embedchain VS gpt-migrate

You can use embedchain[1] to connect various data sources and then get a RAG application running on your local and production very easily. Embedchain is an open source RAG framework and It follows a conventional but configurable approach.

The conventional approach is suitable for software engineer where they may not be less familiar with AI. The configurable approach is suitable for ML engineer where they have sophisticated uses and would want to configure chunking, indexing and retrieval strategies.

[1]: https://github.com/embedchain/embedchain

Langchain is a big wrapper in itself and people can't be bothered to even use that to write 10 lines of code. Look at the traction this project is getting https://github.com/embedchain/embedchain, at it's heart it's just using few modules from langchain. The whole thing, chunking+embedding+retrieval+promoting can be done in 100 lines without langchain and embedchain.

Embedchain: a framework to easily create LLM powered bots over any dataset [Link].

Also, even if it didn't have it you could use GPT-Migrate (shameless plug) https://github.com/0xpayne/gpt-migrate

GPT-Migrate: Easily migrate your codebase from one framework or language to another [Link].

Migrate code from one language/framework to another. I think this is very cool. Though you have to ensure all copyrights are in place. You cannot simply take a non-commercial code and convert it to something else and use it.

Github