector embeddings. Fraud Detection is centered on identifying unauthorized activities aimed at immediate financial gain through deceptive practices. The detection models, therefore, look for specific patterns in transactional data that indicate such activities. For instance, they might focus on high-frequency, low-value transactions, which are common indicators of fraudulent behavior. AML , on the other hand, targets the complex process of disguising the origins of illicitly gained funds. The models here analyze broader and more intricate transaction networks and behaviors to identify potential laundering activities. For instance, AML could look at the relationships between transactions and entities over a longer period. Creation of Vector Embeddings for Fraud and AML Fraud and AML models require different approaches because they target distinct types of criminal activities. To accurately identify these activities, machine learning models use vector embeddings tailored to the features of each type of detection. In this solution highlighted in Figure 1, vector embeddings for fraud detection are created using a combination of text, transaction, and counterparty data. Conversely, the embeddings for AML are generated from data on transactions, relationships between counterparties, and their risk profiles. The selection of data sources, including the use of unstructured data and the creation of one or more vector embeddings, can be customized to meet specific needs. This particular solution utilizes OpenAI for generating vector embeddings, though other software options can also be employed. Historical vector embeddings are representations of past transaction data and customer profiles encoded into a vector format. The demo database is prepopulated with synthetically generated test data for both fraud and AML embeddings. In real-world scenarios, you can create embeddings by encoding historical transaction data and customer profiles as vectors. Regarding the fraud and AML detection workflow , as shown in Figure 1, incoming transaction fraud and AML aggregated text are used to generate embeddings using OpenAI. These embeddings are then analyzed using Atlas Vector Search based on the percentage of previous transactions with similar characteristics that were flagged for suspicious activity. In Figure 1, the term " Classified Transaction " indicates a transaction that has been processed and categorized by the detection system. This classification helps determine whether the transaction is considered normal, potentially fraudulent, or indicative of money laundering, thus guiding further actions. If flagged for fraud: The transaction request is declined. If not flagged: The transaction is completed successfully, and a confirmation message is shown. For rejected transactions, users can contact case management services with the transaction reference number for details. No action is needed for successful transactions. Combining Atlas Vector Search for fraud detection With the use of Atlas Vector Search with OpenAI embeddings, organizations can: Eliminate the need for batch and manual feature engineering required by predictive (Risk 2.0) methods. Dynamically incorporate new data sources to perform more accurate semantic searches, addressing emerging fraud trends. Adopt this method for mobile solutions, as traditional methods are often costly and performance-intensive. Why MongoDB for AML and fraud prevention Fraud and AML detection require a holistic platform approach as they involve diverse data sets that are constantly evolving. Customers choose MongoDB because it is a unified data platform (as shown in Figure 2 below) that eliminates the need for niche technologies, such as a dedicated vector database. What’s more, MongoDB’s document data model incorporates any kind of data—any structure (structured, semi-structured, and unstructured), any format, any source—no matter how often it changes, allowing you to create a holistic picture of customers to better predict transaction anomalies in real time. By incorporating Atlas Vector Search, institutions can: Build intelligent applications powered by semantic search and generative AI over any type of data. Store vector embeddings right next to your source data and metadata. Vectors inserted or updated in the database are automatically synchronized to the vector index. Optimize resource consumption, improve performance, and enhance availability with Search Nodes . Remove operational heavy lifting with the battle-tested, fully managed MongoDB Atlas developer data platform. Figure 2: Unified risk management and fraud detection data platform Given the broad and evolving nature of fraud detection and AML, these areas typically require multiple methods and a multimodal approach. Therefore, a unified risk data platform offers several advantages for organizations that are aiming to build effective solutions. Using MongoDB, you can develop solutions for Risk 1.0, Risk 2.0, and Risk 3.0, either separately or in combination, tailored to meet your specific business needs. The concepts are demonstrated with two examples: a card fraud solution accelerator for Risk 1.0 and Risk 2.0 and a new Vector Search solution for Risk 3.0, as discussed in this blog. It's important to note that the vector search-based Risk 3.0 solution can be implemented on top of Risk 1.0 and Risk 2.0 to enhance detection accuracy and reduce false positives. If you would like to discover more about how MongoDB can help you supercharge your fraud detection systems, take a look at the following resources: Revolutionizing Fraud Detection with Atlas Vector Search Card Fraud solution accelerator (Risk 1.0 and Risk 2.0) Risk 3.0 fraud detection solution GitHub repository

July 17, 2024

Atlas Vector Search Once Again Voted Most Loved Vector Database

The 2024 Retool State of AI report has just been released, and for the second year in a row, MongoDB Atlas Vector Search was named the most loved vector database. Atlas Vector Search received the highest net promoter score (NPS), a measure of how likely a user is to recommend a solution to their peers. This post is also available in: Deutsch , Français , Español , Português , Italiano , 한국어 , 简体中文 . The Retool State of AI report is a global annual survey of developers, tech leaders, and IT decision-makers that provides insights into the current and future state of AI, including vector databases, retrieval-augmented generation (RAG) , AI adoption, and challenges innovating with AI. MongoDB Atlas Vector Search commanded the highest NPS in Retool’s inaugural 2023 report, and it was the second most widely used vector database within just five months of its release. This year, Atlas Vector Search came in a virtual tie for the most popular vector database, with 21.1% of the vote, just a hair behind pgvector (PostgreSQL), which received 21.3%. The survey also points to the increasing adoption of RAG as the preferred approach for generating more accurate answers with up-to-date and relevant context that large language models ( LLMs ) aren't trained on. Although LLMs are trained on huge corpuses of data, not all of that data is up to date, nor does it reflect proprietary data. And in those areas where blindspots exist, LLMs are notorious for confidently providing inaccurate "hallucinations." Fine-tuning is one way to customize the data that LLMs are trained on, and 29.3% of Retool survey respondents leverage this approach. But among enterprises with more than 5,000 employees, one-third now leverage RAG for accessing time-sensitive data (such as stock market prices) and internal business intelligence, like customer and transaction histories. This is where MongoDB Atlas Vector Search truly shines. Customers can easily utilize their stored data in MongoDB to augment and dramatically improve the performance of their generative AI applications, during both the training and evaluation phases. In the course of one year, vector database utilization among Retool survey respondents rose dramatically, from 20% in 2023 to an eye-popping 63.6% in 2024. Respondents reported that their primary evaluation criteria for choosing a vector database were performance benchmarks (40%), community feedback (39.3%), and proof-of-concept experiments (38%). One of the pain points the report clearly highlights is difficulty with the AI tech stack . More than 50% indicated they were either somewhat satisfied, not very satisfied, or not at all satisfied with their AI stack. Respondents also reported difficulty getting internal buy-in, which is often complicated by procurement efforts when a new solution needs to be onboarded. One way to reduce much of this friction is through an integrated suite of solutions that streamlines the tech stack and eliminates the need to onboard multiple unknown vendors. Vector search is a native feature of MongoDB's developer data platform, Atlas, so there's no need to bolt on a standalone solution. If you're already using MongoDB Atlas , creating AI-powered experiences involves little more than adding vector data into your existing data collections in Atlas. If you're a developer and want to start using Atlas Vector Search to start building generative AI-powered apps, we have several helpful resources: Learn how to build an AI research assistant agent that uses MongoDB as the memory provider, Fireworks AI for function calling, and LangChain for integrating and managing conversational components. Get an introduction to LangChain and MongoDB Vector Search and learn to create your own chatbot that can read lengthy documents and provide insightful answers to complex queries. Watch Sachin Smotra of Dataworkz as he delves into the intricacies of scaling RAG (retrieval-augmented generation) applications. Read our tutorial that shows you how to combine Google Gemini's advanced natural language processing with MongoDB, facilitated by Vertex AI Extensions to enhance the accessibility and usability of your database. Browse our Resources Hub for articles, analyst reports, case studies, white papers, and more. Want to find out more about recent AI trends and adoption? Read the full 2024 Retool State of AI report .

June 21, 2024

Exact Nearest Neighbor Vector Search for Precise Retrieval

With its ability to efficiently handle high-dimensional, unstructured data, vector search delivers relevant results even when users don’t know what they’re looking for and uses machine learning models to find similar results across any data type. Rapidly emerging as a key technology for modern applications, vector search empowers developers to build next-generation search and generative AI applications faster and easier. MongoDB Atlas Vector Search goes beyond the approximate nearest neighbor (ANN) methods with the introduction of exact nearest neighbor (ENN) vector search . This innovative capability guarantees retrieval of the absolute closest vectors to your query, eliminating the accuracy limitations inherent in ANN. In sum, ENN vector search can help you unleash a new level of precision for your search and generative AI applications, improving benchmarking and moving to production faster. When exact nearest neighbor (ENN) vector search benefits developers While ANN shines in searching across large datasets, ENN vector search offers advantages in specific scenarios: Small-scale vector data: For datasets under 10,000 vectors, the linear time complexity of ENN vector search makes it a viable option, especially considering the added development complexity of tuning ANN parameters. Recall benchmarking of ANN queries: ANN queries are fast, particularly as the scale of your indexed vectors increases, but it may not be easy to know whether the retrieved documents by vector relevance correspond to the guaranteed closest vectors in your index. Using ENN can help provide that exact result set for comparison with your approximate result set, using jaccard similarity or other rank-aware recall metrics. This will allow you to have much greater confidence that your ANN queries are accurate since you can build quantitative benchmarks as your data evolves. Multi-tenant architectures: Imagine a scenario with millions of vectors categorized by tenants. You might search for the closest vectors within a specific tenant (identified by a tenant ID). In cases where the overall vector collection is large (in the millions) but the number of vectors per tenant is small (a few thousand), ANN's accuracy suffers when applying highly selective filters. ENN vector search thrives in this multi-tenant scenario, delivering precise results even with small result sets. Example use cases The small dataset size allows for exhaustive search within a reasonable timeframe, making exact nearest neighbor approach a viable option for finding the most similar data point, improving accuracy confidence in a number of use cases, such as: Multi-tenant data service: You might be building a business providing an agentic service that understands your customers’ data and takes actions on their behalf. When retrieving relevant proprietary data for that agent, it is critical that the right metadata filter be applied and that ENN be executed to retrieve the right sets of documents only corresponding to the appropriate data tenant IDs. Proof of concept development: For instance, a new recommendation engine might have a limited library compared to established ones. Here, ENN vector search can be used to recommend products to a small set of early adopters. Since the data is limited, an exhaustive search becomes practical, ensuring the user gets the most relevant recommendations from the available options. How ENN vector search works on MongoDB Atlas The ENN vector search feature in Atlas integrates seamlessly with the existing $vectorSearch stage within your Atlas aggregation pipelines. Its key characteristics include: Guaranteed accuracy: Unlike ANN, ENN always returns the closest vectors to your query, adhering to the specified limit. Eventual consistency: Similar to approximate vector search, ENN vector search follows an eventual consistency model. Simplified configuration: Unlike approximate vector search, where tuning numCandidates is crucial, ENN vector search only requires specifying the desired limit of returned vectors. Scalable recall evaluation: Atlas allows querying a large number of indexed vectors, facilitating the calculation of comprehensive recall sets for effective evaluation. Fast query execution: ENN vector search query execution can maintain sub-second latency for unfiltered queries up to 10,000 documents. It can also provide low-latency responses for highly selective filters that restrict a broad set of documents into 10,000 documents or less, ordered by vector relevance. Build more with ENN vector search ENN vector search can be a powerful tool when building a proof of concept for retrieval-augmented generation (RAG), semantic search, or recommendation systems powered by vector search. It simplifies the developer experience by minimizing overhead complexity and latency while giving you the flexibility to implement and benchmark precise retrieval. Explore more use cases and build applications faster, start experimenting with ENN vector search.

June 20, 2024

AI-Powered Media Personalization: MongoDB and Vector Search

In recent years, the media industry has grappled with a range of serious challenges, from adapting to digital platforms and on-demand consumption, to monetizing digital content, and competing with tech giants and new media upstarts. Economic pressures from declining sources of revenue like advertising, trust issues due to misinformation, and the difficulty of navigating regulatory environments have added to the complexities facing the industry. Additionally, keeping pace with technological advancements, ensuring cybersecurity, engaging audiences with personalized and interactive content, and addressing globalization issues all require significant innovation and investment to maintain content quality and relevance. In particular, a surge in digital content has saturated the media market, making it increasingly difficult to capture and retain audience attention. Furthermore, a decline in referral traffic—primarily from social media platforms and search engines—has put significant pressure on traditional media outlets. An industry survey from a sample of more than 300 digital leaders from more than 50 countries and territories shows that traffic to news sites from Facebook fell 48% in 2023, with traffic from X/Twitter declining by 27%. As a result, publishers are seeking ways to stabilize their user bases and to enhance engagement sustainably, with 77% looking to invest more in direct channels to deal with the loss of referrals. Enter artificial intelligence: generative AI-powered personalization has become a critical tool for driving the future of media channels. The approach we discuss here offers a roadmap for publishers navigating the shifting dynamics of news consumption and user engagement. Indeed, using AI for backend news automation ( 56% ) is considered the most important use of the technology by publishers. In this post, we’ll walk you through using MongoDB Atlas and Atlas Vector Search to transform how content is delivered to users. The shift in news consumption Today's audiences rarely rely on a single news source. Instead, they use multiple platforms to stay informed, a trend that's been driven by the rise of social media, video-based news formats, and skepticism towards traditional media due to the prevalence (or fear) of "fake news." This diversification in news sources presents a dilemma for publishers, who have come to depend on traffic from social media platforms like Facebook and Twitter. However, both platforms have started to deprioritize news content in favor of posts from individual creators and non-news content, leading to a sharp decline in media referrals. The key to retaining audiences lies in making content personalized and engaging. AI-powered personalization and recommendation systems are essential tools for achieving this. Content suggestions and personalization By drawing on user data, behavior analytics, and the multi-dimensional vectorization of media content, MongoDB Atlas and Atlas Vector Search can be applied to multiple AI use cases to revolutionize media channels and improve end-user experiences. By doing so, media organizations can suggest content that aligns more closely with individual preferences and past interactions. This not only enhances user engagement but also increases the likelihood of converting free users into paying subscribers. The essence of leveraging Atlas and Vector Search is to understand the user. By analyzing interactions and consumption patterns, the solution not only grasps what content resonates but also predicts what users are likely to engage with in the future. This insight allows for crafting a highly personalized content journey. The below image shows a reference architecture highlighting where MongoDB can be leveraged to achieve AI-powered personalization. To achieve this, you can integrate several advanced capabilities: Content suggestions and personalization: The solution can suggest content that aligns with individual preferences and past interactions. This not only enhances user engagement but also increases the likelihood of converting free users into paying subscribers. By integrating MongoDB's vector search to perform k-nearest neighbor (k-NN) searches , you can streamline and optimize how content is matched. Vectors are embedded directly in MongoDB documents, which has several advantages. For instance: No complexities of a polyglot persistence architecture. No need to extract, transform, and load (ETL) data between different database systems, which simplifies the data architecture and reduces overhead. MongoDB’s built-in scalability and resilience can support vector search operations more reliably. Organizations can scale their operations vertically or horizontally, even choosing to scale search nodes independently from operational database nodes, flexibly adapting to the specific load scenario. Content summarization and reformatting: In an age of information overload, this solution provides concise summaries and adapts content formats based on user preferences and device specifications. This tailored approach addresses the diverse consumption habits of users across different platforms. Keyword extraction: Essential information is drawn from content through advanced keyword extraction, enabling users to grasp key news dimensions quickly and enhancing the searchability of content within the platform. Keywords are fundamental to how content is indexed and found in search engines, and they significantly influence the SEO (search engine optimization) performance of digital content. In traditional publishing workflows, selecting these keywords can be a highly manual and labor-intensive task, requiring content creators to identify and incorporate relevant keywords meticulously. This process is not only time-consuming but also prone to human error, with significant keywords often overlooked or underutilized, which can diminish the content's visibility and engagement. With the help of the underlying LLM, the solution extracts keywords automatically and with high sophistication. Automatic creation of Insights and dossiers: The solution can automatically generate comprehensive insights and dossiers from multiple articles. This feature is particularly valuable for users interested in deep dives into specific topics or events, providing them with a rich, contextual experience. This capability leverages the power of one or more Large Language Models (LLMs) to generate natural language output, enhancing the richness and accessibility of information derived from across multiple source articles. This process is agnostic to the specific LLMs used, providing flexibility and adaptability to integrate with any leading language model that fits the publisher's requirements. Whether the publisher chooses to employ more widely recognized models (like OpenAI's GPT series) or other emerging technologies, our solution seamlessly incorporates these tools to synthesize and summarize vast amounts of data. Here’s a deeper look at how this works: Integration with multiple sources: The system pulls content from a variety of articles and data sources, retrieved with MongoDB Atlas Vector Search. Found items are then compiled into dossiers, which provide users with a detailed and contextual exploration of topics, curated to offer a narrative or analytical perspective that adds value beyond the original content. Customizable output: The output is highly customizable. Publishers can set parameters based on their audience’s preferences or specific project requirements. This includes adjusting the level of detail, the use of technical versus layman terms, and the inclusion of multimedia elements to complement the text. This feature significantly enhances user engagement by delivering highly personalized and context-rich content. It caters to users looking for quick summaries as well as those seeking in-depth analyses, thereby broadening the appeal of the platform and encouraging deeper interaction with the content. By using LLMs to automate these processes, publishers can maintain a high level of productivity and innovation in content creation, ensuring they remain at the cutting edge of media delivery. Future directions As media consumption habits continue to evolve, AI-powered personalization stands out as a vital tool for publishers. By using AI to deliver tailored content and to automate back end processes, publishers can address the decline in traditional referrals and build stronger, more direct relationships with their audiences. If you would like to learn more about AI-Powered Media Personalization, visit the following resources: AI-Powered Personalization to Drive Next-Generation Media Channels AI-Powered Innovation in Telecommunications and Media GitHub Repository : Create a local version of this solution by following the instructions in the repository

June 13, 2024

Transforming Predictive Maintenance with AI: Real-Time Audio-Based Diagnostics with Atlas Vector Search

Wind turbines are a critical component in the shift away from fossil fuels toward more sustainable, green sources of energy. According to the International Energy Agency (IEA), the global capacity of wind energy has been growing rapidly, reaching over 743 gigawatts by 2023. Wind energy, in particular, has one of the greatest potentials to increase countries' renewable capacity growth. Solar PV and wind additions are forecast to more than double by 2028 compared with 2022, continuously breaking records over the forecast period. This growth highlights the increasing reliance on wind power and, consequently, the need for effective maintenance strategies. Keeping wind turbines operating at maximum capacity is essential to ensuring their continued contribution to the energy grid. Like any mechanical device, wind turbines must undergo periodic maintenance to keep them operating at optimal levels. In recent years, advancements in technology—particularly in AI and machine learning—have played a significant role by introducing predictive maintenance breakthroughs to industrial processes like periodic maintenance. By integrating AI into renewable energy systems, organizations of all sizes can reduce costs and gain efficiencies. In this post, we will dig into an AI application use case for real-time anomaly detection through sound input, showcasing the impact of AI and MongoDB Atlas Vector Search for predictive maintenance of wind turbines. Predictive Maintenance in Modern Industries Companies increasingly invest in predictive maintenance to optimize their operations and drive efficiency. Research from Deloitte indicates that predictive maintenance can reduce equipment downtime by 5–15 percent, increase labor productivity by 5–20 percent, and reduce overall new equipment costs by 3–5 percent. This helps organizations maximize their investment in equipment and infrastructure. By implementing predictive maintenance strategies, companies can anticipate equipment failures before they occur, ultimately resulting in longer equipment lifetimes, tighter budget control, and higher overall throughput. More concretely, businesses aim to reduce mean time to repair, optimal ordering of replacement parts, efficient people management, and reduced overall maintenance costs. Leveraging data interoperability, real-time analysis, modeling and simulation, and machine learning techniques, predictive maintenance enables companies to thrive in today's competitive landscape. However, despite its immense potential, predictive maintenance also presents significant challenges. One major hurdle is the consolidation of heterogeneous data, as predictive maintenance systems often need to integrate data from various formats and sources that can be difficult to integrate. Scalability also becomes a concern when dealing with the high volumes of IoT signals generated by numerous devices and sensors. And lastly, managing and analyzing this vast amount of data in real-time poses challenges that must be overcome to realize the full benefits of predictive maintenance initiatives. At its core, predictive maintenance begins with real-time diagnostics, enabling proactive identification and mitigation of potential equipment failures in real-time. Figure 1: Predictive Maintenance starts with real-time diagnostics However, while AI has been employed for real-time diagnostics for some time, the main challenge has been acquiring and utilizing the necessary data for training AI models. Traditional methods have struggled with incorporating unstructured data into these models effectively. Enter gen AI and vector search technologies, positioned to revolutionize this landscape. Flexible data platforms working together with AI algorithms can help generate insights from diverse data types, including images, video, audio, geospatial data, and more, paving the way for more robust and efficient maintenance strategies. In this context, MongoDB Atlas Vector Search stands out as a foundational element for effective and efficient gen AI-powered predictive maintenance models. Why MongoDB and Atlas Vector Search? For several reasons, MongoDB stands out as the preferred database solution for modern applications. Figure 2: MongoDB Atlas Developer Data Platform Document data model One of the reasons why the document model is well-suited to the needs of modern applications is its ability to store diverse data types in BSON (Binary JSON) format, ranging from structured to unstructured. This flexibility essentially eliminates the middle layer necessary to convert to a SQL-like format, resulting in easier-to-maintain applications, lower development times, and faster response to changes. Time series collections MongoDB excels in handling time-series data generated by edge devices, IoT sensors, PLCs, SCADA systems, and more. With dedicated time-series collections, MongoDB provides efficient storage and retrieval of time-stamped data, enabling real-time monitoring and analysis. Real-time data processing and aggregation MongoDB's adeptness in real-time data processing is crucial for immediate diagnostics and responses, ensuring timely interventions to prevent costly repairs and downtime. Its powerful aggregation capabilities facilitate the synthesis of data from multiple sources, providing comprehensive insights into fleet-wide performance trends. Developer data platform Beyond just storing data, MongoDB Atlas is a multi-cloud developer data platform, providing the flexibility required to build a diverse range of applications. Atlas includes features like transactional processing, text-based search, vector search, in-app analytics, and more through an elegant and integrated suite of data services. It offers developers a top-tier experience through a unified query interface, all while meeting the most demanding requirements for resilience, scalability, and cybersecurity. Atlas Vector Search Among the out-of-the-box features offered by MongoDB Atlas, Atlas Vector Search stands out, enabling the search of unstructured data effortlessly. You can generate vector embeddings with machine learning models like the ones found in OpenAI or Hugging Face, and store and index them in Atlas. This feature facilitates the indexing of vector representations of objects and retrieves those that are semantically most similar to your query. Explore the capabilities of Atlas Vector Search . This functionality is especially interesting for unstructured data that was previously hard to leverage, such as text, images, and audio, allowing searches that combine audio, video, metadata, production equipment data, or sensor measurements to provide an answer to a query. Let's delve into how simple it is to leverage AI to significantly enhance the sophistication of predictive maintenance models with MongoDB Atlas. Real-time audio-based diagnostics with Atlas Vector Search In our demonstration, we'll showcase real-time audio-based diagnostics applied to a wind turbine. It's important to note that while we focus on wind turbines here, the concept can be extrapolated to any machine, vehicle, or device emitting sound. To illustrate this concept, we'll utilize a handheld fan as our makeshift wind turbine. Wind turbines emit different sounds depending on their operational status. By continuously monitoring the turbine’s audio, our system can accurately specify the current operational status of the equipment and reduce the risk of unexpected breakdowns. Early detection of potential issues allows for enhanced operational efficiency, minimizing the time and resources required for manual inspections. Additionally, timely identification can prevent costly repairs and reduce overall turbine downtime, thus enhancing cost-effectiveness. Now, let’s have a look at how this demo works! Figure 3: Application Architecture Audio Preparation We begin by capturing the audio from the equipment in different situations (normal operation, high vs. low load, equipment obstructed, not operating, etc.). Once each sound is collected, we use an embedding model to process the audio data to convert it to a vector. This step is crucial because by generating embeddings for each audio track, which are high-dimensional vector representations, we are essentially capturing the unique characteristics of the sound. We then upload these vector embeddings to MongoDB Atlas. By adding just a few examples of sounds in our database, they are ready to be searched (and essentially compared) with the sound emitted by our equipment during its operation in real-time. Audio-based diagnosis Now, we put our equipment into normal operation and start capturing the sound it is making in real-time. In this demonstration, we capture one-second clips of audio. Then, with the same embedding model used before, we take our audio clips and convert them to vector embeddings in real-time. This process happens in milliseconds, allowing us to have real-time monitoring of the audio. The one-second audio clips, now converted to vector embeddings, are then sent to MongoDB Atlas Vector Search, which can search for and find the most similar vectors from the ones we previously recorded in our audio preparation phase. The result is given back with a percentage of similarity, enabling a very accurate prediction of the current status of the operation of the wind turbine. These steps are performed repeatedly every second, leveraging fast embedding of vectors and quick searches, allowing for real-time monitoring based on sound. Check out the video below to see it in action! Transforming Predictive Maintenance with AI and MongoDB Predictive maintenance offers substantial benefits but poses challenges like data integration and scalability. MongoDB stands out as a preferred database solution, offering scalability, flexibility, and real-time data processing. As technology advances, AI integration promises to further revolutionize the industry. Thank you to Ralph Johnson and Han Heloir for their valuable contributions to this demo! Ready to revolutionize your predictive maintenance strategy with AI and MongoDB Atlas Vector Search? Try it out yourself by following the simple steps outlined in our Github repo ! Explore how MongoDB empowers manufacturing operations by visiting these resources: Generative AI in Predictive Maintenance Applications Transforming Industries with MongoDB and AI: Manufacturing and Motion MongoDB for Automotive: Driving Innovation from Factory to Finish Line

May 28, 2024

How the NFSA is Using MongoDB Atlas and AI to Make Aussie Culture Accessible

Where can you find everything from facts about Kylie Minogue, to more than 6,000 Australian home movies, to a 60s pop group playing a song with a drum-playing kangaroo ? The NFSA! Founded in 1935, the National Film and Sound Archive of Australia (NFSA) is one of the oldest archives of its kind in the world. It is tasked with collecting, preserving, and sharing Australia’s audiovisual culture. According to its website, the NFSA “represents not only [Australia’s] technical and artistic achievements, but also our stories, obsessions and myths; our triumphs and sorrows; who we were, are, and want to be.” The NFSA’s collection includes petabytes of audiovisual data—including broadcast-quality news footage, TV shows, and movies, high-resolution photographs, radio shows, and video games—plus millions of physical and contextual items like costumes, scripts, props, photographs, and promotional materials, all tucked away in a warehouse. “Today, we have eight petabytes of data, and our data is growing from one to two petabytes each year,” said Shahab Qamar, software engineering manager at NFSA. Making this wealth of data easily accessible to users across Australia (not to mention all over the world) has led to a number of challenges, which is where MongoDB Atlas—which helps developers simplify and accelerate building with data—comes in. Don’t change (but apply a few updates) Because of its broad appeal, the NFSA's collection website alone receives an average of 100,000 visitors each month. When Qamar joined the NFSA in 2020, he saw an opportunity to improve the organization’s web platform. His aim was to ensure the best possible experience for the site’s high number of daily visitors, which had begun to plateau. This included a website refresh, as well as addressing technical issues related to handling site traffic, due to the site being hosted on on-premises servers. The site also wasn’t “optimized for Google Analytics,” said Qamar. In fact, the NFSA website was invisible to Google and other search engines, so he knew it was time for a significant update, which also presented an opportunity to set up strong data foundations to build deeper capabilities down the line. But first, Qamar and team needed to find a setup that could serve the needs of the NFSA and Australia’s 26 million residents more robustly than their previous solution. Specifically, Qamar said, the NFSA was looking for a fully managed database that could also implement search at scale, as well as a system that his small team of five could easily manage. It also needed to ensure high levels of resiliency and the ability to work with more than one cloud provider. The previous NFSA site also didn’t support content delivery networks , he added. MongoDB Atlas supported all of the use cases the NFSA was looking for, Qamar said, including the ability to support multi-cloud hosting. And because Atlas is fully managed, it would readily meet the NFSA's requirements. In July 2023, after months of development, the new and greatly improved NFSA website was launched. The redesign was immediately impactful: Since the NFSA’s redesigned site was launched, the number of users visiting the collection search website has gone up 200%, and content requests—which the NFSA access team responds to on a case-by-case basis—have gone up 16%. (Getting search) back in black While the previous version of the NFSA site included search, the prior functionality was prone to crashing, and the quality of the results was often poor, Qamar said. For example, search results were delivered alphabetically rather than based on relevance, and the previous search didn’t support fine-tuning of relevance based on matches in specific fields. So, as part of its site redesign, the NFSA was looking to add full text search, relevance-based search results, faceting, and pagination. MongoDB Atlas Search —which integrates the database, search engine, and sync mechanism into a single, unified, fully managed platform—ticked all of those boxes. A search results page on the NFSA website Indeed, the NFSA compared search results from its old site to its new MongoDB Atlas site and “found that MongoDB Atlas-based searches were more relevant and targeted,” Qamar said. Previously, configuring site search required manual coding and meant downtime for the site, he noted. “The whole setup wasn’t very developer friendly and, therefore, a barrier to working efficiently with search configuration and fine-tuning,” Qamar said. In comparison, MongoDB Atlas allowed for simple configuration and fine-tuning of the NFSA's search requirements. The NFSA has also been using MongoDB Atlas Charts . Charts help the NFSA easily visualize its collection by custom grouping (like production year or genre), as well as helping the NFSA see which items are most popular with users. “Charts have helped us understand how our collection is growing and evolving over time,” Qamar said. NFSA’s use of MongoDB Charts Can’t get you (AI) out of my head Now, the NFSA—inspired by Qamar’s own training in machine learning and the broad interest in all things AI—is exploring how it can use Atlas Vector Search and generative AI tools to allow users to explore content buried in the NFSA collection. One example cited is putting transcriptions of audiovisual files in NFSA’s collection into a vector database for retrieval-augmented generation (RAG). The NFSA has approximately 27 years worth—meaning, it would take 27 years to play it all back—of material to transcribe, and is currently developing a model to accurately capture the Australian dialect so the work is transcribed correctly. Ultimately, the NFSA is interested in building a RAG-powered AI bot to provide historically and contextually accurate information about work in the NFSA’s archive. The NFSA is also exploring how it can use RAG to deliver accurate, conversation-like search results without training large language models itself, and whether it can leverage AI to help restore some of the older videos in its collection. Qamar and team are also interested in vectorizing audio-visual material for semantic analysis and genre-based classification of collection material at scale, he said. “Historically, we’ve been very metadata-driven and keyword-driven, and I think that’s a missed opportunity. Because when we talk about what an archive does, we archive stories,” Qamar said of the possibilities offered by vectors. “An example I use is, what if the world ended tomorrow? And what if aliens came to Earth and only saw our metadata, what image of Australia would they see? Is that a true image of what Australia is really like?” Qamar said. “How content is described is important, but content’s imagery, the people in it, and the audio and words being spoken are really important. Full-text search can take you somewhere along the way, but vector search allows you to look things up in a semantic manner. So it’s more about ideas and concepts than very specific keywords,” he said. If you’re interested in learning how MongoDB helps accelerate and simplify time-to-mission for federal, state, and local governments, defense agencies, education, and across the public sector, check out MongoDB for Public Sector . Check out MongoDB Atlas Vector Search to learn more about how Vector Search helps organizations like the NFSA build applications powered by semantic search and gen AI. *Note that this story’s subheads come from Australian song titles!

May 14, 2024

Workload Isolation for More Scalability and Availability: Search Nodes Now on Google Cloud

June 25, 2024: Announcing Search Nodes in general availability on Microsoft Azure Today we’re excited to take the next step in bringing scalable, dedicated architecture to your search experiences with the introduction of Atlas Search Nodes, now in general availability for Google Cloud. This post is also available in: Deutsch , Français , Español , Português , Italiano , 한국어 , 简体中文 . Since our initial announcement of Search Nodes in June of 2023, we’ve been rapidly accelerating access to the most scalable dedicated architecture, starting with general availability on AWS and now expanding to general availability on Google Cloud. We'd like to give you a bit more context on what Search Nodes are and why they're important to any search experience running at scale. Search Nodes provide dedicated infrastructure for Atlas Search and Vector Search workloads to enable even greater control over search workloads. They also allow you to isolate and optimize compute resources to scale search and database needs independently, delivering better performance at scale and higher availability. One of the last things developers want to deal with when building and scaling apps is having to worry about infrastructure problems. Any downtime or poor user experiences can result in lost users or revenue, especially when it comes to your database and search experience. This is one of the reasons developers turn to MongoDB, given the ease of use of having one unified system for your database and search solution. With the introduction of Atlas Search Nodes, we’ve taken the next step in providing our builders with ultimate control, giving them the ability to remain flexible by scaling search workloads without the need to over-provision the database. By isolating your search and database workloads while at the same time automatically keeping your search cluster data synchronized with operational data, Atlas Search and Atlas Vector Search eliminate the need to run a separate ETL tool, which takes time and effort to set up and is yet another fail point for your scaling app. This provides superior performance and higher availability while reducing architectural complexity and wasted engineering time recovering from sync failures. In fact, we’ve seen a 40% to 60% decrease in query time for many complex queries, while eliminating the chances of any resource contention or downtime. With just a quick button click, Search Nodes on Google Cloud offer our existing Atlas Search and Vector Search users the following benefits: Higher availability Increased scalability Workload isolation Better performance at scale Improved query performance We offer both compute-heavy search-specific nodes for relevance-based text search, as well as a memory-optimized option that is optimal for semantic and retrieval augmented generation (RAG) production use cases with Atlas Vector Search. This makes resource contention or availability issues a thing of the past. Search Nodes are easy to opt into and set up — to start, jump on into the MongoDB UI and follow the steps do the following: Navigate to your “Database Deployments” section in the MongoDB UI Click the green “+Create” button On the “Create New Cluster” page, change the radio button for Google Cloud for “Multi-cloud, multi-region & workload isolation” to enable Toggle the radio button for “Search Nodes for workload isolation” to enable. Select the number of nodes in the text box Check the agreement box Click “Create cluster” For existing Atlas Search users, click “Edit Configuration” in the MongoDB Atlas Search UI and enable the toggle for workload isolation. Then the steps are the same as noted above. Jump straight into our docs to learn more!

March 28, 2024

Using Generative AI and MongoDB to Tackle Cybersecurity’s Biggest Challenges

This post is also available in: Deutsch , Français , Español , Português , Italiano , 한국어 , 简体中文 . In the ever-evolving landscape of cybersecurity, organizations face a multitude of challenges that demand innovative solutions harnessing cutting-edge technologies. One of the most pressing issues is the increasing sophistication of cyber threats, including malware, ransomware, and phishing attacks, which are becoming more difficult to detect and mitigate. Additionally, the rapid expansion of digital infrastructures has widened the attack surface, making it harder for security teams to monitor and protect every entry and egress point. Another significant challenge is the shortage of skilled cybersecurity professionals — estimated by independent surveys to number around 4 million staff worldwide 1 — which leaves many organizations vulnerable to attack. These challenges underscore the need for advanced technologies that can augment human efforts to secure digital assets and data. How can generative AI help? Generative AI (gen AI) has emerged as a powerful tool in addressing these cybersecurity challenges. By leveraging large language models (LLMs) to generate new data or patterns based on existing datasets, generative AI can provide innovative solutions in several key areas: Enhanced threat detection and response Generative AI can be used to create simulations of cyber threats, including sophisticated malware and phishing attacks. These simulations can help in training machine learning models to detect new and evolving threats more accurately. Furthermore, gen AI can aid in the development of automated response systems that react to threats in real time. While this will never eliminate the need for human oversight, it will reduce the need for manual intervention and toil, allowing for quicker mitigation of attacks. For example, with the appropriate oversight it can automatically apply patches to vulnerable systems or adjust firewall rules to block attack vectors. This automated rapid response capability is particularly valuable in mitigating zero-day vulnerabilities, where the window between the discovery of a vulnerability and its exploitation by attackers can be very short. Actionable learnings from security event postmortems In the aftermath of a cybersecurity incident, conducting a thorough postmortem analysis is crucial for understanding what happened, why it happened, and how similar events can be prevented in the future. Generative AI can play a pivotal role in this process by synthesizing and summarizing complex data from a multitude of sources, including logs, network traffic, and security alerts. By analyzing this data, gen AI can identify patterns and anomalies that may have contributed to the security breach, offering insights that might be overlooked by human analysts due to the sheer volume and complexity of the information. Furthermore, it can generate comprehensive reports that highlight key findings, causative factors, and potential vulnerabilities, streamlining the postmortem process. This capability not only accelerates the recovery and learning process but also enables organizations to implement more effective remediation strategies, ultimately strengthening their cybersecurity posture. Generating synthetic data for deep model training The shortage of real-world data for training cybersecurity systems is a significant hurdle. Gen AI can create realistic, synthetic data sets that mirror genuine network traffic and user behavior without exposing sensitive information. This synthetic data can be used to train detection systems, improving their accuracy and effectiveness without compromising privacy or security. Automating phishing detection Phishing remains one of the most common attack vectors. Gen AI can analyze patterns in phishing emails and websites, generating models that predict and detect phishing attempts with high accuracy. By integrating these models into email systems and web browsers, organizations can automatically filter out phishing content, protecting users from potential threats. Putting it all together: The opportunities and the risks Generative AI holds the promise of transforming cybersecurity practices by automating complex processes, enhancing threat detection and response, and providing a deeper understanding of cyber threats. As the industry continues to integrate gen AI into cybersecurity strategies, it's crucial to remain vigilant about the ethical use of this technology and the potential for misuse. Nevertheless, the benefits it offers in strengthening digital defenses are undeniable, making it an invaluable asset in the ongoing battle against cyber threats. How does MongoDB help? With MongoDB, your development teams can build and deploy robust, correct, and differentiated real-time cyber defenses faster, and at any scale. To understand how MongoDB does this, consider that the AI technology stack comprises three layers: The underlying compute (GPUs) and LLMs The tooling to fine-tune models along with the tooling for in-context learning and inference against the trained models The AI applications and related end-user experiences MongoDB operates at the second layer of the stack. It enables customers to bring their own proprietary data to any LLM running on any computing infrastructure to build gen AI-powered cybersecurity applications. MongoDB does this by addressing the hardest problems when adopting gen AI for cybersecurity. MongoDB Atlas securely unifies operational data, unstructured data, and vector data in a single, fully managed multi-cloud platform, avoiding the need to copy and sync data between different systems. MongoDB’s document-based architecture also allows development teams to easily model relationships between your application data and vector embeddings. This allows deeper and faster analytics and insights against security-related data. Figure 1: MongoDB Atlas brings together all of the data services needed to build modern cyber security applications in a unified API and developer data platform. MongoDB’s open architecture is integrated with a rich ecosystem of AI developer frameworks, LLMs, and embedding providers. This, combined with our industry-leading multi-cloud capabilities, allows your development teams the flexibility to move quickly and avoid lock-in to any particular cloud provider or AI technology in this rapidly evolving space. Check out our AI resource page to learn more about building AI-powered apps with MongoDB. Applying gen AI and MongoDB to real-world cybersecurity applications Threat intelligence ExTrac utilizes AI-powered analytics and MongoDB Atlas to predict public safety risks by analyzing data from thousands of sources. The platform initially helped Western governments foresee conflicts but is expanding to enterprises for reputational management and more. MongoDB's document data model allows ExTrac to manage complex data efficiently, enhancing real-time threat identification. Atlas Vector Search aids in augmenting language models and managing vector embeddings for texts, images, and videos, speeding up feature development. This approach enables ExTrac to efficiently model trends, track evolving narratives, and predict risk for its customers, leveraging the flexibility and power of MongoDB to handle data of any shape and structure. Learn more in our ExTrac case study . CyberSec assessments VISO TRUST leverages AI to streamline the assessment of third-party cyber risks, making complex vendor security information quickly accessible for informed decision-making. Utilizing Amazon Bedrock and MongoDB Atlas, VISO TRUST's platform automates the due diligence of vendor security, significantly reducing the workload for security teams. Its AI-powered approach involves artifact intelligence that classifies security documents, detects organizations, and predicts security control locations within artifacts. MongoDB Atlas hosts text embeddings for a dense retrieval system that enhances the accuracy of LLMs through retrieval-augmented generation (RAG), providing instant, actionable security insights. This innovative use of technology enables VISO TRUST to offer rapid, scalable cyber risk assessments, boasting significant reductions in work and time for enterprises like InstaCart and Upwork. MongoDB's flexible document database and Atlas Vector Search play critical roles in managing and querying vast amounts of data, supporting VISO TRUST's mission to deliver comprehensive cyber risk intelligence. Learn more in our Viso Trust case study . Steps to get started Generative AI powered by LLMs augmented with your own operational data encoded as vector embeddings is opening up many new possibilities in cyber security. If you want to learn more about the technology and its possibilities, take a look at our Atlas Vector Search learning byte . In just 10 minutes you’ll get an overview of different use cases and how to get started. 1 Hill, M. (2023, April 10). Cybersecurity workforce shortage reaches 4 million despite significant recruitment drive . CSO.

March 13, 2024

How MongoDB Enables Digital Twins in the Industrial Metaverse

The integration of MongoDB into the metaverse marks a pivotal moment for the manufacturing industry, unlocking innovative use cases across design and prototyping, training and simulation, and maintenance and repair. MongoDB's powerful capabilities — combined with Augmented Reality (AR) or Virtual Reality (VR) technologies — are reshaping how manufacturers approach these critical aspects of their operations, while also enabling the realization of innovative product features. But first: What is the metaverse, and why is it so important to manufacturers? We often use the term, "digital twin" to refer to a virtual replication of the physical world. It is commonly used for simulations and documentation. The metaverse goes one step further: Not only is it a virtual representation of a physical device or a complete factory, but the metaverse also reacts and changes in real time to reflect a physical object’s condition. The advent of the industrial metaverse over the past decade has given manufacturers an opportunity to embrace a new era of innovation, one that can enhance collaboration, visualization, and training. The industrial metaverse is also a virtual environment that allows geographically dispersed teams to work together in real-time. Overall, the metaverse transforms the way individuals and organizations interact to produce, purchase, sell, consume, educate, and work together. This paradigm shift is expected to accelerate innovation and affect everything from design to production across the manufacturing industry. Here are some of the ways the metaverse — powered by MongoDB — is having an impact on manufacturing. Design and prototyping Design and prototyping processes are at the core of manufacturing innovation. Within the metaverse, engineers and designers can collaborate seamlessly using VR, exploring virtual spaces to refine and iterate on product designs. MongoDB's flexible document-oriented structure ensures that complex design data, including 3D models and simulations, is efficiently stored and retrieved. This enables real-time collaboration, accelerating the design phase while maintaining the precision required for manufacturing excellence. Training and simulation Taking a digital twin and connecting it to physical assets enables training beyond traditional methods and provides immersive simulations in the metaverse that enhance skill development for manufacturing professionals. VR training, powered by MongoDB's capacity to manage diverse data types — such as time-series, key-values and events — enables realistic simulations of manufacturing environments. This approach allows workers to gain hands-on experience in a safe virtual space, preparing them for real-world challenges without affecting production cycles. Gamification is also one of the most effective ways to learn new things. MongoDB's scalability ensures that training data, including performance metrics and user feedback, is efficiently handled to continuously enlarge the training modules and the necessary resources for the ever-increasing amount of data. Maintenance and repair Maintenance and repair operations are streamlined through AR applications within the metaverse. The incorporation of AR and VR technologies into manufacturing processes amplifies the user experience, making interactions more intuitive and immersive. Technicians equipped with AR devices can access real-time information overlaid onto physical equipment, providing step-by-step guidance for maintenance and repairs. MongoDB's support for large volumes of diverse data types, including multimedia and spatial information, ensures a seamless integration of AR and VR content. This not only enhances the visual representation of data from the digital twin and the physical asset but also provides a comprehensive platform for managing the vast datasets generated during AR and VR interactions within the metaverse. Additionally, MongoDB's geospatial capabilities come into play, allowing manufacturers to manage and analyze location-based data for efficient maintenance scheduling and resource allocation. The result is reduced downtime through more efficient maintenance and improved overall operational efficiency. From the digital twin to metaverse with MongoDB The advantages of a metaverse for manufacturers are enormous, and according to Deloitte many executives are confident the industrial metaverse “ will transform research and development, design, and innovation, and enable new product strategies .” However, the realization is not easy for most companies. Challenges include managing system overload, handling vast amounts of data from physical assets, and creating accurate visualizations. The metaverse must also be easily adaptable to changes in the physical world, and new data from various sources must be continuously implemented seamlessly. Given these challenges, having a data platform that can contextualize all the data generated by various systems and then feed that to the metaverse is crucial. That is where MongoDB Atlas , the leading developer data platform, comes in, providing synchronization capabilities between physical and virtual worlds, enabling flexible data modeling, and providing access to the data via a unified query interface as seen in Figure 1. Figure 1: MongoDB connecting to a physical & virtual factory Generative AI with Atlas Vector Search With MongoDB Atlas, customers can combine three systems — database, search engine, and sync mechanisms — into one, delivering application search experiences for metaverse users 30% to 50% faster . Atlas powers use cases such as similarity search, recommendation engines, Q&A systems, dynamic personalization, and long-term memory for large language models (LLMs). Vector data is integrated with application data and seamlessly indexed for semantic queries, enabling customers to build easier and faster. MongoDB Atlas enables developers to store and access operational data and vector embeddings within a single unified platform. With Atlas Vector Search , users can generate information for maintenance, training, and all the other use cases from all possible information that is accessible. This information can come from text files such as Word, from PDFs, and even from pictures or sound streams from which an LLM then generates an accurate semantic answer. It’s no longer necessary to keep dozens of engineers busy, just creating useful manuals that are outdated at the moment a production line goes through first commissioning. Figure 2: Atlas Vector Search Transforming the manufacturing industry with MongoDB In the digital twin and metaverse-driven future of manufacturing, MongoDB emerges as a linchpin, enabling cost-effective virtual prototyping, enhancing simulation capabilities, and revolutionizing training processes. The marriage of MongoDB with AR and VR technologies creates a symbiotic relationship, fostering innovation and efficiency across design, training, and simulation. As the manufacturing industry continues its journey into the metaverse, the partnership between MongoDB and virtual technologies stands as a testament to the transformative power of digital integration in shaping the future of production. Learn more about how MongoDB is helping organizations innovate with the industrial metaverse by reading how we Build a Virtual Factory with MongoDB Atlas in 5 Simple Steps , how IIoT data can be integrated in 4 steps into MongoDB, or how MongoDB drives Innovations End-To-End in the whole Manufacturing Chain .

March 12, 2024

Building AI with MongoDB: Putting Jina AI’s Breakthrough Open Source Embedding Model To Work

Founded in 2020 and based in Berlin, Germany, Jina AI has swiftly risen as a leader in multimodal AI, focusing on prompt engineering and embedding models. With its commitment to open-source and open research, Jina AI is bridging the gap between advanced AI theory and the real world AI-powered applications being built by developers and data scientists. Over 400,000 users are registered to use the Jina AI platform. Dr. Han Xiao, Founder and CEO at Jina AI, describes the company’s mission: “We envision paving the way towards the future of AI as a multimodal reality. We recognize that the existing machine learning and software ecosystems face challenges in handling multimodal AI. As a response, we're committed to developing pioneering tools and platforms that assist businesses and developers in navigating these complexities. Our vision is to play a crucial role in helping the world harness the vast potential of multimodal AI and truly revolutionize the way we interpret and interact with information." Jina AI’s work in embedding models has caught significant industry interest. As many developers now know, embeddings are essential to generative AI (gen AI). Embedding models are sophisticated algorithms that transform and embed data of any structure into multi-dimensional numerical encodings called vectors. These vectors give data semantic meaning by capturing its patterns and relationships. This means we can analyze and search for unstructured data in the same way we’ve always been able to with structured business data. Considering that over 80% of the data we create every day is unstructured, we start to appreciate how transformational embeddings — when combined with a powerful solution such as MongoDB Atlas Vector Search — are for gen AI. Check out our AI resource page to learn more about building AI-powered apps with MongoDB. Jina AI's jina-embeddings-v2 is the first open-source 8K text embedding model. Its 8K token length provides deeper context comprehension, significantly enhancing accuracy and relevance for tasks like retrieval-augmented generation (RAG) and semantic search . Jina AI’s embeddings offer enhanced data indexing and search capabilities, along with bilingual support. The embedding models are focused on singular languages and language pairs, ensuring state-of-the-art performance on language-specific benchmarks. Currently, Jina Embeddings v2 includes bilingual German-English and Chinese-English models, with other bilingual models in the works. Jina AI’s embedding models excel in classification, reranking, retrieval, and summarization, making them suitable for diverse applications, especially those that are cross-lingual. Recent examples from multinational enterprise customers include the automation of sales sequences, skills matching in HR applications, and payment reconciliation with fraud detection. Figure 1:   Jina AI’s world-class embedding models improve search and RAG systems. In our recently published Jina Embeddings v2 and MongoDB Atlas article we show developers how to get started in bringing vector embeddings into their apps. The article covers: Creating a MongoDB Atlas instance and loading it with your data. (The article uses a sample Airbnb reviews data set.) Creating embeddings for the data set using the Jina Embeddings API. Storing and indexing the embeddings with Atlas Vector Search. Implementing semantic search using the embeddings. Dr. Xiao says, “Our Embedding API is natively integrated with key technologies within the gen AI developer stack including MongoDB Atlas, LangChain, LlamaIndex, Dify, and Haystack. MongoDB Atlas unifies application data and vector embeddings in a single platform, keeping both fully synced. Atlas Triggers keeps embeddings fresh by calling our Embeddings API whenever data is inserted or updated in the database. This integrated approach makes developers more productive as they build new, cutting-edge AI-powered apps for the business.” To get started with MongoDB and Jina AI, register for MongoDB Atlas and read the tutorial . If your team is building its AI apps, sign up for the AI Innovators Program . Successful companies get access to free Atlas credits and technical enablement, as well as connections into the broader AI ecosystem.

February 14, 2024

Building AI with MongoDB: Navigating the Path From Predictive to Generative AI

It should come as no surprise that the organizations unlocking the largest benefits from generative AI (gen AI) today have already been using predictive AI (a.k.a. classic, traditional, or analytical AI). McKinsey made this same observation back in June 2023 with its “Economic Potential of Generative AI 1 ” research. There would seem to be several reasons for this: An internal culture that is willing to experiment and explore what AI can do Access to skills — though we must emphasize that gen AI is way more reliant on developers than the data scientists driving predictive AI Availability of clean and curated data from across the organization that is ready to be fed into gen AI models This doesn’t mean to say that only those teams with prior experience in predictive AI stand to benefit from gen AI. If you take a look at examples from our Building AI case study series , you’ll see many organizations with different AI maturity levels tapping MongoDB for gen AI innovation today. In this latest edition of the Building AI series, we feature two companies that, having built predictive AI apps, are now navigating the path to generative AI: MyGamePlan helps professional football players and coaches improve team performance. helps businesses and consumers build trust by running background checks using public domain data. In both cases, Predictive AI is central to data-driven decision-making. And now both are exploring gen AI to extend their services with new products that further deepen user engagement. The common factor for both? Their use of MongoDB Atlas and its flexibility for any AI use case. Let's dig in. MyGamePlan: Elevating the performance of professional football players with AI-driven insights The use of data and analytics to improve the performance of professional athletes isn’t new. Typically, solutions are highly complex, relying on the integration of multiple data providers, resulting in high costs and slow time-to-insight. MyGamePlan is working to change that for professional football clubs and their players. (For the benefit of my U.S. colleagues, where you see “football” read “soccer.”) MyGamePlan is used by staff and players at successful teams across Europe, including Bayer Leverkusen (current number one in the German Bundesliga), AFC Sunderland in the English Championship, CD Castellón (current number one in the third division of Spain), and Slask Wroclaw (the current number one in the Polish Ekstraklasa). I met with Dries Deprest, CTO and co-founder at MyGamePlan who explains, “We redefine football analysis with cutting-edge analytics, AI, and a user-friendly platform that seamlessly integrates data from match events, player tracking, and video sources. Our platform automates workflows, allowing coaches and players to formulate tactics for each game, empower player development, and drive strategic excellence for the team's success.” At the core of the MyGamePlay platform are custom, Python-based predictive AI models hosted in Amazon Sagemaker. The models analyze passages of gameplay to score the performance of individual players and their impact on the game. Performance and contribution can be tracked over time and used to compare with players on opposing teams to help formulate matchday tactics. Data is key to making the models and predictions accurate. The company uses MongoDB Atlas as its database, storing: Metadata for each game, including matches, teams, and players. Event data from each game such as passes, tackles, fouls, and shots. Tracking telemetry that captures the position of each player on the field every 100ms. This data is pulled from MongoDB into Python DataFrames where it is used alongside third-party data streams to train the company’s ML models. Inferences generated from specific sequences of gameplay are stored back in MongoDB Atlas for downstream analysis by coaches and players. Figure 1:   With MyGamePlans web and mobile apps, coaching staff, and players can instantly assess gameplay and shape tactics. On selecting MongoDB, Deprest says, We are continuously enriching data with AI models and using it for insights and analytics. MongoDB is a great fit for this use case. “We chose MongoDB when we started our development two years ago. Our data has complex multi-way relationships, mapping games to players to events and tracking. The best way to represent this data is with nested elements in rich document data structures. It's way more efficient for my developers to work with and for the app to process. Trying to model these relationships with foreign keys and then joining normalized tables in relational databases would be slow and inefficient.” In terms of development, Deprest says, “We use the PyMongo driver to integrate MongoDB with our Python ML data pipelines in Sagemaker and the MongoDB Node.js driver for our React-based, client-facing web and mobile apps.” Deprest goes on to say, "There are two key factors that differentiate MongoDB from NoSQL databases we also considered: the incredible level of developer adoption it has, meaning my team was immediately familiar and productive with it. And we can build in-app analytics directly on top of our live data, without the time and expense of having to move it out into some data warehouse or data lake. With MongoDB’s aggregation pipelines , we can process and analyze data with powerful roll-ups, transformations, and window functions to slice and dice data any way our users need it." Moving beyond predictive AI, the MyGamePlan team is now evaluating how gen AI can further improve user experience. Deprest says, "We have so much rich data and analytics in our platform, and we want to make it even easier for players and coaches to extract insights from it. We are experimenting with natural language processing via chat and question-answering interfaces on top of the data. Gen AI makes it easy for users to visualize and summarize the data. We are currently evaluating OpenAI’s ChatGPT LLM coupled with sophisticated approaches to prompt engineering, orchestration via Langchain, and retrieval augmented generation (RAG) using LlamaIndex and MongoDB Atlas Vector Search ." As our source data is in the MongoDB Atlas database already, unifying it with vector storage and search is a very productive and elegant solution for my developers. Dries Deprest, CTO and Co-founder, MyGamePlan By building on MongoDB Atlas, MyGamePlan’s team can use the breadth of functionality provided by a developer data platform to support almost any application and AI needs in the future. Check out our AI resource page to learn more about building AI-powered apps with MongoDB. Building trust with relationship intelligence powered by AI and MongoDB Atlas while cutting costs by 30% Across the physical and digital world, we are all constantly building relationships with others. Those relationships can be established through peer-to-peer transactions across online marketplaces, between tradespeople and professionals with their prospective clients, between investors and founders, or in creating new personal connections. All of those relationships rely on trust to work, but building it is hard. was founded to remove the guesswork from building that trust. Ferret is an AI platform architected from the ground up to empower companies and individuals with real-time, unbiased intelligence to identify risks and embrace opportunities. Leveraging cutting-edge predictive and generative AI, hundreds of thousands of global data sources, and billions of public documents, provides curated relationship intelligence and monitoring — once only available to the financial industry — making transparency the new norm. Al Basseri, CTO at Ferret tells us how it works: "We ingest information about individuals from public sources. This includes social networks, trading records, court documents, news archives, corporate ownership, and registered business interests. This data is streamed through Kafka pipelines into our Anyscale/Ray MLops platform where we apply natural language processing through our spaCy extraction and machine learning models. All metadata from our data sources — that's close to three billion documents — along with inferences from our models are stored in MongoDB Atlas . The data in Atlas is consumed by our web and mobile customer apps and by our corporate customers through our upcoming APIs." Figure 2:   Artificial intelligence + real-time data = Relationship Intelligence from Moving beyond predictive AI, the company’s developers are now exploring opportunities to use gen AI in the Ferret platform. "We have a close relationship with the data science team at Nvidia,” says Basseri. “We see the opportunity to summarize the data sources and analysis we provide to help our clients better understand and engage with their contacts. Through our experimentation, the Mistral model with its mixture-of-experts ensemble seems to give us better results with less resource overhead than some of the larger and more generic large language models." As well as managing the data from Ferret’s predictive and gen AI models, customer data and contact lists are also stored in MongoDB Atlas. Through Ferret’s continuous monitoring and scoring of public record sources, any change in an individual's status is immediately detected. As Basseri explains, " MongoDB Atlas Triggers watch for updates to a score and instantly send an alert to consuming apps so our customers get real-time visibility into their relationship networks. It's all fully event-driven and reactive, so my developers just set it and forget it." Basseri also described the other advantages MongoDB provides his developers: Through Atlas, it’s available as a fully managed service with best practices baked in. That frees his developers and data scientists from the responsibilities of running a database so they can focus their efforts on app and AI innovation MongoDB Atlas is mature, having seen it scale in many other high-growth companies The availability of engineers who know MongoDB is important as the team rapidly expands Beyond the database, Ferret is extending its use of the MongoDB Atlas platform into text search. As the company moves into Google Cloud, it is migrating from its existing Amazon OpenSearch service to Atlas Search . Discussing the drivers for the migration, Basseri says, "Unifying both databases and search behind a single API reduces cognitive load for my developers, so they are more productive and build features faster. We eliminate all of the hassle of syncing data between database and search. Again, this frees up engineering cycles. It also means our users get a better experience because previous latency bottlenecks are gone — so as they search across contacts and content on our platform, they get the freshest results, not stale and outdated data." By migrating from OpenSearch to Atlas Search, we also save money and get more freedom. We will reduce our total cloud costs by 30% per month just by eliminating unnecessary data duplication between the database and the search engine. And with Atlas being multi-cloud, we get the optionality to move across cloud providers as and when we need to. Al Basseri, CTO at Once the migration is complete, Basseri and the team will begin development with Atlas Vector Search as they continue to build out the gen AI side of the Ferret platform. What's next? No matter where you are in your AI journey, MongoDB can help. You can get started with your AI-powered apps by registering for MongoDB Atlas and exploring the tutorials available in our AI resources center . Our teams are always ready to come and explore the art of the possible with you. 1

February 13, 2024

Building AI with MongoDB: How Flagler Health's AI-Powered Journey is Revolutionizing Patient Care

Flagler Health is dedicated to supporting patients with chronic diseases by matching them with the right physician for the right care. Typically, patients grappling with severe pain conditions face limited options, often relying on prolonged opioid use or exploring costly and invasive surgical interventions. Unfortunately, the latter approach is not only expensive but also has a long recovery period. Flagler finds these patients and triages them to the appropriate specialist for an advanced and comprehensive evaluation. Current state without Flagler Flagler Health employs sophisticated AI techniques to rapidly process, synthesize, and analyze patient health records to aid physicians in treating patients with advanced pain conditions. This enables medical teams to make well-informed decisions, resulting in improved patient outcomes with an accuracy rate exceeding 90% in identifying and diagnosing patients. As the company built out its offerings, it identified the need to perform similarity searches across patient records to match conditions. Flagler’s engineers identified the need for a vector database but found standalone systems to be inefficient. They decided to use MongoDB Atlas Vector Search . This integrated platform allows the organization to store all data in a single location with a unified interface, facilitating quick access and efficient data querying. What Flagler can offer Will Hu, CTO, and Co-founder of Flagler Health, emphasizes the importance of a flexible database that can evolve with the company's growth. A relational model was deemed too rigid, leading the company to choose MongoDB's document model. This flexibility allows for easy customization of client configuration files, streamlining data editing and evolution. The managed services provided on MongoDB's developer data platform save time and offer reliability at scale throughout the development cycle. Flagler Health collaborates with many clinics, first processing millions of electronic health record (EHR) files in Databricks and transforming PDFs into raw text. Using the MongoDB Spark Connector and Atlas Data Federation , the company seamlessly streams data from AWS S3 to MongoDB. Combined with the transformed data from Databricks, Flagler’s real-time application data in MongoDB is used to generate accurate and personalized treatment plans for its users. MongoDB Atlas Search facilitates efficient data search across Flagler Health's extensive patient records. Beyond AI applications, MongoDB serves critical functions in Flagler Health's business, including its web application and patient engagement suite, fostering seamless communication between patients and clinics. This comprehensive application architecture, consolidated on MongoDB's developer data platform, simplifies Flagler Health's operations, enabling efficient development and increased productivity. By preventing administrative loops, the platform ensures timely access to potentially life-saving care for patients. Looking ahead, Flagler Health aims to enhance patient experiences by developing new features, such as a digital portal offering virtual therapy and mental health services, treatment and recovery tracking, and a repository of physical therapy videos. Leveraging MongoDB’s AI Innovators program for technical support and free Atlas credits, Flagler Health is rapidly integrating new AI-backed functionalities on the MongoDB Atlas developer data platform to further aid patients in need.

February 7, 2024