Fundamentals of Building a FARM Stack App in 5 min
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00:00:03Introduction to FARM Stack
00:00:33React and FastAPI Overview
00:01:07MongoDB and Beanie ODM
00:01:49Defining and Using Endpoints
00:02:25Vector Search in MongoDB
00:03:02Interactive Demo and Conclusion
The main theme of the video is demonstrating how to build a web application using the FARM stack, focusing on the integration of React, FastAPI, and MongoDB, and showcasing the capabilities of vector search within a MongoDB database.
🔑 Key Points
- FARM stack consists of React (frontend), FastAPI (API layer), and MongoDB (backend).
- FastAPI is a web framework with async support, built on Starlet for speed and scalability.
- Beanie, an ODM built on Pydantic, integrates well with FastAPI and MongoDB.
- The video demonstrates creating endpoints for sheep data, including a post endpoint for adding new sheep.
- FastAPI's self-documenting feature generates a web interface to interact with the defined endpoints.
- Vector search capabilities in MongoDB allow for semantic data searches, including images and text.
- The demo includes a camera component in React to take photos and use AWS Bedrock service for vector searches.
🔗 Related Links
Full Video Transcript
hi everyone my name is annia and I'm going to use these next five minutes to chat about building a farm stack application so let me tell you what the farm stack is it's an application with reacts on the front end fast API in the middle and mongod DB on the back end you have probably heard of react it's a very popular JS framework for building Rich web frontend application but you'll often need an API in the back end to offer data and functionality and that is where fast API comes in fast API is a web framework designed to build apis it has async iio support and is built on Starlet so it's fast and scalable just like mongodb the world's most popular document database we have a farm so we have to put some sheep on it here is a great example of our code beanie is an odm that's an object document mapper and it provides a higher level interface on top of the documents that are already inside of our database beanie is built on pantic which integrates really really well with fast API here we have defined a sheep and some qualities that sheep have such as a name age and some Hobbies here we are defining our sheep endpoint to look up a sheep by name notice that because the Sheep is a pantic object we can return it directly and it will automatically serialize to Json for what it's worth beanie will also validate the data coming back from the database to make sure that it matches our object definition so here is an equivalent post endpoint for creating a new sheep or a lamb to put on our farm fast API is self-documenting and it generates this lovely web page we are actually able to browse the endpoints we've defined and try them out yourself obviously these two endpoints are very very simple but it doesn't take a lot more code to store the photos of our sheep in the database along with a vector index that allows us to take a photo of a sheep and look up all of its details inside of the database because as you know sheep are notoriously difficult to recognize Vector search is a way to search semantically this means searching based on meaning rather than by exact wording this opens up your search capabilities by a ton since it allows for you to vectorize your data and search mathematically on your vectors the cool part about Vector search is that it allows for you to search all types of data including images audio video and the meaning of a text with a camera component in reacts which is not shown we are actually able to take a photo send it to fast API and then using this code on the screen we can use the AWS Bedrock service to generate a vector in vetting and use it to look up the closest matching sheep in our database what that gives us is sheep search so come check out our really really fun demo at www. mongod db-- match.com where unfortunately sheep won't be involved but your own faces will check us out to see what I mean by that
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