Interface the database search engine to an AI-based LLM

DigiKam’s Advanced Search is powerful, but translating a natural-language request into the right combination of structured filters can be difficult for many users. This project will add a natural-language input to digiKam’s Advanced Search so users can describe queries such as “landscape photos with red labels taken in Paris last summer,” which will then be converted into editable search criteria such as tags, labels, dates, and locations. The implementation will use a lightweight local language model, structured intent parsing, capability-based resolution into supported digiKam search fields, ambiguity handling for unclear terms, and caching for repeated queries, while reusing digiKam’s existing model-management infrastructure. The deliverables include the Advanced Search UI integration, the local inference and parsing pipeline, criteria resolution and validation, safe clarification behavior, and end-to-end integration with digiKam’s existing Advanced Search workflow.