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