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# Reference: https://openai.com/blog/function-calling-and-other-api-updates
import openai
# To start an OpenAI-like Qwen server, use the following commands:
# git clone https://github.com/QwenLM/Qwen-7B;
# cd Qwen-7B;
# pip install fastapi uvicorn openai pydantic sse_starlette;
# python openai_api.py;
#
# Then configure the api_base and api_key in your client:
openai.api_base = "http://localhost:8000/v1"
openai.api_key = "none"
def call_qwen(messages, functions=None):
print(messages)
if functions:
response = openai.ChatCompletion.create(
model="Qwen", messages=messages, functions=functions
)
else:
response = openai.ChatCompletion.create(model="Qwen", messages=messages)
print(response)
print(response.choices[0].message.content)
return response
def test_1():
messages = [{"role": "user", "content": "你好"}]
call_qwen(messages)
messages.append({"role": "assistant", "content": "你好！很高兴为你提供帮助。"})
messages.append({"role": "user", "content": "给我讲一个年轻人奋斗创业最终取得成功的故事。故事只能有一句话。"})
call_qwen(messages)
messages.append(
{
"role": "assistant",
"content": "故事的主人公叫李明，他来自一个普通的家庭，父母都是普通的工人。李明想要成为一名成功的企业家。……",
}
)
messages.append({"role": "user", "content": "给这个故事起一个标题"})
call_qwen(messages)
def test_2():
functions = [
{
"name_for_human": "谷歌搜索",
"name_for_model": "google_search",
"description_for_model": "谷歌搜索是一个通用搜索引擎，可用于访问互联网、查询百科知识、了解时事新闻等。"
+ " Format the arguments as a JSON object.",
"parameters": [
{
"name": "search_query",
"description": "搜索关键词或短语",
"required": True,
"schema": {"type": "string"},
}
],
},
{
"name_for_human": "文生图",
"name_for_model": "image_gen",
"description_for_model": "文生图是一个AI绘画（图像生成）服务，输入文本描述，返回根据文本作画得到的图片的URL。"
+ " Format the arguments as a JSON object.",
"parameters": [
{
"name": "prompt",
"description": "英文关键词，描述了希望图像具有什么内容",
"required": True,
"schema": {"type": "string"},
}
],
},
]
messages = [{"role": "user", "content": "你好"}]
call_qwen(messages, functions)
messages.append(
{"role": "assistant", "content": "你好！很高兴见到你。有什么我可以帮忙的吗？"},
)
messages.append({"role": "user", "content": "谁是周杰伦"})
call_qwen(messages, functions)
messages.append(
{
"role": "assistant",
"content": "Thought: 我应该使用Google搜索查找相关信息。",
"function_call": {
"name": "google_search",
"arguments": '{"search_query": "周杰伦"}',
},
}
)
messages.append(
{
"role": "function",
"name": "google_search",
"content": "Jay Chou is a Taiwanese singer.",
}
)
call_qwen(messages, functions)
messages.append(
{
"role": "assistant",
"content": "周杰伦（Jay Chou）是一位来自台湾的歌手。",
},
)
messages.append({"role": "user", "content": "他老婆是谁"})
call_qwen(messages, functions)
messages.append(
{
"role": "assistant",
"content": "Thought: 我应该使用Google搜索查找相关信息。",
"function_call": {
"name": "google_search",
"arguments": '{"search_query": "周杰伦 老婆"}',
},
}
)
messages.append(
{"role": "function", "name": "google_search", "content": "Hannah Quinlivan"}
)
call_qwen(messages, functions)
messages.append(
{
"role": "assistant",
"content": "周杰伦的老婆是Hannah Quinlivan。",
},
)
messages.append({"role": "user", "content": "给我画个可爱的小猫吧，最好是黑猫"})
call_qwen(messages, functions)
messages.append(
{
"role": "assistant",
"content": "Thought: 我应该使用文生图API来生成一张可爱的小猫图片。",
"function_call": {
"name": "image_gen",
"arguments": '{"prompt": "cute black cat"}',
},
}
)
messages.append(
{
"role": "function",
"name": "image_gen",
"content": '{"image_url": "https://image.pollinations.ai/prompt/cute%20black%20cat"}',
}
)
call_qwen(messages, functions)
def test_3():
functions = [
{
"name": "get_current_weather",
"description": "Get the current weather in a given location.",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city and state, e.g. San Francisco, CA",
},
"unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
},
"required": ["location"],
},
}
]
messages = [
{
"role": "user",
# Note: The current version of Qwen-7B-Chat (as of 2023.08) performs okay with Chinese tool-use prompts,
# but performs terribly when it comes to English tool-use prompts, due to a mistake in data collecting.
"content": "波士顿天气如何？",
}
]
call_qwen(messages, functions)
messages.append(
{
"role": "assistant",
"content": None,
"function_call": {
"name": "get_current_weather",
"arguments": '{"location": "Boston, MA"}',
},
},
)
messages.append(
{
"role": "function",
"name": "get_current_weather",
"content": '{"temperature": "22", "unit": "celsius", "description": "Sunny"}',
}
)
call_qwen(messages, functions)
def test_4():
from langchain.chat_models import ChatOpenAI
from langchain.agents import load_tools, initialize_agent, AgentType
llm = ChatOpenAI(
model_name="Qwen",
openai_api_base="http://localhost:8000/v1",
openai_api_key="EMPTY",
streaming=False,
)
tools = load_tools(
["arxiv"],
)
agent_chain = initialize_agent(
tools,
llm,
agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
verbose=True,
)
# TODO: The performance is okay with Chinese prompts, but not so good when it comes to English.
agent_chain.run("查一下论文 1605.08386 的信息")
if __name__ == "__main__":
print("### Test Case 1 - No Function Calling (普通问答、无函数调用) ###")
test_1()
print("### Test Case 2 - Use Qwen-Style Functions (函数调用，千问格式) ###")
test_2()
print("### Test Case 3 - Use GPT-Style Functions (函数调用，GPT格式) ###")
test_3()
print("### Test Case 4 - Use LangChain (接入Langchain) ###")
test_4()
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