add structured outputs information to readme (openai#181)

README.md

@@ -17,6 +17,7 @@ It is generated from our [OpenAPI specification](https://github.com/openai/opena
   - [Using the `OpenAIClient` class](#using-the-openaiclient-class)
 - [How to use chat completions with streaming](#how-to-use-chat-completions-with-streaming)
 - [How to use chat completions with tools and function calling](#how-to-use-chat-completions-with-tools-and-function-calling)
+- [How to use structured outputs](#how-to-use-structured-outputs)
 - [How to generate text embeddings](#how-to-generate-text-embeddings)
 - [How to generate images](#how-to-generate-images)
 - [How to transcribe audio](#how-to-transcribe-audio)
@@ -296,6 +297,60 @@ do
 } while (requiresAction);
 ```
 
+## How to use structured outputs
+
+Beginning with the `gpt-4o-mini`, `gpt-4o-mini-2024-07-18`, and `gpt-4o-2024-08-06` model snapshots, structured outputs are available for both top-level response content and tool calls in the chat completion and assistants APIs.
+
+For information about the feature, see [the Structured Outputs guide](https://platform.openai.com/docs/guides/structured-outputs/introduction).
+
+To use structured outputs to constrain chat completion content, set an appropriate `ChatResponseFormat` as in the following example:
+
+```csharp
+ChatCompletionOptions options = new()
+{
+    ResponseFormat = ChatResponseFormat.CreateJsonSchemaFormat(
+        name: "math_reasoning",
+        jsonSchema: BinaryData.FromString("""
+            {
+                "type": "object",
+                "properties": {
+                "steps": {
+                    "type": "array",
+                    "items": {
+                    "type": "object",
+                    "properties": {
+                        "explanation": { "type": "string" },
+                        "output": { "type": "string" }
+                    },
+                    "required": ["explanation", "output"],
+                    "additionalProperties": false
+                    }
+                },
+                "final_answer": { "type": "string" }
+                },
+                "required": ["steps", "final_answer"],
+                "additionalProperties": false
+            }
+            """),
+    strictSchemaEnabled: true)
+};
+
+ChatCompletion chatCompletion = await client.CompleteChatAsync(
+    ["How can I solve 8x + 7 = -23?"],
+    options);
+
+using JsonDocument structuredJson = JsonDocument.Parse(chatCompletion.ToString());
+
+Console.WriteLine($"Final answer: {structuredJson.RootElement.GetProperty("final_answer").GetString()}");
+Console.WriteLine("Reasoning steps:");
+
+foreach (JsonElement stepElement in structuredJson.RootElement.GetProperty("steps").EnumerateArray())
+{
+    Console.WriteLine($"  - Explanation: {stepElement.GetProperty("explanation").GetString()}");
+    Console.WriteLine($"    Output: {stepElement.GetProperty("output")}");
+}
+```
+
 ## How to generate text embeddings
 
 In this example, you want to create a trip-planning website that allows customers to write a prompt describing the kind of hotel that they are looking for and then offers hotel recommendations that closely match this description. To achieve this, it is possible to use text embeddings to measure the relatedness of text strings. In summary, you can get embeddings of the hotel descriptions, store them in a vector database, and use them to build a search index that you can query using the embedding of a given customer's prompt.