Tree-based, vectorless document RAG framework. Connect any LLM via URL/API key.
-
Updated
Apr 7, 2026 - TypeScript
#
vectorless-rag
Here are 12 public repositories matching this topic...
Agentic RAG Harness for long documents, Tree and Graph based reasoning. Cited answers down to the pixel
pdf ocr graph indexing citations knowledge-graph question-answering vlm nanonets rag document-ai llm rag-pipeline vlm-ocr vectorless-rag
-
Updated
Apr 15, 2026 - Python
AI-first manual checklist builder using PageIndex-style vectorless retrieval + local Gemma4 to generate grounded maintenance checklists with strict citations.
redis pageindex rag applied-ai document-ai local-llm rag-pipeline docker-model-runner vectorless-rag gemma4 maintenance-ai
-
Updated
Apr 14, 2026 - TypeScript
Vectorless RAG using reasoning over hierarchical document structure instead of embeddings or vector databases.
-
Updated
Apr 6, 2026 - Python
Implements a vectorless RAG architecture using PageIndex APIs and Groq LLMs, enabling efficient document retrieval and response generation without traditional vector databases.
-
Updated
Apr 9, 2026 - Jupyter Notebook
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.
python ci-cd fintech multi-agent-systems fraud-detection human-in-the-loop mlflow fastapi triton-inference-server ollama langgraph vectorless-rag
-
Updated
Apr 14, 2026 - Python
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.
-
Updated
Mar 13, 2026 - Jupyter Notebook
An enterprise-grade, hybrid Retrieval-Augmented Generation (RAG) pipeline that completely bypasses traditional vector databases.
-
Updated
Feb 28, 2026 - Python
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.
python nlp arxiv bm25 qna scientific-papers rag streamlit local-llm retrieval-augmented-generation ollama vectorless-rag
-
Updated
Apr 6, 2026 - Python
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.
-
Updated
Apr 10, 2026 - Python
⚡ 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.
multi-agent semantic-search ai-agents rag hnsw hybrid-retrieval mcp-server agent-memory vectorless-rag reasoning-rag hybrid-rtree-index
-
Updated
Mar 10, 2026 - JavaScript
devops cloud-computing ai-agents rag huggingface huggingface-transformers llms rag-pipeline agentic-ai vectorless-rag
-
Updated
Apr 6, 2026
Improve this page
Add a description, image, and links to the vectorless-rag topic page so that developers can more easily learn about it.
Add this topic to your repo
To associate your repository with the vectorless-rag topic, visit your repo's landing page and select "manage topics."