I'm building an API that allows you to train semantic search for RAGs

I built this tool because I wanted a way to just take a bunch of URLs or domains, and query their content in RAG applications. It takes away the pain of crawling, extracting content, chunking, vectorizing, and updating periodically. I'm curious to see if it can be useful to others. I meant to launch this six months ago but life got in the way...

I've built an advanced RAG (Retrieval-Augmented Generation) pipeline from scratch to demystify the complex mechanics of modern LLM-powered Question Answering systems. This repository features: -- An implementation of a sub-question query engine from scratch to answer complex user questions. -- Illustrative explanations that unveil the inner workings of the system. -- An analysis of the challenges I faced while working with the system, like prompt engineering and cost estimation. -- Qualitative comparison with similar frameworks like LlamaIndex, offering a broader perspective. Key Takeaway:…

Hey folks, Elias here. Excited to unveil my latest project. Why I Built This: Traditional keyword search isn't cutting it. I've used LLM-embeddings to provide more nuanced, relevant results. How It Works: LLM-embedding similarity on curated datasets for semantically similar results. No need to iterate over keywords any more. Current Datasets: - YC Companies - Show HN Posts, - Ask HN Posts - ProductHunt Startups - Github Top 200k Repos Use Cases: - Validate a product idea's existence - Check if someone already Asked HN something - Have fun - search random terms and see what pops up Want to…

Everything that would be here is in the README. I hope this gets big, it has tons of potential.