Building AI with MongoDB: Accelerating App Development With the Codeium AI Toolkit

Mat Keep
February 21, 2024
#genAI

Of the many use cases set to be transformed by generative AI (gen AI), the bleeding edge of this revolution is underway with software development. Developers are using gen AI to improve productivity by writing higher-quality code faster. Tasks include autocompleting code, writing docs, generating tests, and answering natural language queries across a code base. How does this translate to adoption? A recent survey showed 44% of new code being committed was written by an AI code assistant.

Codeium is one of the leaders in the fast-growing AI code assistant space. Its AI toolkit is used by hundreds of thousands of developers for more than 70 languages across more than 40 IDEs including Visual Studio Code, the JetBrains suite, Eclipse, and Jupyter Notebooks. The company describes its toolkit as “the modern coding superpower,” reflected by its recent $65 million Series B funding round and five-star reviews across extension marketplaces.

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

As Anshul Ramachandran, Head of Enterprise & Partnerships at Codeium explains, “Codeium has been developed by a team of researchers and engineers to build on the industry-wide momentum around large language models, specifically for code. We realized that our specialized generative models, when deployed on our world-class optimized deep learning serving software, could provide users with top-quality AI-based products at the lowest possible costs (or ideally, free). The result of that realization is Codeium."

Codeium has recently trained its models on MongoDB code, libraries, and documentation. Now developers building apps with MongoDB can install the Codeium extension on the IDE of their choice and enjoy rapid code completion and codebase-aware chat and search. Developers can stay in the flow while they build, coding at the speed of thought, knowing that Codeium has ingested MongoDB best practices and documentation.

MongoDB is wildly popular across the developer community. This is because Atlas integrates the fully managed database services that provide a unified developer experience across transactional, analytical, and generative AI apps.

Anshul Ramachandran, Head of Enterprise & Partnerships, Codeium

Ramachandran, goes on to say, “MongoDB APIs are incredibly powerful, but due to the breadth and richness of the APIs, it is possible for developers to be spending more time than necessary looking through API documentation or using the APIs inefficiently for the task at hand. An AI assistant, if trained properly, can effectively assist the developer in retrieval and usage quality of these APIs. Unlike other AI code assistants, we at Codeium build our LLMs from scratch and own the underlying data layer. This means we accelerate and optimize the developer experience in unique and novel ways unmatched by others.”

Figure 1:  By simply typing statement names, the Codeium assistant will automatically provide code completion suggestions directly within your IDE.

In its announcement blog post and YouTube video, the Codeium team shows how to build an app in VSCode with MongoDB serving as the data layer. Developers can ask questions on how to read and write to the database, get code competition suggestions, explore specific functions and syntax, handle errors, and more. This was all done at no cost using the MongoDB Atlas free tier and Codeium 100% free, forever individual plan.

You can get started today by registering for MongoDB Atlas and then downloading the Codeium extension. If you are building your own AI app, sign up for the MongoDB AI Innovators Program. Successful applicants get access to free Atlas credits and technical enablement, as well as connections into the broader AI ecosystem.

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Aussie Fintech Monoova Leads the Way on “Multi” “Cloud” (Not “Multi-Cloud”), to Solve Data Security and Compliance Conundrums

Monoova is a fast-growing Australian Fintech scale-up providing real-time payment solutions to businesses. Having grown from 200,000 to 6 million business accounts in 5 years, Monoova has also created 13% of Australia’s PayID since the inception of NPP, and processed over $100 billion in payments. Dealing with critical financial information from hundreds of organisations in highly regulated industries means Monoova needs to put data security and compliance first. But this is easier said than done. Navigating an increasingly complex security and compliance landscape The increased reliance on the cloud, combined with more regulations requiring extra resilience capabilities, means that financial services organisations are facing increasingly complex data security and compliance challenges. APRA’s chair John Lonsdale recently warned the financial sector about cybersecurity non-compliance, mentioning CPS 230 as one of the upcoming regulations organisations should start preparing to comply with ahead of the July 2025 deadline. On November 3rd, 2023, Brad Jones, Assistant Governor of the Reserve Bank of Australia (RBA) gave a speech in which he listed the “Outside Operational Risk (Cloud Concentration Risk)” as one of the main threats to financial stability . “Multi” “cloud” or “multi-cloud”? A significant number of financial services institutions today aren’t using multi-cloud in a way that would make them resilient in the event of a data security or outage issue. Many say that they are using “multi-cloud” but what they are doing is hosting individual workloads and data sets in different clouds, which doesn’t provide full resilience and data security or meet requirements from regulating industry bodies and government. True multi-cloud resilience means having critical data hosted in different clouds at the same time. For Monoova’s CTO, Nicholas Tan, future-proofing compliance and data security lies in adopting a true multi-cloud approach, and this is exactly the path Monoova has taken by working with MongoDB Atlas. “Whether it’s in critical sectors like financial services or telecommunications, time and time again we see events such as outages seriously impact Australian organisations and their sometimes millions of users - the Optus outage from November 2023 is a perfect example," explains Nicholas Tan. “There are great operational risks of not having diversity in an organisations’ core infrastructure, and this is why building real resilience with a proper multi-cloud approach should be a no-brainer.” Working with MongoDB Atlas a game changer MongoDB Atlas, which is the operational database underpinning all of Monoova’s services, was chosen by Monoova to support extra scale requirements as it was - and still is fast growing, as well as empower developer productivity as the company needs to bring new products to market and innovate fast. Another key driver in working with MongoDB Atlas was the unique functionality that enables users to simply “turn a switch on” to automatically enable selected critical data and workloads to go multi-cloud, allowing data to be easily distributed across different clouds and providing resilience and protection for the workload. Tan and his team simply have to choose the UI to reconfigure their posture to be multi-cloud, which takes only a few minutes. Monoova’s MongoDB Atlas multi-cloud console According to Tan: “Our multi-cloud approach is pretty unique in the current Australian financial services landscape, and this is what has set Monoova to be one of the first Australian financial services organisations to align with the CPS230 framework, as well as at the forefront of ensuring compliance and resilience in an environment heavily reliant on third parties.” “Working with MongoDB has been a game changer because it means we were able to quickly scale up at a fraction of the manpower required; it saved us from recruiting a full team if we had had to do all that multi-cloud work in-house.” Learn more about MongoDB Atlas and our multi-cloud feature on our resources page .

February 20, 2024
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Leveraging BigQuery JSON for Optimized MongoDB Dataflow Pipelines

We're delighted to introduce a major enhancement to our Google Cloud Dataflow templates for MongoDB Atlas. By enabling direct support for JSON data types, users can now seamlessly integrate their MongoDB Atlas data into BigQuery, eliminating the need for complex data transformations. This streamlined approach not only saves users time and resources, but it also empowers customers to unlock the full potential of their data through advanced data analytics and machine learning. Figure 1: JSON feature for user options on Dataflow Templates Limitations without JSON support Traditionally, Dataflow pipelines designed to handle MongoDB Atlas data often necessitate the transformation of data into JSON strings or flattening complex structures to a single level of nesting before loading into BigQuery. Although this approach is viable, it can result in several drawbacks: Increased latency: The multiple data conversions required can lead to increased latency and can significantly slow down the overall pipeline execution time. Higher operational costs: The extra data transformations and storage requirements associated with this approach can lead to increased operational costs. Reduced query performance: Flattening complex document structures in JSON String format can impact query performance and make it difficult to analyze nested data. So, what’s new? BigQuery's Native JSON format addresses these challenges by enabling users to directly load nested JSON data from MongoDB Atlas into BigQuery without any intermediate conversions. This approach offers numerous benefits: Reduced operating costs: By eliminating the need for additional data transformations, users can significantly reduce operational expenses, including those associated with infrastructure, storage, and compute resources. Enhanced query performance: BigQuery's optimized storage and query engine is designed to efficiently process data in Native JSON format, resulting in significantly faster query execution times and improved overall query performance. Improved data flexibility: users can easily query and analyze complex data structures, including nested and hierarchical data, without the need for time-consuming and error-prone flattening or normalization processes. A significant advantage of this pipeline lies in its ability to directly leverage BigQuery's powerful JSON functions on the MongoDB data loaded into BigQuery. This eliminates the need for a complex and time-consuming data transformation process. The JSON data within BigQuery can be queried and analyzed using standard BQML queries. Whether you prefer a streamlined cloud-based approach or a hands-on, customizable solution, the Dataflow pipeline can be deployed either through the Google Cloud console or by running the code from the github repository . Enabling data-driven decision-making To summarize, Google’s Dataflow template provides a flexible solution for transferring data from MongoDB to BigQuery. It can process entire collections or capture incremental changes using MongoDB's Change Stream functionality. The pipeline's output format can be customized to suit your specific needs. Whether you prefer a raw JSON representation or a flattened schema with individual fields, you can easily configure it through the userOption parameter. Additionally, data transformation can be performed during template execution using User-Defined Functions (UDFs). By adopting BigQuery Native JSON format in your Dataflow pipelines, you can significantly enhance the efficiency, performance, and cost-effectiveness of your data processing workflows. This powerful combination empowers you to extract valuable insights from your data and make data-driven decisions. Follow the Google Documentation to learn how to set up the Dataflow templates for MongoDB Atlas and BigQuery. Get started with MongoDB Atlas on Google Marketplace . Learn more about MongoDB Atlas on Google Cloud on our product page .

December 17, 2024