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							- # SPDX-License-Identifier: AGPL-3.0-or-later
 
- # lint: pylint
 
- """.. sidebar:: info
 
-    - :origin:`meilisearch.py <searx/engines/meilisearch.py>`
 
-    - `MeiliSearch <https://www.meilisearch.com>`_
 
-    - `MeiliSearch Documentation <https://docs.meilisearch.com/>`_
 
-    - `Install MeiliSearch
 
-      <https://docs.meilisearch.com/learn/getting_started/installation.html>`_
 
- MeiliSearch_ is aimed at individuals and small companies.  It is designed for
 
- small-scale (less than 10 million documents) data collections.  E.g. it is great
 
- for storing web pages you have visited and searching in the contents later.
 
- The engine supports faceted search, so you can search in a subset of documents
 
- of the collection.  Furthermore, you can search in MeiliSearch_ instances that
 
- require authentication by setting ``auth_token``.
 
- Example
 
- =======
 
- Here is a simple example to query a Meilisearch instance:
 
- .. code:: yaml
 
-   - name: meilisearch
 
-     engine: meilisearch
 
-     shortcut: mes
 
-     base_url: http://localhost:7700
 
-     index: my-index
 
-     enable_http: true
 
- """
 
- # pylint: disable=global-statement
 
- from json import loads, dumps
 
- base_url = 'http://localhost:7700'
 
- index = ''
 
- auth_key = ''
 
- facet_filters = []
 
- _search_url = ''
 
- result_template = 'key-value.html'
 
- categories = ['general']
 
- paging = True
 
- def init(_):
 
-     if index == '':
 
-         raise ValueError('index cannot be empty')
 
-     global _search_url
 
-     _search_url = base_url + '/indexes/' + index + '/search'
 
- def request(query, params):
 
-     if auth_key != '':
 
-         params['headers']['X-Meili-API-Key'] = auth_key
 
-     params['headers']['Content-Type'] = 'application/json'
 
-     params['url'] = _search_url
 
-     params['method'] = 'POST'
 
-     data = {
 
-         'q': query,
 
-         'offset': 10 * (params['pageno'] - 1),
 
-         'limit': 10,
 
-     }
 
-     if len(facet_filters) > 0:
 
-         data['facetFilters'] = facet_filters
 
-     params['data'] = dumps(data)
 
-     return params
 
- def response(resp):
 
-     results = []
 
-     resp_json = loads(resp.text)
 
-     for result in resp_json['hits']:
 
-         r = {key: str(value) for key, value in result.items()}
 
-         r['template'] = result_template
 
-         results.append(r)
 
-     return results
 
 
  |