Tree-based, vectorless document RAG framework. Connect any LLM via URL/API key.
-
Updated
Apr 7, 2026 - TypeScript
Tree-based, vectorless document RAG framework. Connect any LLM via URL/API key.
Agentic RAG for long documents, Tree and Graph based reasoning. Cited answers down to the pixel
Vectorless RAG using reasoning over hierarchical document structure instead of embeddings or vector databases.
Implements a vectorless RAG architecture using PageIndex APIs and Groq LLMs, enabling efficient document retrieval and response generation without traditional vector databases.
An enterprise-grade, hybrid Retrieval-Augmented Generation (RAG) pipeline that completely bypasses 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 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.
⚡ 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.
Add a description, image, and links to the vectorless-rag topic page so that developers can more easily learn about it.
To associate your repository with the vectorless-rag topic, visit your repo's landing page and select "manage topics."