A7medElsharkawy/DocQA
This project leverages LayoutLMv2, a state-of-the-art model for document understanding, fine-tuned specifically for Document Question Answering (DQA) tasks. LayoutLMv2 is designed to effectively combine text, layout, and image information from document data, enabling advanced understanding and contextualization of structured documents
Stars
—
Forks
—
Language
Python
License
—
Category
Last pushed
Feb 07, 2026
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/vector-db/A7medElsharkawy/DocQA"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
VectifyAI/PageIndex
📑 PageIndex: Document Index for Vectorless, Reasoning-based RAG
thearpankumar/GPUaccelerated-multilingual-RAG
GPU - vector DB - AI-powered document processing platform for financial services. Features...
praj2408/RAG-Enhanced-NCERT-Tutor
RAG-Enhanced-NCERT-Tutor is an AI-powered tutor for NCERT curriculum, using Retrieval-Augmented...
justine-george/ai-markdown-llm-retrieval
AI-powered document query system using LangChain, ChromaDB, and OpenAI for efficient RAG-based...
Vikas-ai56/Contextual_RAG
An Advanced RAG system using Python and Langgraph for intelligent, stateful question-answering...