I am the developer of eazyrag.com, and here is the problem I am trying to solve.

When I was working on usegrasp.com (a search engine), I integrated the LLM answer engine, which is basically implementing a retrieval augmented generation (RAG) pipeline. First, I tried the most popular libraries available. I have to say I disliked the developer experience due to confusing APIs and complex abstractions. I wanted something like Algolia for retrieval augmented generation.

So, I built eazyrag.com, which is an easy-to-use API to implement RAG with your own data inside your apps or websites. You can simply index everything and query it. You don't even need a unique ID for the content you are indexing; just send the entire documents in a single API call, and we will handle chunking, splitting, embedding, and prompt with context formation, etc.

Here is a demo I built: I indexed all the Bun.js docs/guides pages on EazyRAG and performed RAG on them: https://eazyrag.com/bun