community[patch]: activeloop ai tql deprecation (#14634)

Co-authored-by: AdkSarsen <adilkhan@activeloop.ai>

libs/community/langchain_community/vectorstores/deeplake.py

@@ -51,6 +51,7 @@ class DeepLake(VectorStore):
     """
 
     _LANGCHAIN_DEFAULT_DEEPLAKE_PATH = "./deeplake/"
+    _valid_search_kwargs = ["lambda_mult"]
 
     def __init__(
         self,
@@ -219,11 +220,7 @@ class DeepLake(VectorStore):
         Returns:
             List[str]: List of IDs of the added texts.
         """
-        if kwargs:
-            unsupported_items = "`, `".join(set(kwargs.keys()))
-            raise TypeError(
-                f"`{unsupported_items}` is/are not a valid argument to add_text method"
-            )
+        self._validate_kwargs(kwargs, "add_texts")
 
         kwargs = {}
         if ids:
@@ -371,6 +368,9 @@ class DeepLake(VectorStore):
         Raises:
             ValueError: if both `embedding` and `embedding_function` are not specified.
         """
+        if kwargs.get("tql_query"):
+            logger.warning("`tql_query` is deprecated. Please use `tql` instead.")
+            kwargs["tql"] = kwargs.pop("tql_query")
 
         if kwargs.get("tql"):
             return self._search_tql(
@@ -384,6 +384,8 @@ class DeepLake(VectorStore):
                 filter=filter,
             )
 
+        self._validate_kwargs(kwargs, "search")
+
         if embedding_function:
             if isinstance(embedding_function, Embeddings):
                 _embedding_function = embedding_function.embed_query
@@ -417,7 +419,6 @@ class DeepLake(VectorStore):
             return_tensors=["embedding", "metadata", "text", self._id_tensor_name],
             deep_memory=deep_memory,
         )
-
         scores = result["score"]
         embeddings = result["embedding"]
         metadatas = result["metadata"]
@@ -445,6 +446,9 @@ class DeepLake(VectorStore):
         ]
 
         if return_score:
+            if not isinstance(scores, list):
+                scores = [scores]
+
             return [(doc, score) for doc, score in zip(docs, scores)]
 
         return docs
@@ -899,3 +903,30 @@ class DeepLake(VectorStore):
             "better to use `db.vectorstore.dataset` instead."
         )
         return self.vectorstore.dataset
+
+    @classmethod
+    def _validate_kwargs(cls, kwargs, method_name):
+        if kwargs:
+            valid_items = cls._get_valid_args(method_name)
+            unsupported_items = cls._get_unsupported_items(kwargs, valid_items)
+
+            if unsupported_items:
+                raise TypeError(
+                    f"`{unsupported_items}` are not a valid "
+                    f"argument to {method_name} method"
+                )
+
+    @classmethod
+    def _get_valid_args(cls, method_name):
+        if method_name == "search":
+            return cls._valid_search_kwargs
+        else:
+            return []
+
+    @staticmethod
+    def _get_unsupported_items(kwargs, valid_items):
+        kwargs = {k: v for k, v in kwargs.items() if k not in valid_items}
+        unsupported_items = None
+        if kwargs:
+            unsupported_items = "`, `".join(set(kwargs.keys()))
+        return unsupported_items

libs/community/tests/integration_tests/vectorstores/test_deeplake.py

@@ -18,7 +18,9 @@ def deeplake_datastore() -> DeepLake:
         embedding_function=FakeEmbeddings(),
         overwrite=True,
     )
-    return docsearch
+    yield docsearch
+
+    docsearch.delete_dataset()
 
 
 @pytest.fixture(params=["L1", "L2", "max", "cos"])
@@ -50,27 +52,14 @@ def test_deeplake_with_metadatas() -> None:
     assert output == [Document(page_content="foo", metadata={"page": "0"})]
 
 
-def test_deeplakewith_persistence() -> None:
+def test_deeplake_with_persistence(deeplake_datastore) -> None:
     """Test end to end construction and search, with persistence."""
-    import deeplake
-
-    dataset_path = "./tests/persist_dir"
-    if deeplake.exists(dataset_path):
-        deeplake.delete(dataset_path)
-
-    texts = ["foo", "bar", "baz"]
-    docsearch = DeepLake.from_texts(
-        dataset_path=dataset_path,
-        texts=texts,
-        embedding=FakeEmbeddings(),
-    )
-
-    output = docsearch.similarity_search("foo", k=1)
-    assert output == [Document(page_content="foo")]
+    output = deeplake_datastore.similarity_search("foo", k=1)
+    assert output == [Document(page_content="foo", metadata={"page": "0"})]
 
     # Get a new VectorStore from the persisted directory
     docsearch = DeepLake(
-        dataset_path=dataset_path,
+        dataset_path=deeplake_datastore.vectorstore.dataset_handler.path,
         embedding_function=FakeEmbeddings(),
     )
     output = docsearch.similarity_search("foo", k=1)
@@ -83,22 +72,12 @@ def test_deeplakewith_persistence() -> None:
     # Or on program exit
 
 
-def test_deeplake_overwrite_flag() -> None:
+def test_deeplake_overwrite_flag(deeplake_datastore) -> None:
     """Test overwrite behavior"""
-    import deeplake
+    dataset_path = deeplake_datastore.vectorstore.dataset_handler.path
 
-    dataset_path = "./tests/persist_dir"
-    if deeplake.exists(dataset_path):
-        deeplake.delete(dataset_path)
-
-    texts = ["foo", "bar", "baz"]
-    docsearch = DeepLake.from_texts(
-        dataset_path=dataset_path,
-        texts=texts,
-        embedding=FakeEmbeddings(),
-    )
-    output = docsearch.similarity_search("foo", k=1)
-    assert output == [Document(page_content="foo")]
+    output = deeplake_datastore.similarity_search("foo", k=1)
+    assert output == [Document(page_content="foo", metadata={"page": "0"})]
 
     # Get a new VectorStore from the persisted directory, with no overwrite (implicit)
     docsearch = DeepLake(
@@ -107,7 +86,7 @@ def test_deeplake_overwrite_flag() -> None:
     )
     output = docsearch.similarity_search("foo", k=1)
     # assert page still present
-    assert output == [Document(page_content="foo")]
+    assert output == [Document(page_content="foo", metadata={"page": "0"})]
 
     # Get a new VectorStore from the persisted directory, with no overwrite (explicit)
     docsearch = DeepLake(
@@ -117,7 +96,7 @@ def test_deeplake_overwrite_flag() -> None:
     )
     output = docsearch.similarity_search("foo", k=1)
     # assert page still present
-    assert output == [Document(page_content="foo")]
+    assert output == [Document(page_content="foo", metadata={"page": "0"})]
 
     # Get a new VectorStore from the persisted directory, with overwrite
     docsearch = DeepLake(
@@ -129,8 +108,9 @@ def test_deeplake_overwrite_flag() -> None:
         output = docsearch.similarity_search("foo", k=1)
 
 
-def test_similarity_search(deeplake_datastore: DeepLake, distance_metric: str) -> None:
+def test_similarity_search(deeplake_datastore) -> None:
     """Test similarity search."""
+    distance_metric = "cos"
     output = deeplake_datastore.similarity_search(
         "foo", k=1, distance_metric=distance_metric
     )
@@ -145,7 +125,6 @@ def test_similarity_search(deeplake_datastore: DeepLake, distance_metric: str) -
         query="foo", tql_query=tql_query, k=1, distance_metric=distance_metric
     )
     assert len(output) == 1
-    deeplake_datastore.delete_dataset()
 
 
 def test_similarity_search_by_vector(
@@ -164,6 +143,7 @@ def test_similarity_search_with_score(
     deeplake_datastore: DeepLake, distance_metric: str
 ) -> None:
     """Test similarity search with score."""
+    deeplake_datastore.vectorstore.summary()
     output, score = deeplake_datastore.similarity_search_with_score(
         "foo", k=1, distance_metric=distance_metric
     )[0]
@@ -281,3 +261,11 @@ def test_ids_backwards_compatibility() -> None:
     )
     output = db.similarity_search("foo", k=1)
     assert len(output) == 1
+
+
+def test_similarity_search_should_error_out_when_not_supported_kwargs_are_provided(
+    deeplake_datastore: DeepLake,
+) -> None:
+    """Test that ids are backwards compatible."""
+    with pytest.raises(TypeError):
+        deeplake_datastore.similarity_search("foo", k=1, not_supported_kwarg=True)