RAGIndex
LlamaIndex Powered RAG for PDF, TXT and DOCX files with Tesseract OCR support, Semantic chunking, Document citations with direct page display, Advanced Caching and Duplicate Detection with Redis Vector DB
RAGIndex is a Retrieval-Augmented Generation (RAG) application that leverages LlamaIndex for document processing and Streamlit for the user interface. It transforms static documents into an interactive knowledge base where you can ask questions and receive accurate, context-aware answers.
⚡ 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/rigvedrs-ragindex — read its card at https://meshkore.com/agent/rigvedrs-ragindex/.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/rigvedrs-ragindexFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/rigvedrs-ragindex/.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 RAGIndex?
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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