Neo4j x LangChain: Deep dive into the new Vector index implementation

The blog post discusses the customization options available in Neo4j Vector Index implementation in LangChain. It explains how to set up a Neo4j environment, use an example dataset, and customize the vector index by changing node labels, text and embedding property names, and more. Additionally, it covers loading additional documents into an existing vector index, retrieving data from similarity searches, and using Cypher queries for customization. The post highlights that Neo4j's new vector index makes it a great fit for most RAG applications as it now works well with both structured and unstructured data.