Alternatives
Products that do what AutoRAG – AutoML tool for RAG. Find an optimal RAG pipeline does
- Discovering the most effective RAG pipeline for your specific data and use case can be daunting. It requires experimenting with various RAG modules and configurations, which are both time-consuming and complex. - AutoRAG addresses this challenge by automatically evaluating different combinations of RAG modules and their parameters. You don't need to write implementation code yourself; everything is set up through a single YAML file. - Our aim is to save you the hassle of continuously adapting to new RAG modules and configurations. Instead, you can focus on developing robust data for your…
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Hey there HN! We’re Antonio, Luca, and Yuhang, and we’re excited to introduce Fast GraphRAG, an open-source RAG approach that leverages knowledge graphs and the 25 years old PageRank for better information retrieval and reasoning. Building a good RAG pipeline these days takes a lot of manual optimizations. Most engineers intuitively start from naive RAG: throw everything in a vector database and hope that semantic search is powerful enough. This can work for use cases where accuracy isn’t too important and hallucinations are tolerable, but it doesn’t work for more difficult queries that…
2024 · github.com
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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:…
2023 · github.com
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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…
2024 · github.com
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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...
2024 · embedding.io
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A RAG has several moving parts: data ingestion, retrieval, re-ranking, generation etc.. Each part comes with numerous options. If we consider a toy example, where you could choose from: 5 different chunking methods, 5 different chunk sizes, 5 different embedding models, 5 different retrievers, 5 different re-rankers/ compressors 5 different prompts 5 different LLMs That’s 78,125 distinct RAG configurations! If you could try evaluating each one in just 5 mins, that’d still take 271 days of non-stop trial-and-error effort! In short, it’s kinda impossible to find your optimal RAG setup…
2024 · github.com
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Hey HN, I’m Zach from Superpowered AI (YC S22). We’ve been working in the RAG space for a little over a year now, and we’ve recently decided to open-source all of our core retrieval tech. spRAG is a retrieval system that’s designed to handle complex real-world queries over dense text, like legal documents and financial reports. As far as we know, it produces the most accurate and reliable results of any RAG system for these kinds of tasks. For example, on FinanceBench, which is an especially challenging open-book financial question answering benchmark, spRAG gets 83% of questions correct,…
2024 · github.com
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RAGLite is a Python package for building Retrieval-Augmented Generation (RAG) applications. RAG applications can be magical when they work well, but anyone who has built one knows how much the output quality depends on the quality of retrieval and augmentation. With RAGLite, we set out to unhobble RAG by mapping out all of its subproblems and implementing the best solutions to those subproblems. For example, RAGLite solves the chunking problem by partitioning documents in provably optimal level 4 semantic chunks. Another unique contribution is its optimal closed-form linear query adapter…
2024 · github.com
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2024 · github.com
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Got tired of wiring up vector stores, embedding models, and chunking logic every time I needed RAG. So I built piragi. from piragi import Ragi kb = Ragi(\["./docs", "./code/\*\*/\*.py", "https://api.example.com/docs"\]) answer = kb.ask("How do I deploy this?") That's the entire setup. No API keys required - runs on Ollama + sentence-transformers locally. What it does: - All formats - PDF, Word, Excel, Markdown, code, URLs, images, audio - Auto-updates - watches sources, refreshes in background, zero query latency - Citations - every answer includes sources…
Dec 2025 · pypi.org
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Hello HN, I'm Owen from SciPhi (https://www.sciphi.ai/), a startup working on simplifying˛Retrieval-Augmented Generation (RAG). Today we’re excited to share R2R (https://github.com/SciPhi-AI/R2R), an open-source framework that makes it simpler to develop and deploy production-grade RAG systems. Just a quick reminder: RAG helps Large Language Models (LLMs) use current information and specific knowledge. For example, it allows a programming assistant to use your latest documents to answer questions. The idea is to gather all the relevant information…
2024 · github.com
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Hey HN! We've just launched Quilt, a robust RAG (Retrieval-Augmented Generation) UI that revolutionizes how you interact with your documents. Key features: - Multi-user setup with private/public document collections - Advanced hybrid RAG pipeline combining full-text & vector search - Smart citations with in-browser PDF preview and highlights - Fully customizable settings and prompts through the UI Making an account is free, no need to even use a strong password: this is only to ensure your documents are separate from the rest. We're keen to hear your thoughts and feedback. What features…
2024 · quilt.fly.dev
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Hello everyone! I am Jan, CTO and one of the creators of Pathway, the real-time data processing framework. I’m excited to share Pathway’s ready-to-use AI Pipelines, configurable with just YAML! These frameworks offer out-of-the-box solutions for AI search, RAG, and more—optimized for real-time indexing and in-memory processing. What makes it simple? YAML templates! The pipeline templates are fully customizable using YAMLs to fit your needs, from changing the data sources to the choice of the LLM model, all without touching Pathway’s Python code. Thanks to the Pathway data processing engine,…
2024 · pathway.com
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Nov 2025 · github.com
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Hi HN! I'm excited to share Autolabel, an open-source Python library to label and enrich text datasets with any Large Language Model (LLM) of your choice. We built Autolabel because access to clean, labeled data is a huge bottleneck for most ML/data science teams. The most capable LLMs are able to label data with high accuracy, and at a fraction of the cost and time compared to manual labeling. With Autolabel, you can leverage LLMs to label any text dataset with <5 lines of code. We’re eager for your feedback!
2023 · github.com
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