Optimizing vector retrieval with advanced graph-based metadata techniques using LangChain and Neo4j.
In retrieval-augmented generation (RAG) applications , text embeddings and vector similarity search help us find documents by understanding their meanings and how similar they are to each other. However, text embeddings aren’t as effective when sorting information based on specific criteria like dates or categories; for example, if you need to find all documents created in a particular year o...
Read it: Graph-based Metadata Filtering to Improve Vector Search in RAG Applications