GLM-ZHIPU

Python

Sample code

from openai import OpenAI
import os

# Initialize the OpenAI client
client = OpenAI(
    # If the environment variable is not set, replace "sk-xxx" with your Alibaba Cloud Model Studio API Key.
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    # China (Beijing) region. Replace {WorkspaceId} with your Bailian workspace ID. URLs vary by region.
    base_url="https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/compatible-mode/v1",
)

messages = [{"role": "user", "content": "Who are you?"}]
completion = client.chat.completions.create(
    model="ZHIPU/GLM-5.3",
    messages=messages,
    # Enable thinking mode by setting enable_thinking in extra_body.
    # reasoning_effort controls the reasoning effort. Optional values: max (default), high, low.
    extra_body={"enable_thinking": True, "reasoning_effort": "max"},
    stream=True,
    stream_options={
        "include_usage": True
    },
)

reasoning_content = ""  # Full reasoning process
answer_content = ""  # Full response
is_answering = False  # Tracks if the model is in the answering phase
print("\n" + "=" * 20 + " Reasoning Process " + "=" * 20 + "\n")

for chunk in completion:
    if not chunk.choices:
        print("\n" + "=" * 20 + " Token Usage " + "=" * 20 + "\n")
        print(chunk.usage)
        continue

    delta = chunk.choices[0].delta

    # Collect only the reasoning content
    if hasattr(delta, "reasoning_content") and delta.reasoning_content is not None:
        if not is_answering:
            print(delta.reasoning_content, end="", flush=True)
        reasoning_content += delta.reasoning_content

    # When content is received, start generating the response
    if hasattr(delta, "content") and delta.content:
        if not is_answering:
            print("\n" + "=" * 20 + " Full Response " + "=" * 20 + "\n")
            is_answering = True
        print(delta.content, end="", flush=True)
        answer_content += delta.content

Response

==================== Reasoning Process ====================

Let me carefully consider the user's question. It seems simple, but it is actually quite profound.

From a linguistic perspective, the user is using English, which means I should respond in English. This is a fundamental self-introduction question, but it may have multiple layers of meaning.

First, I need to be clear that as a language model, I should honestly state my identity and nature. I am not a human, nor do I possess true emotions or consciousness. I am an AI assistant trained with deep learning technology. This is a basic fact.

Second, considering the user's potential needs, they might want to know:
1. What services can I provide?
2. What are my areas of expertise?
3. What are my limitations?
4. How can they interact with me more effectively?

In my answer, I should express a friendly and open attitude while maintaining professionalism and accuracy. I should state my main areas of expertise, such as knowledge Q&A, writing assistance, and creative support, while also frankly pointing out my limitations, such as the lack of real emotional experience.

Furthermore, to make the answer more complete, I should also express a positive attitude and willingness to help users solve problems. I can guide the user to ask more specific questions to better showcase my abilities.

Considering this is an open-ended opening, the answer should be concise and clear, yet contain enough information to give the user a clear understanding of my basic situation and lay a good foundation for subsequent conversations.

Finally, the tone should remain humble and professional, neither too technical nor too casual, to make the user feel comfortable and natural.
==================== Full Response ====================

I am a GLM large language model trained by ZHIPU AI, designed to provide users with information and help solve problems. I am designed to understand and generate human language, and I can answer questions, provide explanations, or participate in discussions on various topics.

I do not store your personal data, and our conversations are anonymous. Is there any topic I can help you understand or explore?
==================== Token Usage ====================

CompletionUsage(completion_tokens=344, prompt_tokens=7, total_tokens=351, completion_tokens_details=None, prompt_tokens_details=None)

Node.js

Sample code

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

// Initialize the OpenAI client
const openai = new OpenAI({
    // If the environment variable is not set, replace "sk-xxx" with your Alibaba Cloud Model Studio API Key.
    apiKey: process.env.DASHSCOPE_API_KEY,
    // China (Beijing) region. Replace {WorkspaceId} with your Bailian workspace ID. URLs vary by region.
    baseURL: 'https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/compatible-mode/v1'
});

let reasoningContent = ''; // Full reasoning process
let answerContent = ''; // Full response
let isAnswering = false; // Tracks if the model is in the answering phase

async function main() {
    try {
        const messages = [{ role: 'user', content: 'Who are you?' }];

        const stream = await openai.chat.completions.create({
            model: 'ZHIPU/GLM-5.3',
            messages,
            // Note: In the Node.js SDK, non-standard parameters like enable_thinking are passed as top-level properties, not within extra_body.
            enable_thinking: true,
            // reasoning_effort controls the reasoning effort. Optional values: max (default), high, low.
            reasoning_effort: 'max',
            stream: true,
            stream_options: {
                include_usage: true
            },
        });

        console.log('\n' + '='.repeat(20) + ' Reasoning Process ' + '='.repeat(20) + '\n');

        for await (const chunk of stream) {
            if (!chunk.choices?.length) {
                console.log('\n' + '='.repeat(20) + ' Token Usage ' + '='.repeat(20) + '\n');
                console.log(chunk.usage);
                continue;
            }

            const delta = chunk.choices[0].delta;

            // Collect only the reasoning content
            if (delta.reasoning_content !== undefined && delta.reasoning_content !== null) {
                if (!isAnswering) {
                    process.stdout.write(delta.reasoning_content);
                }
                reasoningContent += delta.reasoning_content;
            }

            // When content is received, start generating the response
            if (delta.content !== undefined && delta.content) {
                if (!isAnswering) {
                    console.log('\n' + '='.repeat(20) + ' Full Response ' + '='.repeat(20) + '\n');
                    isAnswering = true;
                }
                process.stdout.write(delta.content);
                answerContent += delta.content;
            }
        }
    } catch (error) {
        console.error('Error:', error);
    }
}

main();

Response

==================== Reasoning Process ====================

Let me carefully consider the user's question, "Who are you?" This requires analysis and a response from multiple perspectives.

First, this is a basic identity question. As a GLM large language model, I need to accurately state my identity. I should clearly state that I am an AI assistant developed by ZHIPU AI.

Second, I need to consider the user's possible intentions. They might be first-time users wanting to understand basic functions, or they might want to confirm if I can provide specific help, or they might just be testing my response style. Therefore, I need to give an open and friendly answer.

I also need to consider the completeness of the answer. In addition to introducing my identity, I should briefly explain my main functions, such as Q&A, content creation, and analysis, so the user knows how to use this assistant.

Finally, I need to ensure a friendly and approachable tone, expressing a willingness to help. I can use expressions like "I'm happy to help" to make the user feel the warmth of the interaction.

Based on these considerations, I can craft a concise and clear answer that both addresses the user's question and guides future interaction.
==================== Full Response ====================

I am GLM, a large language model trained by ZHIPU AI. Trained on massive text data, I can understand and generate human language to help users answer questions, provide information, and engage in conversations.

I am continuously learning and improving to provide better services. I'm happy to answer your questions or provide assistance! What can I do for you?
==================== Token Usage ====================

{ prompt_tokens: 7, completion_tokens: 248, total_tokens: 255 }

HTTP

Sample code

# China (Beijing) region. Replace {WorkspaceId} with your Bailian workspace ID. URLs vary by region.
curl -X POST https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/compatible-mode/v1/chat/completions \
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
    "model": "ZHIPU/GLM-5.3",
    "messages": [
        {
            "role": "user",
            "content": "Who are you?"
        }
    ],
    "stream": true,
    "stream_options": {
        "include_usage": true
    },
    "enable_thinking": true,
    "reasoning_effort": "max"
}'

Python

Sample code

from openai import OpenAI
import os

client = OpenAI(
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    # China (Beijing) region. Replace {WorkspaceId} with your Bailian workspace ID. URLs vary by region.
    base_url="https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/compatible-mode/v1",
)

tools = [
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get weather information for a specified city",
            "parameters": {
                "type": "object",
                "properties": {
                    "city": {"type": "string", "description": "The name of the city"}
                },
                "required": ["city"]
            }
        }
    }
]

messages = [{"role": "user", "content": "What is the weather like in Beijing"}]

completion = client.chat.completions.create(
    model="ZHIPU/GLM-5.3",
    tools=tools,
    messages=messages,
    extra_body={
        "tool_stream": True,
    },
    stream=True,
    stream_options={"include_usage": True},
)

for chunk in completion:
    if chunk.choices:
        delta = chunk.choices[0].delta
        if hasattr(delta, 'content') and delta.content:
            print(f"[content] {delta.content}")
        if hasattr(delta, 'tool_calls') and delta.tool_calls:
            for tc in delta.tool_calls:
                print(f"[tool_call] id={tc.id}, name={tc.function.name}, args={tc.function.arguments}")
        if chunk.choices[0].finish_reason:
            print(f"[finish_reason] {chunk.choices[0].finish_reason}")
    if not chunk.choices and chunk.usage:
        print(f"[usage] {chunk.usage}")

Node.js

Sample code

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

const openai = new OpenAI({
    apiKey: process.env.DASHSCOPE_API_KEY,
    // China (Beijing) region. Replace {WorkspaceId} with your Bailian workspace ID. URLs vary by region.
    baseURL: 'https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/compatible-mode/v1'
});

const tools = [
    {
        type: "function",
        "function": {
            name: "get_weather",
            description: "Get weather information for a specified city",
            parameters: {
                type: "object",
                properties: {
                    city: { type: "string", description: "The name of the city" }
                },
                required: ["city"]
            }
        }
    }
];

async function main() {
    try {
        const stream = await openai.chat.completions.create({
            model: 'ZHIPU/GLM-5.3',
            messages: [{ role: 'user', content: 'What is the weather like in Beijing' }],
            tools: tools,
            tool_stream: true,
            stream: true,
            stream_options: {
                include_usage: true
            },
        });

        for await (const chunk of stream) {
            if (!chunk.choices?.length) {
                if (chunk.usage) {
                    console.log(`[usage] ${JSON.stringify(chunk.usage)}`);
                }
                continue;
            }

            const delta = chunk.choices[0].delta;

            if (delta.content) {
                console.log(`[content] ${delta.content}`);
            }

            if (delta.tool_calls) {
                for (const tc of delta.tool_calls) {
                    console.log(`[tool_call] id=${tc.id}, name=${tc.function.name}, args=${tc.function.arguments}`);
                }
            }

            if (chunk.choices[0].finish_reason) {
                console.log(`[finish_reason] ${chunk.choices[0].finish_reason}`);
            }
        }
    } catch (error) {
        console.error('Error:', error);
    }
}

main();

curl

# China (Beijing) region. Replace {WorkspaceId} with your Bailian workspace ID. URLs vary by region.
curl -X POST https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/compatible-mode/v1/chat/completions \
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
    "model": "ZHIPU/GLM-5.3",
    "messages": [
        {
            "role": "user",
            "content": "What is the weather like in Beijing"
        }
    ],
    "tools": [
        {
            "type": "function",
            "function": {
                "name": "get_weather",
                "description": "Get weather information for a specified city",
                "parameters": {
                    "type": "object",
                    "properties": {
                        "city": {"type": "string", "description": "The name of the city"}
                    },
                    "required": ["city"]
                }
            }
        }
    ],
    "stream": true,
    "stream_options": {"include_usage": true},
    "tool_stream": true
}'