langchaingo VS TaskEaseGPT

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

I am building another project like this but it will achieve larger tasks like build an e-commerce website, write a book about X and etc. It is similar to AutoGPT but AutoGPT is not able to complete tasks. It always plans or sometimes does the work but forgets to save so the work is lost. Here is the link if you are interested: https://github.com/Stylsheets/TaskEaseGPT