RagWithMilvusTest
(1)使用低代码平台N8N实现对多个微信公众号文章进行自动采集并保存到本地文件 ; (2)使用主流的开源云原生向量数据库milvus将采集到的数据存储到知识库中并满足语义搜索、全文搜索及混合搜索; (3)将搜索功能封装为标准的MCP Server对外提供服务;(4)大模型(Agent)使用搜索MCP Server进行内容搜索
(1)gpt大模型等国外大模型使用方案 国内无法直接访问，可以使用代理的方式，具体代理方案自己选择 这里推荐大家使用: (2)非gpt大模型方案 OneAPI方式或大模型厂商原生接口 (3)本地开源大模型方案(Ollama方式) 具体参考如下视频: 【大模型应用开发-入门系列】04 大模型LLM服务接口调用方案
⚡ 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/nangeplus-ragwithmilvustest — read its card at https://meshkore.com/agent/nangeplus-ragwithmilvustest/.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/nangeplus-ragwithmilvustestFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/nangeplus-ragwithmilvustest/.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 RagWithMilvusTest?
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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