Graph-based metadata filtering is being used to improve vector search accuracy in Retrieval-Augmented Generation (RAG) applications. This technique allows users to narrow down their search results according to specific attributes, such as dates or categories. By leveraging the extensive structured information stored in graph databases like Neo4j, sophisticated metadata filters can be executed to precisely refine document selection using structured criteria. The filtering process is combined with vector similarity search to increase accuracy and relevance of search results. In this blog post, LangChain support for metadata filtering in Neo4j is introduced, along with an OpenAI function-calling agent that dynamically generates Cypher statements based on user input and retrieves relevant information from the graph database. The code is available on GitHub, providing a basis for implementing similar solutions in RAG applications.