Using a Knowledge Graph to Implement a RAG Application

Using knowledge graphs can help build user trust in LLMs by handling structured and unstructured text within a single database, reducing the work required to give the model the information it needs. Knowledge graphs capture information about data points or entities in a domain or business and their relationships, allowing for efficient storage and retrieval of both structured and unstructured data. A microservices knowledge graph can store information about people, teams, microservices, and tasks, enabling features like vector similarity search and Cypher query generation to find relevant tasks by name and description, and aggregate data using various grouping keys. A GraphRAG application with LangChain can support DevOps teams by leveraging the power of knowledge graphs to provide accurate and up-to-date answers to user queries. The code for this example is available on GitHub, along with other resources and learning materials.