> ## Documentation Index
> Fetch the complete documentation index at: https://www.meilisearch.com/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Combine text and image search

> Use multi-search to run text-based and image-based semantic searches in a single request, leveraging multiple embedders for richer results.

Multi-search lets you query the same index with different embedders in a single request. This is useful when your index has both a text embedder and an image embedder configured, and you want to combine their results.

## Configure multiple embedders

Before running semantic multi-search queries, configure at least two embedders on your index. For example, a text embedder using OpenAI and an image embedder using a multimodal REST provider:

<CodeGroup>
  ```bash theme={null}
  curl \
    -X PATCH 'MEILISEARCH_URL/indexes/products/settings' \
    -H 'Content-Type: application/json' \
    -H 'Authorization: Bearer MEILISEARCH_KEY' \
    --data-binary '{
      "embedders": {
        "text": {
          "source": "openAi",
          "apiKey": "OPEN_AI_API_KEY",
          "model": "text-embedding-3-small",
          "documentTemplate": "A product called {{doc.name}}: {{doc.description}}"
        },
        "image": {
          "source": "rest",
          "url": "https://api.voyageai.com/v1/multimodalembeddings",
          "apiKey": "VOYAGE_API_KEY",
          "request": {
            "inputs": [
              {
                "content": [
                  { "type": "image_url", "image_url": "{{media.image}}" }
                ]
              }
            ],
            "model": "voyage-multimodal-3"
          },
          "response": {
            "data": [{ "embedding": "{{embedding}}" }]
          },
          "indexingFragments": {
            "image": { "value": "{{doc.image_url}}" }
          },
          "searchFragments": {
            "image": { "value": "{{media.image}}" }
          }
        }
      }
    }'
  ```
</CodeGroup>

For more on embedder configuration, see [Configure an OpenAI embedder](/capabilities/hybrid_search/how_to/configure_openai_embedder) and [Image search with a multimodal embedder](/capabilities/hybrid_search/how_to/image_search_with_multimodal).

## Search with text and image in one request

Use federated multi-search to combine a text query and an image query, each targeting a different embedder on the same index:

<CodeGroup>
  ```bash theme={null}
  curl \
    -X POST 'MEILISEARCH_URL/multi-search' \
    -H 'Content-Type: application/json' \
    -H 'Authorization: Bearer MEILISEARCH_KEY' \
    --data-binary '{
      "federation": {},
      "queries": [
        {
          "indexUid": "products",
          "q": "comfortable running shoes",
          "hybrid": {
            "embedder": "text",
            "semanticRatio": 0.8
          }
        },
        {
          "indexUid": "products",
          "media": {
            "image": "https://example.com/red-sneaker.jpg"
          },
          "hybrid": {
            "embedder": "image",
            "semanticRatio": 1.0
          }
        }
      ]
    }'
  ```
</CodeGroup>

Meilisearch runs both queries and merges the results into a single ranked list. Products matching both the text description and the image will rank higher.

## Control the balance between text and image results

Use `federationOptions.weight` to control how much each query contributes to the final ranking:

<CodeGroup>
  ```bash theme={null}
  curl \
    -X POST 'MEILISEARCH_URL/multi-search' \
    -H 'Content-Type: application/json' \
    -H 'Authorization: Bearer MEILISEARCH_KEY' \
    --data-binary '{
      "federation": {},
      "queries": [
        {
          "indexUid": "products",
          "q": "comfortable running shoes",
          "hybrid": {
            "embedder": "text",
            "semanticRatio": 0.8
          },
          "federationOptions": { "weight": 1.0 }
        },
        {
          "indexUid": "products",
          "media": {
            "image": "https://example.com/red-sneaker.jpg"
          },
          "hybrid": {
            "embedder": "image",
            "semanticRatio": 1.0
          },
          "federationOptions": { "weight": 0.5 }
        }
      ]
    }'
  ```
</CodeGroup>

In this example, text results have twice the weight of image results. Adjust the weights to match your use case.

## Combine keyword, text semantic, and image search

You can go further and combine all three search modes in one request: keyword search, semantic text search, and image search.

<CodeGroup>
  ```bash theme={null}
  curl \
    -X POST 'MEILISEARCH_URL/multi-search' \
    -H 'Content-Type: application/json' \
    -H 'Authorization: Bearer MEILISEARCH_KEY' \
    --data-binary '{
      "federation": {},
      "queries": [
        {
          "indexUid": "products",
          "q": "red running shoes",
          "hybrid": {
            "embedder": "text",
            "semanticRatio": 0.0
          },
          "federationOptions": { "weight": 1.0 }
        },
        {
          "indexUid": "products",
          "q": "red running shoes",
          "hybrid": {
            "embedder": "text",
            "semanticRatio": 1.0
          },
          "federationOptions": { "weight": 0.8 }
        },
        {
          "indexUid": "products",
          "media": {
            "image": "https://example.com/red-sneaker.jpg"
          },
          "hybrid": {
            "embedder": "image",
            "semanticRatio": 1.0
          },
          "federationOptions": { "weight": 0.5 }
        }
      ]
    }'
  ```
</CodeGroup>

This sends three queries to the same index:

1. **Keyword search** (`semanticRatio: 0.0`) for exact term matches
2. **Semantic text search** (`semanticRatio: 1.0`) for meaning-based matches
3. **Image search** for visually similar products

Meilisearch merges all results and ranks them using the configured weights.

## Search across multiple indexes with different embedders

You can also target different indexes, each with its own embedders. For example, searching a `products` index with a text embedder and an `inspiration` index with an image embedder:

<CodeGroup>
  ```bash theme={null}
  curl \
    -X POST 'MEILISEARCH_URL/multi-search' \
    -H 'Content-Type: application/json' \
    -H 'Authorization: Bearer MEILISEARCH_KEY' \
    --data-binary '{
      "federation": {},
      "queries": [
        {
          "indexUid": "products",
          "q": "summer dress",
          "hybrid": {
            "embedder": "text",
            "semanticRatio": 0.7
          },
          "federationOptions": { "weight": 1.0 }
        },
        {
          "indexUid": "inspiration",
          "media": {
            "image": "https://example.com/summer-outfit.jpg"
          },
          "hybrid": {
            "embedder": "image",
            "semanticRatio": 1.0
          },
          "federationOptions": { "weight": 0.6 }
        }
      ]
    }'
  ```
</CodeGroup>

## Next steps

<CardGroup cols={2}>
  <Card title="Configure embedders" href="/capabilities/hybrid_search/getting_started">
    Set up text and multimodal embedders for semantic search.
  </Card>

  <Card title="Image search" href="/capabilities/hybrid_search/how_to/image_search_with_multimodal">
    Configure a multimodal embedder for image-based search.
  </Card>

  <Card title="Multiple embedders" href="/capabilities/hybrid_search/advanced/multiple_embedders">
    Learn how to configure and use multiple embedders on the same index.
  </Card>

  <Card title="Boost results" href="/capabilities/multi_search/how_to/boost_results_across_indexes">
    Fine-tune federation weights to control result ranking.
  </Card>
</CardGroup>