ragpipe
RAG pipeline library for Python. Ingest files, git repos, web pages into vector databases with 3 functions: ingest(), query(), pipe(). CLI + YAML configs. Works with Qdrant, Pinecone, Ollama, OpenAI.
RAG (Retrieval-Augmented Generation) is how you give an AI access to your own data. Instead of guessing answers, the AI first searches your documents, finds the relevant parts, and then generates an answer based on what it found.
⚡ 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/avasis-ai-ragpipe — read its card at https://meshkore.com/agent/avasis-ai-ragpipe/.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/avasis-ai-ragpipeFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/avasis-ai-ragpipe/.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 ragpipe?
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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