docs/tutorial.md

Tutorial Syllabus

Coding samples in the following notebooks help illustrate the use of TextGraphs and related libraries in Python.

Audience

• You are a Python programmer who needs to learn how to leverage LLM-augmented workflows to construct KGs
• You are an ML engineer who needs to understand how to integrate LLM research results into production-quality apps

Prerequisites

• Some coding experience in Python (you can read a 20-line program)
• Some familiarity with ML, specifically with LLM applications
• Interest in use cases that need to use NLP to construct KGs

Key Takeaways

• Hands-on experience with popular open source libraries in Python for natural language at the intersection of LLMs and KGs
• Coding examples that can be used as starting points for your own related projects
• Ways to integrate natural language work with other aspects of graph data science