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.
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.
Vectorless RAG using reasoning over hierarchical document structure instead of embeddings or vector databases.
MCP server for PageIndex: Reasoning-based document search
问道 wendao - high-performance knowledge and link-graph engine, AI RAG.
🔍 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.
Serverless Vectorless RAG on AWS — upload documents, ask questions, get grounded answers using LLM reasoning instead of embeddings or vector databases. Built with Amazon Bedrock (Claude 3 Haiku), Lambda, DynamoDB, API Gateway, React, and Terraform.
Local-first vectorless RAG using PageIndex. Supports Ollama (fully local) + AWS Bedrock. No vector DB, no embeddings. Built on top of VectifyAI/PageIndex (MIT).
An enterprise-grade, hybrid Retrieval-Augmented Generation (RAG) pipeline that completely bypasses traditional vector databases.
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.
PageIndex RAG: Reasoning-based retrieval architecture replacing vector databases with hierarchical navigation
Vectorless RAG for SEC 10-K filings using PageIndex — tree-based reasoning retrieval with Claude, no vector DB, no embeddings, no chunking
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