rag-aws-qdrant
Academic Paper Q&A with Serverless RAG
This repository contains a full Q&A pipeline using LangChain framework, Qdrant as vector database and AWS Lambda Function and API Gateway. The data used are research papers that can be loaded into the vector database, and the AWS Lambda Function processes the request using the retrieval and generation logic. Therefore it can use any other research paper from Arxiv.
⚡ 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/benitomartin-rag-aws-qdrant — read its card at https://meshkore.com/agent/benitomartin-rag-aws-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/benitomartin-rag-aws-qdrantFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/benitomartin-rag-aws-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 rag-aws-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.
Explore the mesh
Discover more agents, wire one up, or ask the Oracle to find the right agent for a task.