哈嘍！伙伴們，我是小智，你們的AI向?qū)?。歡迎來到每日的AI學(xué)習(xí)時(shí)間。今天，我們將一起深入AI的奇妙世界，探索“面向開發(fā)者的LLM入門課程-內(nèi)置工具PythonREPLTool”，并學(xué)會(huì)本篇文章中所講的全部知識(shí)點(diǎn)。還是那句話“不必遠(yuǎn)征未知，只需喚醒你的潛能！”跟著小智的步伐，我們終將學(xué)有所成，學(xué)以致用，并發(fā)現(xiàn)自身的更多可能性。話不多說，現(xiàn)在就讓我們開始這場(chǎng)激發(fā)潛能的AI學(xué)習(xí)之旅吧。

面向開發(fā)者的LLM入門課程-內(nèi)置工具PythonREPLTool：

使用LangChain內(nèi)置工具PythonREPLTool

我們創(chuàng)建一個(gè)能將顧客名字轉(zhuǎn)換為拼音的 python 代理，步驟與上一部分的一樣：

from langchain.agents.agent_toolkits import create_python_agent
from langchain.tools.python.tool import PythonREPLTool

agent = create_python_agent(
llm, #使用前面一節(jié)已經(jīng)加載的大語(yǔ)言模型
tool=PythonREPLTool(), #使用Python交互式環(huán)境工具 REPLTool
verbose=True #輸出中間步驟
)
customer_list = [“小明”,”小黃”,”小紅”,”小藍(lán)”,”小橘”,”小綠”,]

agent.run(f”將使用pinyin拼音庫(kù)這些客戶名字轉(zhuǎn)換為拼音，并打印輸出列表: {customer_list}。”)

> Entering new AgentExecutor chain…
I need to use the pinyin library to convert the names to pinyin. I can then print
out the list of converted names.
Action: Python_REPL
Action Input: import pinyin
Observation:
Thought:I have imported the pinyin library. Now I can use it to convert the names
to pinyin.
Action: Python_REPL
Action Input: names = [‘小明’, ‘小黃’, ‘小紅’, ‘小藍(lán)’, ‘小橘’, ‘小綠’]
pinyin_names = [pinyin.get(i, format=’strip’) for i in names]
print(pinyin_names)
Observation: [‘xiaoming’, ‘xiaohuang’, ‘xiaohong’, ‘xiaolan’, ‘xiaoju’, ‘xiaolv’]

Thought:I have successfully converted the names to pinyin and printed out the
list of converted names.
Final Answer: [‘xiaoming’, ‘xiaohuang’, ‘xiaohong’, ‘xiaolan’, ‘xiaoju’,
‘xiaolv’]

> Finished chain.

“[‘xiaoming’, ‘xiaohuang’, ‘xiaohong’, ‘xiaolan’, ‘xiaoju’, ‘xiaolv’]”

在調(diào)試（debug）模式下再次運(yùn)行，我們可以把上面的6步分別對(duì)應(yīng)到下面的具體流程

1. 模型對(duì)于接下來需要做什么，給出思考（Thought）
· [chain/start] [1:chain:AgentExecutor] Entering Chain run with input
· [chain/start] [1:chain:AgentExecutor > 2:chain:LLMChain] Entering Chain run with input
· [llm/start] [1:chain:AgentExecutor > 2:chain:LLMChain > 3:llm:ChatOpenAI] Entering LLM run with input
· [llm/end] [1:chain:AgentExecutor > 2:chain:LLMChain > 3:llm:ChatOpenAI] [1.91s] Exiting LLM run with output
· [chain/end] [1:chain:AgentExecutor > 2:chain:LLMChain] [1.91s] Exiting Chain run with output

2. 模型基于思考采取行動(dòng)（Action), 因?yàn)槭褂玫墓ぞ卟煌?，Action的輸出也和之前有所不同，這里輸出的為python代碼 import pinyin
· [tool/start] [1:chain:AgentExecutor > 4:tool:Python REPL] Entering Tool run with input
· [tool/end] [1:chain:AgentExecutor > 4:tool:Python_REPL] [1.28ms] Exiting Tool run with output

3. 模型得到觀察（Observation）
· [chain/start] [1:chain:AgentExecutor > 5:chain:LLMChain] Entering Chain run with input

4. 基于觀察，模型對(duì)于接下來需要做什么，給出思考（Thought）
· [llm/start] [1:chain:AgentExecutor > 5:chain:LLMChain > 6:llm:ChatOpenAI] Entering LLM run with input
· [llm/end] [1:chain:AgentExecutor > 5:chain:LLMChain > 6:llm:ChatOpenAI] [3.48s] Exiting LLM run with output

5. 給出最終答案（Final Answer）
· [chain/end] [1:chain:AgentExecutor > 5:chain:LLMChain] [3.48s] Exiting Chain run with output

6. 返回最終答案。
· [chain/end] [1:chain:AgentExecutor] [19.20s] Exiting Chain run with output

import langchain
langchain.debug=True
agent.run(f”使用pinyin拼音庫(kù)將這些客戶名字轉(zhuǎn)換為拼音，并打印輸出列表: {customer_list}”)
langchain.debug=False

[chain/start] [1:chain:AgentExecutor] Entering Chain run with input:
{
“input”: “使用pinyin拼音庫(kù)將這些客戶名字轉(zhuǎn)換為拼音，并打印輸出列表: [‘小明’, ‘小黃’, ‘小
紅’, ‘小藍(lán)’, ‘小橘’, ‘小綠’]”
}
[chain/start] [1:chain:AgentExecutor > 2:chain:LLMChain] Entering Chain run with
input:
{
“input”: “使用pinyin拼音庫(kù)將這些客戶名字轉(zhuǎn)換為拼音，并打印輸出列表: [‘小明’, ‘小黃’, ‘小
紅’, ‘小藍(lán)’, ‘小橘’, ‘小綠’]”,
“agent_scratchpad”: “”,
“stop”: [
“nObservation:”,
“ntObservation:”
]
}
[llm/start] [1:chain:AgentExecutor > 2:chain:LLMChain > 3:llm:ChatOpenAI]
Entering LLM run with input:
{
“prompts”: [
“Human: You are an agent designed to write and execute python code to answer
questions.nYou have access to a python REPL, which you can use to execute python
code.nIf you get an error, debug your code and try again.nOnly use the output
of your code to answer the question. nYou might know the answer without running
any code, but you should still run the code to get the answer.nIf it does not
seem like you can write code to answer the question, just return “I don’t know”
as the answer.nnnPython_REPL: A Python shell. Use this to execute python
commands. Input should be a valid python command. If you want to see the output
of a value, you should print it out with `print(…)`.nnUse the following
format:nnQuestion: the input question you must answernThought: you should
always think about what to donAction: the action to take, should be one of
[Python_REPL]nAction Input: the input to the actionnObservation: the result of
the actionn… (this Thought/Action/Action Input/Observation can repeat N
times)nThought: I now know the final answernFinal Answer: the final answer to
the original input questionnnBegin!nnQuestion: 使用pinyin拼音庫(kù)將這些客戶名字轉(zhuǎn)換
為拼音，并打印輸出列表: [‘小明’, ‘小黃’, ‘小紅’, ‘小藍(lán)’, ‘小橘’, ‘小綠’]nThought:”
]
}
[llm/end] [1:chain:AgentExecutor > 2:chain:LLMChain > 3:llm:ChatOpenAI] [2.32s]
Exiting LLM run with output:
{
“generations”: [
[
{
“text”: “I need to use the pinyin library to convert the names to pinyin.
I can then print out the list of converted names.nAction: Python_REPLnAction
Input: import pinyin”,
“generation_info”: {
“finish_reason”: “stop”
},
“message”: {
“lc”: 1,
“type”: “constructor”,
“id”: [
“langchain”,
“schema”,
“messages”,
“AIMessage”
],
“kwargs”: {
“content”: “I need to use the pinyin library to convert the names to
pinyin. I can then print out the list of converted names.nAction:
Python_REPLnAction Input: import pinyin”,
“additional_kwargs”: {}
}
}
}
]
],
“llm_output”: {
“token_usage”: {
“prompt_tokens”: 320,
“completion_tokens”: 39,
“total_tokens”: 359
},
“model_name”: “gpt-3.5-turbo”
},
“run”: null
}
[chain/end] [1:chain:AgentExecutor > 2:chain:LLMChain] [2.33s] Exiting Chain run
with output:
{
“text”: “I need to use the pinyin library to convert the names to pinyin. I can
then print out the list of converted names.nAction: Python_REPLnAction Input:
import pinyin”
}
[tool/start] [1:chain:AgentExecutor > 4:tool:Python_REPL] Entering Tool run with
input:
“import pinyin”
[tool/end] [1:chain:AgentExecutor > 4:tool:Python_REPL] [1.5659999999999998ms]
Exiting Tool run with output:
“”
[chain/start] [1:chain:AgentExecutor > 5:chain:LLMChain] Entering Chain run with
input:
{
“input”: “使用pinyin拼音庫(kù)將這些客戶名字轉(zhuǎn)換為拼音，并打印輸出列表: [‘小明’, ‘小黃’, ‘小
紅’, ‘小藍(lán)’, ‘小橘’, ‘小綠’]”,
“agent_scratchpad”: “I need to use the pinyin library to convert the names to
pinyin. I can then print out the list of converted names.nAction:
Python_REPLnAction Input: import pinyinnObservation: nThought:”,
“stop”: [
“nObservation:”,
“ntObservation:”
]
}
[llm/start] [1:chain:AgentExecutor > 5:chain:LLMChain > 6:llm:ChatOpenAI]
Entering LLM run with input:
{
“prompts”: [
“Human: You are an agent designed to write and execute python code to answer
questions.nYou have access to a python REPL, which you can use to execute python
code.nIf you get an error, debug your code and try again.nOnly use the output
of your code to answer the question. nYou might know the answer without running
any code, but you should still run the code to get the answer.nIf it does not
seem like you can write code to answer the question, just return “I don’t know”
as the answer.nnnPython_REPL: A Python shell. Use this to execute python
commands. Input should be a valid python command. If you want to see the output
of a value, you should print it out with `print(…)`.nnUse the following
format:nnQuestion: the input question you must answernThought: you should
always think about what to donAction: the action to take, should be one of
[Python_REPL]nAction Input: the input to the actionnObservation: the result of
the actionn… (this Thought/Action/Action Input/Observation can repeat N
times)nThought: I now know the final answernFinal Answer: the final answer to
the original input questionnnBegin!nnQuestion: 使用pinyin拼音庫(kù)將這些客戶名字轉(zhuǎn)換
為拼音，并打印輸出列表: [‘小明’, ‘小黃’, ‘小紅’, ‘小藍(lán)’, ‘小橘’, ‘小綠’]nThought:I need
to use the pinyin library to convert the names to pinyin. I can then print out
the list of converted names.nAction: Python_REPLnAction Input: import
pinyinnObservation: nThought:”
]
}
[llm/end] [1:chain:AgentExecutor > 5:chain:LLMChain > 6:llm:ChatOpenAI] [4.09s]
Exiting LLM run with output:
{
“generations”: [
[
{
“text”: “I have imported the pinyin library. Now I can use it to convert
the names to pinyin.nAction: Python_REPLnAction Input: names = [‘小明’, ‘小黃’,
‘小紅’, ‘小藍(lán)’, ‘小橘’, ‘小綠’]npinyin_names = [pinyin.get(i, format=’strip’) for i
in names]nprint(pinyin_names)”,
“generation_info”: {
“finish_reason”: “stop”
},
“message”: {
“lc”: 1,
“type”: “constructor”,
“id”: [
“langchain”,
“schema”,
“messages”,
“AIMessage”
],
“kwargs”: {
“content”: “I have imported the pinyin library. Now I can use it to
convert the names to pinyin.nAction: Python_REPLnAction Input: names = [‘小明’,
‘小黃’, ‘小紅’, ‘小藍(lán)’, ‘小橘’, ‘小綠’]npinyin_names = [pinyin.get(i,
format=’strip’) for i in names]nprint(pinyin_names)”,
“additional_kwargs”: {}
}
}
}
]
],
“llm_output”: {
“token_usage”: {
“prompt_tokens”: 365,
“completion_tokens”: 87,
“total_tokens”: 452
},
“model_name”: “gpt-3.5-turbo”
},
“run”: null
}
[chain/end] [1:chain:AgentExecutor > 5:chain:LLMChain] [4.09s] Exiting Chain run
with output:
{
“text”: “I have imported the pinyin library. Now I can use it to convert the
names to pinyin.nAction: Python_REPLnAction Input: names = [‘小明’, ‘小黃’, ‘小
紅’, ‘小藍(lán)’, ‘小橘’, ‘小綠’]npinyin_names = [pinyin.get(i, format=’strip’) for i
in names]nprint(pinyin_names)”
}
[tool/start] [1:chain:AgentExecutor > 7:tool:Python_REPL] Entering Tool run with
input:
“names = [‘小明’, ‘小黃’, ‘小紅’, ‘小藍(lán)’, ‘小橘’, ‘小綠’]
pinyin_names = [pinyin.get(i, format=’strip’) for i in names]
print(pinyin_names)”
[tool/end] [1:chain:AgentExecutor > 7:tool:Python_REPL] [0.8809999999999999ms]
Exiting Tool run with output:
“[‘xiaoming’, ‘xiaohuang’, ‘xiaohong’, ‘xiaolan’, ‘xiaoju’, ‘xiaolv’]”
[chain/start] [1:chain:AgentExecutor > 8:chain:LLMChain] Entering Chain run with
input:
{
“input”: “使用pinyin拼音庫(kù)將這些客戶名字轉(zhuǎn)換為拼音，并打印輸出列表: [‘小明’, ‘小黃’, ‘小
紅’, ‘小藍(lán)’, ‘小橘’, ‘小綠’]”,
“agent_scratchpad”: “I need to use the pinyin library to convert the names to
pinyin. I can then print out the list of converted names.nAction:
Python_REPLnAction Input: import pinyinnObservation: nThought:I have imported
the pinyin library. Now I can use it to convert the names to pinyin.nAction:
Python_REPLnAction Input: names = [‘小明’, ‘小黃’, ‘小紅’, ‘小藍(lán)’, ‘小橘’, ‘小
綠’]npinyin_names = [pinyin.get(i, format=’strip’) for i in
names]nprint(pinyin_names)nObservation: [‘xiaoming’, ‘xiaohuang’, ‘xiaohong’,
‘xiaolan’, ‘xiaoju’, ‘xiaolv’]nnThought:”,
“stop”: [
“nObservation:”,
“ntObservation:”
]
}
[llm/start] [1:chain:AgentExecutor > 8:chain:LLMChain > 9:llm:ChatOpenAI]
Entering LLM run with input:
{
“prompts”: [
“Human: You are an agent designed to write and execute python code to answer
questions.nYou have access to a python REPL, which you can use to execute python
code.nIf you get an error, debug your code and try again.nOnly use the output
of your code to answer the question. nYou might know the answer without running
any code, but you should still run the code to get the answer.nIf it does not
seem like you can write code to answer the question, just return “I don’t know”
as the answer.nnnPython_REPL: A Python shell. Use this to execute python
commands. Input should be a valid python command. If you want to see the output
of a value, you should print it out with `print(…)`.nnUse the following
format:nnQuestion: the input question you must answernThought: you should
always think about what to donAction: the action to take, should be one of
[Python_REPL]nAction Input: the input to the actionnObservation: the result of
the actionn… (this Thought/Action/Action Input/Observation can repeat N
times)nThought: I now know the final answernFinal Answer: the final answer to
the original input questionnnBegin!nnQuestion: 使用pinyin拼音庫(kù)將這些客戶名字轉(zhuǎn)換
為拼音，并打印輸出列表: [‘小明’, ‘小黃’, ‘小紅’, ‘小藍(lán)’, ‘小橘’, ‘小綠’]nThought:I need
to use the pinyin library to convert the names to pinyin. I can then print out
the list of converted names.nAction: Python_REPLnAction Input: import
pinyinnObservation: nThought:I have imported the pinyin library. Now I can use
it to convert the names to pinyin.nAction: Python_REPLnAction Input: names =
[‘小明’, ‘小黃’, ‘小紅’, ‘小藍(lán)’, ‘小橘’, ‘小綠’]npinyin_names = [pinyin.get(i,
format=’strip’) for i in names]nprint(pinyin_names)nObservation: [‘xiaoming’,
‘xiaohuang’, ‘xiaohong’, ‘xiaolan’, ‘xiaoju’, ‘xiaolv’]nnThought:”
]
}
[llm/end] [1:chain:AgentExecutor > 8:chain:LLMChain > 9:llm:ChatOpenAI] [2.05s]
Exiting LLM run with output:
{
“generations”: [
[
{
“text”: “I have successfully converted the names to pinyin and printed
out the list of converted names.nFinal Answer: [‘xiaoming’, ‘xiaohuang’,
‘xiaohong’, ‘xiaolan’, ‘xiaoju’, ‘xiaolv’]”,
“generation_info”: {
“finish_reason”: “stop”
},
“message”: {
“lc”: 1,
“type”: “constructor”,
“id”: [
“langchain”,
“schema”,
“messages”,
“AIMessage”
],
“kwargs”: {
“content”: “I have successfully converted the names to pinyin and
printed out the list of converted names.nFinal Answer: [‘xiaoming’, ‘xiaohuang’,
‘xiaohong’, ‘xiaolan’, ‘xiaoju’, ‘xiaolv’]”,
“additional_kwargs”: {}
}
}
}
]
],
“llm_output”: {
“token_usage”: {
“prompt_tokens”: 483,
“completion_tokens”: 48,
“total_tokens”: 531
},
“model_name”: “gpt-3.5-turbo”
},
“run”: null
}
[chain/end] [1:chain:AgentExecutor > 8:chain:LLMChain] [2.05s] Exiting Chain run
with output:
{
“text”: “I have successfully converted the names to pinyin and printed out the
list of converted names.nFinal Answer: [‘xiaoming’, ‘xiaohuang’, ‘xiaohong’,
‘xiaolan’, ‘xiaoju’, ‘xiaolv’]”
}
[chain/end] [1:chain:AgentExecutor] [8.47s] Exiting Chain run with output:
{
“output”: “[‘xiaoming’, ‘xiaohuang’, ‘xiaohong’, ‘xiaolan’, ‘xiaoju’,
‘xiaolv’]”
}

嘿，伙伴們，今天我們的AI探索之旅已經(jīng)圓滿結(jié)束。關(guān)于“面向開發(fā)者的LLM入門課程-內(nèi)置工具PythonREPLTool”的內(nèi)容已經(jīng)分享給大家了。感謝你們的陪伴，希望這次旅程讓你對(duì)AI能夠更了解、更喜歡。謹(jǐn)記，精準(zhǔn)提問是解鎖AI潛能的鑰匙哦！如果有小伙伴想要了解學(xué)習(xí)更多的AI知識(shí)，請(qǐng)關(guān)注我們的官網(wǎng)“AI智研社”，保證讓你收獲滿滿呦！