embedchain VS HeimdaLLM

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].

This is solvable by augmenting the database schema with comments.

When you integrate your database with an LLM, you'll notice the LLM will produce flawed queries based on wrinkles in your database schema. This is because the LLM relies on conventional understanding of how the schema is probably tied together. When you see the flawed queries, you augment the schema with a comment that explains why the schema has a wrinkle. The LLM takes that into consideration and the resulting queries are improved.

A concrete example[1]: I found that when querying the Sakila movie rental database, the generated query would frequently attempt to join the `rental` table to the `film` table through a nonexistent `film_id` column on the rental table. By adding the linked comment, the LLM stopped doing that.

1. https://github.com/amoffat/HeimdaLLM/blob/dev/notebooks/saki...

I think we're just scratching the surface of apps. As we figure out how to integrate this technology in novel ways (not just "here's my app + AI!!!!"), it will open new doors.

Shameless self-promotion, I'm trying to build some of those intermediary pieces. I have authored an open source library[1] that lets businesses externalize LLMs to their users, so that users can use natural language to query their data in your database. The goal is to try to simplify UIs to have more natural language components, without needing to send your data to an LLM.

1. https://github.com/amoffat/HeimdaLLM