Large Language Models (LLMs) have advanced significantly since the introduction of the Transformer architecture, yet they face limitations such as hallucinations and reliance on pre-existing training data. To mitigate these issues, LLMs can be enhanced by integrating with external data sources using a Retrieval Augmented Generation (RAG) system. This system utilizes Knowledge Graphs and Vector Databases, each offering distinct advantages and drawbacks. Knowledge Graphs provide structured representations of data, enabling complex queries and precise answers, while Vector Databases store unstructured data as numerical vectors, facilitating efficient similarity searches. The two technologies complement each other, with Knowledge Graphs excelling in relationship analysis and explainability, and Vector Databases being adept at handling large volumes of unstructured data. Combining these approaches can enhance AI applications by providing both broad semantic similarity and robust logical reasoning. FalkorDB offers a unified solution, integrating the strengths of both technologies to improve AI performance and mitigate issues like hallucinations in LLMs.