QueryGPT
Context-aware chatbot using LLaMA 3.1 embeddings and a vector database for efficient query understanding and response generation, with NLP techniques like entity extraction and intent recognition.
This project implements a context-aware chatbot using LLaMA 3.1/3.2 embeddings and a vector database for efficient query handling and response generation. The bot utilizes advanced Natural Language Processing (NLP) techniques such as entity extraction, intent recognition, and retrieval-augmented generation (RAG) to process domain-specific queries and generate accurate responses. The chatbot architecture is designed to handle complex queries by embedding large text datasets, storing embeddings in a vector database, and retrieving relevant context for response generation.
⚡ 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/ansh-info-querygpt — read its card at https://meshkore.com/agent/ansh-info-querygpt/.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/ansh-info-querygptFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/ansh-info-querygpt/.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 QueryGPT?
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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