222 lines
7.5 KiB
Python
222 lines
7.5 KiB
Python
"""
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This script is an example of using the OpenAI API to create various interactions with a ChatGLM3 model.
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It includes 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, showcasing how to make requests
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and handle responses.
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"""
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from openai import OpenAI
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base_url = "http://127.0.0.1:8000/v1/"
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client = OpenAI(api_key="EMPTY", 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",
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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 chat(text):
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# 定义API请求的数据
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data = {
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"model": "chatglm3-6b",
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"prompt": text,
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"temperature": 0.5, # 控制输出结果的随机性，范围从0.0到1.0，越高越随机
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"max_tokens": 75, # 控制输出文本的长度
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"top_p": 1, # 一个更复杂的参数，与temperature类似但更加精细控制
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"n": 1, # 要返回的最完整的文本段落数
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"stream": False # 是否以流的形式返回输出
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}
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# 发送API请求
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response = client.chat.completions.create(**data)
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# 打印响应结果
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print(response.get("choices")[0]["text"])
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def chat2(text):
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messages = [
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{
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"role": "user",
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"content": text
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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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prompt=messages,
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stream=False,
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max_tokens=256,
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temperature=0.8,
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presence_penalty=1.1,
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top_p=0.8)
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if response:
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if False:
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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 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 the user's "
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"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，请你用生动的话语给我讲一个猫和狗的小故事吧"
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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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stream=use_stream,
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max_tokens=256,
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temperature=0.8,
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presence_penalty=1.1,
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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 chat3(text):
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history = [['你好', '你好，有什么帮到你呢？'],['你好，给我讲一个七仙女的故事，大概20字', '七个仙女下凡,来到人间,遇见了王子,经历了许多冒险和考验,最终爱情获胜']]
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messages=[]
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if history is not None:
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for string in history:
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# 打印字符串
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print(string)
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# for his in string:
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# print(his)
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i = 0
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for his in string:
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print(his)
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if i==0:
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dialogue={
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"role": "user",
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"content": his
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}
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elif i==1:
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dialogue={
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"role": "assistant",
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"content": his
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}
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messages.append(dialogue)
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i = 1
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current = {
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"role": "user",
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"content": text
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}
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messages.append(current)
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print("===============messages=========================")
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print(messages)
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print("===============messages=========================")
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# messages = [
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# {
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# "role": "user",
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# "content": text
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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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stream=False,
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max_tokens=256,
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temperature=0.8,
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presence_penalty=1.1,
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top_p=0.8)
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if response:
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if False:
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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=["你好，给我讲一个故事，大概100字"],
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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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chat3("你好，给我讲楚汉相争的故事，大概20字")
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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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# 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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# curl -X POST "http://127.0.0.1:8000/v1/completions" \
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# -H 'Content-Type: application/json' \
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# -d '{"prompt": "请用20字内回复我.你今年多大了", "history": []}'
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# curl -X POST "http://127.0.0.1:8000/v1/completions" \
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# -H 'Content-Type: application/json' \
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# -d '{"prompt": "请用20字内回复我.你今年多大了", "history": [{"你好","你好👋！我是人工智能助手 ChatGLM-6B，很高兴见到你，欢迎问我任何问题。"}]}'
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# curl -X POST "http://127.0.0.1:8000/v1/completions" \
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# -H 'Content-Type: application/json' \
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# -d '{"prompt": "请用20字内回复我.你今年多大了", "history": [["你好","你好👋！我是人工智能助手 ChatGLM-6B，很高兴见到你，欢迎问我任何问题。"]]}'
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# curl -X POST "http://127.0.0.1:8000/v1/completions" \
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# -H 'Content-Type: application/json' \
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# -d '{"prompt": "请用20字内回复我.你今年多大了", "history": ["你好"]}'
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