Votre première requête à la Responses API

Passons à la pratique avec la Responses API d’OpenAI ! Vous allez utiliser la bibliothèque openai pour commencer à envoyer des requêtes au point de terminaison de la Responses API.

La classe OpenAI a déjà été importée depuis openai.

Cet exercice fait partie du cours

Travailler avec l’API OpenAI Responses

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Instructions

Définissez un client OpenAI, en laissant le texte de remplacement api_key inchangé.
Créez une requête OpenAI vers le modèle "gpt-5-mini", en envoyant l’invite "In simple terms, what is the OpenAI Responses API?" ; laissez les paramètres reasoning et max_output_tokens inchangés.
Affichez le texte généré depuis l’objet de réponse.

Exercice interactif pratique

Essayez cet exercice en complétant cet exemple de code.

# Define an OpenAI API client
client = ____(api_key="")

# Create the OpenAI API request
response = client.____.____(
    ____="gpt-5-mini",
    input="____",
    reasoning={"effort": "minimal"},
    max_output_tokens=100
)

# Print the generated text from the response
print(response.____)

Modifier et exécuter le code

Cet exercice fait partie du cours

Travailler avec l’API OpenAI Responses

IntermédiaireNiveau de compétence

4.8+

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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: Responses API : la meilleure alliée des développeurs Exercise 2: Votre première requête à la Responses API

Exercice en cours

Exercise 3: Ajouter des instructions au modèle Exercise 4: Extraire des informations de la réponse Exercise 5: Maîtriser les paramètres de réponse Exercise 6: Expérimenter des modèles plus puissants Exercise 7: Raisonner sur le raisonnement Exercise 8: Enchaîner les conversations Exercise 9: D’un message à l’autre Exercise 10: Activer les conversations de type chat

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 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!