langchaingo VS Gin

Generative AI development has been democratised, thanks to powerful Machine Learning models (specifically Large Language Models such as Claude, Meta's LLama 2, etc.) being exposed by managed platforms/services as API calls. This frees developers from the infrastructure concerns and lets them focus on the core business problems. This also means that developers are free to use the programming language best suited for their solution. Python has typically been the go-to language when it comes to AI/ML solutions, but there is more flexibility in this area. In this post you will see how to leverage the Go programming language to use Vector Databases and techniques such as Retrieval Augmented Generation (RAG) with langchaingo. If you are a Go developer who wants to how to build learn generative AI applications, you are in the right place!

This use-case here is a similar one - a chat application. I will switch back to implementing things in Go using langchaingo (I used Python for the previous one) and continue to use Amazon Bedrock. But there are few unique things you can explore in this blog post:

langchaingo is the LangChain implementation for the Go programming language. This blog post covers how to extend langchaingo to use foundation model from Amazon Bedrock.

Langchain Go is being actively developed https://github.com/tmc/langchaingo

Now let’s define the functions that will be called whenever a request hits our API. All the functions will be referencing the context provided by the Gin web framework. Paste the following code below the sample slice we just added to api.go:

We will be using Gorilla Mux. As per their last update, they have a new group of maintainers, and their repos have shown activity to confirm that. The tutorial can be easily replicated in any other framework or library as well. So, while we will be using Gorilla Mux, you can try to replicate it in Gin or Fiber as well.

Now, let's dive into the fun part – creating our basic ToDo application using the powerful Gin framework. This section will walk you through the steps, breaking down the code into manageable snippets.

Thanks to the AWS Lambda Web Adapter, the application built as a (good old) REST/HTTP API using a familiar library (in this case, Gin.

Excellent Performance: Sponge is built on the gin framework, providing outstanding performance for web service development.

In this blog, we will delve into the fascinating realm of handling images in a Golang application, leveraging the power of the Gin framework for RESTful API development, MongoDB as a robust NoSQL database, and the mongo-driver library for seamless interaction with MongoDB. To store images efficiently, we'll explore the intricacies of GridFS, a specification within MongoDB for storing large files as separate chunks.

It uses Gin as the HTTP framework and PostgreSQL as the database with pgx as the driver and Squirrel as the query builder. It also utilizes Redis as the caching layer with go-redis as the client.

I have created an application with Go in Gin-Gonic. In my frontend (Nuxt3/TypeScript) I always get a CORS error:

We use Gin web framework https://gin-gonic.com for the routing, Gin provides a balance between performance, ease of use and extensibility making it a preferred choice for building and running web applications in Go.