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Building Confidence with LLM Sources

You're building a financial analysis assistant that needs to provide users with current stock market information. Since LLMs have knowledge cutoffs, you need to enable web search to access real-time data. Additionally, for transparency and credibility, you want to show users which sources were consulted during the search.

The OpenAI client has been initialized as client, and you'll be querying for the current price of Netflix stock.

Este exercício faz parte do curso

Working with the OpenAI Responses API

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Instruções do exercício

  • Create a web search-enabled request, making sure to include the web search sources in the response.
  • Loop through the response items and extract only items with type "web_search_call", then print the .sources from each web search call's .action attribute.

Exercício interativo prático

Experimente este exercício completando este código de exemplo.

# Create a response with web search enabled and sources included
response = client.responses.create(
    model="gpt-5-mini",
    tools=[{"type": "web_search"}],
    input="What is the current stock price of Netflix?",
    include=["web_search_call.____.____"]
)

# Extract and print sources from web search calls
for item in response.output:
    if ____:
        print(item.action.sources)
        
print(response.output_text)
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