GLM-4

# 安装最新版本
pip install zai-sdk
# 或指定版本
pip install zai-sdk==0.2.2

import zai
print(zai.__version__)

from zai import ZhipuAiClient

# 初始化客户端
client = ZhipuAiClient(api_key="your-api-key")

# 创建聊天完成请求
response = client.chat.completions.create(
    model="glm-4-plus",
    messages=[
        {"role": "system", "content": "你是一个乐于解答各种问题的助手，你的任务是为用户提供专业、准确、有见地的建议。"},
        {"role": "user", "content": "你好，请介绍一下自己"}
    ],
    max_tokens=4096,
    temperature=0.7
)

# 获取回复
print(response.choices[0].message.content)

from zai import ZhipuAiClient

# 初始化客户端
client = ZhipuAiClient(api_key="your-api-key")

# 创建流式聊天完成请求
stream = client.chat.completions.create(
    model="glm-4-plus",
    messages=[
        {"role": "system", "content": "你是一个乐于解答各种问题的助手，你的任务是为用户提供专业、准确、有见地的建议。"},
        {"role": "user", "content": "请详细介绍一下人工智能的发展历程"}
    ],
    max_tokens=4096,
    temperature=0.7,
    stream=True
)

# 处理流式响应
for chunk in stream:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="")

<dependency>
    <groupId>ai.z.openapi</groupId>
    <artifactId>zai-sdk</artifactId>
    <version>0.3.3</version>
</dependency>

implementation 'ai.z.openapi:zai-sdk:0.3.3'

import ai.z.openapi.ZhipuAiClient;
import ai.z.openapi.service.model.ChatCompletionCreateParams;
import ai.z.openapi.service.model.ChatCompletionResponse;
import ai.z.openapi.service.model.ChatMessage;
import ai.z.openapi.service.model.ChatMessageRole;
import java.util.Arrays;

public class BasicChat {
    public static void main(String[] args) {
        // 初始化客户端
        ZhipuAiClient client = ZhipuAiClient.builder().ofZHIPU()
            .apiKey("your-api-key")
            .build();

        // 创建聊天完成请求
        ChatCompletionCreateParams request = ChatCompletionCreateParams.builder()
            .model("glm-4-plus")
            .messages(Arrays.asList(
                ChatMessage.builder()
                    .role(ChatMessageRole.SYSTEM.value())
                    .content("你是一个乐于解答各种问题的助手，你的任务是为用户提供专业、准确、有见地的建议。")
                    .build(),
                ChatMessage.builder()
                    .role(ChatMessageRole.USER.value())
                    .content("你好，请介绍一下自己")
                    .build()
            ))
            .maxTokens(4096)
            .temperature(0.7f)
            .build();

        // 发送请求
        ChatCompletionResponse response = client.chat().createChatCompletion(request);

        // 获取回复
        if (response.isSuccess()) {
            Object reply = response.getData().getChoices().get(0).getMessage().getContent();
            System.out.println("AI 回复: " + reply);
        } else {
            System.err.println("错误: " + response.getMsg());
        }
    }
}

import ai.z.openapi.ZhipuAiClient;
import ai.z.openapi.service.model.ChatCompletionCreateParams;
import ai.z.openapi.service.model.ChatCompletionResponse;
import ai.z.openapi.service.model.ChatMessage;
import ai.z.openapi.service.model.ChatMessageRole;
import ai.z.openapi.service.model.Delta;
import java.util.Arrays;

public class StreamingChat {
    public static void main(String[] args) {
        // 初始化客户端
        ZhipuAiClient client = ZhipuAiClient.builder().ofZHIPU()
            .apiKey("your-api-key")
            .build();

        // 创建聊天完成请求
        ChatCompletionCreateParams request = ChatCompletionCreateParams.builder()
            .model("glm-4-plus")
            .messages(Arrays.asList(
                ChatMessage.builder()
                    .role(ChatMessageRole.SYSTEM.value())
                    .content("你是一个乐于解答各种问题的助手，你的任务是为用户提供专业、准确、有见地的建议。")
                    .build(),
                ChatMessage.builder()
                    .role(ChatMessageRole.USER.value())
                    .content("请详细介绍一下人工智能的发展历程")
                    .build()
            ))
            .maxTokens(4096)
            .temperature(0.7f)
            .stream(true)
            .build();

        ChatCompletionResponse response = client.chat().createChatCompletion(request);

        if (response.isSuccess()) {
            response.getFlowable().subscribe(
                // Process streaming message data
                data -> {
                    if (data.getChoices() != null && !data.getChoices().isEmpty()) {
                    Delta delta = data.getChoices().get(0).getDelta();
                    System.out.print(delta + "\n");
                }},
                // Process streaming response error
                error -> System.err.println("\nStream error: " + error.getMessage()),
                // Process streaming response completion event
                () -> System.out.println("\nStreaming response completed")
            );
        } else {
            System.err.println("Error: " + response.getMsg());
        }
    }
}

from zhipuai import ZhipuAI

client = ZhipuAI(api_key="your-api-key") # 填写您自己的 APIKey
response = client.chat.completions.create(
    model="glm-4-plus",  # 填写需要调用的模型编码
    messages=[
        {"role": "system", "content": "你是一个乐于解答各种问题的助手，你的任务是为用户提供专业、准确、有见地的建议。"},
        {"role": "user", "content": "农夫需要把狼、羊和白菜都带过河，但每次只能带一样物品，而且狼和羊不能单独相处，羊和白菜也不能单独相处，问农夫该如何过河。"}
    ],
    max_tokens=4096,
    temperature=0.7
)
print(response.choices[0].message)

from zhipuai import ZhipuAI

client = ZhipuAI(api_key="your-api-key") # 填写您自己的APIKey
response = client.chat.completions.create(
    model="glm-4-plus",  # 填写需要调用的模型编码
    messages=[
        {"role": "system", "content": "你是一个乐于解答各种问题的助手，你的任务是为用户提供专业、准确、有见地的建议。"},
        {"role": "user", "content": "请详细介绍一下人工智能的发展历程"}
    ],
    max_tokens=4096,
    temperature=0.7,
    stream=True
)

# 处理流式响应
for chunk in response:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="")