Official Python SDK

# Install latest version
pip install zai-sdk

# Or specify version
pip install zai-sdk==0.0.3.3

import zai
print(zai.__version__)

Domestic API URL: https://api.z.ai/api/paas/v4/

from zai import ZaiClient

# Initialize client
client = ZaiClient(api_key="your-api-key")

# Create chat completion
response = client.chat.completions.create(
    model="glm-4.5",
    messages=[
        {"role": "user", "content": "Hello, please introduce yourself, Z.ai!"}
    ]
)
print(response.choices[0].message.content)

# Create streaming chat request
from zai import ZaiClient

# Initialize client
client = ZaiClient(api_key="your-api-key")

# Create chat completion
response = client.chat.completions.create(
    model='glm-4.5',
    messages=[
        {'role': 'system', 'content': 'You are an AI writer.'},
        {'role': 'user', 'content': 'Tell a story about AI.'},
    ],
    stream=True,
)

for chunk in response:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end='')

from zai import ZaiClient
client = ZaiClient(api_key="your-api-key")
response = client.chat.completions.create(
    model="glm-4.5",  # Please fill in the model name you want to call
    messages=[
        {"role": "user", "content": "As a marketing expert, please create an attractive slogan for my product"},
        {"role": "assistant", "content": "Of course, to create an attractive slogan, please tell me some information about your product"},
        {"role": "user", "content": "Z.AI Open Platform"},
        {"role": "assistant", "content": "Ignite the future, Z.AI draws infinite possibilities, making innovation within reach!"},
        {"role": "user", "content": "Create a more precise and attractive slogan"}
    ],
)
print(response.choices[0].message.content)

from zai import ZaiClient
import os

def main():
    # Initialize client
    client = ZaiClient(api_key=os.getenv("ZAI_API_KEY"))
    
    print("Welcome to Z.ai Chatbot! Type 'quit' to exit.")
    
    # Conversation history
    conversation = [
        {"role": "system", "content": "You are a friendly AI assistant"}
    ]
    
    while True:
        # Get user input
        user_input = input("You: ")
        
        if user_input.lower() == 'quit':
            break
        
        try:
            # Add user message
            conversation.append({"role": "user", "content": user_input})
            
            # Create chat request
            response = client.chat.completions.create(
                model="glm-4.5",
                messages=conversation,
                temperature=0.6,
                max_tokens=1000
            )
            
            # Get AI response
            ai_response = response.choices[0].message.content
            print(f"AI: {ai_response}")
            
            # Add AI response to conversation history
            conversation.append({"role": "assistant", "content": ai_response})
            
        except Exception as e:
            print(f"Error occurred: {e}")
    
    print("Goodbye!")

if __name__ == "__main__":
    main()

from zai import ZaiClient
import zai

def robust_chat(message):
    client = ZaiClient(api_key="your-api-key")
    
    try:
        response = client.chat.completions.create(
            model="glm-4.5",
            messages=[{"role": "user", "content": message}]
        )
        return response.choices[0].message.content
        
    except zai.core.APIStatusError as err:
        return f"API status error: {err}"
    except zai.core.APITimeoutError as err:
        return f"Request timeout: {err}"
    except Exception as err:
        return f"Other error: {err}"

# Usage example
result = robust_chat("Hello")
print(result)

import httpx
from zai import ZaiClient

# Custom HTTP client
httpx_client = httpx.Client(
    limits=httpx.Limits(
        max_keepalive_connections=20,
        max_connections=100
    ),
    timeout=30.0
)

# Create client with custom configuration
client = ZaiClient(
    api_key="your-api-key",
    base_url="https://api.z.ai/api/paas/v4/",
    timeout=httpx.Timeout(timeout=300.0, connect=8.0),
    max_retries=3,
    http_client=httpx_client
)

from zai import ZaiClient
import json

# Define functions
def get_weather(location, date=None):
    """Get weather information"""
    # Simulate weather API call
    return {
        "location": location,
        "date": date or "today",
        "weather": "sunny",
        "temperature": "25°C",
        "humidity": "60%"
    }

def get_stock_price(symbol):
    """Get stock price"""
    # Simulate stock API call
    return {
        "symbol": symbol,
        "price": 150.25,
        "change": "+2.5%"
    }

# Function descriptions
tools = [
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get weather information for a specified location",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {
                        "type": "string",
                        "description": "Location name"
                    },
                    "date": {
                        "type": "string",
                        "description": "Date in YYYY-MM-DD format"
                    }
                },
                "required": ["location"]
            }
        }
    },
    {
        "type": "function",
        "function": {
            "name": "get_stock_price",
            "description": "Get current stock price",
            "parameters": {
                "type": "object",
                "properties": {
                    "symbol": {
                        "type": "string",
                        "description": "Stock symbol"
                    }
                },
                "required": ["symbol"]
            }
        }
    }
]

# Use function calling
client = ZaiClient(api_key="your-api-key")

response = client.chat.completions.create(
    model='glm-4.5',
    messages=[
        {'role': 'user', 'content': 'How\'s the weather in Beijing today?'}
    ],
    tools=tools,
    tool_choice="auto"
)

# Handle function calling
if response.choices[0].message.tool_calls:
    for tool_call in response.choices[0].message.tool_calls:
        function_name = tool_call.function.name
        function_args = json.loads(tool_call.function.arguments)
        
        if function_name == "get_weather":
            result = get_weather(**function_args)
            print(f"Weather info: {result}")
        elif function_name == "get_stock_price":
            result = get_stock_price(**function_args)
            print(f"Stock info: {result}")
else:
    print(response.choices[0].message.content)

from zai import ZaiClient

# Initialize client
client = ZaiClient(api_key="your-api-key")

# Use web search tool
response = client.chat.completions.create(
    model='glm-4.5',
    messages=[
        {'role': 'system', 'content': 'You are a helpful assistant.'},
        {'role': 'user', 'content': 'What is artificial intelligence?'},
    ],
    tools=[
        {
            'type': 'web_search',
            'web_search': {
                'search_query': 'What is artificial intelligence?',
                'search_result': True,
            },
        }
    ],
    temperature=0.5,
    max_tokens=2000,
)

print(response)

from zai import ZaiClient
import time

client = ZaiClient(api_key="your-api-key")

# Submit generation task
response = client.videos.generations(
    model="cogvideox-3",  # Video generation model to use
    image_url=image_url,  # Provided image URL or Base64 encoding
    prompt="Make the scene come alive",
    quality="speed",  # Output mode: "quality" for quality priority, "speed" for speed priority
    with_audio=True,
    size="1920x1080",  # Video resolution, supports up to 4K (e.g., "3840x2160")
    fps=30,  # Frame rate, can be 30 or 60
)
print(response)

# Get generation result
time.sleep(60)  # Wait for a while to ensure video generation is complete
result = client.videos.retrieve_videos_result(id=response.id)
print(result)

class StreamProcessor:
    def __init__(self, client):
        self.client = client
        self.full_response = ""
    
    def stream_chat(self, messages, model="glm-4.5", callback=None):
        """Streaming chat processing"""
        stream = self.client.chat.completions.create(
            model=model,
            messages=messages,
            stream=True
        )
        
        self.full_response = ""
        for chunk in stream:
            if chunk.choices[0].delta.content is not None:
                content = chunk.choices[0].delta.content
                self.full_response += content
                
                if callback:
                    callback(content, self.full_response)
                else:
                    print(content, end="", flush=True)
        
        print()  # New line
        return self.full_response

# Usage example
processor = StreamProcessor(client)

# Custom callback function
def on_token_received(token, full_text):
    # You can implement real-time processing logic here
    print(token, end="", flush=True)

response = processor.stream_chat(
    messages=[{"role": "user", "content": "Write a Python function to calculate Fibonacci sequence"}],
    callback=on_token_received
)