LlamaIndex has introduced several updates and features aimed at enhancing the experience of building and querying Knowledge Graphs (KGs), with a focus on integrating new tools and guides for improved data handling and application development. The Property Graph Index, a major feature, elevates KG construction by enabling joint vector and graph searches, while LlamaParse adds support for parsing complex spreadsheets into LLM-friendly formats. Codestral, a code-generating model from MistralAI, and PostgresML are now integrated, expanding the capabilities for coding and AI application development. Additionally, Milvus Lite integration facilitates vector search, and various guides and demos, such as building custom graph retrievers and GenAI applications, provide step-by-step instructions for users. The platform also highlights projects like Omakase RAG Orchestrator for building scalable RAG applications and a Python script for extracting structured Gmail data. New resources include tutorials on serving LlamaIndex RAG apps as REST APIs, a benchmark dataset for financial question answering, and a webinar on long-term memory in autonomous agents. An upcoming event will discuss using LLMs with financial services data, featuring speakers from Weaviate and Weights & Biases.