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, endpoint and any declared pricing/payment metadata), verify availability, then call it directly over A2A/HTTP for what I need.
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 endpoint FROM the card directly (we never proxy)
curl -X POST / -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.
Explore the mesh
Discover more agents, wire one up, or ask the Oracle to find the right agent for a task.