Vectorless, Reasoning-Based Retrieval-Augmented Generation (RAG)
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Updated
Mar 26, 2026 - Python
Vectorless, Reasoning-Based Retrieval-Augmented Generation (RAG)
Extends pageIndex into an AI document workspace with multi-format parsing, OCR, visual TOC, custom models, citations, and agentic QA.
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.
Ziglang eXtensiable Builder for SQL or JSON, zig version, sql or json query builder, extensible custom for any database, for any orm framework
PageIndex-inspired agentic RAG app for vectorless document QA, FastAPI, multi-document retrieval, context compaction, and self-hosted AI workspaces.
Modular RAG library for Python. Swap any component — LLM, vectorstore, reranker — with one line in a YAML file. No code changes. Just config.
AI-first manual checklist builder using PageIndex-style vectorless retrieval + local Gemma4 to generate grounded maintenance checklists with strict citations.
12-week, project-driven Obsidian curriculum: cloud/infra engineer → AI Agent/LLM engineer. Companion narrative + interview prep for shaneliuyx/agent-prep labs.
Evidence RAG Citation traceability for high-stakes documents. Built on a private hybrid evidence retrieval engine.
Local-first MCP server for indexing and querying PDF/Markdown files using PageIndex — no cloud APIs required
🔍 Empower efficient retrieval with PageIndex, a reasoning-based system that eliminates the need for vector databases and chunking for human-like results.
Implements a vectorless RAG architecture using PageIndex APIs and Groq LLMs, enabling efficient document retrieval and response generation without traditional vector databases.
PostgreSQL extension for PageIndex: PDF/Markdown document trees, tree search, JSONB API (pageindex schema). C + Go c-shared bridge; PGXS; MIT licensed.
PageIndex RAG: Reasoning-based retrieval architecture replacing vector databases with hierarchical navigation
A fully private, local RAG system for selectable-text PDFs using hierarchical tree search and D3 spatial graphs.
A vectorless RAG pipeline that navigates PDF documents using a PageIndex tree structure and Gemini 2.0 Flash — no vector database, just LLM-guided tree search with auto-cited answers.
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