Introducing the Full Stack FastAPI App Generator for Python Developers

Taylor Pacelli
January 11, 2024 | Updated: January 31, 2024
#Python

We are thrilled to announce the release of the Full Stack FastAPI, React, MongoDB (FARM) base application generator, coinciding with FastAPI's emerging status as a leading modern Python framework. Known for its high performance and ease of use, FastAPI is quickly becoming a top choice for Python developers. This launch is a significant advancement for Python developers eager to build and maintain progressive web applications using the powerful combination of FastAPI and MongoDB.

Bridging the Development Gap

While it's always been easy and quick to start building modern web applications with MongoDB and FastAPI, in the past developers still had to make many decisions about other parts of the stack, such as authentication, testing, integration etc., and manually integrate these components in their application.

Our new app generator aims to simplify some of this process further. It enables you to quickly spin up a production-grade, full-stack application, seamlessly integrating FastAPI and MongoDB, thereby significantly enhancing the developer experience.

Simplifying the Development Journey

Now, with the launch of our Full Stack FastAPI App Generator, MongoDB dramatically simplifies the initial stages of project setup for production-grade applications by providing a well-structured app skeleton and reduces the initial learning curve and the time spent setting up the project. For new learners and seasoned developers alike, this means less time figuring out the basics and more time building differentiated experiences for their users.

Key Features Included in the App Generator:

  • Complete Web Application Stack: Generates a foundation for your project development, integrating both front-end and back-end components.

  • Docker Compose Integration: Optimized for local development.

  • Built-in Authentication System: Includes user management schemas, models, CRUD, and APIs, with OAuth2 JWT token support and magic link authentication.


Start now

Accelerate your web application development with MongoDB and FastAPI today.

Visit our Github repository for the app generator and start transforming your web development experience.

Please note: This tool is an experimental project and not yet officially supported by MongoDB.

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Panel: How MongoDB is Helping Drive Innovation for Indian Organisations

At MongoDB.local Delhi a panel of CXOs and IT leaders discussed the strategies and challenges of using software to drive innovation in their organisations. Here are the key lessons they shared. Fostering innovation: tips and challenges Our panel, which included representatives from Appy Pie, Autodit, Formidium, and Tech Mahindra, agreed that the rapid development of data analytics technology, and the scarcity of trained talent on the ground, were key challenges when it comes to driving innovation. To stay on top, Tech Mahindra has a dedicated talent acquisition engine to keep tabs on those technologies, and customer requirements. “We imbibe these learnings, so we’re equipped to deliver solutions on the ground,” explained Shuchi Agrawal, Global Head for Data Analytics, Pre-Sales, and Solutioning at Tech Mahindra (IT services and consulting). “When I think of data and MongoDB, I think MongoDB BIRT (business intelligence reporting tools),” says Shuchi. To accelerate their customers’ journey of transformation, for Tech Mahindra, automation must be based on innovations on the baseline of analytics workloads, i.e. data. “That’s why MongoDB is one of the key elements in most of our solution designs when we’re looking for some of the advanced analytics workloads,” says Shuchi. Choosing technology and evaluating products For Vaibhav Agrawal, Executive Vice President of Formidium, selecting technology to drive innovation comes with key caveats: it must be easy to implement, the talent must exist in the market to do the implementation, zero-trust is essential — as data security is paramount for customers — and it must perform in terms of scalability, efficiency, optimization, and monitoring. “If those things are there, you never have to go to other technology,” says Vaibhav. “And MongoDB Atlas comes in on all those check marks perfectly — that's why we chose it.” Enhancing innovation strategies with database features Vaibhav observed two aspects to any innovation: a) having the idea and creating something, and b) re-innovating it. So, for innovation to perpetuate, ideas must be adapted according to your experience, market changes, and changes in technology — and that means reviewing your products’ performance. “MongoDB Atlas has that amazing 360-degree view of your database activities, and the monitoring of your resources,” says Vaibhav. “Plus, it's very easy to get analytics out of it and change the course of your innovation.” “You always need to keep watch on the performance of a database," he adds. "Then you will be able to keep pace with innovation for years.” To see more announcements and get the latest product updates, visit our What's New page.

January 10, 2024
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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