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Documentation Index

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Meilisearch supports configuring multiple embedders on the same index. Each embedder generates its own set of vectors, and you can target a specific embedder at search time. This lets you combine different search strategies (text, image, semantic) with specialized models for each.

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Why use multiple embedders

A single embedder is a good fit when all your searches are the same type. But real applications often need different search modes:
  • Text + image search: use a text-optimized embedder alongside a multimodal embedder, so users can search with keywords or with images
  • Precision vs speed: use a large, high-quality model for precise searches and a smaller, faster model for search-as-you-type suggestions
  • Different quality levels: use a small model at full precision for quick queries and a large model with binary quantization for deep searches
  • Multilingual: use a language-specific model for your primary language and a multilingual model as a fallback
  • Federated search: combine full-text, semantic, and image results in a single federated search request, each powered by the best model for its task

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Configure multiple embedders

Add multiple keys to the embedders setting. Each key is a named embedder with its own configuration: 
curl \
  -X PATCH 'MEILISEARCH_URL/indexes/products/settings/embedders' \
  -H 'Content-Type: application/json' \
  -H 'Authorization: Bearer MEILISEARCH_KEY' \
  --data-binary '{
    "text": {
      "source": "openAi",
      "apiKey": "OPEN_AI_API_KEY",
      "model": "text-embedding-3-small"
    },
    "image": {
      "source": "rest",
      "url": "https://api.voyageai.com/v1/multimodalembeddings",
      "apiKey": "VOYAGE_API_KEY",
      "indexingFragments": {
        "poster": {
          "value": {
            "content": [
              { "type": "image_url", "image_url": "{{doc.poster_url}}" }
            ]
          }
        }
      },
      "searchFragments": {
        "image": {
          "value": {
            "content": [
              { "type": "image_url", "image_url": "{{media.image}}" }
            ]
          }
        }
      },
      "request": {
        "inputs": ["{{fragment}}", "{{..}}"],
        "model": "voyage-multimodal-3"
      },
      "response": {
        "data": [{ "embedding": "{{embedding}}" }, "{{..}}"]
      }
    }
  }'
This configures two embedders: text for keyword-aware semantic search and image for visual similarity search.

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Search with a specific embedder

Specify which embedder to use with the hybrid.embedder parameter: 
# Semantic text search
curl -X POST 'MEILISEARCH_URL/indexes/products/search' \
  -H 'Content-Type: application/json' \
  -H 'Authorization: Bearer MEILISEARCH_KEY' \
  --data-binary '{
    "q": "comfortable running shoes",
    "hybrid": {
      "embedder": "text",
      "semanticRatio": 0.5
    }
  }'
The most powerful use case for multiple embedders is federated search. You can run full-text, semantic, and image searches in a single request and merge the results: 
curl -X POST 'MEILISEARCH_URL/multi-search' \
  -H 'Content-Type: application/json' \
  -H 'Authorization: Bearer MEILISEARCH_KEY' \
  --data-binary '{
    "federation": {},
    "queries": [
      {
        "indexUid": "products",
        "q": "running shoes",
        "hybrid": {
          "embedder": "text",
          "semanticRatio": 0.0
        },
        "federationOptions": { "weight": 1.0 }
      },
      {
        "indexUid": "products",
        "q": "running shoes",
        "hybrid": {
          "embedder": "text",
          "semanticRatio": 1.0
        },
        "federationOptions": { "weight": 0.8 }
      },
      {
        "indexUid": "products",
        "media": {
          "image": "https://example.com/shoe.jpg"
        },
        "hybrid": {
          "embedder": "image",
          "semanticRatio": 1.0
        },
        "federationOptions": { "weight": 0.5 }
      }
    ]
  }'
This single request combines:
  1. Full-text search (semanticRatio: 0.0) with the highest weight for keyword-relevant results
  2. Semantic text search (semanticRatio: 1.0) for meaning-based matches
  3. Image search using a completely different model for visual similarity
Meilisearch merges all results into one ranked list using the federation weights.

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Considerations

  • Each embedder generates and stores its own vectors. More embedders means more disk usage and longer indexing times.
  • You can use binary quantization on individual embedders to reduce storage (e.g., quantize the large model but keep the small one at full precision).
  • Composite embedders can be combined with multiple embedders: use a fast local model for search and a cloud API for indexing, independently for each named embedder.

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Next steps

Federated search

Merge results from multiple queries into one ranked list

Binary quantization

Reduce storage for high-dimensional embedders

Image search

Set up multimodal embedders for image search

Choose an embedder

Compare embedding providers for your use case