Vectorless RAG

In plain words

Vectorless RAG is an AI tool launched in August 2025 that provides retrieval-augmented generation capabilities without requiring vector databases. It's designed for developers and researchers who need to implement RAG systems but want to avoid the complexity and overhead of traditional vector-based approaches. The tool is accessible through Google Colab, making it easy to experiment with and integrate into existing workflows.

written from the facts on this page · September 2026

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  Vectorize2024 · vectorize.io · ▲246

  Build RAG pipelines that are optimized for your data.

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  Vectorize 2.02025 · platform.vectorize.io · ▲335

  Complete RAG agents (chatbot, MCP) with little or no code

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  Rag About It2024 · ragaboutit.com · ▲48

  Dive deep into AI Retrieval Augmented Generation (RAG)

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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…

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  PageIndex – Vectorless RAG2025 · github.com · ▲192

  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…

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  Turn any website into a knowledge base for LLMs2024 · embedding.io · ▲305

  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...

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