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Products that do what PaperQA2, Agentic RAG for Science does
We're excited to release PaperQA2, an open source RAG library specialized to work with the scientific literature. We've seen some really compelling results with it (https://paper.wikicrow.ai), like superhuman performance at question answering and summarization when compared with expert scientists. PaperQA2 is a major overhaul of our prior PaperQA system, it includes automatically obtained rich metadata for each paper, a CLI to work with local papers directly, a local full-text search engine for keywords searches over PDF files, a state-of-the-art algorithm for LLM-based re-ranking…
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Hi HN! I am Maria, solo founder of DataQA (https://dataqa.ai/), a tool to search and label documents for various NLP tasks (e.g. entity extraction, entity linking, etc). I have worked as a data scientist and ML engineer for the better part of a decade, and over that time have specialised mainly in applications involving natural language processing (NLP). One of the key questions I have always had at the back of my mind is whether my time was well spent. Whenever I spent more time on feature engineering or trying different models, I always wondered whether I would get better…
2021
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Hey HN! We’ve published a series of open-source notebooks showcasing Advanced RAG and Agentic architectures, and we’re excited to share our latest compilation of Agentic RAG techniques! These Colab-ready notebooks are designed to be plug-and-play, making it easy to integrate them into your projects. We're actively expanding the repository and would love your input to shape its future. What Advanced RAG technique should we add next? Drop your ideas in the comments or open an issue on GitHub!
2025 · github.com
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Built this as a toy project to understand knowledge graphs by tackling a real problem: traditional RAG fails badly on legal documents because it misses interconnections between sections. The system actually combines both approaches on every query - gets semantic matches via TF-IDF, retrieves structural relationships from Neo4j, then feeds both contexts to OpenAI for comprehensive answers. Used the Indian Income Tax Act as test data since legal documents have natural graph structures. Queries like "What sections reference Section 80C?" get both the reference network AND content explanations.…
2025 · github.com
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I’ve been frustrated with PDFs and found arXiv HTML lacking, so I built a fully interactive paper reader. Features: • Hover references, citations, equations • Light/dark mode • Auto-generated dependency graphs for definitions/lemmas/theorems • Table of contents that syncs with scroll • Highlighting + annotations • “Copy raw LaTeX” anywhere Featured paper: Video models are zero-shot learners and reasoners (Veo 3) https://www.sciencestack.ai/arxiv/2509.20328v2
Nov 2025 · sciencestack.ai
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Hi HN, We built one of the largest RAG set-ups that exist toady with Usul.ai (6B tokens). We started by using langchain and llamaindex, they were able to get us to a prototype in a couple of days, but took 3 months of taking pieces apart and optimizing them to make it perform well at such large scale. We put all of these learning into an MIT licensed open-source project — Agentset. Our goal to let people get production quality RAG w/o having to understand or optimize the underlying pieces. It supports 22 file formats, agentic search, deep research, citations, and a UI out of the box.…
Oct 2025 · github.com
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Smarter RAG with Agentic Retrieval & Context-Aware MCP
Sep 2025
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Hi all, I find literature searches to be slow and despite all the tools nowadays, it is difficult to map out vast areas of research and then connect my ideas to what I’m trying to do (e.g. writing a review, learning something new, planning a new project, assignments, etc). Some of my peers and teachers also found this to be a problem so I built this MVP app to help people not only leverage AI search capabilities but also go on to connect their ideas, map out research projects, cite various results and organise their “stream of consciousness” as they work. I’d love to get some feedback on it…
2025 · hexle.ai
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In this post, we document the results of some experiments comparing vanilla Graph RAG (just a single pass of text2cypher) vs. a router agent Graph RAG approach that can call vector search tools alongside text2cypher. The routing agent uses an LLM to decide which vector search tool to call, depending on the terms identified in the question, and it works quite well. The results show that recent frontier LLMs like `gpt-4.1` and the trusty workhorse `gemini-2.0-flash` produce great quality Cypher reliably and reproducibly, with some prompt engineering to ensure that the graph schema is formatted…
2025 · blog.kuzudb.com
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Last month, the SambaNova team, in partnership with Stanford and UC Berkeley, introduced the viral paper Agentic Context Engineering (ACE), a framework for building evolving contexts that enable self-improving language models and agents. Today, the team has released the full ACE implementation, available on GitHub, including the complete system architecture, modular components (Generator, Reflector, Curator), and ready-to-run scripts for both Finance and AppWorld benchmarks. The repository provides everything needed to reproduce results, extend to new domains, and experiment with evolving…
Dec 2025 · github.com
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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
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Hi HN, Our research team just released the best performing and most efficient reranker out there, and it's available now as an open weight model on HuggingFace. Reranker v2 was designed specifically for agentic RAG, supports instruction following (our v1 was the first to introduce this), and is multilingual. Along with this, we're also open source our eval set, which allows you to reproduce our benchmark results. By releasing these datasets, we are also advancing instruction-following reranking evaluation, where high-quality benchmarks are currently limited. Please give it a try and let us…
2025 · huggingface.co
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Tired of ads and misinformation clogging up your search results when you just want to see the science? That's why we built Consensus, a new search engine that uses Large Language Models to surface findings straight from scientific research for any question. If you like the product, please support our launch on Product Hunt today: https://www.producthunt.com/posts/consensus-2
2022 · consensus.app
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I built this because I was tired of guessing why my RAG system was failing. It projects user queries vs. documents into 2D space to find 'Red Zones' (high user intent, low documentation). Open source, built with FastAPI + React. Would love feedback on the clustering logic.
Dec 2025 · github.com
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In this blog, we introduce a pure JSON index to enable reasoning-based RAG without relying on any Vector DBs. Any feedback is welcome!
Oct 2025 · vectifyai.notion.site
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We're releasing Project AELLA - an open-science initiative to make scientific knowledge more accessible through AI-generated structured summaries of research papers. Blog: https://inference.net/blog/project-aella Visualizer: https://aella.inference.net Models: https://huggingface.co/inference-net/Aella-Qwen3-14B, https://huggingface.co/inference-net/Aella-Nemotron-12B Highlights: - Released 100K research paper summaries in standardized JSON format with interactive visualization. - Fine-tuned open models (Qwen 3 14B &…
Nov 2025 · aella.inference.net
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Napkinbook is a smart canvas for collaboration, sharing, and discovering insights. I think of it as a more computational version of Excalidraw or Miro: a flexible canvas where you can think, build, present, and collaborate without constantly moving between different tools. Since my first Show HN, I’ve added: * Much better mobile support * AI-assisted brainstorming * Presentation decks with animations * Shareable reports * Live collaboration * Actual Git history The goal is to make the canvas itself the workspace, rather than treating whiteboarding, presenting, reporting, and version history…
23d ago · desk.napkinbook.com
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I created this tool over the weekend because, as someone interested in AI and technology, I find many research papers on arXiv fascinating but often incredibly dense and difficult to understand due to the heavy jargon and technical language. I wanted to make these complex topics more accessible to laypeople like myself by converting the topics described in these papers into full-length books that break down key concepts, making cutting-edge research easier to grasp. You can think of it as generating prerequisite reading materials before being able to read the actual paper. Here are some…
2024 · instabooks.ai
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Hey there HN! We’re Vasilije, Boris, and Laszlo, and we’re excited to introduce cognee, an open-source Python library that approaches building evolving semantic memory using knowledge graphs + data pipelines Before we built cognee, Vasilije(B Economics and Clinical Psychology) worked at a few unicorns (Omio, Zalando, Taxfix), while Boris managed large-scale applications in production at Pera and StuDocu. Laszlo joined after getting his PhD in Graph Theory at the University of Szeged. Using LLMs to connect to large datasets (RAG) has been popularized and has shown great promise.…
2025 · github.com
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The project aims to organize computer science research in a logical, simple, and easy-to-follow way. It is designed to help us find papers worth reading first. I started building Trending Papers because following computer science research has become increasingly hard as the pace of innovation accelerates. The number of new articles on Arxiv has grown at 27% CAGR for the past 20 years. 240 new papers have been filed daily on average over the past 12 months. And the number is growing: last month, there were well over 300 new papers on average every single day. The system is based on some…
2023 · trendingpapers.com
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Hey, I've made a pdf reader with ai assistance, you can quickly ask questions about the text and the images, and it will have context about all of your reading material so you can ask questions freely, try it now, I'm in open beta to find bug and get feedback. upcoming features: - Highlighting - Highlighting with Notes - Bibliography - Automatic Reference file opening - Improved UX and robustness currenlty alot of bugs so please let me know if you found any. feedback appreciated
2025 · pdf-hub.com
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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
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