Web extractor - Alibaba Cloud Model Studio - Alibaba Cloud Documentation Center

The web extractor tool fetches and extracts content from specified URLs, providing models with web information.

Usage

Web extractor supports three invocation methods, each with different configuration parameters:

OpenAI-compatible Responses API

To use web extractor, add both web_search and web_extractor to tools.

When using qwen3-max-2026-01-23, enable_thinking must be true.

For the best results, especially in case of mathematical calculations or data analytics, use also the code_interpreter to improve the accuracy.

# Import dependencies and create client...
response = client.responses.create(
    model="qwen3-max-2026-01-23",
    input="Please visit the official Alibaba Cloud Model Studio documentation, find the code interpreter topic and summarize it",
    tools=[
        {"type": "web_search"},
        {"type": "web_extractor"},
        {"type": "code_interpreter"}
    ],
    extra_body={
      "enable_thinking": True
    }
)

print(response.output_text)

OpenAI-Compatible Chat Completions API

Set enable_search and enable_thinking to true, and set search_strategy to agent_max.

Non-streaming output is not supported.

# Import dependencies and create client...
completion = client.chat.completions.create(
    model="qwen3-max-2026-01-23",
    messages=[{"role": "user", "content": "Please visit the official Alibaba Cloud Model Studio documentation, find the code interpreter topic and summarize it"}],
    extra_body={
        "enable_thinking": True,
        "enable_search": True,
        "search_options": {"search_strategy": "agent_max"}
    },
    stream=True
)

DashScope

Set enable_search and enable_thinking to true, and set search_strategy to agent_max.

Non-streaming output is not supported.

from dashscope import Generation

response = Generation.call(
    model="qwen3-max-2026-01-23",
    messages=[{"role": "user", "content": "Please visit the official Alibaba Cloud Model Studio documentation, find the code interpreter topic and summarize it"}],
    enable_search=True,
    search_options={"search_strategy": "agent_max"},
    enable_thinking=True,
    result_format="message",
    stream=True,
    incremental_output=True
)

Availability

Supports qwen3-max in thinking mode and qwen3-max-2026-01-23 in thinking mode. The Responses API is available only in international region.

Getting started

Call the web extractor tool via the Responses API and automatically summarize a technical document.

First, get an API key and export the API key as an environment variable.

Python

import os
from openai import OpenAI

client = OpenAI(
    # If you haven't configured an environment variable, replace the following line with: api_key="sk-xxx",
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    base_url="https://dashscope-intl.aliyuncs.com/api/v2/apps/protocols/compatible-mode/v1"
)

response = client.responses.create(
    model="qwen3-max-2026-01-23",
    input="Please visit the official Alibaba Cloud Model Studio documentation, find the code interpreter topic and summarize it",
    tools=[
        {
            "type": "web_search"
        },
        {
            "type": "web_extractor"
        },
        {
            "type": "code_interpreter"
        }
    ],
    extra_body = {
        "enable_thinking": True
    }
)
# Uncomment the following line to view intermediate output
# print(response.output)
print("="*20+"Response"+"="*20)
print(response.output_text)
# Print tool invocation count
usage = response.usage
print("="*20+"Tool Invocation Count"+"="*20)
if hasattr(usage, 'x_tools') and usage.x_tools:
    print(f"\nWeb Extractor invocations: {usage.x_tools.get('web_extractor', {}).get('count', 0)}")

Node.js

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

const openai = new OpenAI({
    // If you haven't configured an environment variable, replace the following line with: apiKey: "sk-xxx",
    apiKey: process.env.DASHSCOPE_API_KEY,
    baseURL: "https://dashscope-intl.aliyuncs.com/api/v2/apps/protocols/compatible-mode/v1"
});

async function main() {
    const response = await openai.responses.create({
        model: "qwen3-max-2026-01-23",
        input: "Please visit the official Alibaba Cloud Model Studio documentation, find the code interpreter topic and summarize it",
        tools: [
            { type: "web_search" },
            { type: "web_extractor" },
            { type: "code_interpreter" }
        ],
        enable_thinking: true
    });

    console.log("====================Response====================");
    console.log(response.output_text);

    // Print tool invocation count
    console.log("====================Tool Invocation Count====================");
    if (response.usage && response.usage.x_tools) {
        console.log(`Web Extractor invocations: ${response.usage.x_tools.web_extractor?.count || 0}`);
        console.log(`Web Search invocations: ${response.usage.x_tools.web_search?.count || 0}`);
    }
    // Uncomment the following line to view intermediate output
    // console.log(JSON.stringify(response.output[0], null, 2));
}

main();

curl

curl -X POST https://dashscope-intl.aliyuncs.com/api/v2/apps/protocols/compatible-mode/v1/responses \
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
    "model": "qwen3-max-2026-01-23",
    "input": "Please visit the official Alibaba Cloud Model Studio documentation, find the code interpreter topic and summarize it",
    "tools": [
        {"type": "web_search"},
        {"type": "web_extractor"},
        {"type": "code_interpreter"}
    ],
    "enable_thinking": true
}'

Running the code above produces a response similar to:

====================Response====================
Based on the official Alibaba Cloud Model Studio documentation, here is a summary of the **Code Interpreter** feature:

## Overview

...

> **Source**: Alibaba Cloud Model Studio Official Documentation - [Qwen Code Interpreter](https://www.alibabacloud.com/help/zh/model-studio/qwen-code-interpreter) and [Assistant API Code Interpreter](https://www.alibabacloud.com/help/zh/model-studio/code-interpreter) (Last updated: December 2025)
====================Tool Invocation Count====================

Web Extractor invocations: 1

Streaming output

Web extraction can take some time. Use streaming output to receive intermediate results in real time.

We recommend using the Responses API to retrieve the intermediate execution status of the tool.

OpenAI-Compatible Responses API

Python

import os
from openai import OpenAI

client = OpenAI(
    # If you haven't configured an environment variable, replace the following line with: api_key="sk-xxx",
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    base_url="https://dashscope.aliyuncs.com/api/v2/apps/protocols/compatible-mode/v1"
)

stream = client.responses.create(
    model="qwen3-max-2026-01-23",
    input="Please visit the official Alibaba Cloud Model Studio documentation, find the code interpreter topic and summarize it",
    tools=[
        {"type": "web_search"},
        {"type": "web_extractor"},
        {"type": "code_interpreter"}
    ],
    stream=True,
    extra_body={"enable_thinking": True}
)

reasoning_started = False
output_started = False

for chunk in stream:
    # Print reasoning process
    if chunk.type == 'response.reasoning_summary_text.delta':
        if not reasoning_started:
            print("="*20 + "Reasoning Process" + "="*20)
            reasoning_started = True
        print(chunk.delta, end='', flush=True)
    # Print tool invocation completion
    elif chunk.type == 'response.output_item.done':
        if hasattr(chunk, 'item') and hasattr(chunk.item, 'type'):
            if chunk.item.type == 'web_extractor_call':
                print("\n" + "="*20 + "Tool Invocation" + "="*20)
                print(chunk.item.goal)
                print(chunk.item.output)
            elif chunk.item.type == 'reasoning':
                reasoning_started = False
    # Print response content
    elif chunk.type == 'response.output_text.delta':
        if not output_started:
            print("\n" + "="*20 + "Response" + "="*20)
            output_started = True
        print(chunk.delta, end='', flush=True)
    # Response completed, print tool invocation count
    elif chunk.type == 'response.completed':
        print("\n" + "="*20 + "Tool Invocation Count" + "="*20)
        usage = chunk.response.usage
        if hasattr(usage, 'x_tools') and usage.x_tools:
            print(f"Web Extractor invocations: {usage.x_tools.get('web_extractor', {}).get('count', 0)}")
            print(f"Web Search invocations: {usage.x_tools.get('web_search', {}).get('count', 0)}")

Node.js

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

const openai = new OpenAI({
    // If you haven't configured an environment variable, replace the following line with: apiKey: "sk-xxx",
    apiKey: process.env.DASHSCOPE_API_KEY,
    baseURL: "https://dashscope.aliyuncs.com/api/v2/apps/protocols/compatible-mode/v1"
});

async function main() {
    const stream = await openai.responses.create({
        model: "qwen3-max-2026-01-23",
        input: "Please visit the official Alibaba Cloud Model Studio documentation, find the code interpreter topic and summarize it",
        tools: [
            { type: "web_search" },
            { type: "web_extractor" },
            { type: "code_interpreter" }
        ],
        stream: true,
        enable_thinking: true
    });

    let reasoningStarted = false;
    let outputStarted = false;

    for await (const chunk of stream) {
        // Print reasoning process
        if (chunk.type === 'response.reasoning_summary_text.delta') {
            if (!reasoningStarted) {
                console.log("====================Reasoning Process====================");
                reasoningStarted = true;
            }
            process.stdout.write(chunk.delta);
        }
        // Print tool invocation completion
        else if (chunk.type === 'response.output_item.done') {
            if (chunk.item && chunk.item.type === 'web_extractor_call') {
                console.log("\n" + "====================Tool Invocation====================");
                console.log(chunk.item.goal);
                console.log(chunk.item.output);
            } else if (chunk.item && chunk.item.type === 'reasoning') {
                reasoningStarted = false;
            }
        }
        // Print response content
        else if (chunk.type === 'response.output_text.delta') {
            if (!outputStarted) {
                console.log("\n" + "====================Response====================");
                outputStarted = true;
            }
            process.stdout.write(chunk.delta);
        }
        // Response completed, print tool invocation count
        else if (chunk.type === 'response.completed') {
            console.log("\n" + "====================Tool Invocation Count====================");
            const usage = chunk.response.usage;
            if (usage && usage.x_tools) {
                console.log(`Web Extractor invocations: ${usage.x_tools.web_extractor?.count || 0}`);
                console.log(`Web Search invocations: ${usage.x_tools.web_search?.count || 0}`);
            }
        }
    }
}

main();

curl

curl -X POST https://dashscope.aliyuncs.com/api/v2/apps/protocols/compatible-mode/v1/responses \
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
    "model": "qwen3-max-2026-01-23",
    "input": "Please visit the official Alibaba Cloud Model Studio documentation, find the code interpreter topic and summarize it",
    "tools": [
        {"type": "web_search"},
        {"type": "web_extractor"},
        {"type": "code_interpreter"}
    ],
    "enable_thinking": true,
    "stream": true
}'

OpenAI-Compatible Chat Completions API

Python

import os
from openai import OpenAI

client = OpenAI(
    # If you haven't configured an environment variable, replace the following line with: api_key="sk-xxx",
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    base_url="https://dashscope.aliyuncs.com/compatible-mode/v1"
)

stream = client.chat.completions.create(
    model="qwen3-max-2026-01-23",
    messages=[
        {"role": "user", "content": "Please visit the official Alibaba Cloud Model Studio documentation, find the code interpreter topic and summarize it"}
    ],
    extra_body={
        "enable_search": True,
        "search_options": {"search_strategy": "agent_max"}
    },
    stream=True
)

for chunk in stream:
    print(chunk)

Node.js

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

const openai = new OpenAI({
    // If you haven't configured an environment variable, replace the following line with: apiKey: "sk-xxx",
    apiKey: process.env.DASHSCOPE_API_KEY,
    baseURL: "https://dashscope.aliyuncs.com/compatible-mode/v1"
});

async function main() {
    const stream = await openai.chat.completions.create({
        model: "qwen3-max-2026-01-23",
        messages: [
            { role: "user", content: "Please visit the official Alibaba Cloud Model Studio documentation, find the code interpreter topic and summarize it" }
        ],
        enable_search: true,
        search_options: { search_strategy: "agent_max" },
        stream: true
    });

    for await (const chunk of stream) {
        console.log(chunk);
    }
}

main();

curl

curl -X POST https://dashscope.aliyuncs.com/compatible-mode/v1/chat/completions \
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
    "model": "qwen3-max-2026-01-23",
    "messages": [
        {"role": "user", "content": "Please visit the official Alibaba Cloud Model Studio documentation, find the code interpreter topic and summarize it"}
    ],
    "enable_search": true,
    "search_options": {"search_strategy": "agent_max"},
    "stream": true
}'

DashScope

Java SDK is not supported.

Python

import os
import dashscope
from dashscope import Generation

# If you haven't configured an environment variable, replace the following line with: dashscope.api_key = "sk-xxx"
dashscope.api_key = os.getenv("DASHSCOPE_API_KEY")

response = Generation.call(
    model="qwen3-max-2026-01-23",
    messages=[
        {"role": "user", "content": "Please visit the official Alibaba Cloud Model Studio documentation, find the code interpreter topic and summarize it"}
    ],
    enable_search=True,
    search_options={"search_strategy": "agent_max"},
    enable_thinking=True,
    result_format="message",
    stream=True,
    incremental_output=True
)

reasoning_started = False
output_started = False
last_usage = None

for chunk in response:
    if chunk.status_code == 200:
        message = chunk.output.choices[0].message

        # Print reasoning process
        if hasattr(message, 'reasoning_content') and message.reasoning_content:
            if not reasoning_started:
                print("="*20 + "Reasoning Process" + "="*20)
                reasoning_started = True
            print(message.reasoning_content, end='', flush=True)

        # Print response content
        if hasattr(message, 'content') and message.content:
            if not output_started:
                print("\n" + "="*20 + "Response" + "="*20)
                output_started = True
            print(message.content, end='', flush=True)

        # Save the last usage info
        if hasattr(chunk, 'usage') and chunk.usage:
            last_usage = chunk.usage

# Print tool invocation count
if last_usage:
    print("\n" + "="*20 + "Tool Invocation Count" + "="*20)
    if hasattr(last_usage, 'plugins') and last_usage.plugins:
        print(f"Web Extractor invocations: {last_usage.plugins.get('web_extractor', {}).get('count', 0)}")

curl

curl -X POST https://dashscope.aliyuncs.com/api/v1/services/aigc/text-generation/generation \
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
-H "X-DashScope-SSE: enable" \
-H "Content-Type: application/json" \
-d '{
    "model": "qwen3-max-2026-01-23",
    "input": {
        "messages": [
            {
                "role": "user",
                "content": "Please visit the official Alibaba Cloud Model Studio documentation, find the code interpreter topic and summarize it"
            }
        ]
    },
    "parameters": {
        "enable_thinking": true,
        "enable_search": true,
        "search_options": {
            "search_strategy": "agent_max"
        },
        "result_format": "message"
    }
}'

Billing

Model cost: Extracted web content is appended to the prompt, increasing the model's input tokens. This is billed at the model's standard price, listed in Models.
Tool cost: Includes charges for both web extractor and web search.
- Web search pricing per 1,000 invocations:
  - Mainland China: $0.57341.
  - International: $10.00.
- Web extractor tool is currently free for a limited time.