TriModalRAG_System
*Built upon the integration of text, image, and audio modalities, this Multi-modal RAG task follows a structured, step-by-step pipeline to effectively combine and process diverse data sources.*
The TriModal Retrieval-Augmented Generation (T-RAG) Project is an advanced AI system that combines the power of text, image, and audio data for multi-modal retrieval and generation tasks. This project leverages state-of-the-art deep learning models, and cutting-edge supportive frameworks such as Langchain, DVC, and ZenML. Consequently, a shared embedding space can be built more efficiently where data from all three modalities can be processed, retrieved, and used in a generative pipeline.
⚡ 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/tph-kds-trimodalragsystem — read its card at https://meshkore.com/agent/tph-kds-trimodalragsystem/.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/tph-kds-trimodalragsystemFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/tph-kds-trimodalragsystem/.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 TriModalRAG_System?
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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