docarray · alaeddine-13 · Jun 13, 2022 · Jun 13, 2022 · Jun 13, 2022 · Jun 13, 2022
diff --git a/docarray/array/storage/annlite/find.py b/docarray/array/storage/annlite/find.py
@@ -8,7 +8,8 @@
 
 if TYPE_CHECKING:
     import numpy as np
-    from .... import DocumentArray
+
+from .... import DocumentArray
 
 
 class FindMixin:
@@ -41,3 +42,22 @@ def _find(
         )
 
         return match_docs
+
+    def _filter(
+        self,
+        filter: Dict,
+        limit: Optional[Union[int, float]] = 20,
+        only_id: bool = False,
+    ) -> 'DocumentArray':
+        """Returns a subset of documents by filtering by the given filter (`Annlite` filter).
+
+        :param filter: the input filter to apply in each stored document
+        :param limit: the number of results to get for each query document in search.
+        :param only_id: if set, then returning matches will only contain ``id``
+        :return: a `DocumentArray` containing the `Document` objects that verify the filter.
+        """
+
+        docs = self._annlite.filter(
+            filter=filter, limit=limit, include_metadata=not only_id
+        )
+        return DocumentArray(docs)
diff --git a/docs/advanced/document-store/annlite.md b/docs/advanced/document-store/annlite.md
@@ -55,7 +55,62 @@ The following configs can be set:
 Search with `.find` can be restricted by user-defined filters.
 Filters can be constructed following the guidelines provided in [the AnnLite source repository](https://github.com/jina-ai/annlite).
 
-### Example of `.find` with a filter
+### Example of `.find` with a filter only
+
+
+Consider you store Documents with a certain tag `price` into annlite and you want to retrieve all Documents
+with `price`  lower or equal to  some `max_price` value.
+
+
+You can index such Documents as follows:
+```python
+from docarray import Document, DocumentArray
+import numpy as np
+
+n_dim = 3
+da = DocumentArray(
+    storage='annlite',
+    config={
+        'n_dim': n_dim,
+        'columns': [('price', 'float')],
+    },
+)
+
+with da:
+    da.extend([Document(id=f'r{i}', tags={'price': i}) for i in range(10)])
+
+print('\nIndexed Prices:\n')
+for price in da[:, 'tags__price']:
+    print(f'\t price={price}')
+```
+
+Then you can retrieve all documents whose price is lower than or equal to `max_price` by applying the following
+filter:
+
+```python
+max_price = 3
+n_limit = 4
+
+filter = {'price': {'$lte': max_price}}
+results = da.find(filter=filter)
+
+print('\n Returned examples that verify filter "price at most 3":\n')
+for price in results[:, 'tags__price']:
+    print(f'\t price={price}')
+```
+
+This would print
+
+```
+ Returned examples that satisfy condition "price at most 3":
+
+	 price=0
+	 price=1
+	 price=2
+	 price=3
+```
+
+### Example of `.find` with query vector and filter
 
 Consider Documents with embeddings `[0,0,0]` up to ` [9,9,9]` where the document with embedding `[i,i,i]`
 has as tag `price` with value `i`. We can create such example with the following code:

diff --git a/setup.py b/setup.py
@@ -64,15 +64,15 @@
             'uvicorn',
             'strawberry-graphql',
             'weaviate-client~=3.3.0',
-            'annlite>=0.3.0',
+            'annlite>=0.3.2',
             'qdrant-client~=0.7.3',
             'elasticsearch>=8.2.0',
         ],
         'qdrant': [
             'qdrant-client~=0.7.3',
         ],
         'annlite': [
-            'annlite>=0.3.0',
+            'annlite>=0.3.2',
         ],
         'weaviate': [
             'weaviate-client~=3.3.0',
@@ -100,7 +100,7 @@
             'jupyterlab',
             'transformers>=4.16.2',
             'weaviate-client~=3.3.0',
-            'annlite>=0.3.0',
+            'annlite>=0.3.2',
             'elasticsearch>=8.2.0',
             'jina',
         ],

diff --git a/tests/unit/array/mixins/test_find.py b/tests/unit/array/mixins/test_find.py
@@ -392,6 +392,17 @@ def test_search_pre_filtering(
             )
             for operator in ['gt', 'gte', 'lt', 'lte']
         ],
+        *[
+            tuple(
+                [
+                    'annlite',
+                    lambda operator, threshold: {'price': {operator: threshold}},
+                    numeric_operators_annlite,
+                    operator,
+                ]
+            )
+            for operator in numeric_operators_annlite.keys()
+        ],
     ],
 )
 def test_filtering(storage, filter_gen, operator, numeric_operators, start_storage):