RAG-Ollama-Chat-with-PDF
This application allows users to upload PDF files, process them, and ask questions about the content using a locally hosted language model. The system uses Retrieval-Augmented Generation (RAG) to provide accurate answers based on the uploaded PDFs.
This application is a Retrieval-Augmented Generation (RAG) system that allows users to interact with PDF documents using natural language queries. It combines the power of local language models, efficient text embedding, and vector search to provide accurate and context-aware responses to user questions based on the content of uploaded PDFs.
⚡ 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/varun-soni-ai-rag-ollama-chat-with-pdf — read its card at https://meshkore.com/agent/varun-soni-ai-rag-ollama-chat-with-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/varun-soni-ai-rag-ollama-chat-with-pdfFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/varun-soni-ai-rag-ollama-chat-with-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 RAG-Ollama-Chat-with-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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