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The API that gets the details of Points of Interest in Qwant Maps
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  • Idunn is an API to get points-of-interest information for QwantMaps.
  • The POIs are taken from the mimir ElasticSearch database.
  • It also fetches POI data from Wikipedia API and a custom Wikidata Elasticsearch source.
  • Why Idunn ? Because she is the wife of Bragi that is also the main mimir API.
  • A simple workflow schema of Idunn is presented below.

Idunn workflow


  • The API provides its OpenAPI schema with: GET /schema

The main endpoints are:

  • /v1/places/{place_id}?lang={lang}&type={type}&verbosity={verbosity} to get the details of a place (admin, street, address or POI).
    • type: (optional) parameter belongs to the set {'admin', 'street', 'address', 'poi'}
    • verbosity parameter belongs to the set {'long', 'short'}. The default verbosity is long.
  • /v1/places?bbox={bbox}&category=<category-name>&size={size} to get a list of all points of interest matching the given bbox and categories
    • bbox: left,bot,right,top e.g. bbox=2.0,48.0,3.0,49.0
    • category: multiple values are accepted (e.g. category=leisure&category=museum)
    • size: maximum number of places in the response
    • verbosity: default verbosity is list (equivalent to long, except "information" and "wiki" blocks are not returned)
    • source: (optional) to force a data source (instead of automated selection based on coverage). Accepted values: osm, pages_jaunes
    • q: full-text query (optional, experimental)
  • /v1/places?bbox={bbox}&raw_filter=class,subclass&size={size} to get a list of all points of interest matching the given bbox (=left,bot,right,top e.g. bbox=2,48,3,49) and the raw filters (e.g. raw_filter=*,restaurant&raw_filter=shop,*&raw_filter=bakery,bakery)
  • /v1/categories to get the list of all the categories you can filter on.
  • /v1/pois/{poi_id}?lang={lang} is the deprecated route to get the details of a POI.
  • /v1/status to get the status of the API and associated ES cluster.
  • /v1/metrics to get some metrics on the API that give statistics on the number of requests received, the duration of requests... This endpoint can be scraped by Prometheus.


  • The dependencies are managed with Pipenv.
  • To run the api you need to do:
pipenv install
  • and then:
IDUNN_MIMIR_ES=<url_to_MIMIR_ES> IDUNN_WIKI_ES=<url_to_WIKI_ES> pipenv run python
  • you can query the API on port 5000:
curl localhost:5000/v1/places/toto?lang=fr&type=poi


The configuration can be given from different ways:

  1. a default settings is available in utils/default_settings.yaml
  2. a yaml settings file can be given with an env var IDUNN_CONFIG_FILE (the default settings is still loaded and overriden)
  3. specific variable can be overriden with env var. They need to be given like "IDUNN_{var_name}={value}" eg IDUNN_MIMIR_ES=...

How to contribute ?

  • Idunn comes along with all necessary components to contribute as easily as possible: specifically you don't need to have any Elasticsearch instance running. Idunn uses docker images to simulate the Elasticsearch sources and the Redis. This means that you will need a local docker install to be able to spawn an ES cluster.

  • To contribute the common workflow is:

    1. install the dev dependencies: pipenv install --dev
    2. add a test in ./tests for the new feature you propose
    3. implement your feature
    4. run pytest: pipenv run pytest -vv -x
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