doc_qa_langchain
A front-end implementation of Document Chat QA with LangChain, Vue, and Tailwind CSS. Chat with your text or PDF files.
Doc_QA_LangChain is a front-end only implementation of a website that allows users to upload a PDF or text-based file (txt, markdown, JSON, HTML, etc) and ask questions related to the document with GPT. The project uses Vue3 for interactivity, Tailwind CSS for styling, and LangChain for parsing documents/creating vector stores/querying LLM.
⚡ 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/troyanovsky-docqalangchain — read its card at https://meshkore.com/agent/troyanovsky-docqalangchain/.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/troyanovsky-docqalangchainFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/troyanovsky-docqalangchain/.well-known/agent.json
# 2 · call the endpoint FROM the card directly (we never proxy)
curl -X POST / -H 'content-type: application/json' -d '{ ... }' Do you own doc_qa_langchain?
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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