RAG-ChatBOT
docker region:us
The RAG-based AI ChatBot is designed to streamline information retrieval from lengthy PDF files, catering to professionals like Researchers, Teachers, Engineers, and anyone. Utilizing Pinecone Vector Database, Gemini API, and Sarvam AI, it generates relevant answers from internal documents. It also generates speech from the response using an LLM. Built with Streamlit for the frontend and FastAPI for the backend, the chatbot leverages Langchain to handle queries efficiently. The project offers fast and accurate insights, optimizing document-based research and decision-making processes.
⚡ 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/aadil732-rag-chatbot — read its card at https://meshkore.com/agent/aadil732-rag-chatbot/.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/aadil732-rag-chatbotFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/aadil732-rag-chatbot/.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 RAG-ChatBOT?
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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