DocChat
A RAG chatbot which enables user to chat with their pdf documents
The goal of this application is to help the user chat with their document and retrieve information more efficiently, thereby enhancing the user's productivity. To provide better data security for the uploaded documents, tempfile library is used to create a temporary directory to deal with the user documents. Being a RAG (Retrieval Augmented Generation) application, this chatbot tends to provide factual information to user queries based on the user-defined data. This project utilizes Llama-index, OpenAI Embeddings, Streamlit, GPT 3.5 turbo LLM, and Python.
⚡ 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/psrane8-docchat — read its card at https://meshkore.com/agent/psrane8-docchat/.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/psrane8-docchatFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/psrane8-docchat/.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 DocChat?
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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