search with facets and autocomplete

Sure — if you want search with facets and autocomplete, here’s the typical pattern.

What it means

• Search: full-text query over items/documents
• Facets: filters like category, brand, price range, tags
• Autocomplete: suggestions as the user types

Common architecture

1. Index your data in a search engine

   • Elasticsearch / OpenSearch
   • Algolia
   • Meilisearch
   • Typesense
   • Azure AI Search

2. Store searchable fields

   • title, description, tags
   • facet fields like category, brand, price, rating

3. Autocomplete endpoint

   • returns suggested queries or matching items
   • usually triggered on each keystroke with debounce

4. Faceted search endpoint

   • accepts query + facet filters
   • returns matching results + facet counts

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Example API shape

Autocomplete

GET /search/autocomplete?q=iph

Response:

{
  "suggestions": [
    "iphone 15",
    "iphone 15 pro",
    "iphone charger"
  ]
}

Search with facets

GET /search?q=iphone&category=phones&brand=apple&price_min=500&price_max=1500

Response:

{
  "results": [
    {
      "id": 1,
      "title": "iPhone 15 Pro"
    }
  ],
  "facets": {
    "brand": [
      { "value": "Apple", "count": 120 }
    ],
    "category": [
      { "value": "Phones", "count": 80 }
    ]
  },
  "total": 80
}

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If you’re using Elasticsearch/OpenSearch

You’d typically use:

• match / multi_match for search
• terms aggregations for facets
• suggest, completion, or edge n-grams for autocomplete

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If you’re using Algolia

You get this almost out of the box:

• facets via attributesForFaceting
• autocomplete via searchable attributes + typo tolerance + query suggestions

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If you want, I can help with any of these:

1. Design the API
2. Implement in Elasticsearch/OpenSearch
3. Implement in Algolia
4. Implement in React/Next.js UI
5. Implement in SQL + backend
6. Add autocomplete + facets to an existing app

If you tell me your stack, I can give you a concrete implementation.