spring-ai-rag-ollama
This project contains source code to demonstrate the usage of Retrival Augmented Generation (RAG) feature using Spring AI using PG Vector as the Database and Ollama as the Local LLM
This project demonstrates the implementation of Retrieval Augmented Generation (RAG) using Spring AI, Ollama, and PGVector Database. The application serves as a personal assistant that can answer questions about Spring Boot by referencing the Spring Boot Reference Documentation PDF.
⚡ 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/saiupadhyayula-spring-ai-rag-ollama — read its card at https://meshkore.com/agent/saiupadhyayula-spring-ai-rag-ollama/.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/saiupadhyayula-spring-ai-rag-ollamaFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/saiupadhyayula-spring-ai-rag-ollama/.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 spring-ai-rag-ollama?
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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