📑 PageIndex: Document Index for Vectorless, Reasoning-based RAG

MCP server for PageIndex. PageIndex is a vectorless reasoning-based RAG system which uses multi-step reasoning and tree search to retrieve information like a human expert would.

Vectorless, Reasoning-Based Retrieval-Augmented Generation (RAG)

Tree-based, vectorless document RAG framework. Connect any LLM via URL/API key.

A production-ready, Neo4j Graph Database implementation of the PageIndex Vectorless RAG architecture.

No-brainer vectorless RAG combining docling and toon-python

A lightweight, reasoning-based RAG system that uses LLM-guided tree traversal instead of traditional vector databases and embeddings. It's clean, interpretable, and highly efficient.

Atlas - Enterprise document indexing plugin for OpenClaw. Vectorless RAG using PageIndex with async indexing, incremental updates, and smart caching. Scales from 10 to 5000+ documents. Perfect for financial reports, legal docs, technical manuals, and research papers.

A microservice RAG system using PageIndex's reasoning-based approach to achieve high retrieva accuracy without vector embeddings. Built with 9 FastAPI services and Next.js, eliminating theneed for vector databases.

Vectorless, reasoning-based RAG using hierarchical document indexing with Vertex AI. No embeddings, no vector DB - just LLM reasoning.

PageIndex Implementation : Building advanced vectorless, reasoning-based RAG for long document analysis. No chunking, no vectors—pure LLM tree navigation

Agentic RAG for long documents, Tree and Graph based reasoning. Cited answers down to the pixel

Oracle AI Database-powered document intelligence with SQL Property Graphs, vectorless RAG, and knowledge graph visualization

📄 Vectorless, reasoning-based RAG system that builds hierarchical tree indices from PDFs and Markdown using LLM reasoning

Vectorless RAG with PageIndex - upload PDFs and chat with them using full-document, model-driven reasoning without chunking, embeddings, or a vector database.

No description available

Vectorless RAG: Reasoning-based Retrieval Reasoning-native RAG ◦ No Vector DB ◦ No Chunking ◦ Human-like Tree Search

Tree-based RAG using PageIndex: LLM reasons over doc structure. No vectors/DB. FastAPI, async, Docker. 98.7% FinanceBench.

Vectorless RAG with concurrent LLM retrieval. No vector DB, no embeddings.

Tree-based document intelligence — no vectors, no chunks, full explainability.

Implements a vectorless RAG architecture using PageIndex APIs and Groq LLMs, enabling efficient document retrieval and response generation without traditional vector databases.

No description available

No description available

Vectorless RAG using reasoning over hierarchical document structure instead of embeddings or vector databases.

A powerful Chatbot based on PageIndex Vectorless RAG Framework

Vectorless, Reasoning-based RAG with AWS Bedrock - Port of PageIndex

Self-hosted PageIndex MCP server for Claude Code — vectorless RAG for PDFs

Vectorless, reasoning-based RAG for documents — tree-structured indexing with LLM reasoning that achieves 98.7% accuracy on FinanceBench without embeddings or vector databases.

Thisi is an vectorless rag project

No description available