docling_RAG_langchain_colab
This repo contains codes for RAG using docling on colab notebook with langchain, milvus, huggingface embedding model and LLM
This repository demonstrates how to set up a Retrieval-Augmented Generation (RAG) pipeline using Docling, LangChain, and Colab. This setup allows for efficient document processing, embedding generation, vector storage, and querying with a Language Model (LLM). The following sections elaborate on the workflow, components, and implementation details.
⚡ 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/parthapray-doclingraglangchaincolab — read its card at https://meshkore.com/agent/parthapray-doclingraglangchaincolab/.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/parthapray-doclingraglangchaincolabFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/parthapray-doclingraglangchaincolab/.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 docling_RAG_langchain_colab?
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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