Merge branch 'main' into demo/chart-gallery

vizro-ai/changelog.d/20240626_224646_anna_xiong_azure_openai.md

@@ -0,0 +1,48 @@
+<!--
+A new scriv changelog fragment.
+
+Uncomment the section that is right (remove the HTML comment wrapper).
+-->
+
+<!--
+### Highlights ✨
+
+- A bullet item for the Highlights ✨ category with a link to the relevant PR at the end of your entry, e.g. Enable feature XXX ([#1](https://github.com/mckinsey/vizro/pull/1))
+
+-->
+<!--
+### Removed
+
+- A bullet item for the Removed category with a link to the relevant PR at the end of your entry, e.g. Enable feature XXX ([#1](https://github.com/mckinsey/vizro/pull/1))
+
+-->
+<!--
+### Added
+
+- A bullet item for the Added category with a link to the relevant PR at the end of your entry, e.g. Enable feature XXX ([#1](https://github.com/mckinsey/vizro/pull/1))
+
+-->
+<!--
+### Changed
+
+- A bullet item for the Changed category with a link to the relevant PR at the end of your entry, e.g. Enable feature XXX ([#1](https://github.com/mckinsey/vizro/pull/1))
+
+-->
+<!--
+### Deprecated
+
+- A bullet item for the Deprecated category with a link to the relevant PR at the end of your entry, e.g. Enable feature XXX ([#1](https://github.com/mckinsey/vizro/pull/1))
+
+-->
+<!--
+### Fixed
+
+- A bullet item for the Fixed category with a link to the relevant PR at the end of your entry, e.g. Enable feature XXX ([#1](https://github.com/mckinsey/vizro/pull/1))
+
+-->
+<!--
+### Security
+
+- A bullet item for the Security category with a link to the relevant PR at the end of your entry, e.g. Enable feature XXX ([#1](https://github.com/mckinsey/vizro/pull/1))
+
+-->

vizro-ai/src/vizro_ai/chains/_llm_models.py

@@ -3,41 +3,24 @@
 from langchain_core.language_models.chat_models import BaseChatModel
 from langchain_openai import ChatOpenAI
 
-# TODO constant of model inventory, can be converted to yaml and link to docs
-PREDEFINED_MODELS: Dict[str, Dict[str, Union[int, BaseChatModel]]] = {
-    "gpt-3.5-turbo-0613": {
-        "max_tokens": 4096,
-        "wrapper": ChatOpenAI,
-    },
-    "gpt-4-0613": {
-        "max_tokens": 8192,
-        "wrapper": ChatOpenAI,
-    },
-    "gpt-3.5-turbo-1106": {
-        "max_tokens": 16385,
-        "wrapper": ChatOpenAI,
-    },
-    "gpt-4-1106-preview": {
-        "max_tokens": 128000,
-        "wrapper": ChatOpenAI,
-    },
-    "gpt-3.5-turbo-0125": {
-        "max_tokens": 16385,
-        "wrapper": ChatOpenAI,
-    },
-    "gpt-3.5-turbo": {
-        "max_tokens": 16385,
-        "wrapper": ChatOpenAI,
-    },
-    "gpt-4-turbo": {
-        "max_tokens": 128000,
-        "wrapper": ChatOpenAI,
-    },
+SUPPORTED_MODELS = {
+    "OpenAI": [
+        "gpt-4-0613",
+        "gpt-3.5-turbo-1106",
+        "gpt-4-1106-preview",
+        "gpt-3.5-turbo-0125",
+        "gpt-3.5-turbo",
+        "gpt-4-turbo",
+        "gpt-4o",
+    ]
 }
 
+DEFAULT_WRAPPER_MAP: Dict[str, BaseChatModel] = {"OpenAI": ChatOpenAI}
 DEFAULT_MODEL = "gpt-3.5-turbo"
 DEFAULT_TEMPERATURE = 0
 
+model_to_vendor = {model: key for key, models in SUPPORTED_MODELS.items() for model in models}
+
 
 def _get_llm_model(model: Optional[Union[ChatOpenAI, str]] = None) -> BaseChatModel:
     """Fetches and initializes an instance of the LLM.
@@ -54,14 +37,21 @@ def _get_llm_model(model: Optional[Union[ChatOpenAI, str]] = None) -> BaseChatMo
     """
     if not model:
         return ChatOpenAI(model_name=DEFAULT_MODEL, temperature=DEFAULT_TEMPERATURE)
-    if isinstance(model, ChatOpenAI):
+
+    if isinstance(model, BaseChatModel):
         return model
-    if isinstance(model, str) and model in PREDEFINED_MODELS:
-        return PREDEFINED_MODELS.get(model)["wrapper"](model_name=model, temperature=DEFAULT_TEMPERATURE)
+
+    if isinstance(model, str):
+        if any(model in model_list for model_list in SUPPORTED_MODELS.values()):
+            vendor = model_to_vendor[model]
+            return DEFAULT_WRAPPER_MAP.get(vendor)(model_name=model, temperature=DEFAULT_TEMPERATURE)
+
     raise ValueError(
         f"Model {model} not found! List of available model can be found at https://vizro.readthedocs.io/projects/vizro-ai/en/latest/pages/explanation/faq/#which-llms-are-supported-by-vizro-ai"
     )
 
 
 if __name__ == "__main__":
-    llm_chat_openai = _get_llm_model()
+    llm_chat_openai = _get_llm_model(model="gpt-3.5-turbo")
+    print(repr(llm_chat_openai))  # noqa: T201
+    print(llm_chat_openai.model_name)  # noqa: T201

vizro-core/changelog.d/20240703_091955_huong_li_nguyen_fix_selector_return_type.md

@@ -0,0 +1,47 @@
+<!--
+A new scriv changelog fragment.
+
+Uncomment the section that is right (remove the HTML comment wrapper).
+-->
+
+<!--
+### Highlights ✨
+
+- A bullet item for the Highlights ✨ category with a link to the relevant PR at the end of your entry, e.g. Enable feature XXX ([#1](https://github.com/mckinsey/vizro/pull/1))
+
+-->
+<!--
+### Removed
+
+- A bullet item for the Removed category with a link to the relevant PR at the end of your entry, e.g. Enable feature XXX ([#1](https://github.com/mckinsey/vizro/pull/1))
+
+-->
+<!--
+### Added
+
+- A bullet item for the Added category with a link to the relevant PR at the end of your entry, e.g. Enable feature XXX ([#1](https://github.com/mckinsey/vizro/pull/1))
+
+-->
+<!--
+### Changed
+
+- A bullet item for the Changed category with a link to the relevant PR at the end of your entry, e.g. Enable feature XXX ([#1](https://github.com/mckinsey/vizro/pull/1))
+
+-->
+<!--
+### Deprecated
+
+- A bullet item for the Deprecated category with a link to the relevant PR at the end of your entry, e.g. Enable feature XXX ([#1](https://github.com/mckinsey/vizro/pull/1))
+
+-->
+
+### Fixed
+
+- Ensure that categorical selectors always return a list of values. ([#562](https://github.com/mckinsey/vizro/pull/562))
+
+<!--
+### Security
+
+- A bullet item for the Security category with a link to the relevant PR at the end of your entry, e.g. Enable feature XXX ([#1](https://github.com/mckinsey/vizro/pull/1))
+
+-->

vizro-core/src/vizro/actions/_actions_utils.py

@@ -138,12 +138,19 @@ def _get_parametrized_config(target: ModelID, ctd_parameters: List[CallbackTrigg
     config["data_frame"] = {}
 
     for ctd in ctd_parameters:
-        selector_value = ctd[
-            "value"
-        ]  # TODO: needs to be refactored so that it is independent of implementation details
+        # TODO: needs to be refactored so that it is independent of implementation details
+        selector_value = ctd["value"]
+
         if hasattr(selector_value, "__iter__") and ALL_OPTION in selector_value:  # type: ignore[operator]
             selector: SelectorType = model_manager[ctd["id"]]
-            selector_value = selector.options
+
+            # Even if options are provided as List[Dict], the Dash component only returns a List of values.
+            # So we need to ensure that we always return a List only as well to provide consistent types.
+            if all(isinstance(option, dict) for option in selector.options):
+                selector_value = [option["value"] for option in selector.options]
+            else:
+                selector_value = selector.options
+
         selector_value = _validate_selector_value_none(selector_value)
         selector_actions = _get_component_actions(model_manager[ctd["id"]])
 

vizro-core/src/vizro/models/_action/_action.py

@@ -105,7 +105,7 @@ def _action_callback_function(
     ) -> Any:
         logger.debug("===== Running action with id %s, function %s =====", self.id, self.function._function.__name__)
         if logger.isEnabledFor(logging.DEBUG):
-            logger.debug("Action inputs:\n%s", pformat(inputs, depth=2, width=200))
+            logger.debug("Action inputs:\n%s", pformat(inputs, depth=3, width=200))
             logger.debug("Action outputs:\n%s", pformat(outputs, width=200))
 
         if isinstance(inputs, Mapping):

vizro-core/tests/unit/vizro/actions/conftest.py

@@ -10,6 +10,11 @@ def gapminder_2007(gapminder):
     return gapminder.query("year == 2007")
 
 
+@pytest.fixture
+def iris():
+    return px.data.iris()
+
+
 @pytest.fixture
 def gapminder_dynamic_first_n_last_n_function(gapminder):
     return lambda first_n=None, last_n=None: (
@@ -44,6 +49,16 @@ def scatter_chart(gapminder_2007, scatter_params):
     return px.scatter(gapminder_2007, **scatter_params).update_layout(margin_t=24)
 
 
+@pytest.fixture
+def scatter_matrix_params():
+    return {"dimensions": ["sepal_width", "sepal_length", "petal_width", "petal_length"]}
+
+
+@pytest.fixture
+def scatter_matrix_chart(iris, scatter_matrix_params):
+    return px.scatter_matrix(iris, **scatter_matrix_params).update_layout(margin_t=24)
+
+
 @pytest.fixture
 def scatter_chart_dynamic_data_frame(scatter_params):
     return px.scatter("gapminder_dynamic_first_n_last_n", **scatter_params).update_layout(margin_t=24)
@@ -110,3 +125,16 @@ def managers_one_page_two_graphs_one_table_one_aggrid_one_button(
         ],
     )
     Vizro._pre_build()
+
+
+@pytest.fixture
+def managers_one_page_one_graph_with_dict_param_input(scatter_matrix_chart):
+    """Instantiates a model_manager and data_manager with a page and a graph that requires a list input."""
+    vm.Page(
+        id="test_page",
+        title="My first dashboard",
+        components=[
+            vm.Graph(id="scatter_matrix_chart", figure=scatter_matrix_chart),
+        ],
+    )
+    Vizro._pre_build()

vizro-core/tests/unit/vizro/actions/test_parameter_action.py

@@ -15,6 +15,13 @@ def target_scatter_parameter_y(request, gapminder_2007, scatter_params):
     return px.scatter(gapminder_2007, **scatter_params).update_layout(margin_t=24)
 
 
+@pytest.fixture
+def target_scatter_matrix_parameter_dimensions(request, iris, scatter_matrix_params):
+    dimensions = request.param
+    scatter_matrix_params["dimensions"] = dimensions
+    return px.scatter_matrix(iris, **scatter_matrix_params).update_layout(margin_t=24)
+
+
 @pytest.fixture
 def target_scatter_parameter_hover_data(request, gapminder_2007, scatter_params):
     hover_data = request.param
@@ -95,6 +102,38 @@ def ctx_parameter_y(request):
     return context_value
 
 
+@pytest.fixture
+def ctx_parameter_dimensions(request):
+    """Mock dash.ctx that represents `dimensions` Parameter value selection."""
+    y = request.param
+    mock_ctx = {
+        "args_grouping": {
+            "external": {
+                "filter_interaction": [],
+                "filters": [],
+                "parameters": [
+                    CallbackTriggerDict(
+                        id="dimensions_parameter",
+                        property="value",
+                        value=y,
+                        str_id="dimensions_parameter",
+                        triggered=False,
+                    )
+                ],
+                "theme_selector": CallbackTriggerDict(
+                    id="theme_selector",
+                    property="checked",
+                    value=False,
+                    str_id="theme_selector",
+                    triggered=False,
+                ),
+            }
+        }
+    }
+    context_value.set(AttributeDict(**mock_ctx))
+    return context_value
+
+
 @pytest.fixture
 def ctx_parameter_hover_data(request):
     """Mock dash.ctx that represents hover_data Parameter value selection."""
@@ -497,3 +536,36 @@ def test_data_frame_parameters_multiple_targets(
         }
 
         assert result == expected
+
+    @pytest.mark.usefixtures("managers_one_page_one_graph_with_dict_param_input")
+    @pytest.mark.parametrize(
+        "ctx_parameter_dimensions, target_scatter_matrix_parameter_dimensions",
+        [("ALL", ["sepal_length", "sepal_width", "petal_length", "petal_width"]), (["sepal_width"], ["sepal_width"])],
+        indirect=True,
+    )
+    def test_one_parameter_with_dict_input_as_options(
+        self, ctx_parameter_dimensions, target_scatter_matrix_parameter_dimensions
+    ):
+        # If the options are provided as a list of dictionaries, the value should be correctly passed to the
+        # target as a list. So when "ALL" is selected, a list of all possible values should be returned.
+        dimensions_parameter = vm.Parameter(
+            id="test_parameter_dimensions",
+            targets=["scatter_matrix_chart.dimensions"],
+            selector=vm.RadioItems(
+                id="dimensions_parameter",
+                options=[
+                    {"label": "sepal_length", "value": "sepal_length"},
+                    {"label": "sepal_width", "value": "sepal_width"},
+                    {"label": "petal_length", "value": "petal_length"},
+                    {"label": "petal_width", "value": "petal_width"},
+                ],
+            ),
+        )
+        model_manager["test_page"].controls = [dimensions_parameter]
+        dimensions_parameter.pre_build()
+
+        # Run action by picking the above added action function and executing it with ()
+        result = model_manager[f"{PARAMETER_ACTION_PREFIX}_test_parameter_dimensions"].function()
+        expected = {"scatter_matrix_chart": target_scatter_matrix_parameter_dimensions}
+
+        assert result == expected