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# SPDX-License-Identifier: AGPL-3.0-or-later
""".. 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
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