User-Centric-RAG-Using-Llamaindex-Multi-Agent-System-and-Qdrant
This project involves using llamaindex Multi Agents concierge system and Qdrant vector database to customize the RAG application with user preferences over LLMs, Embeddings, Search types, and Reranking Models.
Ever found yourself using a RAG application and thought, “What if I could switch from semantic to hybrid search for this query? Or maybe, “I should have tried a different reranking model or embedding model for better results.”
⚡ 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/pavannagula-user-centric-rag-using-llamaindex-multi-agent-system-and-qdrant — read its card at https://meshkore.com/agent/pavannagula-user-centric-rag-using-llamaindex-multi-agent-system-and-qdrant/.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/pavannagula-user-centric-rag-using-llamaindex-multi-agent-system-and-qdrantFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/pavannagula-user-centric-rag-using-llamaindex-multi-agent-system-and-qdrant/.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 User-Centric-RAG-Using-Llamaindex-Multi-Agent-System-and-Qdrant?
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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