Fix documentation example to ensure it runs correctly

docsite/posts/index/overview.md

@@ -45,33 +45,40 @@ yarn run:index --config your_pipeline.yml # custom config mode
 ### Python API
 
 ```python
+import pandas as pd
+import asyncio
+
 from graphrag.index import run_pipeline
 from graphrag.index.config import PipelineWorkflowReference
 
-workflows: list[PipelineWorkflowReference] = [
-    PipelineWorkflowReference(
-        steps=[
-            {
-                # built-in verb
-                "verb": "derive",  # https://github.com/microsoft/datashaper/blob/main/python/datashaper/datashaper/verbs/derive.py
-                "args": {
-                    "column1": "col1",  # from above
-                    "column2": "col2",  # from above
-                    "to": "col_multiplied",  # new column name
-                    "operator": "*",  # multiply the two columns
-                },
-                # Since we're trying to act on the default input, we don't need explicitly to specify an input
-            }
-        ]
-    ),
-]
-
-dataset = pd.DataFrame([{"col1": 2, "col2": 4}, {"col1": 5, "col2": 10}])
-outputs = []
-async for output in await run_pipeline(dataset=dataset, workflows=workflows):
-    outputs.append(output)
-pipeline_result = outputs[-1]
-print(pipeline_result)
+async def main():
+    workflows: list[PipelineWorkflowReference] = [
+        PipelineWorkflowReference(
+            steps=[
+                {
+                    # built-in verb
+                    "verb": "derive",  # https://github.com/microsoft/datashaper/blob/main/python/datashaper/datashaper/verbs/derive.py
+                    "args": {
+                        "column1": "col1",  # from above
+                        "column2": "col2",  # from above
+                        "to": "col_multiplied",  # new column name
+                        "operator": "*",  # multiply the two columns
+                    },
+                    # Since we're trying to act on the default input, we don't need explicitly to specify an input
+                }
+            ]
+        ),
+    ]
+
+    dataset = pd.DataFrame([{"col1": 2, "col2": 4}, {"col1": 5, "col2": 10}])
+    outputs = []
+    async for output in run_pipeline(dataset=dataset, workflows=workflows):
+        outputs.append(output)
+    pipeline_result = outputs[-1]
+    print(pipeline_result)
+
+if __name__ == "__main__":
+    asyncio.run(main())
 ```
 
 ## Further Reading