This repository demonstrates how to build a semantic knowledge graph and represent knowledge in AI using RDF (Resource Description Framework). It also shows how to interact with Large Language Models (LLMs) for inferencing and information extraction.

• Building semantic knowledge graphs with RDF
• Representing structured knowledge for AI applications
• Interacting with LLMs for reasoning and extracting insights from the graph

1. Create RDF triples to model your domain knowledge.
2. Use SPARQL or other query languages to interact with the graph.
3. Connect with LLM APIs to perform inferencing and extract information.

• Python 3.x
• RDFLib (for RDF handling)
• Access to LLM API (Ollama, OpenAI, Hugging Face, etc.)

• follow this tutorial for deploying Ollama/local LLM https://github.com/ignitewala/llm-local-k8s
• try installing rdflib -- pip install rdflib requests