Alternatives
Products that do what PageIndex for Reasoning-Based RAG does
In this blog, we introduce a pure JSON index to enable reasoning-based RAG without relying on any Vector DBs. Any feedback is welcome!
- 1PV
Not all improvements come from adding complexity — sometimes it's about removing it. PageIndex takes a different approach to RAG. Instead of relying on vector databases or artificial chunking, it builds a hierarchical tree structure from documents and uses reasoning-based tree search to locate the most relevant sections. This mirrors how humans approach reading: navigating through sections and context rather than matching embeddings. As a result, the retrieval feels transparent, structured, and explainable. It moves RAG away from approximate "semantic vibes" and toward explicit reasoning…
2025 · github.com
- 2FB
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
- 3RN
We built PageIndex, a document indexing system that turns documents into hierarchical search trees to support reasoning-based RAG. Traditional vector-based RAG often struggles with retrieval accuracy because it optimizes for similarity, not relevance. But what we really need in retrieval is relevance — which requires reasoning. When working with professional documents that demand domain expertise and multi-step reasoning, vector-based RAG and similarity search often fall short. So we started exploring a more reasoning-driven approach to RAG. Reasoning-based RAG enables LLMs to think and…
2025 · github.com
- 4TA
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
- 5HL
Not all improvements come from adding complexity — sometimes it's about removing it. PageIndex takes a different approach to RAG. Instead of relying on vector databases or artificial chunking, it builds a hierarchical tree structure from documents and uses reasoning-based tree search to locate the most relevant sections. This mirrors how humans actually read: navigating through sections and context rather than relying on embedding similarity. As a result, the retrieval feels transparent, structured, and explainable. It moves RAG away from approximate "semantic vibes" and toward explicit…
2025 · github.com
- 6DA
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
- 7JF
After two years of improvement, I think it's time to share it with you all. Here’s a quick overview: - Common features include validation, formatting, minification, and more. - Visualize JSON in a graph or table view. - Structured comparison with fallback to text comparison. - Navigate though JSON using JSON pointer. - Supports jq. Would love to hear the community's questions, thoughts and comments!
2024 · github.com
- 8JA
2020 · github.com
- 9BA
Made this in a free evening. Index an permissive license translation of the Bible (WEB) into a RAG database to allow returning passages of similar semantic meaning. Lots of fun. For example, "more money more problems" returns Ecclesiastes 5:9-13 which, I'll just say, is spot on.. "Moreover the profit of the earth is for all. The king profits from the field. He who loves silver shall not be satisfied with silver, nor he who loves abundance, with increase. This also is vanity. When goods increase, those who eat them are increased; and what advantage is there to its owner, except to feast on…
Jun 2026 · crosscanon.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
- 13AV
The word "index" originally came from how humans retrieve info: book indexes and tables of contents that guide us to the right place in documents. Computers later borrowed the term for data structures: e.g., B-trees, hash tables, and more recently, vector indexes. They are highly efficient for machines; but abstract and unnatural: not something a human, or an LLM, can understand and directly use as a reasoning aid. This creates a gap between how indexes work for computers and how they should work for models that reason like humans. PageIndex is a new step that "looks back to move forward".…
Oct 2025 · github.com
- 14QP
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
- 15RA
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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2016 · github.com
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2023 · github.com
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2013 · github.com
- 19IB
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
- 20VR
2025 · colab.research.google.com
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RAG pipelines have become bloated: embeddings, vector DBs, rerankers, and ad-hoc pipelines everywhere. Projects like Claude Code showed a simpler path: In-Context Retrieval — letting the LLM reason directly over context for retrieval instead of outsourcing search to external infrastructure. PageIndex takes that one step further with In-Context Indexing. If retrieval happens in-context, the index should live there too. Each document is transformed into a hierarchical, human-readable tree structure (like a table-of-contents tree index) inside the model's context window. The LLM reads the…
Oct 2025 · github.com
- 24HN
Hey HN! We're college friends building HelixDB. It's a database that natively supports both graph and vector types. It’s designed for AI-driven apps like RAG, vector search, code indexing, and agent frameworks where you need both explicit relationships and similarity. We came up with the idea for Helix at university, while building a graph database as a side project in Rust. Reading some research papers on RAG setups, I realised there was a lot of infrastructure setup to get started. You need your own server, a graph database, a vector database and then some bespoke middleman software to…
2025 · github.com
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