MongoDB Provider for Entity Framework Core Now Available in Public Preview

Shelby Carpenter and Megha Bharadwaj
October 12, 2023

We are pleased to announce that the MongoDB Provider for Entity Framework Core (EF Core) is now available in Public Preview. This makes it possible for developers using EF Core to build C#/.NET applications with MongoDB and take advantage of our powerful developer data platform while continuing to use APIs and design patterns they already know and love.

Building for the C#/.NET community

Nearly one-third of all developers use C# to build applications, with the population of C# developers reaching upwards of 10 million developers worldwide. Forty-one percent of C# developers use EF Core, which is beloved as an abstraction layer to simplify working with data during development. In the past, C# developers could use MongoDB’s C# driver but didn’t have first-party support for EF Core; some turned to community-built projects that could be helpful but lacked official backing or ongoing support from MongoDB.

With the official MongoDB Provider for EF Core now available in Public Preview, developers can use C# and EF Core with confidence when building with MongoDB.

What's in the New Provider for EF Core

In this initial Public Preview release, the MongoDB Provider for EF Core offers developers the following capabilities:

  • Support for code-first workflows: Allows users to build without an initial database; you first create the classes for your application and then match your data model to the classes, not the other way around.
  • Basic CRUD methods: Basic create, read, update, and delete (CRUD) operations are supported.
  • String and numeric type operators: String and numeric type operators needed for basic CRUD operations will be supported. We anticipate adding support for more complex operators in future iterations of the Provider.
  • Embedded documents: The Provider supports embedded documents, making it easier to store related information in the same database record.
  • Class mapping and serialization: Your classes in C# will map to MongoDB in a predictable way, including when working with IDs as well as date and/or time values.
  • LINQ query support: The Provider will support LINQ3 queries with fluent query syntax.
  • Change tracking: The Provider allows you to track and save changes made to entities with each DbContext instance back to your MongoDB database.

And this is just a start. Stay tuned for more advanced functionality when we release the MongoDB provider in General Availability (targeted for 2024).

Benefits of using the provider for EF Core

With the MongoDB Provider for EF Core, C# developers can unlock the full power of MongoDB's developer data platform to build modern applications while leveraging a familiar API interface, query paradigm (LINQ), and design patterns. Developers looking to modernize their data layer can do so with MongoDB while remaining free from cloud vendor lock-in since MongoDB works with all major cloud providers and for multi-cloud deployments

How to get started with MongoDB Provider for Entity Framework Core

All you need to do is download the MongoDB Provider for EF Core from the NuGet package manager and build a DbContext that points to a MongoDB Provider instance. The Provider connects to MongoDB and handles the rest, so you can quickly harness the joint value of EF Core and MongoDB.

You can learn more by diving into our documentation. After you try the new Provider for EF Core, feel free to leave us feedback in our user feedback portal. Your input is important for helping us continue to improve the product experience.

Get started today to unleash the power of your data with MongoDB and EF Core.

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Create and Customize your Own Billing Dashboard with Atlas Charts

When it comes to managing tech stacks, controlling expenses is a top priority for our customers. This summer, we've made it easier than ever to optimize your spend with new ways for visualizing and analyzing your Atlas billing data. Through Cost Explorer , you can conveniently analyze your billing data right within the Billing section of the Atlas UI. But that's not all! We know many of you prefer to craft your own Atlas billing dashboards – allowing you to customize, embed, and share tailor-made charts to align with your optimization strategies. With the latest release of Atlas Charts , we’ve made this easier than ever with a new out-of-the-box billing dashboard! Gone are the days of navigating to a Github repository , running scripts from your command line and manually importing a dashboard to gain insights into your Atlas bill. We understand the importance of quickly accessing and being able to answer questions about your Atlas spending, so we’ve made it a top priority to enhance your user experience by streamlining this process. Now, when you enter Charts, you’ll find a dedicated Ingestions page conveniently located in the left navigation menu. Here, you can effortlessly set up your scheduled billing data ingestion by entering your Organization API keys (or create them up following the steps we’ve included), and selecting the deployment and database where you’d like your data to live. Once you’ve completed the configuration and hit Save , your first ingestion job will be kicked off. Fresh data will be retrieved from the Atlas Billing API once a day to ensure the data in your nominated database is always up-to-date. After this initial job is complete, you can click Add and open to explore your new billing dashboard. Just like any other dashboard, you have the flexibility to filter and highlight charts within the billing dashboard, and customize or add new ones to tailor the dashboard to your specific needs. You could even share the billing dashboard with your team or schedule regular dashboard reports to be sent to your email. You have the freedom to manage your ingestion configuration at any time from the Ingestions page, whether you need to update your API key pair or change the database in which your data is stored. To learn more about visualizing your billing data in Atlas Charts, be sure to check out our documentation . We’re always listening to feature requests that will enhance using Charts across teams, so if you have any requests or feedback, please share them with us here . New to Atlas Charts? Get started today by logging into or signing up for MongoDB Atlas , deploying or selecting a cluster, and activating Charts for free.

October 12, 2023
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Building AI With MongoDB: Integrating Vector Search And Cohere to Build Frontier Enterprise Apps

Cohere is the leading enterprise AI platform, building large language models (LLMs) which help businesses unlock the potential of their data. Operating at the frontier of AI, Cohere’s models provide a more intuitive way for users to retrieve, summarize, and generate complex information. Cohere offers both text generation and embedding models to its customers. Enterprises running mission-critical AI workloads select Cohere because its models offer the best performance-cost tradeoff and can be deployed in production at scale. Cohere’s platform is cloud-agnostic. Their models are accessible through their own API as well as popular cloud managed services, and can be deployed on a virtual private cloud (VPC) or even on-prem to meet companies where their data is, offering the highest levels of flexibility and control. Cohere’s leading Embed 3 and Rerank 3 models can be used with MongoDB Atlas Vector Search to convert MongoDB data to vectors and build a state-of-the-art semantic search system. Search results also can be passed to Cohere’s Command R family of models for retrieval augmented generation (RAG) with citations. Check out our AI resource page to learn more about building AI-powered apps with MongoDB. A new approach to vector embeddings It is in the realm of embedding where Cohere has made a host of recent advances. Described as “AI for language understanding,” Embed is Cohere’s leading text representation language model. Cohere offers both English and multilingual embedding models, and gives users the ability to specify the type of data they are computing an embedding for (e.g., search document, search query). The result is embeddings that improve the accuracy of search results for traditional enterprise search or retrieval-augmented generation. One challenge developers faced using Embed was that documents had to be passed one by one to the model endpoint, limiting throughput when dealing with larger data sets. To address that challenge and improve developer experience, Cohere has recently announced its new Embed Jobs endpoint . Now entire data sets can be passed in one operation to the model, and embedded outputs can be more easily ingested back into your storage systems. Additionally, with only a few lines of code, Rerank 3 can be added at the final stage of search systems to improve accuracy. It also works across 100+ languages and offers uniquely high accuracy on complex data such as JSON, code, and tabular structure. This is particularly useful for developers who rely on legacy dense retrieval systems. Demonstrating how developers can exploit this new endpoint, we have published the How to use Cohere embeddings and rerank modules with MongoDB Atlas tutorial . Readers will learn how to store, index, and search the embeddings from Cohere. They will also learn how to use the Cohere Rerank model to provide a powerful semantic boost to the quality of keyword and vector search results. Figure 1: Illustrating the embedding generation and search workflow shown in the tutorial Why MongoDB Atlas and Cohere? MongoDB Atlas provides a proven OLTP database handling high read and write throughput backed by transactional guarantees. Pairing these capabilities with Cohere’s batch embeddings is massively valuable to developers building sophisticated gen AI apps. Developers can be confident that Atlas Vector Search will handle high scale vector ingestion, making embeddings immediately available for accurate and reliable semantic search and RAG. Increasing the speed of experimentation, developers and data scientists can configure separate vector search indexes side by side to compare the performance of different parameters used in the creation of vector embeddings. In addition to batch embeddings, Atlas Triggers can also be used to embed new or updated source content in real time, as illustrated in the Cohere workflow shown in Figure 2. Figure 2: MongoDB Atlas Vector Search supports Cohere’s batch and real time workflows. (Image courtesy of Cohere) Supporting both batch and real-time embeddings from Cohere makes MongoDB Atlas well suited to highly dynamic gen AI-powered apps that need to be grounded in live, operational data. Developers can use MongoDB’s expressive query API to pre-filter query predicates against metadata, making it much faster to access and retrieve the more relevant vector embeddings. The unification and synchronization of source application data, metadata, and vector embeddings in a single platform, accessed by a single API, makes building gen AI apps faster, with lower cost and complexity. Those apps can be layered on top of the secure, resilient, and mature MongoDB Atlas developer data platform that is used today by over 45,000 customers spanning startups to enterprises and governments handling mission-critical workloads. What's next? To start your journey into gen AI and Atlas Vector Search, review our 10-minute Learning Byte . In the video, you’ll learn about use cases, benefits, and how to get started using Atlas Vector Search.

April 25, 2024