Ziglang eXtensiable Builder for SQL or JSON, zig version, sql or json query builder, extensible custom for any database, for any orm framework
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Updated
Nov 4, 2025 - Zig
Ziglang eXtensiable Builder for SQL or JSON, zig version, sql or json query builder, extensible custom for any database, for any orm framework
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.
AI-first manual checklist builder using PageIndex-style vectorless retrieval + local Gemma4 to generate grounded maintenance checklists with strict citations.
Modular RAG library for Python. Swap any component — LLM, vectorstore, reranker — with one line in a YAML file. No code changes. Just config.
12-week, project-driven Obsidian curriculum: cloud/infra engineer → AI Agent/LLM engineer. Companion narrative + interview prep for shaneliuyx/agent-prep labs.
PageIndex RAG: Reasoning-based retrieval architecture replacing vector databases with hierarchical navigation
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.
Local-first MCP server for indexing and querying PDF/Markdown files using PageIndex — no cloud APIs required
PostgreSQL extension for PageIndex: PDF/Markdown document trees, tree search, JSONB API (pageindex schema). C + Go c-shared bridge; PGXS; MIT licensed.
A comprehensive research project comparing different Retrieval-Augmented Generation (RAG) techniques applied to medical question-answering in obstetrics.
MCP server for PageIndex: Reasoning-based document search
问道 wendao - high-performance knowledge and link-graph engine, AI RAG.
Vectorless RAG via hierarchical tree indexing — Go reimplementation of PageIndex with zero external deps
Implements a vectorless RAG architecture using PageIndex APIs and Groq LLMs, enabling efficient document retrieval and response generation without traditional vector databases.
🔍 Empower efficient retrieval with PageIndex, a reasoning-based system that eliminates the need for vector databases and chunking for human-like results.
An enterprise-grade, hybrid Retrieval-Augmented Generation (RAG) pipeline that completely bypasses traditional vector databases.
Implementation of vectorless rag Using pageindex and Gemini
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