VectifyAI/PageIndex
- Source
- GitHub
- First trending
- Category
- RAG and search
- GitHub stars
- 36,322
- Main language
- Python
This page introduces an external open-source repository. It is not an HDATF product.

What it does
PageIndex builds a hierarchical tree index of a document and lets an LLM reason through it to find the relevant sections, without a vector database or chunking. Its SDK can index and retrieve locally or through PageIndex Cloud.
How it helps ATF
Worth comparing for Company Brain retrieval over long company documents, since it follows a section tree instead of similarity search. It could also be a reference for how LabChin reads long sources found during a search.
License
MIT Permissive. Commercial use and changes are allowed if the copyright notice is kept.
More in this category
- tobi/qmd
QMD is a local CLI search engine for markdown notes, meeting transcripts and documentation. It combines BM25 full-text search, vector search and LLM reranking, all running on the device with GGUF models. - HKUDS/DeepTutor
DeepTutor is a personalized AI tutoring system with chats, knowledge bases, quizzes and books, plus a CLI for agents. Recent releases add search across the whole conversation history and a tool that looks beyond the knowledge base. - mvanhorn/last30days-skill
An agent skill that searches Reddit, X, YouTube, Hacker News, Polymarket and the web on a topic in parallel, scores results by engagement such as upvotes and likes, and synthesizes one grounded brief. - pathwaycom/llm-app
Ready-to-run app templates for RAG and enterprise search that stay in sync with sources such as Google Drive, SharePoint, S3, Kafka and PostgreSQL. Built-in indexing supports vector, hybrid and full-text search in memory. - alibaba/zvec
An open-source vector database that runs in-process, embedded directly into applications. It offers similarity search and full-text search, and is used as a file store backend in the ReMe agent memory kit.
Only repositories in the ranked Trendshift lists are included, and the lists are used only to find candidates. We do not copy their ranks. Descriptions, licenses and star counts come from each GitHub repository. The notes are our own reading. We have not tested these projects, and a place on a trending list does not prove quality.