RAGless – similar to RAG, but $0 LLM API costs at runtime
RAGless is a semantic retrieval system that answers questions about your documentation, without using an LLM at runtime. - EmilResearch/RAGless
What it does
RAGless is a retrieval-only question-answering system with zero LLM calls at runtime . Source documents are converted into self-contained informational blocks, indexed into a local vector database (Qdrant), and queried via asymmetric Gemini embeddings. Zero hallucinations at runtime. Minimal latency. Near-zero cost per query. Clone or download the repository and navigate to the projectfrom github.com
Does the same job
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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...

- RORagas – Open-source library for evaluating RAG pipelines2024 · github.com · ▲121
Ragas is an open-source library for evaluating and testing RAG and other LLM applications. Github: https://docs.ragas.io/en/stable/, docs: https://docs.ragas.io/. Ragas provides you with different sets of metrics and methods like synthetic test data generation to help you evaluate your RAG applications. Ragas started off by scratching our own itch for evaluating our RAG chatbots last year. Problems Ragas can solve - How do you choose the best components for your RAG, such as the retriever, reranker, and LLM? - How do you formulate a test dataset…

- DADemystifying Advanced RAG Pipelines2023 · github.com · ▲131
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:…
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I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
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