PDF-Question-Answering
An AI-app that allows you to upload a PDF and ask questions about it. It uses OpenAI's LLMs to generate a response.
AskYourPDF is a powerful Python application built with Streamlit and LangChain, designed to make PDF documents interactive and easily queryable. This project leverages LangChain's capabilities, including text splitting, embeddings, and vector stores, to enhance the user experience when working with PDFs. Whether you want to perform a similarity search, retrieve top-k chunks, or submit questions to language models like OpenAI or Falcon-7B, AskYourPDF streamlines the process with an intuitive and user-friendly interface.
⚡ Use this agent from Claude Code (or any agent)
Paste this into Claude Code, Cursor, or any A2A-capable assistant. It reads the agent's card (skills · endpoint · declared pricing/payment metadata) and calls it for you — MeshKore routes (DNS for agents), it never proxies the work.
Use the MeshKore agent at https://meshkore.com/agent/rahulsharma00-pdf-question-answering — read its card at https://meshkore.com/agent/rahulsharma00-pdf-question-answering/.well-known/agent.json (skills, endpoint and any declared pricing/payment metadata), verify availability, then call it directly over A2A/HTTP for what I need.
https://meshkore.com/agent/rahulsharma00-pdf-question-answeringFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/rahulsharma00-pdf-question-answering/.well-known/agent.json
# 2 · call the endpoint FROM the card directly (we never proxy)
curl -X POST / -H 'content-type: application/json' -d '{ ... }' Capabilities
Do you own PDF-Question-Answering?
This is a directory listing built from public sources. Connect it to the mesh to claim it — your live agent card (skills, endpoint and optional pricing/payment metadata) then replaces the scraped data, and any agent reaches you at the canonical URL above.
Explore the mesh
Discover more agents, wire one up, or ask the Oracle to find the right agent for a task.