1. support openai_api.py to the latest openai sdk(>=1.0.0); 2. add st…

examples/openai_api_demo/function_call_examples_v2.py

@@ -0,0 +1,307 @@
+# Reference: https://openai.com/blog/function-calling-and-other-api-updates
+import json
+from openai import OpenAI
+
+# To start an Latest OpenAI-like Qwen server, use the following commands:
+# git clone https://github.com/QwenLM/Qwen;
+# cd Qwen;
+# pip install fastapi uvicorn openai pydantic sse_starlette;
+# python examples/openai_api_demo/openai_api.py;
+#
+# Then configure the api_base and api_key in your client:
+client = OpenAI(
+ api_key="EMPTY",
+ base_url="http://localhost:8000/v1/",
+)
+
+
+# Change the default values of stream parameter to enable streaming
+def call_qwen(messages, functions=None, stream=False):
+ print(messages)
+ if functions:
+ response = client.chat.completions.create(
+ model="Qwen", messages=messages, functions=functions, stream=stream
+ )
+ else:
+ response = client.chat.completions.create(model="Qwen", messages=messages, stream=stream)
+ if stream:
+ for part in response:
+ print(part.choices[0].delta.content or "", end="", flush=True)
+ else:
+ # 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 get_current_weather(location, unit="fahrenheit"):
+ """Get the current weather in a given location"""
+ if "tokyo" in location.lower():
+ return json.dumps({"location": "Tokyo", "temperature": "10", "unit": unit})
+ elif "san francisco" in location.lower():
+ return json.dumps({"location": "San Francisco", "temperature": "72", "unit": unit})
+ elif "paris" in location.lower():
+ return json.dumps({"location": "Paris", "temperature": "22", "unit": unit})
+ else:
+ return json.dumps({"location": location, "temperature": "unknown"})
+
+
+# Parallel function calling
+def test_4():
+ # Step 1: send the conversation and available functions to the model
+ messages = [{"role": "user", "content": "What's the weather like in San Francisco, Tokyo, and Paris?"}]
+ tools = [
+ {
+ "type": "function",
+ "function": {
+ "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"],
+ },
+ },
+ }
+ ]
+ response = client.chat.completions.create(
+ model="qwen",
+ messages=messages,
+ tools=tools,
+ tool_choice="auto", # auto is default, but we'll be explicit
+ )
+ response_message = response.choices[0].message
+ print(f"first_response: {response_message} \n")
+
+ tool_calls = response_message.tool_calls
+ # Step 2: check if the model wanted to call a function
+ if tool_calls:
+ # Step 3: call the function
+ # Note: the JSON response may not always be valid; be sure to handle errors
+ available_functions = {
+ "get_current_weather": get_current_weather,
+ } # only one function in this example, but you can have multiple
+ messages.append(response_message) # extend conversation with assistant's reply
+ # Step 4: send the info for each function call and function response to the model
+ for tool_call in tool_calls:
+ function_name = tool_call.function.name
+ function_to_call = available_functions[function_name]
+ function_args = json.loads(tool_call.function.arguments)
+ function_response = function_to_call(
+ location=function_args.get("location"),
+ unit=function_args.get("unit"),
+ )
+ print(f"function_response: {function_response} \n")
+
+ messages.append(
+ {
+ "tool_call_id": tool_call.id,
+ "role": "tool",
+ "name": function_name,
+ "content": function_response,
+ }
+ ) # extend conversation with function response
+
+ print(f"second_messages: {messages} \n")
+ second_response = client.chat.completions.create(
+ model="qwen",
+ messages=messages
+ ) # get a new response from the model where it can see the function response
+ print(f"second_response: {second_response}")
+
+
+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()
+ # # Qwen has not optimized parallel tool calls, often unable to parse a parallel call instruction into multiple tool_calls
+ # print("### Test Case 4 - Parallel function calling (并行函数调用，GPT格式) ###")
+ # test_4()