RAG & Knowledge
ragapp
RAGApp: deploy a RAG app over your documents with Docker — configure it like a custom GPT
★ 4.4k starsApache-2.0
View guide →PageIndex: vectorless, reasoning-based RAG that indexes documents like a human expert would
PageIndex is an open-source rag & knowledge project by VectifyAI (VectifyAI/PageIndex on GitHub). It has 38,984 GitHub stars and 3,378 forks, is released under the MIT license, and was last updated on GitHub on 8 October 2026.
Decide what you want from PageIndex and check the README and requirements to confirm it fits your setup and Python environment.
Clone the repository from GitHub (github.com/VectifyAI/PageIndex) or use the documented install channel if you do not need the source.
Follow the README quickstart to install dependencies and set any required configuration or credentials.
Work through the README's first example end to end, then adapt it to your own project or data.
Retrieval without a vector database — reasoning-based document indexing instead.
Builds a structured document index that an LLM can navigate by reasoning.
Reports leading accuracy on FinanceBench in the README's comparisons.
Use it through the PageIndex SDK client, an agent integration, or PageIndex Cloud.
Can run locally with `pip install -U pageindex`.
Designed for agentic, multi-step retrieval over long documents.
Start by getting the source and entering the project directory.
git clone https://github.com/VectifyAI/PageIndex.git
cd PageIndexInstall the Python package from PyPI.
pip install -U pageindexFollow the README/SDK guide to build an index for a PDF and query it.
# see README for the exact command — SDK getting startedIt is maintained by VectifyAI and contributors on GitHub, where the repository github.com/VectifyAI/PageIndex is the canonical home for code, issues and releases.
Yes — it is open source under the MIT license. Read the license in the repository for what is permitted, including any conditions on redistribution or commercial use.
It needs an LLM for reasoning-based retrieval — use your own provider key locally, or the hosted PageIndex Cloud.
Compared with classic vector RAG: PageIndex skips embeddings and vector stores, using a reasoning-navigable index — which can beat vector RAG on structured professional documents.
Clone github.com/VectifyAI/PageIndex, follow the README quickstart to install it, and run the documented first example before adapting it to your own work.
RAGApp: deploy a RAG app over your documents with Docker — configure it like a custom GPT
Open-source LLM knowledge platform: turn raw documents into a queryable RAG, an autonomous reasoning agent, and a self-maintaining Wiki.
Knowhere turns documents into structured, hierarchy-native chunks ready for agents and RAG.
Microsoft's graph-based RAG: index a corpus into a knowledge graph, then query it with an LLM
Incremental engine that keeps RAG indexes and agent context fresh by processing only the delta.
The smallest vector index in the world: private RAG over everything on your laptop with 97% less storage.
The most-starred other RAG & Knowledge projects in the LibreAI catalogue — the realistic shortlist if PageIndex is not the right fit.
Build Agentic workflows, RAG pipelines, with rich AI model and tool support on one collaborative workspace. Deploy on cloud, VPC, or…
RAGFlow is a leading open-source Retrieval-Augmented Generation (RAG) engine that fuses cutting-edge RAG with Agent capabilities to create a superior context…
Lightweight OCR toolkit turning PDFs and images into structured, LLM-ready data in 100+ languages
Stop renting your intelligence. Own it with AnythingLLM. Everything you need for a powerful local-first agent experience
Ready-to-run templates for RAG, AI pipelines and enterprise search that stay in sync with live data
Free and open — grab the source, star the repo, and start creating.