GLM-ZHIPU

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"),
    # Singapore region. Replace {WorkspaceId} with your Bailian workspace ID. URLs vary by region.
    base_url="https://{WorkspaceId}.ap-southeast-1.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

==================== 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)

from openai import OpenAI
import os

client = OpenAI(
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    # Singapore region. Replace {WorkspaceId} with your Bailian workspace ID. URLs vary by region.
    base_url="https://{WorkspaceId}.ap-southeast-1.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}")