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# Reference: https://openai.com/blog/function-calling-and-other-api-updates
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import openai
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# To start an OpenAI-like Qwen server, use the following commands:
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# git clone https://github.com/QwenLM/Qwen-7B;
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# cd Qwen-7B;
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# pip install fastapi uvicorn openai pydantic sse_starlette;
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# python openai_api.py;
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#
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# Then configure the api_base and api_key in your client:
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openai.api_base = "http://localhost:8000/v1"
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openai.api_key = "none"
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def call_qwen(messages, functions=None):
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print(messages)
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if functions:
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response = openai.ChatCompletion.create(
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model="Qwen", messages=messages, functions=functions
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)
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else:
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response = openai.ChatCompletion.create(model="Qwen", messages=messages)
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print(response)
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print(response.choices[0].message.content)
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return response
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def test_1():
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messages = [{"role": "user", "content": "你好"}]
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call_qwen(messages)
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messages.append({"role": "assistant", "content": "你好！很高兴为你提供帮助。"})
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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。"})
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call_qwen(messages)
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messages.append(
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{
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"role": "assistant",
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"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。……",
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}
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)
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messages.append({"role": "user", "content": "给这个故事起一个标题"})
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call_qwen(messages)
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def test_2():
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functions = [
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{
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"name_for_human": "谷歌搜索",
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"name_for_model": "google_search",
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"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。"
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+ " Format the arguments as a JSON object.",
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"parameters": [
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{
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"name": "search_query",
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"description": "搜索关键词或短语",
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"required": True,
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"schema": {"type": "string"},
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}
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],
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},
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{
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"name_for_human": "文生图",
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"name_for_model": "image_gen",
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"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。"
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+ " Format the arguments as a JSON object.",
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"parameters": [
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{
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"name": "prompt",
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"description": "英文关键词，描述了希望图像具有什么内容",
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"required": True,
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"schema": {"type": "string"},
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}
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],
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},
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]
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messages = [{"role": "user", "content": "你好"}]
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call_qwen(messages, functions)
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messages.append(
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{"role": "assistant", "content": "你好！很高兴见到你。有什么我可以帮忙的吗？"},
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)
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messages.append({"role": "user", "content": "谁是周杰伦"})
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call_qwen(messages, functions)
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messages.append(
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{
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"role": "assistant",
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"content": "Thought: 我应该使用Google搜索查找相关信息。",
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"function_call": {
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"name": "google_search",
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"arguments": '{"search_query": "周杰伦"}',
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},
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}
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)
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messages.append(
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{
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"role": "function",
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"name": "google_search",
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"content": "Jay Chou is a Taiwanese singer.",
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}
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)
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call_qwen(messages, functions)
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messages.append(
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{
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"role": "assistant",
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"content": "周杰伦（Jay Chou）是一位来自台湾的歌手。",
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},
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)
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messages.append({"role": "user", "content": "他老婆是谁"})
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call_qwen(messages, functions)
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messages.append(
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{
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"role": "assistant",
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"content": "Thought: 我应该使用Google搜索查找相关信息。",
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"function_call": {
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"name": "google_search",
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"arguments": '{"search_query": "周杰伦 老婆"}',
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},
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}
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)
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messages.append(
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{"role": "function", "name": "google_search", "content": "Hannah Quinlivan"}
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)
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call_qwen(messages, functions)
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messages.append(
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{
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"role": "assistant",
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"content": "周杰伦的老婆是Hannah Quinlivan。",
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},
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)
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messages.append({"role": "user", "content": "给我画个可爱的小猫吧，最好是黑猫"})
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call_qwen(messages, functions)
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messages.append(
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{
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"role": "assistant",
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"content": "Thought: 我应该使用文生图API来生成一张可爱的小猫图片。",
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"function_call": {
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"name": "image_gen",
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"arguments": '{"prompt": "cute black cat"}',
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},
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}
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)
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messages.append(
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{
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"role": "function",
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"name": "image_gen",
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"content": '{"image_url": "https://image.pollinations.ai/prompt/cute%20black%20cat"}',
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}
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)
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call_qwen(messages, functions)
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def test_3():
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functions = [
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{
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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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messages = [
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{
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"role": "user",
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# Note: The current version of Qwen-7B-Chat (as of 2023.08) performs okay with Chinese tool-use prompts,
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# but performs terribly when it comes to English tool-use prompts, due to a mistake in data collecting.
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"content": "波士顿天气如何？",
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}
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]
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call_qwen(messages, functions)
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messages.append(
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{
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"role": "assistant",
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"content": None,
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"function_call": {
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"name": "get_current_weather",
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"arguments": '{"location": "Boston, MA"}',
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},
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},
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)
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messages.append(
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{
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"role": "function",
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"name": "get_current_weather",
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"content": '{"temperature": "22", "unit": "celsius", "description": "Sunny"}',
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}
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)
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call_qwen(messages, functions)
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def test_4():
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from langchain.chat_models import ChatOpenAI
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from langchain.agents import load_tools, initialize_agent, AgentType
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llm = ChatOpenAI(
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model_name="Qwen",
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openai_api_base="http://localhost:8000/v1",
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openai_api_key="EMPTY",
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streaming=False,
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)
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tools = load_tools(
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["arxiv"],
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)
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agent_chain = initialize_agent(
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tools,
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llm,
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agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
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verbose=True,
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)
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# TODO: The performance is okay with Chinese prompts, but not so good when it comes to English.
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agent_chain.run("查一下论文 1605.08386 的信息")
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if __name__ == "__main__":
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print("### Test Case 1 - No Function Calling (普通问答、无函数调用) ###")
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test_1()
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print("### Test Case 2 - Use Qwen-Style Functions (函数调用，千问格式) ###")
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test_2()
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print("### Test Case 3 - Use GPT-Style Functions (函数调用，GPT格式) ###")
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test_3()
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print("### Test Case 4 - Use LangChain (接入Langchain) ###")
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test_4()
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