40 lines
1.7 KiB
Python
40 lines
1.7 KiB
Python
from zhipuai import ZhipuAI
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class ZhipuService:
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def __init__(self):
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self.model_name = "glm-4"
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self.app_secret_key = "d54f764a1d67c17d857bd3983b772016.GRjowY0fyiMNurLc"
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def talk_to_zhipu(self, message):
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client = ZhipuAI(api_key=self.app_secret_key) # 请填写您自己的APIKey
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response = client.chat.completions.create(
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model=self.model_name, # 填写需要调用的模型名称
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messages=[
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{"role": "user", "content": message},
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],
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stream=True, # 流式输出
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temperature= 0.01, #随机度，越大越发散，0.01则是一个比较确定和稳定的输出
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top_p= 0.1, #选择前 10% 概率的 tokens 作为候选，也是影响随机程度
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)
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accum_resp = ""
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for chunk in response:
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print(chunk.choices[0].delta.content)
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accum_resp = accum_resp + chunk.choices[0].delta.content
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print(accum_resp)
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return accum_resp
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def talk_to_zhipu_sse(self, message):
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client = ZhipuAI(api_key=self.app_secret_key) # 请填写您自己的APIKey
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response = client.chat.completions.create(
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model=self.model_name, # 填写需要调用的模型名称
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messages=[
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{"role": "user", "content": message},
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],
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stream=True, # 流式输出
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temperature= 0.01, #随机度，越大越发散，0.01则是一个比较确定和稳定的输出
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top_p= 0.1, #选择前 10% 概率的 tokens 作为候选，也是影响随机程度
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)
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for chunk in response:
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print(chunk.choices[0].delta.content)
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yield chunk.choices[0].delta.content |