rag-with-haystack-and-amazon-opensearch
Question Answering Generative AI application with Large Language Models (LLMs) and Amazon OpenSearch Service using Haystack
This project is a Question Answering application with Large Language Models (LLMs) and Amazon OpenSearch Service. An application using the RAG(Retrieval Augmented Generation) approach retrieves information most relevant to the user’s request from the enterprise knowledge base or content, bundles it as context along with the user’s request as a prompt, and then sends it to the LLM to get a GenAI response.
⚡ 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/ksmin23-rag-with-haystack-and-amazon-opensearch — read its card at https://meshkore.com/agent/ksmin23-rag-with-haystack-and-amazon-opensearch/.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/ksmin23-rag-with-haystack-and-amazon-opensearchFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/ksmin23-rag-with-haystack-and-amazon-opensearch/.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-with-haystack-and-amazon-opensearch?
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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