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- 全新ui - 全面优化websocket逻辑，提高数字人和ui连接的稳定性及资源开销 - 全面优化唤醒逻辑，提供稳定的普通唤醒模式和前置词唤醒模式 - 优化拾音质量，支持多声道麦克风拾音 - 优化自动播放服务器的对接机制，提供稳定和兼容旧版ue工程的对接模式 - 数字人接口输出机器人表情，以适应新fay ui及单片机的数字人表情输出 - 使用更高级的音频时长计算方式，可以更精准控制音频播放完成后的逻辑 - 修复点击关闭按钮会导致程序退出的bug - 修复没有麦克风的设备开启麦克风会出错的问题 - 为服务器主机地址提供配置项，以方便服务器部署
101 lines
3.1 KiB
Python
101 lines
3.1 KiB
Python
"""
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This script is an example of using the Zhipu API to create various interactions with a ChatGLM3 model. It includes
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functions to:
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1. Conduct a basic chat session, asking about weather conditions in multiple cities.
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2. Initiate a simple chat in Chinese, asking the model to tell a short story.
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3. Retrieve and print embeddings for a given text input.
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Each function demonstrates a different aspect of the API's capabilities,
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showcasing how to make requests and handle responses.
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Note: Make sure your Zhipu API key is set as an environment
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variable formate as xxx.xxx (just for check, not need a real key).
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"""
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from zhipuai import ZhipuAI
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base_url = "http://127.0.0.1:8000/v1/"
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client = ZhipuAI(api_key="EMP.TY", base_url=base_url)
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def function_chat():
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messages = [{"role": "user", "content": "What's the weather like in San Francisco, Tokyo, and Paris?"}]
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tools = [
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{
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"type": "function",
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"function": {
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"name": "get_current_weather",
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"description": "Get the current weather in a given location",
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"parameters": {
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"type": "object",
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"properties": {
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"location": {
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"type": "string",
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"description": "The city and state, e.g. San Francisco, CA",
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},
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"unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
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},
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"required": ["location"],
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},
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},
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}
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]
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response = client.chat.completions.create(
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model="chatglm3_6b",
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messages=messages,
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tools=tools,
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tool_choice="auto",
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)
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if response:
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content = response.choices[0].message.content
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print(content)
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else:
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print("Error:", response.status_code)
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def simple_chat(use_stream=True):
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messages = [
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{
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"role": "system",
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"content": "You are ChatGLM3, a large language model trained by Zhipu.AI. Follow "
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"the user's instructions carefully. Respond using markdown.",
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},
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{
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"role": "user",
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"content": "你好，请你介绍一下chatglm3-6b这个模型"
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}
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]
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response = client.chat.completions.create(
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model="chatglm3_",
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messages=messages,
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stream=use_stream,
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max_tokens=256,
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temperature=0.8,
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top_p=0.8)
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if response:
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if use_stream:
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for chunk in response:
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print(chunk.choices[0].delta.content)
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else:
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content = response.choices[0].message.content
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print(content)
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else:
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print("Error:", response.status_code)
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def embedding():
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response = client.embeddings.create(
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model="bge-large-zh-1.5",
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input=["ChatGLM3-6B 是一个大型的中英双语模型。"],
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)
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embeddings = response.data[0].embedding
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print("嵌入完成，维度：", len(embeddings))
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if __name__ == "__main__":
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simple_chat(use_stream=False)
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simple_chat(use_stream=True)
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embedding()
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function_chat()
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