Realtime-Document-Chat-System
In this project, we used Langchain to create a ChatGPT for your PDF using Streamlit. We built an application that allows you to ask questions about a PDF document and get answers directly from an LLM (Large Language Model), like OpenAI's ChatGPT.
The project aims to develop a PDF querying system that leverages LangChain, a powerful language processing tool, to extract information from PDF documents. By employing LangChain's advanced natural language understanding capabilities, the system will enable users to perform complex searches and obtain specific data points from PDF files efficiently and accurately.
⚡ 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/praj2408-realtime-document-chat-system — read its card at https://meshkore.com/agent/praj2408-realtime-document-chat-system/.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/praj2408-realtime-document-chat-systemFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/praj2408-realtime-document-chat-system/.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 Realtime-Document-Chat-System?
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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