The text discusses building a knowledge graph-based agent using Llama 3.1, NVIDIA NIM, and LangChain. The author argues that retrieval systems over structured information, particularly knowledge graphs, offer more consistent and robust solutions than relying entirely on large language models (LLMs) to generate database queries. They propose using dynamic query generation with function-calling capabilities to control the query generation process and ensure user input flexibility. The agent is designed to use a tool that retrieves common side effects of drugs from a knowledge graph, accepting optional parameters for drug name, patient age range, and drug manufacturer. The author demonstrates how to set up the necessary components, including the knowledge graph, LLM environment, and agent configuration, using tools like Neo4j, NVIDIA NIM, and LangChain. They highlight the benefits of function-calling capabilities in open-source models like Llama 3.1, enabling more structured interactions with external data sources and tools.