Kedhareswer/QuantumPDF_ChatApp_VectorDB
QuantumPDF V1.3 enables intelligent conversations with PDF documents. Built with Next.js 15 and React 19, it uses Retrieval-Augmented Generation (RAG) to provide accurate, context-aware responses from your documents.
This platform helps you have intelligent conversations with your PDF, DOCX, XLSX, and CSV documents. You input your files, and the system allows you to ask questions, providing accurate, context-aware answers complete with clickable citations to the original sources. Anyone who needs to quickly find information, understand complex documents, or extract key insights from large datasets would benefit from this.
Use this if you need to quickly get precise answers and insights from your business reports, research papers, financial spreadsheets, or other extensive documents without manually sifting through them.
Not ideal if you only need to perform simple keyword searches or if your documents contain highly sensitive, proprietary information that cannot be processed by external AI services.
Stars
7
Forks
7
Language
TypeScript
License
GPL-3.0
Category
Last pushed
Feb 25, 2026
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/vector-db/Kedhareswer/QuantumPDF_ChatApp_VectorDB"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
biocypher/biochatter
Backend library for conversational AI in biomedicine
pgalko/BambooAI
A Python library powered by Language Models (LLMs) for conversational data discovery and analysis.
redis-developer/ArXivChatGuru
Use ArXiv ChatGuru to talk to research papers. This app uses LangChain, OpenAI, Streamlit, and...
7-docs/7-docs
Use local files or public GitHub repository as a source and ask questions through ChatGPT about it
redis-developer/LLM-Document-Chat
Using LlamaIndex, Redis, and OpenAI to chat with PDF documents. Supplementary material for blog...