Large language models have evolved to become increasingly sophisticated and efficient, with the emergence of multimodal LLMs. These models often generate inaccurate responses, known as hallucinations, which can be mitigated using approaches such as Retrieval Augmented Generation (RAG), fine-tuning, and prompt engineering. RAG is a more sophisticated solution that uses knowledge graphs to provide contextually relevant responses. Knowledge graphs are structured representations of complex information that enable LLMs to understand relationships and context among data points effectively. By storing information in a graph format, knowledge graphs provide a more intuitive and flexible way to model real-world scenarios, making it easier to retrieve and utilize relevant information. RAG systems can be built using either vector databases or knowledge graphs, each offering distinct advantages and methodologies for information retrieval and response generation. The integration of GraphRAG with LLMs leverages frameworks like LangChain, simplifying knowledge graph construction by automating entity recognition and relationship mapping. SingleStore database is a suitable choice for building RAG applications, providing a robust platform that supports all types of data and can handle tasks such as semantic caching, vector search, hybrid search, building full-stack AI apps, vector data storage, integration for AI frameworks, etc.