前言
本文使用最新的大模型 chatglm3-6b ，调用自定义工具计算两个地址的经纬度之间的距离。
安装 11.8 的 cuda
在 https://pytorch.org/get-started/locally/ 中可以查看 pytorch 最常见的是支持 11.8 cuda 版本，然后进入 https://developer.nvidia.com/cuda-toolkit-archive 找到 CUDA Toolkit 11.8 进行下载，下载结束之后双击基本上是傻瓜式下一步按钮即可，不懂的可以见参考中的链接。此时重新打开命令行，查看 nvcc -V 已经变成了 11.8 版本：
yaml
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2022 NVIDIA Corporation
Built on Wed_Sep_21_10:41:10_Pacific_Daylight_Time_2022
Cuda compilation tools, release 11.8, V11.8.89
Build cuda_11.8.r11.8/compiler.31833905_0搭建 conda 虚拟环境
创建 3.10 的 python 版本来生成虚拟环境。
ini
conda create -n torch-2.x-py-3.10 python=3.10激活虚拟环境
conda activate torch-2.x-py-3.10
在浏览器 pytorch.org/get-started... 页面中找到支持 CUDA 11.8 的 pytorch2.1 命令在虚拟环境中进行安装。
perl
pip3.exe install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118拉取项目 ChatGLM3 到本地
bash
git clone https://github.com/THUDM/ChatGLM3安装项目所需要的库
pip.exe install -r requirements.txt
修改模型绝对路径
从 huggingface.co/THUDM/chatg... 将完整的模型下载到本地，将 langchain_demo/main.py 文件改为下面的代码，其中主要修改的部分是 model_path 是模型的绝对路径，另外使用 run_tool 调用 Calculator 。
ini
from typing import List
from ChatGLM3 import ChatGLM3
from langchain.agents import load_tools
from Tool.Calculator import Calculator
from langchain.agents import initialize_agent
from langchain.agents import AgentType
def run_tool(tools, llm, prompt_chain: List[str]):
loaded_tolls = []
for tool in tools:
if isinstance(tool, str):
loaded_tolls.append(load_tools([tool], llm=llm)[0])
else:
loaded_tolls.append(tool)
agent = initialize_agent(
loaded_tolls, llm,
agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION,
verbose=True,
handle_parsing_errors=True
)
for prompt in prompt_chain:
agent.run(prompt)
if __name__ == "__main__":
model_path = "D:\chatglm3-6b"
llm = ChatGLM3()
llm.load_model(model_name_or_path=model_path)
# calculator: 单个工具调用示例 3
run_tool([Calculator()], llm, [
"复兴城市家园和清河家园经的经纬度是120.15235、30.210432 和 120.1531、30.20865，计算他们之间的距离？",
])将 langchain_demo/Tool/Calculator.py 改成下面的代码，主要实现了一个计算两个经纬度之间的直线距离的函数。
python
import abc
from typing import Any
from langchain.tools import BaseTool
from math import radians, sin, cos, sqrt, atan2
class Calculator(BaseTool, abc.ABC):
name = "Calculator"
description = "Useful for when you need to answer questions about math"
def __init__(self):
super().__init__()
async def _arun(self, *args: Any, **kwargs: Any) -> Any:
# 用例中没有用到 arun 不予具体实现
pass
def haversine_distance(self, lon1, lat1, lon2, lat2 ):
lat1, lon1, lat2, lon2 = map(radians, [lat1, lon1, lat2, lon2])
dlat = lat2 - lat1
dlon = lon2 - lon1
a = sin(dlat / 2) ** 2 + cos(lat1) * cos(lat2) * sin(dlon / 2) ** 2
c = 2 * atan2(sqrt(a), sqrt(1 - a))
R = 6371.0 * 1000
distance = R * c
return distance
def _run(self, para: str) -> str:
A,B = para.split('-')
lon1, lat1 = A.split(",")
lon2, lat2 = B.split(",")
return str(self.haversine_distance(float(lon1), float(lat1), float(lon2), float(lat2))) + "米"
if __name__ == "__main__":
calculator_tool = Calculator()
result = calculator_tool.run("120.15235,30.210432-120.1531,30.20865")
print(result)启动 main.py ，可以看到打印下面的结果，大模型从输入中提取出来了两个经纬度，并且传入到了自定义的计算距离的工具，可以返回结果为 210.849 米 ，下面是整个思考和结论过程：
vbnet
Loading checkpoint shards: 100%|██████████| 7/7 [00:03<00:00, 2.21it/s]
> Entering new AgentExecutor chain...
======
System: Respond to the human as helpfully and accurately as possible. You have access to the following tools:
Calculator: Useful for when you need to answer questions about math, args: {{'para': {{'title': 'Para', 'type': 'string'}}}}
Use a json blob to specify a tool by providing an action key (tool name) and an action_input key (tool input).
Valid "action" values: "Final Answer" or Calculator
Provide only ONE action per $JSON_BLOB, as shown:
```
{
"action": $TOOL_NAME,
"action_input": $INPUT
}
```
Follow this format:
Question: input question to answer
Thought: consider previous and subsequent steps
Action:
```
$JSON_BLOB
```
Observation: action result
... (repeat Thought/Action/Observation N times)
Thought: I know what to respond
Action:
```
{
"action": "Final Answer",
"action_input": "Final response to human"
}
```
Begin! Reminder to ALWAYS respond with a valid json blob of a single action. Use tools if necessary. Respond directly if appropriate. Format is Action:```$JSON_BLOB```then Observation:.
Thought:
Human: 复兴城市家园和清河家园经的经纬度是120.15235、30.210432 和 120.1531、30.20865，计算他们之间的距离？
======
Action:
```
{"action": "Calculator", "action_input": "120.15235,30.210432-120.1531,30.20865"}
```
Observation: 210.84897095947352米
Thought:======
System: Respond to the human as helpfully and accurately as possible. You have access to the following tools:
Calculator: Useful for when you need to answer questions about math, args: {{'para': {{'title': 'Para', 'type': 'string'}}}}
Use a json blob to specify a tool by providing an action key (tool name) and an action_input key (tool input).
Valid "action" values: "Final Answer" or Calculator
Provide only ONE action per $JSON_BLOB, as shown:
```
{
"action": $TOOL_NAME,
"action_input": $INPUT
}
```
Follow this format:
Question: input question to answer
Thought: consider previous and subsequent steps
Action:
```
$JSON_BLOB
```
Observation: action result
... (repeat Thought/Action/Observation N times)
Thought: I know what to respond
Action:
```
{
"action": "Final Answer",
"action_input": "Final response to human"
}
```
Begin! Reminder to ALWAYS respond with a valid json blob of a single action. Use tools if necessary. Respond directly if appropriate. Format is Action:```$JSON_BLOB```then Observation:.
Thought:
Human: 复兴城市家园和清河家园经的经纬度是120.15235、30.210432 和 120.1531、30.20865，计算他们之间的距离？
This was your previous work (but I haven't seen any of it! I only see what you return as final answer):
Action:
```
{"action": "Calculator", "action_input": "120.15235,30.210432-120.1531,30.20865"}
```
Observation: 210.84897095947352米
Thought:
======
Action:
```
{"action": "Final Answer", "action_input": "复兴城市家园和清河家园之间的距离约为210.849米。"}
```
> Finished chain.