docarray · JoanFM · Dec 1, 2022 · Nov 23, 2022 · Nov 23, 2022 · Nov 23, 2022
diff --git a/docarray/array/storage/weaviate/find.py b/docarray/array/storage/weaviate/find.py
@@ -94,7 +94,6 @@ def _find_similar_vectors(
             doc = Document.from_base64(result['_serialized'], **self._serialize_config)
 
             distance = result['_additional']['distance']
-            doc.scores['cosine_similarity'] = NamedScore(value=distance)
             doc.scores['distance'] = NamedScore(value=distance)
 
             certainty = result['_additional'].get('certainty', None)

diff --git a/docs/advanced/document-store/elasticsearch.md b/docs/advanced/document-store/elasticsearch.md
@@ -193,7 +193,12 @@ Consider we want the nearest vectors to the embedding `[8. 8. 8.]`, with the res
 prices must follow a filter. As an example, let's consider that retrieved documents must have `price` value lower
 or equal than `max_price`. We can encode this information in ElasticSearch using `filter = {'range': {'price': {'lte': max_price}}}`.
 
-Then the search with the proposed filter can be implemented and used with the following code:
+Then the search with the proposed filter can be implemented and used with the following code. 
+
+````{admonition} Note
+:class: note
+For Elasticsearch, the distance scores can be accessed in the Document's `.scores` dictionary under the key `'score'`.
+````
 
 ```python
 max_price = 7

diff --git a/docs/advanced/document-store/qdrant.md b/docs/advanced/document-store/qdrant.md
@@ -76,25 +76,26 @@ Other functions behave the same as in-memory DocumentArray.
 
 The following configs can be set:
 
-| Name                  | Description                                                                                                                              | Default                                          |
-|-----------------------|------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------|
-| `n_dim`               | Number of dimensions of embeddings to be stored and retrieved                                                                            | **This is always required**                      |
-| `collection_name`     | Qdrant collection name client                                                                                                            | **Random collection name generated**             |
-| `distance`            | Distance metric to use during search. Can be 'cosine', 'dot' or 'euclidean'                                                              | `'cosine'`                                       |
-| `host`                | Hostname of the Qdrant server                                                                                                            | `'localhost'`                                    |
-| `port`                | Port of the Qdrant server                                                                                                                | `6333`                                           |
-| `grpc_port`           | Port of the Qdrant gRPC interface                                                                                                        | `6334`                                           |
-| `prefer_grpc`         | Set `True` to use gPRC interface whenever possible in custom methods                                                                     | `False`                                          |
-| `api_key`             | API key for authentication in Qdrant Cloud                                                                                               | `None`                                           |
-| `https`               | Set `True` to use HTTPS(SSL) protocol                                                                                                    | `None`                                           |
-| `serialize_config`    | [Serialization config of each Document](../../../fundamentals/document/serialization.md)                                                 | `None`                                           |
-| `scroll_batch_size`   | Batch size used when scrolling over the storage                                                                                          | `64`                                             |
-| `ef_construct`        | Number of neighbours to consider during the index building.  Larger = more accurate search, more time to build index                     | `None`, defaults to the default value in Qdrant* |
-| `full_scan_threshold` | Minimal size (in KiloBytes) of vectors for additional payload-based indexing                                                             | `None`, defaults to the default value in Qdrant* |
-| `m`                   | Number of edges per node in the index graph. Larger = more accurate search, more space required                                          | `None`, defaults to the default value in Qdrant* |
-| `columns`             | Other fields to store in Document                                                                                                        | `None`                                           |
-| `list_like`           | Controls if ordering of Documents is persisted in the Database. Disabling this breaks list-like features, but can improve performance.   | True                                             |
-| `root_id`             | Boolean flag indicating whether to store `root_id` in the tags of chunk level Documents                                                  | True                                             |
+| Name                  | Description                                                                                                                            | Default                                          |
+|-----------------------|----------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------|
+| `n_dim`               | Number of dimensions of embeddings to be stored and retrieved                                                                          | **This is always required**                      |
+| `collection_name`     | Qdrant collection name client                                                                                                          | **Random collection name generated**             |
+| `distance`            | Distance metric to use during search. Can be 'cosine' (similarity), 'dot' or 'euclidean'                                               | `'cosine'`                                       |
+| `host`                | Hostname of the Qdrant server                                                                                                          | `'localhost'`                                    |
+| `port`                | Port of the Qdrant server                                                                                                              | `6333`                                           |
+| `grpc_port`           | Port of the Qdrant gRPC interface                                                                                                      | `6334`                                           |
+| `prefer_grpc`         | Set `True` to use gPRC interface whenever possible in custom methods                                                                   | `False`                                          |
+| `api_key`             | API key for authentication in Qdrant Cloud                                                                                             | `None`                                           |
+| `https`               | Set `True` to use HTTPS(SSL) protocol                                                                                                  | `None`                                           |
+| `serialize_config`    | [Serialization config of each Document](../../../fundamentals/document/serialization.md)                                               | `None`                                           |
+| `scroll_batch_size`   | Batch size used when scrolling over the storage                                                                                        | `64`                                             |
+| `ef_construct`        | Number of neighbours to consider during the index building.  Larger = more accurate search, more time to build index                   | `None`, defaults to the default value in Qdrant* |
+| `full_scan_threshold` | Minimal size (in KiloBytes) of vectors for additional payload-based indexing                                                           | `None`, defaults to the default value in Qdrant* |
+| `m`                   | Number of edges per node in the index graph. Larger = more accurate search, more space required                                        | `None`, defaults to the default value in Qdrant* |
+| `columns`             | Other fields to store in Document                                                                                                      | `None`                                           |
+| `list_like`           | Controls if ordering of Documents is persisted in the Database. Disabling this breaks list-like features, but can improve performance. | True                                             |
+| `root_id`             | Boolean flag indicating whether to store `root_id` in the tags of chunk level Documents                                                | True                                             |
+
 
 
 *You can read more about the HNSW parameters and their default values [here](https://qdrant.tech/documentation/indexing/#vector-index)
@@ -217,6 +218,12 @@ Embeddings Nearest Neighbours with "price" at most 7:
 	embedding=[5. 5. 5.],	 price=5
 	embedding=[4. 4. 4.],	 price=4
 ```
+
+````{admonition} Note
+:class: note
+For Qdrant, the distance scores can be accessed in the Document's `.scores` dictionary by the key `f'{distance_metric}_similarity'`. For example, for `distance = 'euclidean'` the key would be `'euclidean_similarity'`.
+````
+
 ### Example of `.filter` with a filter
 The following example shows how to use DocArray with Qdrant Document Store in order to filter text documents.
 Consider Documents have the tag `price` with a value of `i`. We can create these with the following code:

diff --git a/docs/advanced/document-store/redis.md b/docs/advanced/document-store/redis.md
@@ -204,7 +204,13 @@ Consider the case where you want the nearest vectors to the embedding `[8.,  8.,
 @price:[-inf {max_price}] @color:{color} @stock:[1 1]
 ```
 
-Then the search with the proposed filter can be used as follows:
+Then the search with the proposed filter can be used as follows. 
+
+````{admonition} Note
+:class: note
+For Redis, the distance scores can be accessed in the Document's `.scores` dictionary under the key `'score'`.
+````
+
 ```python
 max_price = 7
 color = "blue"

diff --git a/docs/advanced/document-store/weaviate.md b/docs/advanced/document-store/weaviate.md
@@ -453,10 +453,10 @@ results = da.find(
         model,
         collate_fn=collate_fn,
     ),
-    query_params={"certainty": 0.9},
+    query_params={"certainty": 0.995},
 )
 
-print("Only results that have a 'weaviate_certainty' of higher than 0.9 should show:")
+print("Only results that have a 'weaviate_certainty' of higher than 0.995 should show:")
 for res in results:
     print(f"\t text={res[:, 'text']}")
     print(f"\t scores={res[:, 'scores']}")
@@ -467,9 +467,14 @@ This should return something similar to:
 ```bash
 Only results that have a 'weaviate_certainty' of higher than 0.9 should show:
          text=['Persist Documents with Weaviate.']
-         scores=[{'weaviate_certainty': {'value': 1.0000001}, 'cosine_similarity': {'value': 1.0000002000000001}}]
+         scores=[{'distance': {'value': -3.5762787e-07}}]
 ```
 
+````{admonition} Note
+:class: note
+For Weaviate, the distance scores can be accessed in the Document's `.scores` dictionary under the key `'distance'`.
+````
+
 ## Include additional properties in the return
 
 GraphQL additional properties can be used on data objects in Get{} Queries to get additional information about the 

diff --git a/tests/unit/array/mixins/test_find.py b/tests/unit/array/mixins/test_find.py
@@ -74,31 +74,55 @@ def test_find(storage, config, limit, query, start_storage):
     # annlite uses cosine distance by default
     if n_dim == 1:
         if storage == 'weaviate':
+            distances = [t['distance'].value for t in result[:, 'scores']]
+            assert sorted(distances, reverse=False) == distances
+            assert len(distances) == limit
+        elif storage == 'qdrant':
             cosine_similarities = [
                 t['cosine_similarity'].value for t in result[:, 'scores']
             ]
-            assert sorted(cosine_similarities, reverse=False) == cosine_similarities
-        if storage == 'redis':
-            cosine_distances = [t['score'].value for t in da[:, 'scores']]
+            assert sorted(cosine_similarities, reverse=True)
+            assert len(cosine_similarities) == limit
+        elif storage == 'elasticsearch':
+            cosine_similarities = [t['score'].value for t in result[:, 'scores']]
+            assert sorted(cosine_similarities, reverse=True) == cosine_similarities
+            assert len(cosine_similarities) == limit
+        elif storage == 'redis':
+            cosine_distances = [t['score'].value for t in result[:, 'scores']]
             assert sorted(cosine_distances, reverse=False) == cosine_distances
-        elif storage in ['memory', 'annlite', 'elasticsearch']:
-            cosine_distances = [t['cosine'].value for t in da[:, 'scores']]
+            assert len(cosine_distances) == limit
+        elif storage in ['memory', 'annlite']:
+            cosine_distances = [t['cosine'].value for t in result[:, 'scores']]
             assert sorted(cosine_distances, reverse=False) == cosine_distances
+            assert len(cosine_distances) == limit
     else:
         if storage == 'weaviate':
+            for da in result:
+                distances = [t['distance'].value for t in da[:, 'scores']]
+                assert sorted(distances, reverse=False) == distances
+                assert len(distances) == limit
+        elif storage == 'qdrant':
             for da in result:
                 cosine_similarities = [
                     t['cosine_similarity'].value for t in da[:, 'scores']
                 ]
-                assert sorted(cosine_similarities, reverse=False) == cosine_similarities
-        if storage == 'redis':
+                assert sorted(cosine_similarities, reverse=True)
+                assert len(cosine_similarities) == limit
+        elif storage == 'elasticsearch':
+            for da in result:
+                cosine_similarities = [t['score'].value for t in da[:, 'scores']]
+                assert sorted(cosine_similarities, reverse=True) == cosine_similarities
+                assert len(cosine_similarities) == limit
+        elif storage == 'redis':
             for da in result:
                 cosine_distances = [t['score'].value for t in da[:, 'scores']]
                 assert sorted(cosine_distances, reverse=False) == cosine_distances
-        elif storage in ['memory', 'annlite', 'elasticsearch']:
+                assert len(cosine_distances) == limit
+        elif storage in ['memory', 'annlite']:
             for da in result:
                 cosine_distances = [t['cosine'].value for t in da[:, 'scores']]
                 assert sorted(cosine_distances, reverse=False) == cosine_distances
+                assert len(cosine_distances) == limit
 
 
 @pytest.mark.parametrize(