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activeloopai · May 11, 2023 · ddec666 · ddec666
1 parent 9f2c5c7
commit ddec666
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Showing 6 changed files with 10 additions and 12 deletions.
diff --git a/deeplake/core/dataset/dataset.py b/deeplake/core/dataset/dataset.py
@@ -2122,7 +2122,7 @@ def query(
 
         if return_indices_and_scores:
             raise ValueError(
-                "return_indices_and_scores is not supported. Please add `db_engine=True` if you want to return indices and scores"
+                "return_indices_and_scores is not supported. Please add `runtime = {'db_engine': True}` if you want to return indices and scores"
             )
 
         from deeplake.enterprise import query

diff --git a/deeplake/core/vectorstore/deeplake_vectorstore.py b/deeplake/core/vectorstore/deeplake_vectorstore.py
@@ -202,8 +202,8 @@ def delete(
         dataset_utils.delete_and_commit(self.dataset, ids)
         return True
 
-    @classmethod
-    def force_delete_by_path(cls, path: str) -> None:
+    @staticmethod
+    def force_delete_by_path(path: str) -> None:
         """Force delete dataset by path"""
         deeplake.delete(path, large_ok=True, force=True)
 

diff --git a/deeplake/core/vectorstore/test_deeplake_vectorstore.py b/deeplake/core/vectorstore/test_deeplake_vectorstore.py
@@ -17,8 +17,7 @@
 
 
 @requires_libdeeplake
-# @pytest.mark.parametrize("distance_metric", ["L1", "L2", "COS", "MAX", "DOT"])
-@pytest.mark.parametrize("distance_metric", ["L2"])
+@pytest.mark.parametrize("distance_metric", ["L1", "L2", "COS", "MAX", "DOT"])
 def test_search(distance_metric):
     k = 4
     query_embedding = np.random.randint(0, 255, (1, embedding_dim))

diff --git a/deeplake/core/vectorstore/vector_search/dataset/dataset.py b/deeplake/core/vectorstore/vector_search/dataset/dataset.py
@@ -46,7 +46,7 @@ def dataset_exists(dataset_path, token, creds, **kwargs):
 def load_dataset(dataset_path, token, creds, logger, read_only, **kwargs):
     if dataset_path == DEFAULT_DEEPLAKE_PATH:
         logger.warning(
-            f"Default deeplake path location is used: {DEFAULT_DEEPLAKE_PATH}"
+            f"The default deeplake path location is used: {DEFAULT_DEEPLAKE_PATH}"
             " and it is not free. All addtionally added data will be added on"
             " top of already existing deeplake dataset."
         )
@@ -134,9 +134,9 @@ def get_embedding(embedding, query, embedding_function=None):
                 "Either embedding array or embedding_function should be specified!"
             )
 
-        if embedding is not None:
-            always_warn("both embedding and embedding_function are specified. ")
-        embedding = embedding_function.embed_query(query)  # type: ignore
+    if embedding is not None and embedding_function is not None:
+        always_warn("both embedding and embedding_function are specified. ")
+    embedding = embedding_function.embed_query(query)  # type: ignore
 
     if embedding.dtype != "float32":
         embedding = np.array(embedding, dtype=np.float32)

diff --git a/deeplake/core/vectorstore/vector_search/filter/filter.py b/deeplake/core/vectorstore/vector_search/filter/filter.py
@@ -17,7 +17,7 @@ def dp_filter(x: dict, filter: Dict[str, str]) -> bool:
 
 
 def attribute_based_filtering(view, filter, exec_option):
-    filtering_execption(filter=filter, exec_option=exec_option)
+    filtering_exception(filter=filter, exec_option=exec_option)
     # attribute based filtering
     if filter is not None:
         if isinstance(filter, dict):
@@ -29,7 +29,7 @@ def attribute_based_filtering(view, filter, exec_option):
     return view
 
 
-def filtering_execption(filter, exec_option):
+def filtering_exception(filter, exec_option):
     if exec_option in ("indra", "db_engine") and filter is not None:
         case_specific_exception = ""
         if "db_engine":

diff --git a/deeplake/core/vectorstore/vector_search/python/vector_search.py b/deeplake/core/vectorstore/vector_search/python/vector_search.py
@@ -9,7 +9,6 @@
     "max": lambda a, b: np.linalg.norm(a - b, axis=1, ord=np.inf),
     "cos": lambda a, b: np.dot(a, b.T)
     / (np.linalg.norm(a) * np.linalg.norm(b, axis=1)),
-    "dot": lambda a, b: np.dot(a, b.T),
 }