docarray · JoanFM · Oct 3, 2022 · Oct 3, 2022 · Oct 3, 2022
diff --git a/docarray/array/mixins/find.py b/docarray/array/mixins/find.py
@@ -131,7 +131,6 @@ def find(
 
         :return: a list of DocumentArrays containing the closest Document objects for each of the queries in `query`.
         """
-
         index_da = self._get_index(subindex_name=on)
         if index_da is not self:
             return index_da.find(
@@ -145,7 +144,6 @@ def find(
                 index,
                 on=None,
             )
-
         from docarray import Document, DocumentArray
 
         if isinstance(query, dict):

diff --git a/docarray/array/storage/elastic/backend.py b/docarray/array/storage/elastic/backend.py
@@ -1,6 +1,6 @@
 import copy
 import uuid
-from dataclasses import dataclass, field, asdict
+from dataclasses import dataclass, field
 import warnings
 
 from typing import (

diff --git a/docarray/array/storage/elastic/find.py b/docarray/array/storage/elastic/find.py
@@ -48,7 +48,6 @@ def _find_similar_vectors(
         :return: DocumentArray containing the closest documents to the query if it is a single query, otherwise a list of DocumentArrays containing
            the closest Document objects for each of the queries in `query`.
         """
-
         query = to_numpy_array(query)
         is_all_zero = np.all(query == 0)
         if is_all_zero:
@@ -151,7 +150,6 @@ def _find_with_filter(self, query: Dict, limit: Optional[Union[int, float]] = 20
             query=query,
             size=limit,
         )
-
         list_of_hits = resp['hits']['hits']
 
         da = DocumentArray()

diff --git a/tests/unit/array/storage/elastic/test_find.py b/tests/unit/array/storage/elastic/test_find.py
@@ -33,3 +33,74 @@ def _mock_knn_search(**kwargs):
     np_query = np.array([2, 1, 3])
 
     elastic_doc.find(np_query, limit=10, num_candidates=num_candidates)
+
+
+def test_filter(start_storage):
+    import random
+    import string
+
+    elastic_da = DocumentArray(
+        storage='elasticsearch',
+        config={
+            'n_dim': 2,
+            'columns': {
+                'A': 'str',
+                'B': 'str',
+                'V': 'str',
+                'D': 'str',
+                'E': 'str',
+                'F': 'str',
+                'G': 'str',
+            },
+        },
+    )
+
+    def ran():
+        return ''.join(random.choices(string.ascii_uppercase + string.digits, k=10))
+
+    def ran_size():
+        sizes = ['S', 'M', 'L', 'XL']
+        return sizes[random.randint(0, len(sizes) - 1)]
+
+    def ran_type():
+        types = ['A', 'B', 'C', 'D']
+        return types[random.randint(0, len(types) - 1)]
+
+    def ran_stype():
+        stypes = ['SA', 'SB', 'SC', 'SD']
+        return stypes[random.randint(0, len(stypes) - 1)]
+
+    docs = DocumentArray(
+        [
+            Document(
+                id=f'r{i}',
+                embedding=np.random.rand(2),
+                tags={
+                    'A': ran(),
+                    'B': ran_stype(),
+                    'C': ran_size(),
+                    'D': ran_type(),
+                    'E': ran(),
+                    'F': ran_type(),
+                    'G': f'G{i}',
+                },
+            )
+            for i in range(50)
+        ]
+    )
+
+    with elastic_da:
+        elastic_da.extend(docs)
+
+    res = elastic_da.find(query=Document(embedding=docs[0].embedding))
+    assert len(res) > 0
+    assert res[0][0].tags['G'] == 'G0'
+    filter_ = {'match': {'G': 'G3'}}
+
+    res = elastic_da.find(filter=filter_)
+    assert len(res) > 0
+    assert res[0].tags['G'] == 'G3'
+
+    res = elastic_da.find(query=Document(embedding=docs[0].embedding), filter=filter_)
+    assert len(res) > 0
+    assert res[0][0].tags['G'] == 'G3'