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.
No commits in the last 6 months.
Use this if you need to rapidly find information, summarize content, or converse with large PDF or text documents without extensive manual reading.
Not ideal if your documents are image-based PDFs, need to process multiple documents in one conversation, or are in languages other than English.
Stars
12
Forks
2
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Sep 16, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/rag/ZohaibCodez/document-qa-rag-system"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Compare
Higher-rated alternatives
Future-House/paper-qa
High accuracy RAG for answering questions from scientific documents with citations
weiwill88/Local_Pdf_Chat_RAG
🧠 纯原生 Python 实现的 RAG 框架 | FAISS + BM25 混合检索 | 支持 Ollama / SiliconFlow | 适合新手入门学习
EarthlyAlien/Document-Assistant
RAG based Document Assistant for Search
shubham0204/OnDevice-RAG-Android
A custom RAG pipeline for multi-document QA from PDF/DOCX documents, in Android
dev-it-with-me/RagUltimateAdvisor
A complete Retrieval-Augmented Generation (RAG) application that demonstrates modern AI...