Alternatives
Products that do what Dabarqus does
Add PDF chat to your LLM app in less than 9 lines of code
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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…
2025 · joinable.ai
- 8AB
All LLM user interfaces I've seen so far are somewhat frustrating: * ChatGPT web requires a lot of copy-paste, it rewrites whole document even if you need to update a part of it, etc. * Github Copilot completions are rather unreliable and do not leave an option to specify what you want; Copilot's chat sidebar is little more than ChatGPT integrated into the IDE * Google Docs have right UI for non-code text, but they use really dumb model (not Gemini 1.5 Pro). Also won't work for code. So... I wrote a Emacs Lisp function which calls LLM with contents of the buffer to generate text according to…
2024 · x.com
- 9AS
Upload markdown docs, get a support chatbot that answers with citations. The entire backend is one JS file — storage, search, and conversation history are handled by the runtime. Stack: Next.js + OnCell + OpenRouter. Demo: https://youtu.be/rWIU43pOH5Y
Apr 2026 · github.com
- 10RW
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
- 11AI
Hi I am Jan, CTO @ Pathway. A use case we have been working on with LLMs is to let people know when an answer to their query changes due to revisions of source documents. Obviously, we want to avoid periodically re-computing all queries for the LLM. Why I think it’s cool? - We don’t spin in a loop to repeat with the LLM. - Alerts are LLM-deduplicated - no spamming users with typo fixes - And the best - our framework, Pathway takes care of handling the updates, the example looks nearly like a regular, static RAG chatbot. More context + GIF of how it works for Google Drive document alerts:…
2023 · github.com
- 12CF
Over this past month I had the idea to build a 100% open-source MIT-licensed tool to simplify sharing code with LLMs, without the vendor lock-in you get from most SDKs. Right now, it’s way too hard to export your data or work freely with models like o1 PRO or Grok 3, especially since they don’t even have API access. So I built OpenRepoPrompt, an open-source tool from wildberry-source that serializes files and folders into XML for LLMs. I coded/designed from 12PM -> 11PM on Saturday and 8AM -> 11PM on Sunday. There are still tons of features missing (I'm working on better file filtering…
2025 · github.com
- 13EL
Hey HN! I built Experiment to solve a common frustration in LLM development: the lack of proper tools for prompt engineering experimentation. Here's what makes it different: Key Features: - Load and edit chat completion logs from CSV files - Fork and modify specific conversation entries - Run inference via Anthropic, Mistral, and OpenAI - Define custom tools using JSONSchema format - Visual tool usage analysis with collapsible, sorted key-value pairs - Full mobile support and available as installable PWA Technical Highlights: - Built with React using custom isomorphic architecture -…
2025 · github.com
- 14XT
I just built XYMake (https://xymake.com), a tool that lets you convert any X (Twitter) thread into clean markdown, making your conversations accessible for LLMs, MCPs, or any API. ## What it does: - Transforms any X thread URL into markdown by simply changing "x.com" to "xymake.com" in the URL - OAuth2 login to "free your data" and make your threads available - Auto-generates OG images with token counts and participant info for easy sharing - Serves different content types based on whether the request is from a crawler, browser, or agent ## Why I built it: I believe people should…
2025 · xymake.com
- 15MI
Hi HN! I lead product at Vectara and we've just released a new LLM in our platform that outperforms GPT4 and Gemini 1.5 Pro on RAG tasks. Vectara is a Retrieval Augmented Generation (RAG) platform primarily deployed as a SaaS service which includes a generous free tier so you can try it for free. The way we've been able to offer a "better but cheaper" is that we focus a lot of our attention on taking smaller models (which can be hosted in a cost efficient way) and fine tuning them to specific tasks: in this case RAG. This ends up with a model that is less capable of arbitrary tasks like…
2024 · vectara.com
- 16LF
LLM from URL —— A free AI chat completion service directly from URL Usage: In the address bar of any web browser, type your question after https://818233.xyz/ and hit Enter to get the instant answer. You know the best part of this? Whitespace in the url is supported in most web browsers! You can also use curl or Wget to retrieve the appended url by replacing any whitespace with a '+' character. If you need to have an actual '+' character in your question, just use '++'. Example: The url "https://818233.xyz/hi there" in any web browser will return the same answer…
2025 · 818233.xyz
- 17AO
I've built an airgapped Retrieval-Augmented Generation (RAG) system for question-answering on documents, running entirely offline with local inference. Using Llama 3, Mistral, and Gemini, this setup allows secure, private NLP on your own machine. Perfect for researchers, data scientists, and developers who need to process sensitive data without cloud dependencies. Built with Llama C++, LangChain, and Streamlit, it supports quantized models and provides a sleek UI for document processing. Check it out, contribute, or suggest new features!
2024 · github.com
- 18AE
Hi all, Sharing a repo I was working on for a while. It’s open-source and includes many different strategies for RAG (currently 17), including tutorials, and visualizations. This is great learning and reference material. Open issues, suggest more strategies, and use as needed. Enjoy!
2024 · github.com
- 19IB
I often get strange or funny outputs from LLMs (ChatGPT, Claude, etc.), and wanted a simple way to save and share them. So I built this small side project https://llmquotes.com. What it does: - Submit quotes (no login needed, optional Twitter handle) - Upvote and favorite - Spam is manually moderated (for now) - Best ones also get posted to https://x.com/LlmQuotes Backstory: This started because my friends and I kept screenshotting odd LLM replies and losing them in chats. I built a little place to collect them. Nothing fancy, just centralizing and sharing. Tech:…
Sep 2025 · llmquotes.com
- 20IB
I’ve been working on something called Davia — a platform for creating interactive documents. These are “living documents”: each page is self-contained, combining editable content, interactive components, and data. You can read them, edit them, and actually interact with them — more like mini-apps than static docs. Here’s what makes it interesting: The interactive components are pieces of code generated on the fly, so you’re not limited to a fixed set of blocks like in Notion. You can create infinitely flexible functionality right inside a doc. Technically, an interactive page is an ensemble…
Sep 2025 · old.reddit.com
- 21GR
I built a RAG application that retrieves relevant Bhagavad Gita verses for ethical dilemmas and generates structured guidance. The problem: The Gita has 701 verses. Finding applicable wisdom for a specific situation requires either deep familiarity or hours of reading. How it works: 1. User describes their ethical dilemma 2. Query is embedded using sentence-transformers 3. ChromaDB retrieves top-k semantically similar verses 4. LLM generates structured output: 3 options with tradeoffs, implementation steps, verse citations Tech stack: - Backend: FastAPI, PostgreSQL, Redis - Vector DB:…
Dec 2025 · geetanjaliapp.com
- 22IB
After fine-tuning GPT for a personal project, I realized how tedious it is to write plain text in a massive JSON file. That's why I built this app for my own use, and I want to see if others could benefit from a tool like this as well ;)
2024 · finetuna-ui.com
- 23AO
2024 · orac-interface.vercel.app
- 24CV
I built Codag because I kept getting lost in my own AI code. You're chaining 3 LLM calls across 5 files. A prompt change breaks something downstream. Which call? Which branch? You grep for "openai.chat", open 8 tabs, trace the flow manually. Codag automates this: - Point it at your codebase and it extracts every LLM call, decision branch, and processing step - Renders an interactive and shareable DAG with clickable nodes that link back to source - Live updates as you edit using tree-sitter — no waiting for re-analysis Supports OpenAI, Anthropic, Gemini, LangChain, LangGraph, CrewAI, and…
Feb 2026 · github.com
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