Chat with Knowledge Graph using LLMs

Geraldus Wilsen’s tutorial explains how to integrate knowledge graphs with large language models (LLMs) using Python. He begins by defining knowledge graphs as networks of real-world entities and their relationships, stored in graph databases like Neo4J. Wilsen emphasizes the importance of knowledge graphs in enhancing LLM performance by providing context and connections that traditional retrieval methods might miss. The tutorial covers setting up a Neo4J database, using Langchain and the open-source Gemini model, and implementing prompting strategies to improve model performance. Wilsen demonstrates how to insert data into Neo4J using Cypher query language and build a graph chain to process user queries. He evaluates the model’s performance by comparing generated Cypher queries to expected results, identifying common issues like inaccurate translations and hallucinations. To address these, he introduces prompting strategies, providing the model with examples to improve its understanding. Additionally, he discusses creating dynamic prompts using semantic similarity to select the most relevant examples. The tutorial includes practical steps, such as setting up API keys for Google Gemini and Hugging Face, and running Python code to interact with the Neo4J database. Wilsen concludes by highlighting the benefits of using knowledge graphs with LLMs and encouraging viewers to experiment with these techniques in their projects.