RAG и базы знаний
ragapp
RAGApp: deploy a RAG app over your documents with Docker — configure it like a custom GPT
★ 4,4 тыс. звёздApache-2.0
Смотреть руководство →PageIndex — проект с открытым исходным кодом из категории «RAG и базы знаний» от VectifyAI (VectifyAI/PageIndex на GitHub). У него 38 984 звезды на GitHub и 3 378 форков, он распространяется по лицензии MIT и последний раз обновлялся на GitHub 8 октября 2026.
PageIndex: vectorless, reasoning-based RAG that indexes documents like a human expert would
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.
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