网络搜索

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from openai import OpenAI
client = OpenAI()

response = client.responses.create(
    model="gpt-5.5",
    tools=[{"type": "web_search"}],
    input="What was a positive news story from today?"
)

print(response.output_text)

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curl "https://api.openai.com/v1/responses" \
    -H "Content-Type: application/json" \
    -H "Authorization: Bearer $OPENAI_API_KEY" \
    -d '{
        "model": "gpt-5.5",
        "tools": [{"type": "web_search"}],
        "input": "what was a positive news story from today?"
}'

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openai responses create \
  --model gpt-5.5 \
  --raw-output \
  --transform 'output.#(type=="message").content.0.text' <<'YAML'
tools:
  - type: web_search
input: What was a positive news story from today?
YAML

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using OpenAI.Responses;

string key = Environment.GetEnvironmentVariable("OPENAI_API_KEY")!;
OpenAIResponseClient client = new(model: "gpt-5.5", apiKey: key);

ResponseCreationOptions options = new();
options.Tools.Add(ResponseTool.CreateWebSearchTool());

OpenAIResponse response = (OpenAIResponse)client.CreateResponse([
    ResponseItem.CreateUserMessageItem([
        ResponseContentPart.CreateInputTextPart("What was a positive news story from today?"),
    ]),
], options);

Console.WriteLine(response.GetOutputText());

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from openai import OpenAI
client = OpenAI()

completion = client.chat.completions.create(
    model="gpt-5-search-api",
    web_search_options={},
    messages=[
        {
            "role": "user",
            "content": "What was a positive news story from today?",
        }
    ],
)

print(completion.choices[0].message.content)

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curl -X POST "https://api.openai.com/v1/chat/completions" \
    -H "Authorization: Bearer $OPENAI_API_KEY" \
    -H "Content-type: application/json" \
    -d '{
        "model": "gpt-5-search-api",
        "web_search_options": {},
        "messages": [{
            "role": "user",
            "content": "What was a positive news story from today?"
        }]
    }'

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from openai import OpenAI
client = OpenAI()

response = client.responses.create(
    model="gpt-5.5",
    tools=[{
        "type": "web_search",
        "search_context_size": "low",
    }],
    input="What movie won best picture in 2025?",
)

print(response.output_text)

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using OpenAI.Responses;

string key = Environment.GetEnvironmentVariable("OPENAI_API_KEY")!;
OpenAIResponseClient client = new(model: "gpt-5.5", apiKey: key);

ResponseCreationOptions options = new();
options.Tools.Add(ResponseTool.CreateWebSearchTool(
    searchContextSize: WebSearchToolContextSize.Low
));

OpenAIResponse response = (OpenAIResponse)client.CreateResponse([
    ResponseItem.CreateUserMessageItem([
        ResponseContentPart.CreateInputTextPart(
            "What movie won best picture in 2025?"
        )
    ])
], options);

Console.WriteLine(response.GetOutputText());

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curl "https://api.openai.com/v1/responses" \
    -H "Content-Type: application/json" \
    -H "Authorization: Bearer $OPENAI_API_KEY" \
    -d '{
        "model": "gpt-5.5",
        "tools": [{
            "type": "web_search",
            "search_context_size": "low"
        }],
        "input": "What movie won best picture in 2025?"
    }'

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import OpenAI from "openai";
const client = new OpenAI();

const response = await client.responses.create({
    model: "gpt-5.5",
    reasoning: { effort: "xhigh" },
    tools: [
        {
            type: "web_search",
            return_token_budget: "unlimited",
        },
    ],
    input: [
        "Research the economic impact of semaglutide on global healthcare systems.",
        "",
        "Do:",
        "- Include specific figures, trends, statistics, and measurable outcomes.",
        "- Prioritize reliable, up-to-date sources: peer-reviewed research, health organizations (e.g., WHO, CDC), regulatory agencies, or pharmaceutical earnings reports.",
        "- Include inline citations and return all source metadata.",
        "",
        "Be analytical, avoid generalities, and ensure that each section supports data-backed reasoning that could inform healthcare policy or financial modeling.",
    ].join("\n"),
});

console.log(response.output_text);

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from openai import OpenAI
client = OpenAI()

response = client.responses.create(
    model="gpt-5.5",
    reasoning={"effort": "xhigh"},
    tools=[
        {
            "type": "web_search",
            "return_token_budget": "unlimited",
        }
    ],
    input="""Research the economic impact of semaglutide on global healthcare systems.

Do:
- Include specific figures, trends, statistics, and measurable outcomes.
- Prioritize reliable, up-to-date sources: peer-reviewed research, health organizations (e.g., WHO, CDC), regulatory agencies, or pharmaceutical earnings reports.
- Include inline citations and return all source metadata.

Be analytical, avoid generalities, and ensure that each section supports data-backed reasoning that could inform healthcare policy or financial modeling.""",
)

print(response.output_text)

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import OpenAI from "openai";
const client = new OpenAI();

const response = await client.responses.create({
    model: "gpt-5.5",
    reasoning: { effort: "low" },
    tools: [
        {
            type: "web_search",
            filters: {
                allowed_domains: [
                    "pubmed.ncbi.nlm.nih.gov",
                    "clinicaltrials.gov",
                    "www.who.int",
                    "www.cdc.gov",
                    "www.fda.gov",
                ],
                blocked_domains: [
                    "reddit.com",
                    "quora.com",
                    "wikipedia.org",
                ],
            },
        },
    ],
    tool_choice: "auto",
    include: ["web_search_call.action.sources"],
    input: "Please perform a web search on how semaglutide is used in the treatment of diabetes.",
});

console.log(response.output_text);

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from openai import OpenAI
client = OpenAI()

response = client.responses.create(
    model="gpt-5.5",
    reasoning={"effort": "low"},
    tools=[
        {
            "type": "web_search",
            "filters": {
                "allowed_domains": [
                    "pubmed.ncbi.nlm.nih.gov",
                    "clinicaltrials.gov",
                    "www.who.int",
                    "www.cdc.gov",
                    "www.fda.gov",
                ],
                "blocked_domains": [
                    "reddit.com",
                    "quora.com",
                    "wikipedia.org",
                ],
            },
        }
    ],
    tool_choice="auto",
    include=["web_search_call.action.sources"],
    input="Please perform a web search on how semaglutide is used in the treatment of diabetes.",
)

print(response.output_text)

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from openai import OpenAI
client = OpenAI()

response = client.responses.create(
    model="gpt-5.5",
    tools=[{
        "type": "web_search",
        "user_location": {
            "type": "approximate",
            "country": "GB",
            "city": "London",
            "region": "London",
        }
    }],
    input="What are the best restaurants near me?",
)

print(response.output_text)

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using OpenAI.Responses;

string key = Environment.GetEnvironmentVariable("OPENAI_API_KEY")!;
OpenAIResponseClient client = new(model: "gpt-5.5", apiKey: key);

ResponseCreationOptions options = new();
options.Tools.Add(ResponseTool.CreateWebSearchTool(
    userLocation: WebSearchToolLocation.CreateApproximateLocation(
        country: "GB",
        city: "London",
        region: "Granary Square"
    )
));

OpenAIResponse response = (OpenAIResponse)client.CreateResponse([
    ResponseItem.CreateUserMessageItem([
        ResponseContentPart.CreateInputTextPart(
            "What are the best restaurants near me?"
        )
    ])
], options);

Console.WriteLine(response.GetOutputText());

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curl "https://api.openai.com/v1/responses" \
    -H "Content-Type: application/json" \
    -H "Authorization: Bearer $OPENAI_API_KEY" \
    -d '{
        "model": "gpt-5.5",
        "tools": [{
            "type": "web_search",
            "user_location": {
                "type": "approximate",
                "country": "GB",
                "city": "London",
                "region": "London"
            }
        }],
        "input": "What are the best restaurants near me?"
    }'

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from openai import OpenAI
client = OpenAI()

completion = client.chat.completions.create(
    model="gpt-5-search-api",
    web_search_options={
        "user_location": {
            "type": "approximate",
            "approximate": {
                "country": "GB",
                "city": "London",
                "region": "London",
            }
        },
    },
    messages=[{
        "role": "user",
        "content": "What are the best restaurants near me?",
    }],
)

print(completion.choices[0].message.content)

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curl -X POST "https://api.openai.com/v1/chat/completions" \
    -H "Authorization: Bearer $OPENAI_API_KEY" \
    -H "Content-type: application/json" \
    -d '{
        "model": "gpt-5-search-api",
        "web_search_options": {
            "user_location": {
                "type": "approximate",
                "approximate": {
                    "country": "GB",
                    "city": "London",
                    "region": "London"
                }
            }
        },
        "messages": [{
            "role": "user",
            "content": "What are the best restaurants near me?"
        }]
    }'

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import OpenAI from "openai";
const client = new OpenAI();

const response = await client.responses.create({
model: "gpt-5.5",
tools: [
{ type: "web_search", external_web_access: false },
],
tool_choice: "auto",
input: "Find when the Eiffel Tower opened to the public and cite the source.",
});

console.log(response.output_text);

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from openai import OpenAI
client = OpenAI()

resp = client.responses.create(
model="gpt-5.5",
tools=[{ "type": "web_search", "external_web_access": False }],
tool_choice="auto",
input="Find when the Eiffel Tower opened to the public and cite the source.",
)
print(resp.output_text)