search-engine-setuplisted

# Search Engine Setup ## Overview This skill helps AI agents implement production-quality search in applications. It covers index design with custom analyzers, database-to-index sync pipelines, search APIs with faceting and highlights, autocomplete, and relevance tuning based on real query data. ## Instructions ### Index Design (Elasticsearch) 1. Map source database columns to Elasticsearch field types: - Text columns users search → `text` with custom analyzer - Enum/category columns for filtering → `keyword` - Numeric columns for range filters → `integer`, `float` - Boolean flags → `boolean` - Dates → `date` - Fields for autocomplete → `completion` 2. Custom analyzer template for product/content search: ```json { "analyzer": { "content_analyzer": { "tokenizer": "standard", "filter": ["lowercase", "synonym_filter", "edge_ngram_filter"] } }, "filter": { "synonym_filter": { "type": "synonym", "synonyms_path": "synonyms.txt" }, "edge_ngram_filter": { "type": "edge_ngram", "min_gram": 3, "max_gram": 15 } } } ``` 3. Boost fields by search importance: title/name (3-5x), tags (2x), description (1x). 4. Always add a `suggest` field of type `completion` for typeahead. ### Index Design (Algolia) 1. Set `searchableAttributes` in priority order: `["name", "category", "description"]`. 2. Set `attributesForFaceting`: prefix filterable attributes with `filterOnly()` for non-displayed fa