Text-to-image search

import os
import json
from openai import OpenAI

client = OpenAI(
    # If you have not configured the environment variable, replace the following line with api_key="sk-xxx" (not recommended), using your Model Studio API key.
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    # The following URL is for the China (Beijing) region. Replace {WorkspaceId} with your Workspace ID. URLs vary by region.
    base_url="https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/compatible-mode/v1"
)

response = client.responses.create(
    model="qwen3.7-plus",
    input="Find a tech-style background image for a PPT cover",
    tools=[
        {
            "type": "web_search_image"
        }
    ]
)

for item in response.output:
    if item.type == "web_search_image_call":
        print(f"[Tool Call] Text-to-image search (status: {item.status})")
        # Parse and display the list of searched images
        if item.output:
            images = json.loads(item.output)
            print(f"  Found {len(images)} images:")
            for img in images[:5]:  # Display the first 5 images
                print(f"  [{img['index']}] {img['title']}")
                print(f"      {img['url']}")
            if len(images) > 5:
                print(f"  ... {len(images)} images in total")
    elif item.type == "message":
        print(f"\n[Model Response]")
        print(response.output_text)

# Display token usage and tool call statistics
print(f"\n[Token Usage] Input: {response.usage.input_tokens}, Output: {response.usage.output_tokens}, Total: {response.usage.total_tokens}")
if hasattr(response.usage, 'x_tools') and response.usage.x_tools:
    for tool_name, info in response.usage.x_tools.items():
        print(f"[Tool Statistics] {tool_name} calls: {info.get('count', 0)}")

import OpenAI from "openai";
import process from 'process';

const openai = new OpenAI({
    // If you have not configured the environment variable, replace the following line with apiKey: "sk-xxx", using your Model Studio API key.
    apiKey: process.env.DASHSCOPE_API_KEY,
    // The following URL is for the China (Beijing) region. Replace {WorkspaceId} with your Workspace ID. URLs vary by region.
    baseURL: "https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/compatible-mode/v1"
});

async function main() {
    const response = await openai.responses.create({
        model: "qwen3.7-plus",
        input: "Find a tech-style background image for a PPT cover",
        tools: [
            { type: "web_search_image" }
        ]
    });

    for (const item of response.output) {
        if (item.type === "web_search_image_call") {
            console.log(`[Tool Call] Text-to-image search (status: ${item.status})`);
            // Parse and display the list of searched images
            if (item.output) {
                const images = JSON.parse(item.output);
                console.log(`  Found ${images.length} images:`);
                images.slice(0, 5).forEach(img => {
                    console.log(`  [${img.index}] ${img.title}`);
                    console.log(`      ${img.url}`);
                });
                if (images.length > 5) {
                    console.log(`  ... ${images.length} images in total`);
                }
            }
        } else if (item.type === "message") {
            console.log(`\n[Model Response]`);
            console.log(response.output_text);
        }
    }

    // Display token usage and tool call statistics
    console.log(`\n[Token Usage] Input: ${response.usage.input_tokens}, Output: ${response.usage.output_tokens}, Total: ${response.usage.total_tokens}`);
    if (response.usage && response.usage.x_tools) {
        for (const [toolName, info] of Object.entries(response.usage.x_tools)) {
            console.log(`[Tool Statistics] ${toolName} calls: ${info.count || 0}`);
        }
    }
}

main();

# The following URL is for the China (Beijing) region. Replace {WorkspaceId} with your Workspace ID. URLs vary by region.
curl -X POST https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/compatible-mode/v1/responses \
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
    "model": "qwen3.7-plus",
    "input": "Find a tech-style background image for a PPT cover",
    "tools": [
        {"type": "web_search_image"}
    ]
}'

import os
import json
from openai import OpenAI

client = OpenAI(
    # If you have not configured the environment variable, replace the following line with api_key="sk-xxx" (not recommended), using your Model Studio API key.
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    # The following URL is for the China (Beijing) region. Replace {WorkspaceId} with your Workspace ID. URLs vary by region.
    base_url="https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/compatible-mode/v1"
)

stream = client.responses.create(
    model="qwen3.7-plus",
    input="Find a tech-style background image for a PPT cover",
    tools=[{"type": "web_search_image"}],
    stream=True
)

for event in stream:
    # Tool call starts
    if event.type == "response.output_item.added":
        if event.item.type == "web_search_image_call":
            print("[Tool Call] Text-to-image search in progress...")
    # Tool call is complete. Parse and display the list of searched images.
    elif event.type == "response.output_item.done":
        if event.item.type == "web_search_image_call":
            print(f"[Tool Call] Text-to-image search complete (status: {event.item.status})")
            if event.item.output:
                images = json.loads(event.item.output)
                print(f"  Found {len(images)} images:")
                for img in images[:5]:  # Display the first 5 images
                    print(f"  [{img['index']}] {img['title']}")
                    print(f"      {img['url']}")
                if len(images) > 5:
                    print(f"  ... {len(images)} images in total")
    # Model response starts
    elif event.type == "response.content_part.added":
        print(f"\n[Model Response]")
    # Streamed text output
    elif event.type == "response.output_text.delta":
        print(event.delta, end="", flush=True)
    # Response is complete. Output usage.
    elif event.type == "response.completed":
        usage = event.response.usage
        print(f"\n\n[Token Usage] Input: {usage.input_tokens}, Output: {usage.output_tokens}, Total: {usage.total_tokens}")
        if hasattr(usage, 'x_tools') and usage.x_tools:
            for tool_name, info in usage.x_tools.items():
                print(f"[Tool Statistics] {tool_name} calls: {info.get('count', 0)}")

import OpenAI from "openai";
import process from 'process';

const openai = new OpenAI({
    // If you have not configured the environment variable, replace the following line with apiKey: "sk-xxx", using your Model Studio API key.
    apiKey: process.env.DASHSCOPE_API_KEY,
    // The following URL is for the China (Beijing) region. Replace {WorkspaceId} with your Workspace ID. URLs vary by region.
    baseURL: "https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/compatible-mode/v1"
});

async function main() {
    const stream = await openai.responses.create({
        model: "qwen3.7-plus",
        input: "Find a tech-style background image for a PPT cover",
        tools: [{ type: "web_search_image" }],
        stream: true
    });

    for await (const event of stream) {
        // Tool call starts
        if (event.type === "response.output_item.added") {
            if (event.item && event.item.type === "web_search_image_call") {
                console.log("[Tool Call] Text-to-image search in progress...");
            }
        }
        // Tool call is complete. Parse and display the list of searched images.
        else if (event.type === "response.output_item.done") {
            if (event.item && event.item.type === "web_search_image_call") {
                console.log(`[Tool Call] Text-to-image search complete (status: ${event.item.status})`);
                if (event.item.output) {
                    const images = JSON.parse(event.item.output);
                    console.log(`  Found ${images.length} images:`);
                    images.slice(0, 5).forEach(img => {
                        console.log(`  [${img.index}] ${img.title}`);
                        console.log(`      ${img.url}`);
                    });
                    if (images.length > 5) {
                        console.log(`  ... ${images.length} images in total`);
                    }
                }
            }
        }
        // Model response starts
        else if (event.type === "response.content_part.added") {
            console.log(`\n[Model Response]`);
        }
        // Streamed text output
        else if (event.type === "response.output_text.delta") {
            process.stdout.write(event.delta);
        }
        // Response is complete. Output usage.
        else if (event.type === "response.completed") {
            const usage = event.response.usage;
            console.log(`\n\n[Token Usage] Input: ${usage.input_tokens}, Output: ${usage.output_tokens}, Total: ${usage.total_tokens}`);
            if (usage && usage.x_tools) {
                for (const [toolName, info] of Object.entries(usage.x_tools)) {
                    console.log(`[Tool Statistics] ${toolName} calls: ${info.count || 0}`);
                }
            }
        }
    }
}

main();

# The following URL is for the China (Beijing) region. Replace {WorkspaceId} with your Workspace ID. URLs vary by region.
curl -X POST https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/compatible-mode/v1/responses \
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
    "model": "qwen3.7-plus",
    "input": "Find a tech-style background image for a PPT cover",
    "tools": [
        {"type": "web_search_image"}
    ],
    "stream": true
}'