【学习笔记】Langchain基础

zoopk0079 · 发表于 5 天前

作者：CSDN博客
文章目录

https://www.bilibili.com/video/BV1ax4y1s7Sk

1 Langchain基础回顾、LECL、Tool Use、RAG 以及 LangSmith

参考资料：

https://python.langchain.com/v0.2/docs/concepts/#langchain-expression-language-lcel
https://ai.plainenglish.io/understanding-large-language-model-based-agents-27bee5c82cec

import os
from dotenv import load_dotenv
# LANGCHAIN_TRACING_V2=true # 免费# LANGCHAIN_API_KEY= # 免费# OPENAI_API_KEY= # 付费，但可以用别的
load_dotenv()

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所谓的 agent 开发，LLMs workflows，GenAI 时代的软件工程
与 AutoGen 等相比，更多地面向开发者，面向软件工程
推荐《大模型应用开发动手做AI Agent》（https://www.bilibili.com/opus/935785456083140628）

LCEL (LangChain Expression Language)

所谓的最佳实践

# 自带的|运算符，是二进制位运算# (2 | 3) > 32|3>2# True2|3# 3

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from langchain_core.runnables import(
RunnablePassthrough,
RunnableLambda,
RunnableParallel
)
os.environ["LANGCHAIN_PROJECT"]='lcel_test'# 设置一个新的projectfrom langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
prompt = ChatPromptTemplate.from_template("Tell me a short joke about {topic}")
output_parser = StrOutputParser()
llm = ChatOpenAI(model="gpt-3.5-turbo")# lcel
chain =({"topic": RunnablePassthrough()}| prompt
| llm
| output_parser
)
chain.invoke("ice cream")# 'Why did the ice cream truck break down?\n\nBecause it had too many "scoops"!'

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这里prompt的输入就是{"topic": RunnablePassthrough()}，这样上一步的输出是下一步的输入，如此成chain
比如直接写成常见的串行语法：

prompt.invoke({'topic':'ice cream'})# ChatPromptValue(messages=[HumanMessage(content='Tell me a short joke about ice cream')])
llm.invoke(prompt.invoke({'topic':'ice cream'}))# AIMessage(content='Why did the ice cream truck break down?\n\nIt had too many "scoops" of ice cream!', response_metadata={'token_usage': {'completion_tokens': 22, 'prompt_tokens': 15, 'total_tokens': 37}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-fb46f4ce-665b-45f7-9fd0-9dd559465fcc-0', usage_metadata={'input_tokens': 15, 'output_tokens': 22, 'total_tokens': 37})
output_parser.invoke(llm.invoke(prompt.invoke({'topic':'ice cream'})))# 'Why did the ice cream truck break down? It had too many "scoops" on board!'

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runnables

# ex1
chain = RunnablePassthrough()| RunnablePassthrough ()| RunnablePassthrough ()
chain.invoke("hello")# 'hello'# ex2
chain = RunnablePassthrough()| RunnableLambda(lambda x: x.upper())
chain.invoke("hello")# 'HELLO'# ex3
chain = RunnablePassthrough()| RunnableLambda(lambda x: x.upper())| RunnablePassthrough()
chain.invoke("hello")# 'HELLO'

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2 conversational agents，ReAct、agent_scratchpad 历史过程信息维护

一些概念：

过程信息（所谓的对话式 agent，conversational agents，就是带记忆）
- intermediate_steps： list of actions/tools (input, output)
- 第一次 query-resp 之后，才会有这些过程信息
chat history
langsmith: 监控所有的 messages (input, output)

前一种是隐式的，后者是显式的。
ReAct（Reasoning & Acting）

ReAct（经典、且general、应用广泛的 prompt）
- User query => (Thought -> Action (Action Input) -> Observation(Action Ouptut)) * N
  multi-step/hops reasoning/interacting跟 function calling（LangChain 中的 tools）天然地适配；

from langchain import hub
prompt = hub.pull("hwchase17/react")type(prompt), prompt
"""
(langchain_core.prompts.prompt.PromptTemplate,
PromptTemplate(input_variables=['agent_scratchpad', 'input', 'tool_names', 'tools'], metadata={'lc_hub_owner': 'hwchase17', 'lc_hub_repo': 'react', 'lc_hub_commit_hash': 'd15fe3c426f1c4b3f37c9198853e4a86e20c425ca7f4752ec0c9b0e97ca7ea4d'}, template='Answer the following questions as best you can. You have access to the following tools:\n\n{tools}\n\nUse the following format:\n\nQuestion: the input question you must answer\nThought: you should always think about what to do\nAction: the action to take, should be one of [{tool_names}]\nAction Input: the input to the action\nObservation: the result of the action\n... (this Thought/Action/Action Input/Observation can repeat N times)\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question\n\nBegin!\n\nQuestion: {input}\nThought:{agent_scratchpad}'))
"""

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# 一个完整的 prompt 模板，历史对话/函数调用（输入输出）信息不断地追加在这一个 prompt 中，而不是维护成 messages listprint(prompt.template)"""
Answer the following questions as best you can. You have access to the following tools:
{tools}
Use the following format:
Question: the input question you must answer
Thought: you should always think about what to do
Action: the action to take, should be one of [{tool_names}]
Action Input: the input to the action
Observation: the result of the action
... (this Thought/Action/Action Input/Observation can repeat N times)
Thought: I now know the final answer
Final Answer: the final answer to the original input question
Begin!
Question: {input}
Thought:{agent_scratchpad}
"""

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LangChain Projects

next_action = agent.get_action(...)while next_action != AgentFinish:
observation = run(next_action)
next_action = agent.get_action(..., next_action, observation)return next_action

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os.environ["LANGCHAIN_PROJECT"]='conversational_agents'from langchain_openai import ChatOpenAI
from langchain.tools import tool
from langchain.agents import AgentExecutor, create_react_agent
@tooldefget_employee_id(name):"""
To get employee id, it takes employee name as arguments
name(str): Name of the employee
"""
fake_employees ={"Alice":"E001","Bob":"E002","Charlie":"E003","Diana":"E004","Evan":"E005","Fiona":"E006","George":"E007","Hannah":"E008","Ian":"E009","Jasmine":"E010"}return fake_employees.get(name,"Employee not found")# Custom tool for the Agent # ValueError: Function must have a docstring if description not provided.@tooldefget_employee_salary(employee_id):"""
To get the salary of an employee, it takes employee_id as input and return salary
"""
employee_salaries ={"E001":56000,"E002":47000,"E003":52000,"E004":61000,"E005":45000,"E006":58000,"E007":49000,"E008":53000,"E009":50000,"E010":55000}return employee_salaries.get(employee_id,"Employee not found")
prompt = hub.pull("hwchase17/react")
llm = ChatOpenAI(model='gpt-4o')
tools =[get_employee_salary, get_employee_id]
agent = create_react_agent(llm, tools, prompt)
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
agent_executor.invoke({"input":"What is the Salary of Evan?"})

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输出结果：

> Entering new AgentExecutor chain...
To determine Evan's salary, I first need to obtain his employee ID.
Action: get_employee_id
Action Input:"Evan"E005Now that I have Evan's employee ID, I can retrieve his salary using this ID.
Action: get_employee_salary
Action Input: E00545000I now know the final answer.
Final Answer: The salary of Evan is45000.> Finished chain.{'input':'What is the Salary of Evan?','output':'The salary of Evan is 45000.'}

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first query-resp
LangSmith: https://smith.langchain.com/
LangSmith是评估大模型能力好坏的评估工具，能够量化评估基于大模型的系统的效果。LangSmith通过记录langchain构建的大模型应用的中间过程，从而能够更好的调整提示词等中间过程做优化。
使用的Prompt：

Answer the following questions as best you can. You have access to the following tools:
get_employee_salary(employee_id)- To get the salary of an employee, it takes employee_id asinputandreturn salary
get_employee_id(name)- To get employee id, it takes employee name as arguments
name(str): Name of the employee
Use the following format:
Question: the input question you must answer
Thought: you should always think about what to do
Action: the action to take, should be one of [get_employee_salary, get_employee_id]
Action Input: the input to the action
Observation: the result of the action
...(this Thought/Action/Action Input/Observation can repeat N times)
Thought: I now know the final answer
Final Answer: the final answer to the original input question
Begin!
Question: What is the Salary of Evan?
Thought:

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To determine Evan's salary, I first need to obtain his employee ID.
Action: get_employee_id
Action Input:"Evan"

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get_employee_id

second query-resp

Answer the following questions as best you can. You have access to the following tools:
get_employee_salary(employee_id)- To get the salary of an employee, it takes employee_id asinputandreturn salary
get_employee_id(name)- To get employee id, it takes employee name as arguments
name(str): Name of the employee
Use the following format:
Question: the input question you must answer
Thought: you should always think about what to do
Action: the action to take, should be one of [get_employee_salary, get_employee_id]
Action Input: the input to the action
Observation: the result of the action
...(this Thought/Action/Action Input/Observation can repeat N times)
Thought: I now know the final answer
Final Answer: the final answer to the original input question
Begin!
Question: What is the Salary of Evan?
Thought:To determine Evan's salary, I first need to obtain his employee ID.
Action: get_employee_id
Action Input:"Evan"
Observation: E005
Thought:

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Now that I have Evan's employee ID, I can retrieve his salary using this ID.
Action: get_employee_salary
Action Input: E005

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get_employee_salary

finally

Answer the following questions as best you can. You have access to the following tools:
get_employee_salary(employee_id)- To get the salary of an employee, it takes employee_id asinputandreturn salary
get_employee_id(name)- To get employee id, it takes employee name as arguments
name(str): Name of the employee
Use the following format:
Question: the input question you must answer
Thought: you should always think about what to do
Action: the action to take, should be one of [get_employee_salary, get_employee_id]
Action Input: the input to the action
Observation: the result of the action
...(this Thought/Action/Action Input/Observation can repeat N times)
Thought: I now know the final answer
Final Answer: the final answer to the original input question
Begin!
Question: What is the Salary of Evan?
Thought:To determine Evan's salary, I first need to obtain his employee ID.
Action: get_employee_id
Action Input:"Evan"
Observation: E005
Thought: Now that I have Evan's employee ID, I can retrieve his salary using this ID.
Action: get_employee_salary
Action Input: E005
Observation:45000
Thought:

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I now know the final answer.
Final Answer: The salary of Evan is45000.

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显式地操作agent_scratchpad与intermediate_steps
https://python.langchain.com/v0.1/docs/modules/agents/how_to/custom_agent/

os.environ["LANGCHAIN_PROJECT"]='memory_agents'@tooldefget_word_length(word:str)->int:"""Returns the length of a word."""returnlen(word)
get_word_length.invoke("abc")# 3
tools =[get_word_length]from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
prompt = ChatPromptTemplate.from_messages([("system","You are very powerful assistant, but don't know current events",),("user","{input}"),
MessagesPlaceholder(variable_name="agent_scratchpad"),])
llm = ChatOpenAI(model="gpt-4o", temperature=0)
llm_with_tools = llm.bind_tools(tools)from langchain.agents.format_scratchpad.openai_tools import(
format_to_openai_tool_messages,)from langchain.agents.output_parsers.openai_tools import OpenAIToolsAgentOutputParser
# runnable Sequence，根据需要循环执行这 4 步；
agent =({"input":lambda x: x["input"],"agent_scratchpad":lambda x: format_to_openai_tool_messages(
x["intermediate_steps"]),}| prompt
| llm_with_tools
| OpenAIToolsAgentOutputParser())
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
process_list =list(agent_executor.stream({"input":"How many letters in the word eudca"}))"""
> Entering new AgentExecutor chain...
Invoking: `get_word_length` with `{'word': 'eudca'}`
5The word "eudca" has 5 letters.
> Finished chain.
"""

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上面的process_list变量形如：

[{'actions':[ToolAgentAction(tool='get_word_length', tool_input={'word':'eudca'}, log="\nInvoking: `get_word_length` with `{'word': 'eudca'}`\n\n\n", message_log=[AIMessageChunk(content='', additional_kwargs={'tool_calls':[{'index':0,'id':'call_P3LQGFQ50JvKMtLpLXJ2SSsZ','function':{'arguments':'{"word":"eudca"}','name':'get_word_length'},'type':'function'}]}, response_metadata={'finish_reason':'tool_calls','model_name':'gpt-4o-2024-05-13','system_fingerprint':'fp_3aa7262c27'},id='run-94428a05-fa61-4853-8eeb-7c12f68b447e', tool_calls=[{'name':'get_word_length','args':{'word':'eudca'},'id':'call_P3LQGFQ50JvKMtLpLXJ2SSsZ','type':'tool_call'}], tool_call_chunks=[{'name':'get_word_length','args':'{"word":"eudca"}','id':'call_P3LQGFQ50JvKMtLpLXJ2SSsZ','index':0,'type':'tool_call_chunk'}])], tool_call_id='call_P3LQGFQ50JvKMtLpLXJ2SSsZ')],'messages':[AIMessageChunk(content='', additional_kwargs={'tool_calls':[{'index':0,'id':'call_P3LQGFQ50JvKMtLpLXJ2SSsZ','function':{'arguments':'{"word":"eudca"}','name':'get_word_length'},'type':'function'}]}, response_metadata={'finish_reason':'tool_calls','model_name':'gpt-4o-2024-05-13','system_fingerprint':'fp_3aa7262c27'},id='run-94428a05-fa61-4853-8eeb-7c12f68b447e', tool_calls=[{'name':'get_word_length','args':{'word':'eudca'},'id':'call_P3LQGFQ50JvKMtLpLXJ2SSsZ','type':'tool_call'}], tool_call_chunks=[{'name':'get_word_length','args':'{"word":"eudca"}','id':'call_P3LQGFQ50JvKMtLpLXJ2SSsZ','index':0,'type':'tool_call_chunk'}])]},{'steps':[AgentStep(action=ToolAgentAction(tool='get_word_length', tool_input={'word':'eudca'}, log="\nInvoking: `get_word_length` with `{'word': 'eudca'}`\n\n\n", message_log=[AIMessageChunk(content='', additional_kwargs={'tool_calls':[{'index':0,'id':'call_P3LQGFQ50JvKMtLpLXJ2SSsZ','function':{'arguments':'{"word":"eudca"}','name':'get_word_length'},'type':'function'}]}, response_metadata={'finish_reason':'tool_calls','model_name':'gpt-4o-2024-05-13','system_fingerprint':'fp_3aa7262c27'},id='run-94428a05-fa61-4853-8eeb-7c12f68b447e', tool_calls=[{'name':'get_word_length','args':{'word':'eudca'},'id':'call_P3LQGFQ50JvKMtLpLXJ2SSsZ','type':'tool_call'}], tool_call_chunks=[{'name':'get_word_length','args':'{"word":"eudca"}','id':'call_P3LQGFQ50JvKMtLpLXJ2SSsZ','index':0,'type':'tool_call_chunk'}])], tool_call_id='call_P3LQGFQ50JvKMtLpLXJ2SSsZ'), observation=5)],'messages':[FunctionMessage(content='5', name='get_word_length')]},{'output':'The word "eudca" has 5 letters.','messages':[AIMessage(content='The word "eudca" has 5 letters.')]}]

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换一个例子：

from langchain_core.prompts import MessagesPlaceholder
MEMORY_KEY ="chat_history"
prompt = ChatPromptTemplate.from_messages([("system","You are very powerful assistant, but bad at calculating lengths of words.",),
MessagesPlaceholder(variable_name=MEMORY_KEY),("user","{input}"),
MessagesPlaceholder(variable_name="agent_scratchpad"),])from langchain_core.messages import AIMessage, HumanMessage
chat_history =[]
agent =({"input":lambda x: x["input"],"agent_scratchpad":lambda x: format_to_openai_tool_messages(
x["intermediate_steps"]),"chat_history":lambda x: x["chat_history"],}| prompt
| llm_with_tools
| OpenAIToolsAgentOutputParser())
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
input1 ="how many letters in the word educa?"
result = agent_executor.invoke({"input": input1,"chat_history": chat_history})
chat_history.extend([
HumanMessage(content=input1),
AIMessage(content=result["output"]),])
agent_executor.invoke({"input":"is that a real word?","chat_history": chat_history})"""
> Entering new AgentExecutor chain...
Invoking: `get_word_length` with `{'word': 'educa'}`
5The word "educa" has 5 letters.
> Finished chain.
> Entering new AgentExecutor chain...
"Educa" is not a standard English word. It might be a truncated form of "education" or a name, but it is not commonly recognized as a standalone word in English.
> Finished chain.
{'input': 'is that a real word?',
'chat_history': [HumanMessage(content='how many letters in the word educa?'),
AIMessage(content='The word "educa" has 5 letters.')],
'output': '"Educa" is not a standard English word. It might be a truncated form of "education" or a name, but it is not commonly recognized as a standalone word in English.'}
"""

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messages

chat_history =[]# https://smith.langchain.com/hub/hwchase17/openai-functions-agent# input_variables=['agent_scratchpad', 'input'], optional_variables=['chat_history']
prompt = hub.pull('hwchase17/openai-functions-agent')
agent =({"input":lambda x: x["input"],"agent_scratchpad":lambda x: format_to_openai_tool_messages(
x["intermediate_steps"]),"chat_history":lambda x: x["chat_history"],}| prompt
| llm_with_tools
| OpenAIToolsAgentOutputParser())
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
input1 ="how many letters in the word dadebfdr?"
result = agent_executor.invoke({"input": input1,"chat_history": chat_history})
chat_history.extend([
HumanMessage(content=input1),
AIMessage(content=result["output"]),])
agent_executor.invoke({"input":"is that a real word?","chat_history": chat_history})

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输出结果：

> Entering new AgentExecutor chain...
Invoking: `get_word_length` with `{'word':'dadebfdr'}`
8The word "dadebfdr" has 8 letters.> Finished chain.> Entering new AgentExecutor chain...
No,"dadebfdr" does not appear to be a real word in the English language. It seems to be a random string of letters.> Finished chain.{'input':'is that a real word?','chat_history':[HumanMessage(content='how many letters in the word dadebfdr?'),
AIMessage(content='The word "dadebfdr" has 8 letters.')],'output':'No, "dadebfdr" does not appear to be a real word in the English language. It seems to be a random string of letters.'}

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3 LangGraph 基本概念（AgentState、StateGraph，nodes，edges）

langgraph studio
https://github.com/langchain-ai/langgraph/blob/main/examples/reflexion/reflexion.ipynbhttps://github.com/langchain-ai/langgraph/blob/main/examples/agent_executor/force-calling-a-tool-first.ipynb

所有用到的包

import os
from typing import TypedDict, Annotated, List, Union, Sequence
import operator
import json
from dotenv import load_dotenv
from langchain import hub
from langchain.tools import BaseTool, StructuredTool, Tool, tool
from langchain.tools.render import format_tool_to_openai_function
from langchain_core.utils.function_calling import convert_to_openai_function
from langchain_openai.chat_models import ChatOpenAI
from langchain.agents import create_openai_functions_agent
from langchain_core.utils.function_calling import convert_to_openai_function
from langchain_core.agents import AgentAction, AgentFinish
from langchain_core.messages import BaseMessage, HumanMessage, SystemMessage, FunctionMessage
from langgraph.graph import END, StateGraph, MessagesState
from langgraph.prebuilt import ToolNode, ToolInvocation
from langgraph.prebuilt.tool_executor import ToolExecutor
os.environ['http_proxy']='http://127.0.0.1:7890'
os.environ['https_proxy']='http://127.0.0.1:7890'

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核心概念

langchain => langgraph

chain: 更多是 linear chain，链式顺序执行；
graph：可以有分支/判断（是否到达 AgentFinish），有循环

Graph

nodes, edges
state 沿着 edge 从一个 node 到另一个 node 时，state 会发生变化（携带上一个 node 的执行输出信息）

LangGraph的执行中间过程同样可以在LangSmith里查看
案例一：Agent Executor

classAgentState(TypedDict):input:str
chat_history:list[BaseMessage]
agent_outcome: Union[AgentAction, AgentFinish,None]# 不断追加和传递
intermediate_steps: Annotated[list[tuple[AgentAction,str]], operator.add]

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tools

第一个tool的回答作为第二个tool的输入
Tools are interfaces that an agent can use to interact with the world. They combine a few things:

import random
@tool("upper_case", return_direct=True)defto_upper_case(input:str)->str:"""Returns the input as all upper case."""returninput.upper()@tool("random_number", return_direct=True)defrandom_number_maker(input:str)->str:"""Returns a random number between 0-100."""return random.randint(0,100)
tools =[to_upper_case,random_number_maker]
random_number_maker.run('random')
to_upper_case.run('hello WORLD')

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Agent

# https://smith.langchain.com/hub/hwchase17/openai-functions-agent
prompt = hub.pull('hwchase17/openai-functions-agent')
llm = ChatOpenAI(model="gpt-4o", streaming=True)
prompt
# ChatPromptTemplate(input_variables=['agent_scratchpad', 'input'], optional_variables=['chat_history'], input_types={'chat_history': typing.List[typing.Union[langchain_core.messages.ai.AIMessage, langchain_core.messages.human.HumanMessage, langchain_core.messages.chat.ChatMessage, langchain_core.messages.system.SystemMessage, langchain_core.messages.function.FunctionMessage, langchain_core.messages.tool.ToolMessage]], 'agent_scratchpad': typing.List[typing.Union[langchain_core.messages.ai.AIMessage, langchain_core.messages.human.HumanMessage, langchain_core.messages.chat.ChatMessage, langchain_core.messages.system.SystemMessage, langchain_core.messages.function.FunctionMessage, langchain_core.messages.tool.ToolMessage]]}, partial_variables={'chat_history': []}, metadata={'lc_hub_owner': 'hwchase17', 'lc_hub_repo': 'openai-functions-agent', 'lc_hub_commit_hash': 'a1655024b06afbd95d17449f21316291e0726f13dcfaf990cc0d18087ad689a5'}, messages=[SystemMessagePromptTemplate(prompt=PromptTemplate(input_variables=[], template='You are a helpful assistant')), MessagesPlaceholder(variable_name='chat_history', optional=True), HumanMessagePromptTemplate(prompt=PromptTemplate(input_variables=['input'], template='{input}')), MessagesPlaceholder(variable_name='agent_scratchpad')])

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上面的输出表明模型其实并没有使用tools，因为这只是一个step，不是整个pipeline，下面我们看如何执行整个graph

agent = create_openai_functions_agent(llm, tools, prompt)# langchain_core.runnables.base.RunnableSequence
inputs ={"input":"give me a random number and then write in words and make it upper case.","chat_history":[],"intermediate_steps":[]}
agent_outcome = agent.invoke(inputs)# AgentActionMessageLog(tool='random_number', tool_input={'input': 'Generate a random number'}, log="\nInvoking: `random_number` with `{'input': 'Generate a random number'}`\n\n\n", message_log=[AIMessage(content='', additional_kwargs={'function_call': {'arguments': '{"input":"Generate a random number"}', 'name': 'random_number'}}, response_metadata={'finish_reason': 'function_call', 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_3aa7262c27'}, id='run-e2652fc2-03db-4c6c-9b26-310d80e0f06a-0')])

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tools

tool_executor = ToolExecutor(tools)# tool_executor = ToolNode(tools)defrun_agent(state):
agent_outcome = agent.invoke(state)# AgentState.agent_outcome: 当前输出，决策动作return{"agent_outcome": agent_outcome}defexecute_tools(state):
agent_action = state['agent_outcome']
output = tool_executor.invoke(agent_action)print(f"The agent action is {agent_action}")print(f"The tool result is: {output}")# AgentState.intermediate_steps: 追加逻辑return{"intermediate_steps":[(agent_action,str(output))]}defshould_continue(state):ifisinstance(state['agent_outcome'], AgentFinish):return"end"return"continue"

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graphs

workflow = StateGraph(AgentState)
workflow.add_node("agent", run_agent)
workflow.add_node("action", execute_tools)
workflow.set_entry_point("agent")# 条件分支
workflow.add_conditional_edges("agent",
should_continue,{"continue":"action","end": END
})
workflow.add_edge('action','agent')
graph = workflow.compile()from IPython.display import Image, display
try:
display(Image(graph.get_graph().draw_mermaid_png()))except Exception:# This requires some extra dependencies and is optionalpass

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