Streaming Semantic Events

You're building a weather assistant that provides real-time forecasts. The OpenAI client has been initialized and configured to work with the Responses API. You'll stream semantic events to track when the response starts, when text blocks finish, and when the full response is complete. This creates a more engaging user experience by showing progress as the model generates the forecast.

This exercise is part of the course

Working with the OpenAI Responses API

Exercise instructions

Handle the "response.created" event by printing a start message.
Handle the "response.output_text.done" event by printing a completion message.
Handle the "response.completed" event by printing the full response text stored in current_text.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

prompt = "Explain how to read a weather forecast in one sentence for a beginner hiker."

with client.responses.create(model="gpt-5-mini", input=prompt, stream=True) as stream:
    for event in stream:
        # Find response created events
        if event.type == "____":
            print("Forecast generation started...\n")

        # Find output text completed events
        elif event.type == "____":
            print("\n--- Forecast complete ---\n")

        # Find response completed events
        elif event.type == "____":
            print(f"\nFull forecast:\n{current_text}")

Edit and Run Code

This exercise is part of the course

Working with the OpenAI Responses API

IntermediateSkill Level

5.0+

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Building AI applications has never been easier with the Responses API! Learn how to prompt models using a simple interface, best practices for working with reasoning models, and how to build back-and-forth conversations with LLMs.

Exercise 1: The Responses API: a Developer's Best Friend Exercise 2: Your First Responses API Call Exercise 3: Adding Model Instructions Exercise 4: Extracting Information from the Response Exercise 5: Mastering Response Parameters Exercise 6: Experimenting with More Powerful Models Exercise 7: Reasoning About Reasoning Exercise 8: Conversation Chaining Exercise 9: From One Message to Another Exercise 10: Enabling Chat Conversations

LLMs are powerful, but they're even more powerful once tools are introduced! Learn how to integrate web search with your LLM systems with no additional API keys or credentials. Discover that tools can be, well... pretty much anything! Build your own tools for LLMs to call to go beyond the model's base capabilities.

Exercise 1: Response Items: The Building Blocks Exercise 2: Extracting Output Items and Their Contents Exercise 3: Using Items for Custom Handling Exercise 4: Web Search That Just Works Exercise 5: Combining LLMs and Web Search Exercise 6: Building Confidence with LLM Sources Exercise 7: Function-Calling Tools: Defining the Function Exercise 8: The Function-Calling Tool Workflow Exercise 9: Defining a Function for Converting Timezones Exercise 10: Function-Calling Tools: The Full Workflow Exercise 11: Building the OpenAI Tool Definition Exercise 12: Integrating Function-Calling Tools

Move your LLM systems from proof-of-concept to production with three key design paradigms. Learn how to generate consistent and reliable structured outputs, like JSON, using LLMs. You'll also use semantic events to stream updates to your users, creating interactive and enjoyable experiences. Finally, go multi-modal and learn how to combine text and images with OpenAI's GPT-5 models.

Exercise 1: Generating Structured Outputs Exercise 2: Consistent Outputs, Every Time!Exercise 3: Nesting Pydantic Classes Exercise 4: Streaming with Semantic Events Exercise 5: Streaming OpenAI Responses Exercise 6: Streaming Semantic Events

Current Exercise

Exercise 7: Streaming with Function Calls Exercise 8: Input Flexibility and Multimodality Exercise 9: Role-Based Inputs Exercise 10: Prompting with Images from URLs Exercise 11: Prompting with Local Image Files Exercise 12: Congratulations!