68 lines
2.7 KiB
Python
68 lines
2.7 KiB
Python
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# 使用curl命令测试返回
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# curl -X POST "http://127.0.0.1:8000/v1/chat/completions" \
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# -H "Content-Type: application/json" \
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# -d "{\"model\": \"chatglm3-6b\", \"messages\": [{\"role\": \"system\", \"content\": \"You are ChatGLM3, a large language model trained by Zhipu.AI. Follow the user's instructions carefully. Respond using markdown.\"}, {\"role\": \"user\", \"content\": \"你好，给我讲一个故事，大概100字\"}], \"stream\": false, \"max_tokens\": 100, \"temperature\": 0.8, \"top_p\": 0.8}"
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# 使用Python代码测返回
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import requests
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import json
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import urllib3
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########################################
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# 该文件实现了与大模型的简单通信
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########################################
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# 忽略https的安全性警告
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urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)
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base_url = "https://45.125.46.134:25344" # 本地部署的地址,或者使用你访问模型的API地址
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def create_chat_completion(model, messages, use_stream=False):
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data = {
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"model": model, # 模型名称
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"messages": messages, # 会话历史
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"stream": use_stream, # 是否流式响应
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"max_tokens": 100, # 最多生成字数
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"temperature": 0.8, # 温度
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"top_p": 0.8, # 采样概率
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}
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response = requests.post(f"{base_url}/v1/chat/completions", json=data, stream=use_stream, verify=False)
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if response.status_code == 200:
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if use_stream:
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# 处理流式响应
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for line in response.iter_lines():
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if line:
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decoded_line = line.decode('utf-8')[6:]
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try:
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response_json = json.loads(decoded_line)
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content = response_json.get("choices", [{}])[0].get("delta", {}).get("content", "")
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print(content)
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except:
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print("Special Token:", decoded_line)
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else:
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# 处理非流式响应
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decoded_line = response.json()
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print(decoded_line)
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content = decoded_line.get("choices", [{}])[0].get("message", "").get("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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return None
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if __name__ == "__main__":
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chat_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 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，给我讲一个故事，大概100字"
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}
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]
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create_chat_completion("chatglm3-6b", chat_messages, use_stream=False)
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