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.

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.

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.

Vectorless RAG for SEC 10-K filings using PageIndex — tree-based reasoning retrieval with Claude, no vector DB, no embeddings, no chunking

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

An autonomous support triage agent powered by Vectorless RAG (PageIndex) and local LLMs to intelligently classify, route, and resolve customer tickets.

Document Q&A system using LLM Tree RAG — no vector embeddings needed. Upload files (PDF, DOCX, XLSX), build hierarchical summary trees, and ask questions with cited answers. Supports Ollama, OpenAI, Gemini & Anthropic.

Vectorless semantic indexing SDK that converts large text into searchable knowledge trees for fast, structured retrieval.

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

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 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.

🎙️ Vaani RAG — Multi-Language Voice Agent

An enterprise-grade, hybrid Retrieval-Augmented Generation (RAG) pipeline that completely bypasses traditional vector databases.

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

⚡ The Agent-Native Retrieval Engine — Hybrid Vector + Reasoning + Memory for AI Agents. HNSW indexing, tree-based reasoning retrieval, multi-agent orchestration, MCP server, and built-in RAG.