What is self-query retrieval and how does it work?

Self-query retrieval uses a language model to parse the user's natural-language query into a structured query that includes both a semantic search component and explicit metadata filters. For example, a query like "action movies from the 1990s" becomes a vector search on "action movies" with a filter on year between 1990 and 1999. This handles queries that mix semantic intent with structured constraints, which naive vector search cannot express. It requires a schema description of filterable metadata fields.