Vectorless, Reasoning-Based Retrieval-Augmented Generation (RAG)
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Updated
Mar 26, 2026 - Python
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.
AI-first manual checklist builder using PageIndex-style vectorless retrieval + local Gemma4 to generate grounded maintenance checklists with strict citations.
Implements a vectorless RAG architecture using PageIndex APIs and Groq LLMs, enabling efficient document retrieval and response generation without traditional vector databases.
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.
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 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.
Hybrid RAG approach which blends Vector, Graph Database and Vectorless RAG for Retrieval of data and which will scale
Vector RAG vs. Vectorless RAG vs. OKF
Enterprise-grade vectorless retrieval platform engineered for deterministic knowledge orchestration, explainable AI search, contextual document intelligence, and scalable enterprise retrieval workflows without vector embeddings.
Vectorless RAG using reasoning over hierarchical document structure instead of embeddings or vector databases.
Self-hosted vectorless RAG for grounded Q&A over local PDFs with PageIndex, FastAPI, and TanStack Start.
A retrieval-augmented generation (RAG) system for querying ML/AI research papers using BM25 sparse retrieval — no vector embeddings or external APIs required. Users ask natural language questions and receive grounded answers with citations to the source papers.
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.
A scalable, agentic document intelligence system inspired by PageIndex, designed to process long documents and enable reasoning-driven retrieval instead of vector similarity search.
A vector-less RAG works fully in local NO internet needed, with webUI
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