Chat With Tools
源码 examples/offline_inference/chat_with_tools.py
# SPDX-License-Identifier: Apache-2.0
# ruff: noqa
import json
import random
import string
from vllm import LLM
from vllm.sampling_params import SamplingParams
# This script is an offline demo for function calling
# 此脚本是用于函数调用的离线演示
#
# If you want to run a server/client setup, please follow this code:
# 如果要运行服务器/客户端设置，请按以下代码:
#
# - Server:
# - 服务器:
#
# ```bash
# vllm serve mistralai/Mistral-7B-Instruct-v0.3 --tokenizer-mode mistral --load-format mistral --config-format mistral
# ```
#
# - Client:
# - 客户端:
#
# ```bash
# curl --location 'http://<your-node-url>:8000/v1/chat/completions' \
# --header 'Content-Type: application/json' \
# --header 'Authorization: Bearer token' \
# --data '{
# "model": "mistralai/Mistral-7B-Instruct-v0.3"
# "messages": [
# {
# "role": "user",
# "content": [
# {"type" : "text", "text": "Describe this image in detail please."},
# {"type": "image_url", "image_url": {"url": "https://s3.amazonaws.com/cms.ipressroom.com/338/files/201808/5b894ee1a138352221103195_A680%7Ejogging-edit/A680%7Ejogging-edit_hero.jpg"}},
# {"type" : "text", "text": "and this one as well. Answer in French."},
# {"type": "image_url", "image_url": {"url": "https://www.wolframcloud.com/obj/resourcesystem/images/a0e/a0ee3983-46c6-4c92-b85d-059044639928/6af8cfb971db031b.png"}}
# ]
# }
# ]
# }'
# ```
#
# Usage:
# 用法:
# python demo.py simple
# python demo.py advanced
model_name = "mistralai/Mistral-7B-Instruct-v0.3"
# or switch to "mistralai/Mistral-Nemo-Instruct-2407"
# or "mistralai/Mistral-Large-Instruct-2407"
# or any other mistral model with function calling ability
# 或切换到 "Mistralai/Mistral-Nemo-Instruct-2407"
# 或 "Mistralai/Mistral-Large-Instruct-2407"
# 或具有功能通话能力的任何其他 Mistral 模型
sampling_params = SamplingParams(max_tokens=8192, temperature=0.0)
llm = LLM(model=model_name,
tokenizer_mode="mistral",
config_format="mistral",
load_format="mistral")
def generate_random_id(length=9):
characters = string.ascii_letters + string.digits
random_id = ''.join(random.choice(characters) for _ in range(length))
return random_id
# simulate an API that can be called
# 模拟可以调用的 API
def get_current_weather(city: str, state: str, unit: 'str'):
return (f"The weather in {city}, {state} is 85 degrees {unit}. It is "
"partly cloudly, with highs in the 90's.")
tool_funtions = {"get_current_weather": get_current_weather}
tools = [{
"type": "function",
"function": {
"name": "get_current_weather",
"description": "Get the current weather in a given location",
"parameters": {
"type": "object",
"properties": {
"city": {
"type":
"string",
"description":
"The city to find the weather for, e.g. 'San Francisco'"
},
"state": {
"type":
"string",
"description":
"the two-letter abbreviation for the state that the city is"
" in, e.g. 'CA' which would mean 'California'"
},
"unit": {
"type": "string",
"description": "The unit to fetch the temperature in",
"enum": ["celsius", "fahrenheit"]
}
},
"required": ["city", "state", "unit"]
}
}
}]
messages = [{
"role":
"user",
"content":
"Can you tell me what the temperate will be in Dallas, in fahrenheit?"
}]
outputs = llm.chat(messages, sampling_params=sampling_params, tools=tools)
output = outputs[0].outputs[0].text.strip()
# append the assistant message
# 附加助手消息
messages.append({
"role": "assistant",
"content": output,
})
# let's now actually parse and execute the model's output simulating an API call by using the
# above defined function
# 现在让我们实际上解析并执行模型的输出，以模拟 API 调用
# 上面定义的功能
tool_calls = json.loads(output)
tool_answers = [
tool_funtions[call['name']](**call['arguments']) for call in tool_calls
]
# append the answer as a tool message and let the LLM give you an answer
# 附加答案到工具消息中，让 LLM 给您答案
messages.append({
"role": "tool",
"content": "\n\n".join(tool_answers),
"tool_call_id": generate_random_id(),
})
outputs = llm.chat(messages, sampling_params, tools=tools)
print(outputs[0].outputs[0].text.strip())
# yields
# 结果
# 'The weather in Dallas, TX is 85 degrees fahrenheit. '
# 'It is partly cloudly, with highs in the 90's.'