The article explores the process of adding new data loaders to LlamaIndex, a toolkit designed to enhance Large Language Models (LLMs) with personalized data by utilizing in-context learning. Initially, a simple CSV loader is demonstrated, followed by the creation and application of loaders for graph databases and GraphQL APIs. These loaders enable LlamaIndex to retrieve relevant context from extensive knowledge bases, thus improving the accuracy of responses from the LLMs. The process of developing these loaders is straightforward, involving the integration of existing technologies and transforming query results into YAML documents for LlamaIndex. Examples provided include using a graph database to query movie plots and a GraphQL endpoint for retrieving country and capital data. The article highlights the ease of implementing such loaders and the potential for further exploration, especially with graph database integrations.