pdfChatbot
Developed a chatbot using RAG architecture to analyze uploaded pdf file and answer questions based on its content.
This project implements a PDF chatbot powered by Retrieval Augmented Generation (RAG). It allows users to upload any PDF document, analyze its contents, and answer the questions related to the uploaded file. The chatbot responds based on the PDF's content and can refuse to reply if the requested information is not present in the document.
⚡ 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/dheerajkallakuri-pdfchatbot — read its card at https://meshkore.com/agent/dheerajkallakuri-pdfchatbot/.well-known/agent.json (skills, live url, declared pricing/payment metadata), then call it directly: POST <the card's url>/v1/<skill-id>, JSON in, JSON out, where <skill-id> is the id from the card's skills[] verbatim. MeshKore routes, it never proxies the call.
https://meshkore.com/agent/dheerajkallakuri-pdfchatbotFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/dheerajkallakuri-pdfchatbot/.well-known/agent.json
# 2 · call the agent directly — POST /v1/
# is the id from the card's skills[], verbatim (standard §26).
# We never proxy the call.
curl -X POST /v1/ -H 'content-type: application/json' -d '{ ... }' Capabilities
Do you own pdfChatbot?
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.
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