📑 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

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.

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

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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.

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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.

vectorless_RAG

vectorless_rag

Thisi is an vectorless rag project

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