mirror of
https://github.com/QwenLM/Qwen.git
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250 lines
7.9 KiB
Python
250 lines
7.9 KiB
Python
# Reference: https://openai.com/blog/function-calling-and-other-api-updates
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import json
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from pprint import pprint
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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('input:')
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pprint(messages, indent=2)
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if functions:
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response = openai.ChatCompletion.create(model='Qwen',
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messages=messages,
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functions=functions)
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else:
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response = openai.ChatCompletion.create(model='Qwen',
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messages=messages)
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response = response.choices[0]['message']
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response = json.loads(json.dumps(response,
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ensure_ascii=False)) # fix zh rendering
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print('output:')
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pprint(response, indent=2)
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print()
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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({
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'role': 'user',
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'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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})
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call_qwen(messages)
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messages.append({
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'role':
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'assistant',
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'content':
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'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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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':
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'谷歌搜索',
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'name_for_model':
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'google_search',
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'description_for_model':
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'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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'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': {
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'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':
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'文生图',
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'name_for_model':
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'image_gen',
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'description_for_model':
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'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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'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': {
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'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': '（请不要调用工具）\n\n你好'}]
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call_qwen(messages, functions)
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messages.append({
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'role': 'assistant',
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'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': '搜索一下谁是周杰伦'})
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call_qwen(messages, functions)
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messages.append({
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'role': 'assistant',
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'content': '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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messages.append({
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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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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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messages.append({'role': 'user', 'content': '搜索一下他老婆是谁'})
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call_qwen(messages, functions)
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messages.append({
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'role': 'assistant',
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'content': '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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messages.append({
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'role': 'function',
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'name': 'google_search',
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'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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messages.append({'role': 'user', 'content': '用文生图工具画个可爱的小猫吧，最好是黑猫'})
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call_qwen(messages, functions)
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messages.append({
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'role': 'assistant',
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'content': '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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messages.append({
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'role':
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'function',
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'name':
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'image_gen',
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'content':
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'{"image_url": "https://image.pollinations.ai/prompt/cute%20black%20cat"}',
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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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'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':
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'The city and state, e.g. San Francisco, CA',
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},
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'unit': {
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'type': 'string',
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'enum': ['celsius', 'fahrenheit']
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},
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},
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'required': ['location'],
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},
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}]
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messages = [{
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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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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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messages.append({
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'role':
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'function',
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'name':
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'get_current_weather',
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'content':
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'{"temperature": "22", "unit": "celsius", "description": "Sunny"}',
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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.agents import AgentType, initialize_agent, load_tools
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from langchain.chat_models import ChatOpenAI
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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(['arxiv'], )
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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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