Geosearch

Filter and sort search results based on their geographic location.

Meilisearch allows you to filter and sort results based on their geographic location. This can be useful when you only want results within a specific area or when sorting results based on their distance from a specific location.

Due to Meilisearch allowing malformed _geo fields in the following versions (v0.27, v0.28 and v0.29), please ensure the _geo field follows the correct format.

Preparing documents for location-based search

In order to start filtering and sorting documents based on their geographic location, you must make sure they contain a valid _geo field.

_geo is a reserved field. If you include it in your documents, Meilisearch expects its value to conform to a specific format.

When using JSON and NDJSON, _geo must contain an object with two keys: lat and lng. Both fields must contain either a floating point number or a string indicating, respectively, latitude and longitude:

{
  …
  "_geo": {
    "lat": 0.0,
    "lng": "0.0"
  }
}

Examples

Suppose we have a JSON array containing a few restaurants:

[
  {
    "id": 1,
    "name": "Nàpiz' Milano",
    "address": "Viale Vittorio Veneto, 30, 20124, Milan, Italy",
    "type": "pizza",
    "rating": 9
  },
  {
    "id": 2,
    "name": "Bouillon Pigalle",
    "address": "22 Bd de Clichy, 75018 Paris, France",
    "type": "french",
    "rating": 8
  },
  {
    "id": 3,
    "name": "Artico Gelateria Tradizionale",
    "address": "Via Dogana, 1, 20123 Milan, Italy",
    "type": "ice cream",
    "rating": 10
  }
]

Our restaurant dataset looks like this once we add geopositioning data:

[
  {
    "id": 1,
    "name": "Nàpiz' Milano",
    "address": "Viale Vittorio Veneto, 30, 20124, Milan, Italy",
    "type": "pizza",
    "rating": 9,
    "_geo": {
      "lat": 45.4777599,
      "lng": 9.1967508
    }
  },
  {
    "id": 2,
    "name": "Bouillon Pigalle",
    "address": "22 Bd de Clichy, 75018 Paris, France",
    "type": "french",
    "rating": 8,
    "_geo": {
      "lat": 48.8826517,
      "lng": 2.3352748
    }
  },
  {
    "id": 3,
    "name": "Artico Gelateria Tradizionale",
    "address": "Via Dogana, 1, 20123 Milan, Italy",
    "type": "ice cream",
    "rating": 10,
    "_geo": {
      "lat": 45.4632046,
      "lng": 9.1719421
    }
  }
]

Trying to index a dataset with one or more documents containing badly formatted _geo values will cause Meilisearch to throw an invalid_document_geo_field error. In this case, the update will fail and no documents will be added or modified.

Using _geo with CSV

If your dataset is formatted as CSV, the file header must have a _geo column. Each row in the dataset must then contain a column with a comma-separated string indicating latitude and longitude:

"id:number","name:string","address:string","type:string","rating:number","_geo:string"
"1","Nàpiz Milano","Viale Vittorio Veneto, 30, 20124, Milan, Italy","pizzeria",9,"45.4777599,9.1967508"
"2","Bouillon Pigalle","22 Bd de Clichy, 75018 Paris, France","french",8,"48.8826517,2.3352748"
"3","Artico Gelateria Tradizionale","Via Dogana, 1, 20123 Milan, Italy","ice cream",10,"48.8826517,2.3352748"

Filtering results with _geoRadius and _geoBoundingBox

You can use _geo data to filter queries so you only receive results located within a given geographic area.

Configuration

In order to filter results based on their location, you must add the _geo attribute to the filterableAttributes list:

cURL

curl \
  -X PUT 'MEILISEARCH_URL/indexes/restaurants/settings/filterable-attributes' \
  -H 'Content-type:application/json' \
  --data-binary '["_geo"]'

Meilisearch will rebuild your index whenever you update filterableAttributes. Depending on the size of your dataset, this might take a considerable amount of time.

You can read more about configuring filterableAttributes in our dedicated filtering guide.

Usage

Use the filter search parameter along with _geoRadius or _geoBoundingBox. These are special filter rules that ensure Meilisearch only returns results located within a specific geographic area.

_geoRadius

_geoRadius(lat, lng, distance_in_meters)

_geoBoundingBox

_geoBoundingBox([{lat}, {lng}], [{lat}, {lng}])

Examples

Using our example dataset, we can search for places to eat near the center of Milan with _geoRadius:

cURL

curl \
  -X POST 'MEILISEARCH_URL/indexes/restaurants/search' \
  -H 'Content-type:application/json' \
  --data-binary '{ "filter": "_geoRadius(45.472735, 9.184019, 2000)" }'

We also make a similar query using _geoBoundingBox:

cURL

curl \
  -X POST 'MEILISEARCH_URL/indexes/restaurants/search' \
  -H 'Content-type:application/json' \
  --data-binary '{ "filter": "_geoBoundingBox([45.494181, 9.214024], [45.449484, 9.179175])" }'

[
  {
    "id": 1,
    "name": "Nàpiz' Milano",
    "address": "Viale Vittorio Veneto, 30, 20124, Milan, Italy",
    "type": "pizza",
    "rating": 9,
    "_geo": {
      "lat": 45.4777599,
      "lng": 9.1967508
    }
  },
  {
    "id": 3,
    "name": "Artico Gelateria Tradizionale",
    "address": "Via Dogana, 1, 20123 Milan, Italy",
    "type": "ice cream",
    "rating": 10,
    "_geo": {
      "lat": 45.4632046,
      "lng": 9.1719421
    }
  }
]

It is also possible to combine _geoRadius and _geoBoundingBox with other filters. We can narrow down our previous search so it only includes pizzerias:

cURL

curl \
  -X POST 'MEILISEARCH_URL/indexes/restaurants/search' \
  -H 'Content-type:application/json' \
  --data-binary '{ "filter": "_geoRadius(45.472735, 9.184019, 2000) AND type = pizza" }'

[
  {
    "id": 1,
    "name": "Nàpiz' Milano",
    "address": "Viale Vittorio Veneto, 30, 20124, Milan, Italy",
    "type": "pizza",
    "rating": 9,
    "_geo": {
      "lat": 45.4777599,
      "lng": 9.1967508
    }
  }
]

_geo, _geoDistance, and _geoPoint are not valid filter rules. Trying to use any of them with the filter search parameter will result in an invalid_search_filter error.

Sorting results with _geoPoint

Configuration

Before using geosearch for sorting, you must add the _geo attribute to the sortableAttributes list:

cURL

curl \
  -X PUT 'MEILISEARCH_URL/indexes/restaurants/settings/sortable-attributes' \
  -H 'Content-type:application/json' \
  --data-binary '["_geo"]'

Read more about sortableAttributes here.

Usage

_geoPoint(0.0, 0.0):asc

Examples

The _geoPoint sorting function can be used like any other sorting rule. We can order documents based on how close they are to the Eiffel Tower:

cURL

curl \
  -X POST 'MEILISEARCH_URL/indexes/restaurants/search' \
  -H 'Content-type:application/json' \
  --data-binary '{ "sort": ["_geoPoint(48.8561446,2.2978204):asc"] }'

With our restaurants dataset, the results look like this:

[
  {
    "id": 2,
    "name": "Bouillon Pigalle",
    "address": "22 Bd de Clichy, 75018 Paris, France",
    "type": "french",
    "rating": 8,
    "_geo": {
      "lat": 48.8826517,
      "lng": 2.3352748
    }
  },
  {
    "id": 3,
    "name": "Artico Gelateria Tradizionale",
    "address": "Via Dogana, 1, 20123 Milan, Italy",
    "type": "ice cream",
    "rating": 10,
    "_geo": {
      "lat": 45.4632046,
      "lng": 9.1719421
    }
  },
  {
    "id": 1,
    "name": "Nàpiz' Milano",
    "address": "Viale Vittorio Veneto, 30, 20124, Milan, Italy",
    "type": "pizza",
    "rating": 9,
    "_geo": {
      "lat": 45.4777599,
      "lng": 9.1967508
    }
  }
]

_geoPoint also works when used together with other sorting rules. We can sort restaurants based on their proximity to the Eiffel Tower and their rating:

cURL

curl \
  -X POST 'MEILISEARCH_URL/indexes/restaurants/search' \
  -H 'Content-type:application/json' \
  --data-binary '{
    "sort": [
      "_geoPoint(48.8561446,2.2978204):asc",
      "rating:desc"
    ]
  }'

[
  {
    "id": 2,
    "name": "Bouillon Pigalle",
    "address": "22 Bd de Clichy, 75018 Paris, France",
    "type": "french",
    "rating": 8,
    "_geo": {
      "lat": 48.8826517,
      "lng": 2.3352748
    }
  },
  {
    "id": 3,
    "name": "Artico Gelateria Tradizionale",
    "address": "Via Dogana, 1, 20123 Milan, Italy",
    "type": "ice cream",
    "rating": 10,
    "_geo": {
      "lat": 45.4632046,
      "lng": 9.1719421
    }
  },
  {
    "id": 1,
    "name": "Nàpiz' Milano",
    "address": "Viale Vittorio Veneto, 30, 20124, Milan, Italy",
    "type": "pizza",
    "rating": 9,
    "_geo": {
      "lat": 45.4777599,
      "lng": 9.1967508
    }
  }
]