Large language models (LLMs) and large vision models (LVMs) often face limitations due to their reliance on static, pre-trained data, resulting in outdated or incomplete responses. Retrieval-Augmented Generation (RAG) offers a solution by enabling LLMs to access real-time, contextually relevant information from multiple data sources. LlamaIndex, an open-source framework, facilitates the development of LLM-powered applications by connecting LLMs with private or domain-specific data sources, and supports the ingestion, structuring, and indexing of data from diverse formats. When combined with FalkorDB, a scalable knowledge graph database with vector indexing capabilities, LlamaIndex enhances RAG systems by providing richer data retrieval and mitigating issues such as context window limitations. This synergy allows for the creation of GraphRAG systems, where RAG implementations are enriched by knowledge graphs, thus providing more accurate and contextually grounded responses. By leveraging tools like LlamaIndex and FalkorDB, developers can construct scalable, data-rich applications that maintain relevance and accuracy through real-time information retrieval and knowledge graph integration.