Alternatives
Products that do what Joinable's RAG-in-a-Box – fastest way to build a RAG App for your data does
Hey HN, I’m Julia, my team and I are building Rag-in-a-Box (https://www.joinable.ai/rag-in-a-box), hosted RAG service that let’s builders of any skill level launch their own RAG app loaded with their own data in minutes. [ What can you do ] 1. Load your documents (PDFs, CSV, PPTs, Word Docs, etc) and make them searchable instantly. All your data stays private and encrypted. 2. Choose latest open source LLM (Llama 4, Deepseek, GPT-oss, etc) to interact with your docs 3. Access your hosted RAG via API - build your own custom front end or integrate with your existing product…
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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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Hey hacker news, We’re the cofounders at Psychic.dev (http://psychic.dev) where we help companies connect LLMs to private data. With the launch of Llama 2, we think it’s finally viable to self-host an internal application that’s on-par with ChatGPT, so we did exactly that and made it an open source project. We also included a vector DB and API server so you can upload files and connect Llama 2 to your own data. The RAG in RAGstack stands for Retrieval Augmented Generation, a technique where the capabilities of a large language model (LLM) are augmented by retrieving information…
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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TL;DR: Vector-based RAG performs poorly for many real-world applications like codebase chats, and you should consider 'language maps'. Part of our mission at Mutable.ai is to make it much easier for developers to build and understand software. One of the natural ways to do this is to create a codebase chat, that answer questions about your repo and help you build features. It might seem simple to plug in your codebase into a state-of-the-art LLM, but LLMs have two limitations that make human-level assistance with code difficult: 1. They currently have context windows that are too small to…
2024 · twitter.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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Hey all! I wanted to share this project I've been working on that can maybe help you or your developer friends out. I built a RAG system for our product a while back and didn't realize how easy they were to get started. So I put together my learnings into this online course. It's not quite ready but if you sign up and mention HackerNews, I can get you early access. I'm looking to get feedback on the following: (1) materials — is it engaging & did you learn something? (2) UI/UX of the platform — did you have any issues that prevented you from starting or finishing the tutorial? (3)…
2024 · takehomes.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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Hi everyone, my cofounder and I built Dera - a platform to help manage chunks and embeddings. We built this because of the pain points we experienced while building RAG applications for side projects. The biggest pain point we encountered was that we were constantly trying out different chunking strategies, but there’s no easy way to check how the strategies are performing in terms of retrieval when given the same query. We tried searching for a tool for this but couldn’t find any (most LLM dev tools focus on prompts management). We hope this tool will be useful for people building RAG apps.…
2024 · getdera.com
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Ragas is an open-source library designed for evaluating and testing RAG (Retrieval-Augmented Generation) and other LLM applications. It offers a diverse set of metrics and methods, including synthetic test data generation, to help you assess your RAG applications. Ragas was initially developed to address our own needs for evaluating RAG chatbots last year. ### Problems Ragas Can Solve: - How can you select the best components for your RAG, such as the retriever, reranker, and LLM? - How can you create a test dataset without incurring significant expenses and time? We believe there's a need…
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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RAG Web UI is designed to be the most straightforward way to build your own knowledge-based Q&A system. While other RAG (Retrieval-Augmented Generation) projects might be complex, we focus on making it super easy to understand and use. Why It's The Most Beginner-Friendly: Simple Document Management - Just upload your documents (PDF, DOCX, Markdown, Text) - System handles all the complex processing automatically - No need to worry about document chunking or vectorization - Documents update automatically in the background Easy-to-Use Chat Interface - Ask questions in plain language - Get…
2025 · github.com
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Hey folks! I wanted to share one of the latest projects I worked on, called Vectara Portal. It's an application that allows users of our platform (https://vectara.com/) to create shareable pages that let you and other users (privately, if you wish) chat with, search through, or get answers from your documents. This began as a side project resulting from a question over lunch: "How do we put the power of our platform into the hands of users, without requiring them to code?" One hackathon weekend later, plus a few more days' worth of improvements, Portal was born. It's…
2024 · portal.demo.vectara.com
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