Migrate from Elasticsearch to Vespa — map ES indices and mappings to Vespa schemas, translate Query DSL to YQL, plan reindexing, and bridge ranking differences. Use when the user mentions migrating from Elasticsearch, ES→Vespa, porting an ES index, or replacing Elasticsearch with Vespa.
Writing, validating, and evolving Vespa .sd schema files — covers field types, indexing pipelines, match modes, tensors, rank profiles, structs, fieldsets, and common pitfalls.
Scaffold and configure Vespa application packages, including services.xml, schemas, deployment.xml, query profiles, and embedder components.
Vespa document CRUD operations and bulk feeding — covers document ID format, JSON wire format for put/update/remove, REST API endpoints, CLI commands, partial updates, conditional writes, bulk feeding, and document visiting/export.
Python API for Vespa.ai — define schemas, deploy applications, feed documents, query, and manage Vespa from Python using pyvespa.
Build Vespa YQL queries and design rank profiles. Covers YQL syntax, operators, grouping, rank-profile phases, ML model integration, and query tensor inputs.
Vespa CLI for deploying, managing, and debugging Vespa.ai applications -- covers target configuration, authentication, deployment lifecycle, production pipelines, document operations, log inspection, testing, and CI/CD integration.