vectorless-rag

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

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

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

Local-first AI knowledge base for PDFs, Office docs, web clips and Markdown notes — ask your whole library and get citations that jump back to the source. Vectorless RAG, wikilinks, local Codex/Claude agents.

PageIndex-inspired agentic RAG app for vectorless document QA, FastAPI, multi-document retrieval, context compaction, and self-hosted AI workspaces.

AI-powered codebase intelligence platform replacing vector chunking with Vectorless RAG via AST graph traversal. Features multi-agent LangGraph workflows, interactive Neo4j NVL graph visualizer, 3-hop blast radius analysis, and automated architectural PDF report generation.

A complete, structured RAG bootcamp covering every layer of modern AI pipelines, data ingestion, vector search, agentic architectures, memory, guardrails, and real-world evaluation. Built with LangChain, LangGraph, and Python.

AI-first manual checklist builder using PageIndex-style vectorless retrieval + local Gemma4 to generate grounded maintenance checklists with strict citations.

Reasoning-based, vectorless RAG over a large document using a hierarchical tree (PageIndex) and a Vision-Language Model (Llama 4 Scout), no embeddings, no vector store, no text chunking.

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

RAG on PDF documents without a vector database. Uploads are indexed into a hierarchical document tree via the PageIndex API — at query time, Llama 3.1 (Groq) walks the tree to select the most relevant sections, then generates a grounded answer from their full content. Includes a FastAPI backend and a simple web UI.

A Claude Code skill that reads long documents the way a person does: it turns PDFs, Word files, decks and spec folders into a table of contents, finds the right sections on your machine, and answers with the page it read. No API key. Includes an offline node-graph viewer of every index.

MAXXKI CodeIndex: A vectorless, AST-based RAG framework for local code analysis. Leverages hierarchical LLM routing and abstract syntax tree indexing instead of vector embeddings. Provides precise, local-first code Q&A without the overhead of vector databases. MAXXKI CodeIndex — Local-first, offline code intelligence for Python codebases. Ask natur

Hybrid RAG approach which blends Vector, Graph Database and Vectorless RAG for Retrieval of data and which will scale

A production-grade, LangGraph-orchestrated fraud detection system built for regulated financial environments. Combines ML risk scoring, LLM-powered document forensics, and a Human-in-the-Loop compliance workflow — end-to-end.

This repository accompanies the labs from the book **Secure AI Systems** and demonstrates both vulnerable and hardened AI architectures through practical security-focused implementations.

Enterprise-grade vectorless retrieval platform engineered for deterministic knowledge orchestration, explainable AI search, contextual document intelligence, and scalable enterprise retrieval workflows without vector embeddings.

Vector RAG vs. Vectorless RAG vs. OKF

Self-hosted vectorless RAG for grounded Q&A over local PDFs with PageIndex, FastAPI, and TanStack Start.

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