openvino-chatbot-rag-pdf
LLM Chatbot-RAG by OpenVINO. The chatbot can read a PDF file and answer to the related questions.
This project demonstrates how to extend the LLM models' capability to answer a question related to a given document. The project consists of two programs. One is for preparation, and the other is for question and answering using LLM. The preparation program will read a PDF file and generate a database (vector store). The LLM model will pick up a collection of a fraction of the input document that is related to the given query from the user and then answer the query by referring to the picked-up documents. This technique is so called RAG (Retrieval Augmented Generation).
⚡ 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/yas-sim-openvino-chatbot-rag-pdf — read its card at https://meshkore.com/agent/yas-sim-openvino-chatbot-rag-pdf/.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/yas-sim-openvino-chatbot-rag-pdfFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/yas-sim-openvino-chatbot-rag-pdf/.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 openvino-chatbot-rag-pdf?
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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