SNOW-1856438: Directly support Snowflake Cortex functions Summarize and Sentiment with apply

CHANGELOG.md

@@ -1,5 +1,20 @@
 # Release History
 
+## 1.28.0 (TBD)
+
+### Snowpark Python API Updates
+
+#### Deprecations:
+
+- Deprecated Snowpark Python function `snowflake_cortex_summarize`. Users can install snowflake-ml-python and use the snowflake.cortex.summarize function instead.
+- Deprecated Snowpark Python function `snowflake_cortex_sentiment`. Users can install snowflake-ml-python and use the snowflake.cortex.sentiment function instead.
+
+### Snowpark pandas API Updates
+
+#### New Features
+
+- Added support for applying Snowflake Cortex functions `Summarize` and `Sentiment`.
+
 ## 1.27.0 (2025-02-03)
 
 ### Snowpark Python API Updates

setup.py

@@ -202,6 +202,7 @@ def run(self):
             "scikit-learn",  # Snowpark pandas 3rd party library testing
             # plotly version restricted due to foreseen change in query counts in version 6.0.0+
             "plotly<6.0.0",  # Snowpark pandas 3rd party library testing
+            "snowflake-ml-python",
         ],
         "localtest": [
             "pandas",

src/snowflake/snowpark/functions.py

@@ -212,6 +212,7 @@
     publicapi,
     validate_object_name,
     check_create_map_parameter,
+    deprecated,
 )
 from snowflake.snowpark.column import (
     CaseExpr,
@@ -10779,13 +10780,16 @@ def make_interval(
     return res
 
 
+@deprecated(
+    version="1.28.0",
+    extra_warning_text="Please consider installing snowflake-ml-python and using `snowflake.cortex.summarize` instead.",
+    extra_doc_string="Use :meth:`snowflake.cortex.summarize` instead.",
+)
 def snowflake_cortex_summarize(text: ColumnOrLiteralStr):
     """
     Summarizes the given English-language input text.
-
     Args:
         text: A string containing the English text from which a summary should be generated.
-
     Returns:
         A string containing a summary of the original text.
     """
@@ -10794,10 +10798,14 @@ def snowflake_cortex_summarize(text: ColumnOrLiteralStr):
     return builtin(sql_func_name)(text_col)
 
 
+@deprecated(
+    version="1.28.0",
+    extra_warning_text="Please consider installing snowflake-ml-python and using `snowflake.cortex.sentiment` instead.",
+    extra_doc_string="Use :meth:`snowflake.cortex.sentiment` instead.",
+)
 def snowflake_cortex_sentiment(text: ColumnOrLiteralStr):
     """
     A string containing the text for which a sentiment score should be calculated.
-
     Args:
         text: A string containing the English text from which a summary should be generated.
     Returns:

src/snowflake/snowpark/modin/plugin/_internal/apply_utils.py

@@ -97,10 +97,23 @@
     sp_func.floor,
     sp_func.trunc,
     sp_func.sqrt,
-    sp_func.snowflake_cortex_summarize,
-    sp_func.snowflake_cortex_sentiment,
 }
 
+try:
+    import snowflake.cortex
+
+    SUPPORTED_SNOWFLAKE_CORTEX_FUNCTIONS_IN_APPLY = {
+        snowflake.cortex.Summarize,
+        snowflake.cortex.Sentiment,
+    }
+
+    ALL_SNOWFLAKE_CORTEX_FUNCTIONS = tuple(
+        i[1] for i in inspect.getmembers(snowflake.cortex)
+    )
+except ImportError:
+    SUPPORTED_SNOWFLAKE_CORTEX_FUNCTIONS_IN_APPLY = set()
+    ALL_SNOWFLAKE_CORTEX_FUNCTIONS = tuple()
+
 
 class GroupbyApplySortMethod(Enum):
     """

src/snowflake/snowpark/modin/plugin/compiler/snowflake_query_compiler.py

@@ -202,6 +202,8 @@
     is_supported_snowpark_python_function,
     sort_apply_udtf_result_columns_by_pandas_positions,
     make_series_map_snowpark_function,
+    SUPPORTED_SNOWFLAKE_CORTEX_FUNCTIONS_IN_APPLY,
+    ALL_SNOWFLAKE_CORTEX_FUNCTIONS,
 )
 from collections import defaultdict
 from snowflake.snowpark.modin.plugin._internal.binary_op_utils import (
@@ -8471,6 +8473,29 @@ def apply(
                 )
             return self._apply_snowpark_python_function_to_columns(func, kwargs)
 
+        if func in SUPPORTED_SNOWFLAKE_CORTEX_FUNCTIONS_IN_APPLY:
+            if axis != 0:
+                ErrorMessage.not_implemented(
+                    f"Snowpark pandas apply API doesn't yet support Snowflake Cortex function `{func.__name__}` with with axis = {axis}.'"
+                )
+            if raw is not False:
+                ErrorMessage.not_implemented(
+                    f"Snowpark pandas apply API doesn't yet support Snowflake Cortex function `{func.__name__}`with raw = {raw}."
+                )
+            if args:
+                ErrorMessage.not_implemented(
+                    f"Snowpark pandas apply API doesn't yet support Snowflake Cortex function `{func.__name__}` with args == '{args}'"
+                )
+            if kwargs:
+                ErrorMessage.not_implemented(
+                    f"Snowpark pandas apply API doesn't yet support Snowflake Cortex function `{func.__name__}` with kwargs == '{kwargs}'"
+                )
+            return self._apply_snowflake_cortex_function_to_columns(func)
+        elif func in ALL_SNOWFLAKE_CORTEX_FUNCTIONS:
+            ErrorMessage.not_implemented(
+                f"Snowpark pandas apply API doesn't yet support Snowflake Cortex function `{func.__name__}`"
+            )
+
         sf_func = NUMPY_UNIVERSAL_FUNCTION_TO_SNOWFLAKE_FUNCTION.get(func)
         if sf_func is not None:
             return self._apply_snowpark_python_function_to_columns(sf_func, kwargs)
@@ -8786,6 +8811,22 @@ def sf_function(col: SnowparkColumn) -> SnowparkColumn:
             self._modin_frame.apply_snowpark_function_to_columns(sf_function)
         )
 
+    def _apply_snowflake_cortex_function_to_columns(
+        self,
+        snowflake_function: Callable,
+    ) -> "SnowflakeQueryCompiler":
+        """Apply Snowflake Cortex function to columns."""
+
+        def sf_function(col: SnowparkColumn) -> SnowparkColumn:
+            resolved_positional = []
+            resolved_positional.append(col)
+
+            return snowflake_function(*resolved_positional)
+
+        return SnowflakeQueryCompiler(
+            self._modin_frame.apply_snowpark_function_to_columns(sf_function)
+        )
+
     def applymap(
         self,
         func: AggFuncType,
@@ -8819,6 +8860,25 @@ def applymap(
                 )
             return self._apply_snowpark_python_function_to_columns(func, kwargs)
 
+        if func in SUPPORTED_SNOWFLAKE_CORTEX_FUNCTIONS_IN_APPLY:
+            if na_action:
+                ErrorMessage.not_implemented(
+                    f"Snowpark pandas applymap API doesn't yet support Snowflake Cortex function `{func.__name__}` with na_action == '{na_action}'"
+                )
+            if args:
+                ErrorMessage.not_implemented(
+                    f"Snowpark pandas applymap API doesn't yet support Snowflake Cortex function `{func.__name__}` with args == '{args}'"
+                )
+            if kwargs:
+                ErrorMessage.not_implemented(
+                    f"Snowpark pandas applymap API doesn't yet support Snowflake Cortex function `{func.__name__}` with kwargs == '{kwargs}'"
+                )
+            return self._apply_snowflake_cortex_function_to_columns(func)
+        elif func in ALL_SNOWFLAKE_CORTEX_FUNCTIONS:
+            ErrorMessage.not_implemented(
+                f"Snowpark pandas apply API doesn't yet support Snowflake Cortex function `{func.__name__}`"
+            )
+
         # Check if the function is a known numpy function that can be translated
         # to Snowflake function.
         sf_func = NUMPY_UNIVERSAL_FUNCTION_TO_SNOWFLAKE_FUNCTION.get(func)

tests/integ/modin/test_apply_snowflake_cortex_functions.py

@@ -0,0 +1,149 @@
+#
+# Copyright (c) 2012-2025 Snowflake Computing Inc. All rights reserved.
+#
+
+import modin.pandas as pd
+import pytest
+from pytest import param
+
+
+from tests.integ.utils.sql_counter import SqlCounter, sql_count_checker
+from tests.utils import running_on_jenkins
+from snowflake.cortex import Sentiment, Summarize, Translate
+
+
+@pytest.mark.skipif(
+    running_on_jenkins(),
+    reason="TODO: SNOW-1859087 snowflake.cortex.summarize SSL error",
+)
+def test_apply_snowflake_cortex_summarize(session):
+
+    # TODO: SNOW-1758914 snowflake.cortex.summarize error on GCP
+    with SqlCounter(query_count=0):
+        if session.connection.host == "sfctest0.us-central1.gcp.snowflakecomputing.com":
+            return
+
+    with SqlCounter(query_count=1):
+        content = """pandas on Snowflake lets you run your pandas code in a distributed manner directly on your data in
+        Snowflake. Just by changing the import statement and a few lines of code, you can get the familiar pandas experience
+        you know and love with the scalability and security benefits of Snowflake. With pandas on Snowflake, you can work
+        with much larger datasets and avoid the time and expense of porting your pandas pipelines to other big data
+        frameworks or provisioning large and expensive machines. It runs workloads natively in Snowflake through
+        transpilation to SQL, enabling it to take advantage of parallelization and the data governance and security
+        benefits of Snowflake. pandas on Snowflake is delivered through the Snowpark pandas API as part of the Snowpark
+        Python library, which enables scalable data processing of Python code within the Snowflake platform.
+        """
+        s = pd.Series([content])
+        summary = s.apply(Summarize).iloc[0]
+        # this length check is to get around the fact that this function may not be deterministic
+        assert 0 < len(summary) < len(content)
+
+
+@pytest.mark.skipif(
+    running_on_jenkins(),
+    reason="TODO: SNOW-1859087 snowflake.cortex.sentiment SSL error",
+)
+def test_apply_snowflake_cortex_sentiment_series(session):
+
+    # TODO: SNOW-1758914 snowflake.cortex.sentiment error on GCP
+    with SqlCounter(query_count=0):
+        if session.connection.host == "sfctest0.us-central1.gcp.snowflakecomputing.com":
+            return
+
+    with SqlCounter(query_count=1):
+        content = "A very very bad review!"
+        s = pd.Series([content])
+        sentiment = s.apply(Sentiment).iloc[0]
+        assert -1 <= sentiment <= 0
+
+
+def test_apply_snowflake_cortex_sentiment_df(session):
+
+    # TODO: SNOW-1758914 snowflake.cortex.sentiment error on GCP
+    with SqlCounter(query_count=0):
+        if session.connection.host == "sfctest0.us-central1.gcp.snowflakecomputing.com":
+            return
+    text_list = [
+        "A first row of text.",
+        "This is a very bad test.",
+        "This is the best test ever.",
+    ]
+
+    content_frame = pd.DataFrame(text_list, columns=["content"])
+    with SqlCounter(query_count=4):
+        res = content_frame.apply(Sentiment)
+        sent_row_2 = res["content"][1]
+        sent_row_3 = res["content"][2]
+        assert -1 <= sent_row_2 <= 0
+        assert 0 <= sent_row_3 <= 1
+
+
+@pytest.mark.skipif(
+    running_on_jenkins(),
+    reason="TODO: SNOW-1859087 snowflake.cortex.sentiment SSL error",
+)
+@sql_count_checker(query_count=0)
+@pytest.mark.parametrize(
+    "is_series, operation",
+    [
+        param(
+            True,
+            (lambda s: s.apply(Translate, source_language="en", target_language="de")),
+            id="series_cortex_unsupported_function_translate",
+        ),
+        param(
+            False,
+            (
+                lambda df: df.apply(
+                    Translate, source_language="en", target_language="de"
+                )
+            ),
+            id="df_cortex_unsupported_function_translate",
+        ),
+        param(
+            True,
+            (lambda s: s.apply(Sentiment, args=("hello",))),
+            id="series_cortex_unsupported_args",
+        ),
+        param(
+            False,
+            (lambda df: df.apply(Sentiment, args=("hello",))),
+            id="df_cortex_unsupported_args",
+        ),
+        param(
+            True,
+            (lambda s: s.apply(Sentiment, extra="hello")),
+            id="series_cortex_unsupported_kwargs",
+        ),
+        param(
+            False,
+            (lambda df: df.apply(Sentiment, extra="hello")),
+            id="df_cortex_unsupported_kwargs",
+        ),
+        param(
+            True,
+            (lambda s: s.apply(Sentiment, na_action="ignore")),
+            id="series_cortex_unsupported_na_action",
+        ),
+        param(
+            False,
+            (lambda df: df.apply(Sentiment, raw=True)),
+            id="df_cortex_unsupported_raw",
+        ),
+        param(
+            False,
+            (lambda df: df.apply(Sentiment, axis=1)),
+            id="df_cortex_unsupported_axis_1",
+        ),
+    ],
+)
+def test_apply_snowflake_cortex_negative(session, is_series, operation):
+
+    # TODO: SNOW-1758914 snowflake.cortex.sentiment error on GCP
+    if session.connection.host == "sfctest0.us-central1.gcp.snowflakecomputing.com":
+        return
+
+    content = "One day I will see the world."
+    modin_input = (pd.Series if is_series else pd.DataFrame)([content])
+    with pytest.raises(NotImplementedError):
+        operation(modin_input)

tests/integ/modin/test_apply_snowpark_python_functions.py

@@ -10,8 +10,7 @@
 import pytest
 
 from tests.integ.modin.utils import assert_frame_equal, assert_series_equal
-from tests.integ.utils.sql_counter import sql_count_checker, SqlCounter
-from tests.utils import running_on_jenkins
+from tests.integ.utils.sql_counter import sql_count_checker
 
 
 @sql_count_checker(query_count=4)
@@ -70,50 +69,3 @@ def test_apply_snowpark_python_function_not_implemented():
         pd.DataFrame({"a": [1, 2, 3]}).apply(asc, axis=1)
     with pytest.raises(NotImplementedError):
         pd.DataFrame({"a": [1, 2, 3]}).apply(asc, args=(1, 2))
-
-
-@pytest.mark.skipif(
-    running_on_jenkins(),
-    reason="TODO: SNOW-1859087 snowflake.cortex.summarize SSL error",
-)
-def test_apply_snowflake_cortex_summarize(session):
-    from snowflake.snowpark.functions import snowflake_cortex_summarize
-
-    # TODO: SNOW-1758914 snowflake.cortex.summarize error on GCP
-    with SqlCounter(query_count=0):
-        if session.connection.host == "sfctest0.us-central1.gcp.snowflakecomputing.com":
-            return
-
-    with SqlCounter(query_count=1):
-        content = """pandas on Snowflake lets you run your pandas code in a distributed manner directly on your data in
-        Snowflake. Just by changing the import statement and a few lines of code, you can get the familiar pandas experience
-        you know and love with the scalability and security benefits of Snowflake. With pandas on Snowflake, you can work
-        with much larger datasets and avoid the time and expense of porting your pandas pipelines to other big data
-        frameworks or provisioning large and expensive machines. It runs workloads natively in Snowflake through
-        transpilation to SQL, enabling it to take advantage of parallelization and the data governance and security
-        benefits of Snowflake. pandas on Snowflake is delivered through the Snowpark pandas API as part of the Snowpark
-        Python library, which enables scalable data processing of Python code within the Snowflake platform.
-        """
-        s = pd.Series([content])
-        summary = s.apply(snowflake_cortex_summarize).iloc[0]
-        # this length check is to get around the fact that this function may not be deterministic
-        assert 0 < len(summary) < len(content)
-
-
-@pytest.mark.skipif(
-    running_on_jenkins(),
-    reason="TODO: SNOW-1859087 snowflake.cortex.sentiment SSL error",
-)
-def test_apply_snowflake_cortex_sentiment(session):
-    from snowflake.snowpark.functions import snowflake_cortex_sentiment
-
-    # TODO: SNOW-1758914 snowflake.cortex.sentiment error on GCP
-    with SqlCounter(query_count=0):
-        if session.connection.host == "sfctest0.us-central1.gcp.snowflakecomputing.com":
-            return
-
-    with SqlCounter(query_count=1):
-        content = "A very very bad review!"
-        s = pd.Series([content])
-        sentiment = s.apply(snowflake_cortex_sentiment).iloc[0]
-        assert -1 <= sentiment <= 0