Morphia Version 0.101 Released

MongoDB
July 11, 2013
#Releases

The Java Team has released Morphia, version 0.101. Rumors of Morphia’s demise have been greatly exaggerated.

This release formalizes the 0.99-SNAPSHOT code that many have been using for years. Apart from some formatting and minor style changes, there is no functional change from what’s been on Google Code for the past few years.

We’re currently doing a follow-up release to incorporate the enhancements made in James Green’s fork. At that point, we can start working through the backlog of issues logged against Morphia and begin evolving the project.

As part of the mission to revive the project, we’ve migrated the Morphia code (and wiki, and issues) from Google Code to GitHub. This should make it much easier to contribute to Morphia via pull requests. Feel free to submit pull requests for the bugs or irritating documentation fixes that you want. The Google Code site will notify users that Github is now the place to be. You can find the Github repo here

We released 0.101 into Maven Central, so you’ll be able to get your updated version and future versions easily through Maven or Gradle. You can find the release notes here Github.

Further down the line, Morphia will run on the new Java driver that we’ve been working on all year.

We’re aware that the community has been somewhat split between the snapshot release and James’s fork, and we’ve spoken to James Green, who has maintained Morphia for awhile, about our plans. We want to make sure that we’re all moving in the same direction and that we involve the community in our efforts. We’d like to thank James for the work he’s done so far keeping Morphia alive, and look forward to working with him and the whole Morphia community more closely in the future.

The Java Team: Jeff Yemin, Justin Lee & Trisha Gee

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hackNY Founders on 10gen’s Sponsorship, NYC Startup Ecosystem and MongoDB for Rapid Prototyping

Last month, 10gen announced its sponsorship for hackNY , the non-profit aiming to federate the next generation of hackers for New York City. We’ve been longtime supporters of hackNY and were excited to present the founders, Evan Korth and Chris Wiggins, with a donation of $75,000 at MongoNYC . At the conference, Chris and Evan sat for a brief interview, where they talked about how technology is transforming the key industries in New York City, from media, advertising, publishing, finance and beyond. They explained how hackNY’s model, which organizes student hackathons and summer fellowship programs, give students practical, hands-on experience with programming that they don’t receive at university. Open source technologies like MongoDB are a great fit for hackathons since they enable developers to rapidly prototype, with the knowledge that they can scale their applications.

July 9, 2013
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Retrieval Augmented Generation for Claim Processing: Combining MongoDB Atlas Vector Search and Large Language Models

Following up on our previous blog, AI, Vectors, and the Future of Claims Processing: Why Insurance Needs to Understand The Power of Vector Databases , we’ll pick up the conversation right where we left it. We discussed extensively how Atlas Vector Search can benefit the claim process in insurance and briefly covered Retrieval Augmented Generation (RAG) and Large Language Models (LLMs). MongoDB.local NYC Join us in person on May 2, 2024 for our keynote address, announcements, and technical sessions to help you build and deploy mission-critical applications at scale. Use Code Web50 for 50% off your ticket! Learn More One of the biggest challenges for claim adjusters is pulling and aggregating information from disparate systems and diverse data formats. PDFs of policy guidelines might be stored in a content-sharing platform, customer information locked in a legacy CRM, and claim-related pictures and voice reports in yet another tool. All of this data is not just fragmented across siloed sources and hard to find but also in formats that have been historically nearly impossible to index with traditional methods. Over the years, insurance companies have accumulated terabytes of unstructured data in their data stores but have failed to capitalize on the possibility of accessing and leveraging it to uncover business insights, deliver better customer experiences, and streamline operations. Some of our customers even admit they’re not fully aware of all the data in their archives. There’s a tremendous opportunity to leverage this unstructured data to benefit the insurer and its customers. Our image search post covered part of the solution to these challenges, opening the door to working more easily with unstructured data. RAG takes it a step further, integrating Atlas Vector Search and LLMs, thus allowing insurers to go beyond the limitations of baseline foundational models, making them context-aware by feeding them proprietary data. Figure 1 shows how the interaction works in practice: through a chat prompt, we can ask questions to the system, and the LLM returns answers to the user and shows what references it used to retrieve the information contained in the response. Great! We’ve got a nice UI, but how can we build an RAG application? Let’s open the hood and see what’s in it! Figure 1: UI of the claim adjuster RAG-powered chatbot Architecture and flow Before we start building our application, we need to ensure that our data is easily accessible and in one secure place. Operational Data Layers (ODLs) are the recommended pattern for wrangling data to create single views. This post walks the reader through the process of modernizing insurance data models with Relational Migrator, helping insurers migrate off legacy systems to create ODLs. Once the data is organized in our MongoDB collections and ready to be consumed, we can start architecting our solution. Building upon the schema developed in the image search post , we augment our documents by adding a few fields that will allow adjusters to ask more complex questions about the data and solve harder business challenges, such as resolving a claim in a fraction of the time with increased accuracy. Figure 2 shows the resulting document with two highlighted fields, “claimDescription” and its vector representation, “claimDescriptionEmbedding” . We can now create a Vector Search index on this array, a key step to facilitate retrieving the information fed to the LLM. Figure 2: document schema of the claim collection, the highlighted fields are used to retrieve the data that will be passed as context to the LLM Having prepared our data, building the RAG interaction is straightforward; refer to this GitHub repository for the implementation details. Here, we’ll just discuss the high-level architecture and the data flow, as shown in Figure 3 below: The user enters the prompt, a question in natural language. The prompt is vectorized and sent to Atlas Vector Search; similar documents are retrieved. The prompt and the retrieved documents are passed to the LLM as context. The LLM produces an answer to the user (in natural language), considering the context and the prompt. Figure 3: RAG architecture and interaction flow It is important to note how the semantics of the question are preserved throughout the different steps. The reference to “adverse weather” related accidents in the prompt is captured and passed to Atlas Vector Search, which surfaces claim documents whose claim description relates to similar concepts (e.g., rain) without needing to mention them explicitly. Finally, the LLM consumes the relevant documents to produce a context-aware question referencing rain, hail, and fire, as we’d expect based on the user's initial question. So what? To sum it all up, what’s the benefit of combining Atlas Vector Search and LLMs in a Claim Processing RAG application? Speed and accuracy: Having the data centrally organized and ready to be consumed by LLMs, adjusters can find all the necessary information in a fraction of the time. Flexibility: LLMs can answer a wide spectrum of questions, meaning applications require less upfront system design. There is no need to build custom APIs for each piece of information you’re trying to retrieve; just ask the LLM to do it for you. Natural interaction: Applications can be interrogated in plain English without programming skills or system training. Data accessibility: Insurers can finally leverage and explore unstructured data that was previously hard to access. Not just claim processing The same data model and architecture can serve additional personas and use cases within the organization: Customer Service: Operators can quickly pull customer data and answer complex questions without navigating different systems. For example, “Summarize this customer's past interactions,” “What coverages does this customer have?” or “What coverages can I recommend to this customer?” Customer self-service: Simplify your members’ experience by enabling them to ask questions themselves. For example, “My apartment is flooded. Am I covered?” or “How long do windshield repairs take on average?” Underwriting: Underwriters can quickly aggregate and summarize information, providing quotes in a fraction of the time. For example, “Summarize this customer claim history.” “I Am renewing a customer policy. What are the customer's current coverages? Pull everything related to the policy entity/customer. I need to get baseline info. Find relevant underwriting guidelines.” If you would like to discover more about Converged AI and Application Data Stores with MongoDB, take a look at the following resources: RAG for claim processing GitHub repository From Relational Databases to AI: An Insurance Data Modernization Journey Modernize your insurance data models with MongoDB and Relational Migrator

April 18, 2024