chat-with-pdf-website
RAG technique for QA tasks with PDF, website and video, leveraging LLMs and the OpenAI API for interactive applications
The codebase demonstrates the fundamental application of RAG for question-answering tasks, leveraging the power of open-source Large Language Models (LLMs) from HuggingFace for interacting with PDFs (chat_with_pdf) and utilizing the OpenAI API key for a simple question-answering website (chat_with_website) and a simple question-answering video (chat_with_video) using langchain.
⚡ 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/dinhquy-nguyen-1704-chat-with-pdf-website — read its card at https://meshkore.com/agent/dinhquy-nguyen-1704-chat-with-pdf-website/.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/dinhquy-nguyen-1704-chat-with-pdf-websiteFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/dinhquy-nguyen-1704-chat-with-pdf-website/.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 chat-with-pdf-website?
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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