noorjotk/local-rag-engine
Local RAG app with zero-config Docker setup. FastAPI + Streamlit + Qdrant + Ollama. Just run `docker-compose up --build`! 🚀
This tool helps researchers, legal professionals, or anyone working with extensive documentation quickly find answers within their PDF files. You simply upload your documents, and it uses AI to understand their content. Then, you can ask questions in plain language and receive accurate, AI-generated responses based on your uploaded information.
No commits in the last 6 months.
Use this if you need to extract specific information or get summaries from a collection of PDF documents without sharing sensitive data online, and prefer a straightforward, one-command setup.
Not ideal if you need to process document types other than PDFs, require integration with external web services, or do not have Docker installed on your machine.
Stars
9
Forks
1
Language
Python
License
MIT
Category
Last pushed
Jul 26, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/vector-db/noorjotk/local-rag-engine"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
yichuan-w/LEANN
[MLsys2026]: RAG on Everything with LEANN. Enjoy 97% storage savings while running a fast,...
byerlikaya/SmartRAG
Multi-Modal RAG for .NET — query databases, documents, images and audio in natural language....
aws-samples/layout-aware-document-processing-and-retrieval-augmented-generation
Advanced document extraction and chunking techniques for retrieval augmented generation that is...
sourangshupal/simple-rag-langchain
Exploring the Basics of Langchain
sion42x/llama-index-milvus-example
Open AI APIs with Llama Index and Milvus Vector DB for Retrieval Augmented Generation (RAG) testing