document-qa-rag-system and rag-app

document-qa-rag-system

Emerging

rag-app

Experimental

Maintenance 2/25

Adoption 5/25

Maturity 15/25

Community 11/25

Maintenance 0/25

Adoption 6/25

Maturity 8/25

Community 15/25

Stars: 12

Forks: 2

Downloads: —

Commits (30d): 0

Language: Jupyter Notebook

License: MIT

Stars: 16

Forks: 5

Downloads: —

Commits (30d): 0

Language: JavaScript

License: —

Stale 6m No Package No Dependents

No License Stale 6m No Package No Dependents

About document-qa-rag-system

ZohaibCodez/document-qa-rag-system

A simple Retrieval-Augmented Generation (RAG) project built with LangChain and Streamlit. Upload documents (PDF/TXT) and interact with them using natural language questions powered by embeddings and vector search.

This tool helps you quickly get answers from your documents by turning any PDF or plain text file into an interactive Q&A experience. You upload your document, and then you can ask questions about its content in everyday language, getting direct answers back. It's ideal for professionals, researchers, or students who need to extract specific information or summarize key points from reports, articles, or books without manually sifting through pages.

information-retrieval document-analysis research-assistant knowledge-management study-aid

About rag-app

ajaykrupalk/rag-app

An RAG (retrieval augmented generation) app which iterates through a PDF document and can answer user's questions based on the document uploaded. This application needs a Google API Key.

Related comparisons

document-qa-rag-system and paper-qa document-qa-rag-system and rag-qa document-qa-rag-system and Agentic_RAG document-qa-rag-system and paper-qa

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