embedchain VS llmo

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].

Aider is such an awesome project! I didn't know about it until I read this comment. I also wanted a way to provide my code as context from within the terminal without having to copy and paste back and forth. The tool I wrote (llmo) seems pretty similar to yours, although it uses the Textual library and Rich.

https://github.com/knowsuchagency/llmo

I'm really excited to try out aider, thanks for making it!

Hello HN!

LLMO (Elmo) is an AI pair programming tool I created that's become an indispensable part of my workflow.

https://github.com/knowsuchagency/llmo

LLMO is designed to meet you where you are – your terminal. It provides real-time, interactive programming assistance. With its "staging area" feature, you can keep files in the context window and update the AI about your ongoing coding tasks without the hassle of copying and pasting every time you make changes to your code.

Key features include: - Interactive Chat: Get real-time programming assistance directly in your terminal. - Staging Area: No need to copy and paste updates. Simply add your files to the AI's context. - Model Customization: Choose the OpenAI model that fits your needs. - Personality: By default, Elmo loves to make bodybuilding references. You can turn this off through a CLI flag or environment variable

The recommended way to install LLMO is via `pipx install llmo` https://pypa.github.io/pipx/

As a sidenote, LLMO uses Textual which runs the terminal in application mode, meaning that you can't simply copy content as you would normally. In iterm2, you can hold down the `option` key to select text. You'll need to refer to the documentation for your own terminal for more information.

I hope you find LLMO as useful as I have!