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								# SPDX-License-Identifier: AGPL-3.0-or-later
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											2023-06-30 18:07:02 +02:00
										 
									 
								 
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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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											2021-09-07 13:26:59 +02:00
										 
									 
								 
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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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