Graph-based metadata filtering for improving vector search in RAG applications

This guest blog post by Tomaz Bratanic discusses the use of advanced graph-based metadata techniques to optimize vector retrieval, specifically focusing on LangChain and Neo4j. Text embeddings and vector similarity search are effective in finding documents based on their meanings but struggle with sorting information according to specific criteria like dates or categories. Metadata filtering or filtered vector search can handle these structured filters, allowing users to narrow their search results based on particular attributes. The blog post demonstrates how to implement graph-based metadata filtering using LangChain in combination with OpenAI function-calling agent and provides an example dataset for testing the implementation.