This blog post details how to ingest data to later be used by a vector and GraphRAG agent using Milvus and Neo4j. Traditional RAG systems rely solely on vector databases to retrieve relevant documents, but incorporating Neo4j allows for the capture of relationships between entities and concepts, providing a more nuanced understanding of the information. To build this ingestion process, several technologies are needed, including LangChain, Ollama, Milvus, Neo4j, and Dotenv. The agent will require retrieving information from both a Milvus Vectorstore and a Neo4j Graph Database. The ingestion process involves reading source data, chunking the text content into smaller elements, upserting vector data into a local Milvus instance, and converting source documents into structured graph documents using an LLM. These graph documents can then be added to a Neo4j database instance. Running this process will output the number of documents and chunks created, as well as the nodes and relationships in the Neo4j database. The code presented in this article can be found on GitHub, with plans for future articles on creating agents using LangGraph to query both datastores for answers.