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