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vizro-ai/docs/pages/user-guides/add-generated-chart-usecase.md

@@ -10,18 +10,17 @@ This guide explains the different ways in which you can add a chart generated by
 
     !!! example "Vizro-AI chart"
         === "Code for the cell"
-
             ```python
             import vizro_ai
             from vizro_ai import VizroAI
             import vizro.plotly.express as px
             from dotenv import load_dotenv
-    
+
             load_dotenv()
-    
+
             df = px.data.gapminder()
             vizro_ai = VizroAI(model="gpt-4o")
-    
+
             result = vizro_ai.plot(
                 df,
                 """Plot a bubble chart to show the changes in life expectancy
@@ -30,8 +29,8 @@ This guide explains the different ways in which you can add a chart generated by
                     Add animation on yearly basis, and do not use facets.
                     Put the legend on top""",
                 return_elements=True,
-               )
-    
+            )
+
             print(f"Insight:\n{result.chart_insights}\n")
             print(f"Code explanation:\n{result.code_explanation}\n\nCode:\n{result.code_vizro}\n")
             result.get_fig_object(df).show()
@@ -40,7 +39,7 @@ This guide explains the different ways in which you can add a chart generated by
         === "Result"
             [![VizroAIChart]][vizroaichart]
 
-2. Insert the resulting chart into a dashboard.
+1. Insert the resulting chart into a dashboard.
 
     Once you are satisfied with the chart, you can add it to a [Vizro](https://github.com/mckinsey/vizro/tree/main/vizro-core) dashboard.
 

vizro-ai/docs/pages/user-guides/customize-vizro-ai.md

@@ -18,7 +18,6 @@ vizro_ai = VizroAI(model="<chosen model>")
     For the string settings to work, you must supply your API key via environment variables. The relevant variable names to be set are noted in each vendor tab.
 
 === "OpenAI"
-
     | Environment variable | Name(s)           |
     | -------------------- | ----------------- |
     | API key              | `OPENAI_API_KEY`  |

vizro-ai/docs/pages/user-guides/vizro-ai-langchain-guide.md

@@ -115,9 +115,7 @@ chain = llm_with_tools | inject_df | tool_router.map()
 
 Now you can use the chain to generate charts or dashboards based on natural language queries. The chain will generate code that you can use to create visualizations.
 
-
 !!! example "Generate chart code"
-
     === "Code"
         ```python
         # Load sample data
@@ -126,17 +124,17 @@ Now you can use the chain to generate charts or dashboards based on natural lang
         plot_response = chain.invoke("Plot GDP per capita for each continent")
         print(plot_response[0].content)
         ```
+
     === "Vizro-AI Generated Code"
         ```python
         import plotly.graph_objects as go
         from vizro.models.types import capture
 
+
         @capture("graph")
         def custom_chart(data_frame):
             continent_gdp = data_frame.groupby("continent")["gdpPercap"].mean().reset_index()
-            fig = go.Figure(
-                data=[go.Bar(x=continent_gdp["continent"], y=continent_gdp["gdpPercap"])]
-            )
+            fig = go.Figure(data=[go.Bar(x=continent_gdp["continent"], y=continent_gdp["gdpPercap"])])
             fig.update_layout(
                 title="GDP per Capita by Continent",
                 xaxis_title="Continent",
@@ -146,14 +144,16 @@ Now you can use the chain to generate charts or dashboards based on natural lang
         ```
 
 !!! example "Generate dashboard code"
-
     === "Code"
         ```python
         dfs = [px.data.gapminder()]
 
-        dashboard_response = chain.invoke("Create a dashboard. This dashboard has a chart showing the correlation between gdpPercap and lifeExp.")
+        dashboard_response = chain.invoke(
+            "Create a dashboard. This dashboard has a chart showing the correlation between gdpPercap and lifeExp."
+        )
         print(dashboard_response[0].content)
         ```
+
     === "Vizro-AI Generated Code"
         ```python
         ############ Imports ##############
@@ -166,9 +166,7 @@ Now you can use the chain to generate charts or dashboards based on natural lang
         @capture("graph")
         def gdp_life_exp_graph(data_frame):
             fig = go.Figure()
-            fig.add_trace(
-                go.Scatter(x=data_frame["gdpPercap"], y=data_frame["lifeExp"], mode="markers")
-            )
+            fig.add_trace(go.Scatter(x=data_frame["gdpPercap"], y=data_frame["lifeExp"], mode="markers"))
             fig.update_layout(
                 title="GDP per Capita vs Life Expectancy",
                 xaxis_title="GDP per Capita",