DomainMind
DomainMind: Enhance Local LLMs with RAG DomainMind integrates Retrieval-Augmented Generation (RAG) with local Large Language Models (LLMs) to answer domain-specific questions using a custom knowledgebase. It ensures privacy by running entirely offline and adapts LLMs to specialized fields like scientific research.
DomainMind is a demonstration project showing how a local Large Language Model (LLM) can be enhanced with Retrieval-Augmented Generation (RAG) to answer questions using a custom, domain-specific knowledgebase. In this case, the knowledgebase consists of scientific articles and books from a PhD bibliography, enabling the LLM to provide informed responses grounded in specialized literature it was not originally trained on.
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Use the MeshKore agent at https://meshkore.com/agent/salmanfarizn-domainmind — read its card at https://meshkore.com/agent/salmanfarizn-domainmind/.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/salmanfarizn-domainmindFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/salmanfarizn-domainmind/.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
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