typo

site/en/tutorials/structured_data_extraction.ipynb

@@ -450,7 +450,7 @@
         "id": "TgC_wkHPmkHn"
       },
       "source": [
-        "That's relatively simple and often works, but you can porentially make this more strict/robust by defining the schema using the API's Function Calling feature."
+        "That's relatively simple and often works, but you can potentially make this more strict/robust by defining the schema using the API's function calling feature."
       ]
     },
     {
@@ -459,7 +459,7 @@
         "id": "CxMC28LAOfUf"
       },
       "source": [
-        "## Use Function Calling"
+        "## Use function calling"
       ]
     },
     {
@@ -468,9 +468,9 @@
         "id": "x-V6PJn83Kh9"
       },
       "source": [
-        "If you haven't gone through the [Function Calling basics](https://ai.google.dev/tutorials/function_calling_python_quickstart) tutorial yet, make sure you do that first.\n",
+        "If you haven't gone through the [Function calling basics](https://ai.google.dev/tutorials/function_calling_python_quickstart) tutorial yet, make sure you do that first.\n",
         "\n",
-        "With Function Calling your function and its parameters are described to the API as a `glm.FunctionDeclaration`. In basic cases the SDK can build the `FunctionDeclaration` from the function and its annotations. The SDK doesn't currently handle the description of nested `OBJECT` (`dict`) parameters. So you'll need to define them explicitly, for now."
+        "With function calling your function and its parameters are described to the API as a `glm.FunctionDeclaration`. In basic cases the SDK can build the `FunctionDeclaration` from the function and its annotations. The SDK doesn't currently handle the description of nested `OBJECT` (`dict`) parameters. So you'll need to define them explicitly, for now."
       ]
     },
     {
@@ -481,7 +481,7 @@
       "source": [
         "### Define the schema\n",
         "\n",
-        "Start by defining `person` as an object with strting-fields `name`, `description`, `start_place_name`, `end_place_name`."
+        "Start by defining `person` as an object with string fields `name`, `description`, `start_place_name`, `end_place_name`."
       ]
     },
     {
@@ -645,7 +645,7 @@
       "source": [
         "### Call the API\n",
         "\n",
-        "Like you saw in [Function Calling basics](https://ai.google.dev/tutorials/function_calling_python_quickstart) now you can pass this `FunctionDeclaration` to the `tools` argument of the `genai.GenerativeModel` constructor (the constructor would also accept an equivalent JSON representation of the function declaration):"
+        "Like you saw in [Function calling basics](https://ai.google.dev/tutorials/function_calling_python_quickstart) now you can pass this `FunctionDeclaration` to the `tools` argument of the `genai.GenerativeModel` constructor (the constructor would also accept an equivalent JSON representation of the function declaration):"
       ]
     },
     {
@@ -883,7 +883,7 @@
       "source": [
         "## Conclusion\n",
         "\n",
-        "While the API can handle structured data extraction problems with pure text input and text output, using Function Calling is likely more reliable since it lets you define a strict schema, and eliminates a potentially error-prone parsing step."
+        "While the API can handle structured data extraction problems with pure text input and text output, using function calling is likely more reliable since it lets you define a strict schema, and eliminates a potentially error-prone parsing step."
       ]
     }
   ],