CACHE Archives - icodebroker

[PYTHON/STREAMLIT] @cache_data 데코레이터 : 함수에서 반환 데이터 캐시하기

■ @cache_data 데코레이터를 사용해 함수에서 반환 데이터를 캐시하는 방법을 보여준다. ▶ main.py


import streamlit as st
import pandas    as pd

@st.cache_data
def loadData(url, rowCount):
    dataFrame = pd.read_csv(url, nrows = rowCount)
    changeToLowerCaseString = lambda x: str(x).lower()
    dataFrame.rename(changeToLowerCaseString, axis = "columns", inplace = True)
    dataFrame["date/time"] = pd.to_datetime(dataFrame["date/time"])
    return dataFrame

st.title("Uber pickups in NYC")

loadingDataText = st.text("Loading data...")

dataFrame = loadData("https://s3-us-west-2.amazonaws.com/streamlit-demo-data/uber-raw-data-sep14.csv.gz", 10000)

loadingDataText.text("Done! (using st.cache_data)")

st.subheader("Raw data")

st.write(dataFrame)

import streamlit as st

import pandas as pd

@st.cache_data

def loadData(url, rowCount):

dataFrame = pd.read_csv(url, nrows = rowCount)

changeToLowerCaseString = lambda x: str(x).lower()

dataFrame.rename(changeToLowerCaseString, axis = "columns", inplace = True)

dataFrame["date/time"] = pd.to_datetime(dataFrame["date/time"])

return dataFrame

st.title("Uber pickups in NYC")

loadingDataText = st.text("Loading data...")

dataFrame = loadData("https://s3-us-west-2.amazonaws.com/streamlit-demo-data/uber-raw-data-sep14.csv.gz", 10000)

loadingDataText.text("Done! (using st.cache_data)")

st.subheader("Raw data")

st.write(dataFrame)

▶ requirements.txt


altair==5.3.0
attrs==23.2.0
blinker==1.8.2
cachetools==5.3.3
certifi==2024.6.2
charset-normalizer==3.3.2
click==8.1.7
gitdb==4.0.11
GitPython==3.1.43
idna==3.7
Jinja2==3.1.4
jsonschema==4.22.0
jsonschema-specifications==2023.12.1
markdown-it-py==3.0.0
MarkupSafe==2.1.5
mdurl==0.1.2
numpy==2.0.0
packaging==24.1
pandas==2.2.2
pillow==10.3.0
protobuf==5.27.2
pyarrow==16.1.0
pydeck==0.9.1
Pygments==2.18.0
python-dateutil==2.9.0.post0
pytz==2024.1
referencing==0.35.1
requests==2.32.3
rich==13.7.1
rpds-py==0.18.1
six==1.16.0
smmap==5.0.1
streamlit==1.36.0
tenacity==8.4.2
toml==0.10.2
toolz==0.12.1
tornado==6.4.1
typing_extensions==4.12.2
tzdata==2024.1
urllib3==2.2.2
watchdog==4.0.1

altair==5.3.0

attrs==23.2.0

blinker==1.8.2

cachetools==5.3.3

certifi==2024.6.2

charset-normalizer==3.3.2

click==8.1.7

gitdb==4.0.11

GitPython==3.1.43

idna==3.7

Jinja2==3.1.4

jsonschema==4.22.0

jsonschema-specifications==2023.12.1

markdown-it-py==3.0.0

MarkupSafe==2.1.5

mdurl==0.1.2

numpy==2.0.0

packaging==24.1

pandas==2.2.2

pillow==10.3.0

protobuf==5.27.2

pyarrow==16.1.0

pydeck==0.9.1

Pygments==2.18.0

python-dateutil==2.9.0.post0

pytz==2024.1

referencing==0.35.1

requests==2.32.3

rich==13.7.1

rpds-py==0.18.1

six==1.16.0

smmap==5.0.1

streamlit==1.36.0

tenacity==8.4.2

toml==0.10.2

toolz==0.12.1

tornado==6.4.1

typing_extensions==4.12.2

tzdata==2024.1

urllib3==2.2.2

watchdog==4.0.1

※ pip install streamlit 명령을 실행했다.

[PYTHON/LANGCHAIN] set_llm_cache 함수 : SQLiteCache 객체를 사용해 SQLITE 데이터베이스 파일 캐시 설정하기

■ set_llm_cache 함수에서 SQLiteCache 객체를 사용해 SQLITE 데이터베이스 파일 캐시를 설정하는 방법을 보여준다. ※ OPENAI_API_KEY 환경 변수 값은 .env 파일에 정의한다. ▶

[PYTHON/LANGCHAIN] set_llm_cache 함수 : InMemoryCache 객체를 사용해 메모리 캐시 설정하기

■ set_llm_cache 함수에서 InMemoryCache 객체를 설정해 메모리 캐시를 사용하는 방법을 보여준다. ※ OPENAI_API_KEY 환경 변수 값은 .env 파일에 정의한다. ▶ main.py


from dotenv                    import load_dotenv
from langchain_community.cache import InMemoryCache
from langchain.globals         import set_llm_cache
from langchain_openai          import OpenAI

load_dotenv()

set_llm_cache(InMemoryCache())

openAI = OpenAI(model_name = "gpt-3.5-turbo-instruct", n = 2, best_of = 2)

responseString = openAI.invoke("Tell me a joke")

print(responseString)

"""
Why did the tomato turn red?

Because it saw the salad dressing!
"""

responseString = openAI.invoke("Tell me a joke")

print(responseString)

"""
Why did the tomato turn red?

Because it saw the salad dressing!
"""

from dotenv import load_dotenv

from langchain_community.cache import InMemoryCache

from langchain.globals import set_llm_cache

from langchain_openai import OpenAI

load_dotenv()

set_llm_cache(InMemoryCache())

openAI = OpenAI(model_name = "gpt-3.5-turbo-instruct", n = 2, best_of = 2)

responseString = openAI.invoke("Tell me a joke")

print(responseString)

"""

Why did the tomato turn red?

Because it saw the salad dressing!

"""

responseString = openAI.invoke("Tell me a joke")

print(responseString)

"""

Why did the tomato turn red?

Because it saw the salad dressing!

"""

[PYTHON/LANGCHAIN] set_llm_cache 함수 : SQLiteCache 객체를 캐시로 설정하기

■ set_llm_cache 함수를 사용해 SQLiteCache 객체를 캐시로 설정하는 방법을 보여준다. ※ OPENAI_API_KEY 환경 변수 값은 .env 파일에 정의한다. ▶ main.py


from dotenv                    import load_dotenv
from langchain.globals         import set_llm_cache
from langchain_community.cache import SQLiteCache
from langchain_openai          import ChatOpenAI

load_dotenv()

sqliteCache = SQLiteCache(database_path = "cache.db")

set_llm_cache(sqliteCache)

chatOpenAI = ChatOpenAI(model = "gpt-3.5-turbo-0125")

llmResult1 = chatOpenAI.invoke("고양이 울음소리는?")

print(llmResult1)

"""
content='"야옹"이라고 합니다.' response_metadata={'token_usage': {'completion_tokens': 11, 'prompt_tokens': 18, 'total_tokens': 29}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None} id='run-70cc38f8-64a1-43a0-b7a5-6e0b7dce0be9-0' usage_metadata={'input_tokens': 18, 'output_tokens': 11, 'total_tokens': 29}
"""

llmResult2 = chatOpenAI.invoke("고양이 울음소리는?")

print(llmResult2)

"""
content='"야옹"이라고 합니다.' response_metadata={'token_usage': {'completion_tokens': 11, 'prompt_tokens': 18, 'total_tokens': 29}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None} id='run-70cc38f8-64a1-43a0-b7a5-6e0b7dce0be9-0' usage_metadata={'input_tokens': 18, 'output_tokens': 11, 'total_tokens': 29}
"""

llmResult3 = chatOpenAI.invoke("까마귀 울음소리는?")

print(llmResult3)

"""
content='까악 까악!' response_metadata={'token_usage': {'completion_tokens': 10, 'prompt_tokens': 20, 'total_tokens': 30}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None} id='run-61db093c-e5d5-462f-b6a8-702eadcb522c-0' usage_metadata={'input_tokens': 20, 'output_tokens': 10, 'total_tokens': 30}
"""

from dotenv import load_dotenv

from langchain.globals import set_llm_cache

from langchain_community.cache import SQLiteCache

from langchain_openai import ChatOpenAI

load_dotenv()

sqliteCache = SQLiteCache(database_path = "cache.db")

set_llm_cache(sqliteCache)

chatOpenAI = ChatOpenAI(model = "gpt-3.5-turbo-0125")

llmResult1 = chatOpenAI.invoke("고양이 울음소리는?")

print(llmResult1)

"""

content='"야옹"이라고 합니다.' response_metadata={'token_usage': {'completion_tokens': 11, 'prompt_tokens': 18, 'total_tokens': 29}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None} id='run-70cc38f8-64a1-43a0-b7a5-6e0b7dce0be9-0' usage_metadata={'input_tokens': 18, 'output_tokens': 11, 'total_tokens': 29}

"""

llmResult2 = chatOpenAI.invoke("고양이 울음소리는?")

print(llmResult2)

"""

llmResult3 = chatOpenAI.invoke("까마귀 울음소리는?")

print(llmResult3)

"""

content='까악 까악!' response_metadata={'token_usage': {'completion_tokens': 10, 'prompt_tokens': 20, 'total_tokens': 30}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None} id='run-61db093c-e5d5-462f-b6a8-702eadcb522c-0' usage_metadata={'input_tokens': 20, 'output_tokens': 10, 'total_tokens': 30}

"""

▶

[PYTHON/LANGCHAIN] set_llm_cache 함수 : InMemoryCache 객체를 캐시로 설정하기

■ set_llm_cache 함수를 사용해 InMemoryCache 객체를 캐시로 설정하는 방법을 보여준다. ※ OPENAI_API_KEY 환경 변수 값은 .env 파일에 정의한다. ▶ main.py


from dotenv                    import load_dotenv
from langchain.globals         import set_llm_cache
from langchain_community.cache import InMemoryCache
from langchain_openai          import ChatOpenAI

load_dotenv()

set_llm_cache(InMemoryCache())

chatOpenAI = ChatOpenAI(model = "gpt-3.5-turbo-0125")

llmResult1 = chatOpenAI.invoke("고양이 울음소리는?")

print(llmResult1)

"""
content='"야옹" 혹은 "냐옹"이라고 소리를 지르는 소리를 내는 동물입니다.' response_metadata={'token_usage': {'completion_tokens': 39, 'prompt_tokens': 18, 'total_tokens': 57}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None} id='run-ccd305bc-54fe-48fe-b602-e61bd08e057f-0' usage_metadata={'input_tokens': 18, 'output_tokens': 39, 'total_tokens': 57}
"""

llmResult2 = chatOpenAI.invoke("고양이 울음소리는?")

print(llmResult2)

"""
content='"야옹" 혹은 "냐옹"이라고 소리를 지르는 소리를 내는 동물입니다.' response_metadata={'token_usage': {'completion_tokens': 39, 'prompt_tokens': 18, 'total_tokens': 57}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None} id='run-ccd305bc-54fe-48fe-b602-e61bd08e057f-0' usage_metadata={'input_tokens': 18, 'output_tokens': 39, 'total_tokens': 57}
"""

llmResult3 = chatOpenAI.invoke("까마귀 울음소리는?")

print(llmResult3)

"""
content='"까악까악"' response_metadata={'token_usage': {'completion_tokens': 10, 'prompt_tokens': 20, 'total_tokens': 30}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None} id='run-da58ae63-ff3c-47ca-bab2-8ab3d981189b-0' usage_metadata={'input_tokens': 20, 'output_tokens': 10, 'total_tokens': 30}
"""

from dotenv import load_dotenv

from langchain.globals import set_llm_cache

from langchain_community.cache import InMemoryCache

from langchain_openai import ChatOpenAI

load_dotenv()

set_llm_cache(InMemoryCache())

chatOpenAI = ChatOpenAI(model = "gpt-3.5-turbo-0125")

llmResult1 = chatOpenAI.invoke("고양이 울음소리는?")

print(llmResult1)

"""

content='"야옹" 혹은 "냐옹"이라고 소리를 지르는 소리를 내는 동물입니다.' response_metadata={'token_usage': {'completion_tokens': 39, 'prompt_tokens': 18, 'total_tokens': 57}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None} id='run-ccd305bc-54fe-48fe-b602-e61bd08e057f-0' usage_metadata={'input_tokens': 18, 'output_tokens': 39, 'total_tokens': 57}

"""

llmResult2 = chatOpenAI.invoke("고양이 울음소리는?")

print(llmResult2)

"""

llmResult3 = chatOpenAI.invoke("까마귀 울음소리는?")

print(llmResult3)

"""

content='"까악까악"' response_metadata={'token_usage': {'completion_tokens': 10, 'prompt_tokens': 20, 'total_tokens': 30}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None} id='run-da58ae63-ff3c-47ca-bab2-8ab3d981189b-0' usage_metadata={'input_tokens': 20, 'output_tokens': 10, 'total_tokens': 30}

"""

▶

[C#/ASP.NET MVC/.NETCORE] Microsoft.Extensions.Caching.Memory 누겟 설치하기

■ Microsoft.Extensions.Caching.Memory 누겟을 설치하는 방법을 보여준다. 1. Visual Studio를 실행한다. 2. [도구] / [NuGet 패키지 관리자] / [패키지 관리자 콘솔] 메뉴를 실행한다.

[C#/ASP.NET] HttpCachePolicy 클래스 : 코드 기반 캐시 설정하기

■ HttpCachePolicy 클래스를 사용해 코드 기반 캐시를 설정하는 방법을 보여준다. ▶ MainPage.aspx


<%@ Page Language="C#" AutoEventWireup="true" CodeBehind="MainPage.aspx.cs" Inherits="TestProject.MainPage" %>
<!DOCTYPE html>
<html xmlns="http://www.w3.org/1999/xhtml">
    <head runat="server">
        <meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
        <title>HttpCachePolicy 클래스 : 코드 기반 캐시 사용하기</title>
    </head>
    <body>
        <form id="form" runat="server">
            <div />
        </form>
    </body>
</html>

<%@ Page Language="C#" AutoEventWireup="true" CodeBehind="MainPage.aspx.cs" Inherits="TestProject.MainPage" %>

<!DOCTYPE html>

<title>HttpCachePolicy 클래스 : 코드 기반 캐시 사용하기</title>

</head>

<body>

<div />

</form>

</body>

</html>

▶ MainPage.aspx.cs


using System;
using System.Web;
using System.Web.UI;

namespace TestProject
{
    /// <summary>
    /// 메인 페이지
    /// </summary>
    public partial class MainPage : Page
    {
        //////////////////////////////////////////////////////////////////////////////////////////////////// Method
        ////////////////////////////////////////////////////////////////////////////////////////// Protected

        #region 페이지 로드시 처리하기 - Page_Load(sender, e)

        /// <summary>
        /// 페이지 로드시 처리하기
        /// </summary>
        /// <param name="sender">이벤트 발생자</param>
        /// <param name="e">이벤트 인자</param>
        protected void Page_Load(object sender, EventArgs e)
        {
            Response.Write(DateTime.Now.ToString());

            Response.Cache.SetCacheability(HttpCacheability.Public);

            Response.Cache.SetExpires(DateTime.Now.AddSeconds(60));

            Response.Cache.VaryByParams["*"] = true;
        }

        #endregion
    }
}

using System;

using System.Web;

using System.Web.UI;

namespace TestProject

{

/// <summary>

/// 메인 페이지

/// </summary>

public partial class MainPage : Page

{

//////////////////////////////////////////////////////////////////////////////////////////////////// Method

////////////////////////////////////////////////////////////////////////////////////////// Protected

#region 페이지 로드시 처리하기 - Page_Load(sender, e)

/// <summary>

/// 페이지 로드시 처리하기

/// </summary>

/// <param name="sender">이벤트 발생자</param>

/// <param name="e">이벤트 인자</param>

protected void Page_Load(object sender, EventArgs e)

{

Response.Write(DateTime.Now.ToString());

Response.Cache.SetCacheability(HttpCacheability.Public);

Response.Cache.SetExpires(DateTime.Now.AddSeconds(60));

Response.Cache.VaryByParams["*"] = true;

}

#endregion

}

TestProject.zip

[C#/ASP.NET] OutputCache 지시자 : 캐시 설정하기

■ OutputCache 지시자를 사용해 캐시를 설정하는 방법을 보여준다. ▶ TimeControl.ascx


<%@ Control Language="C#" AutoEventWireup="true" CodeBehind="TimeControl.ascx.cs" Inherits="TestProject.TimeControl" %>
<%@ OutputCache Duration="5" VaryByParam="None" %>
<asp:Label ID="timeLabel" runat="server" />

<%@ Control Language="C#" AutoEventWireup="true" CodeBehind="TimeControl.ascx.cs" Inherits="TestProject.TimeControl" %>

<%@ OutputCache Duration="5" VaryByParam="None" %>

<asp:Label ID="timeLabel" runat="server" />

▶ TimeControl.ascx.cs


using System;
using System.Web.UI;

namespace TestProject
{
    /// <summary>
    /// 시간 컨트롤
    /// </summary>
    public partial class TimeControl : UserControl
    {
        //////////////////////////////////////////////////////////////////////////////////////////////////// Method
        ////////////////////////////////////////////////////////////////////////////////////////// Protected

        #region 페이지 로드시 처리하기 - Page_Load(sender, e)

        /// <summary>
        /// 페이지 로드시 처리하기
        /// </summary>
        /// <param name="sender">이벤트 발생자</param>
        /// <param name="e">이벤트 인자</param>
        protected void Page_Load(object sender, EventArgs e)
        {
            this.timeLabel.Text = DateTime.Now.ToString();
        }

        #endregion
    }
}

using System;

using System.Web.UI;

namespace TestProject

{

/// <summary>

/// 시간 컨트롤

/// </summary>

public partial class TimeControl : UserControl

{

//////////////////////////////////////////////////////////////////////////////////////////////////// Method

////////////////////////////////////////////////////////////////////////////////////////// Protected

#region 페이지 로드시 처리하기 - Page_Load(sender, e)

/// <summary>

/// 페이지 로드시 처리하기

/// </summary>

/// <param name="sender">이벤트 발생자</param>

/// <param name="e">이벤트 인자</param>

protected void Page_Load(object sender, EventArgs e)

{

this.timeLabel.Text = DateTime.Now.ToString();

}

#endregion

}

▶ MainPage.aspx


<%@ Page Language="C#" AutoEventWireup="true" CodeBehind="MainPage.aspx.cs" Inherits="TestProject.MainPage" %>
<%@ Register TagPrefix="local" Src="~/TimeControl.ascx"
    TagName="TimeControl" %>
<!DOCTYPE html>
<html xmlns="http://www.w3.org/1999/xhtml">
    <head runat="server">
        <meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
        <title>OutputCache 지시자 : 캐시 사용하기</title>
    </head>
    <body>
        <form id="form" runat="server">
            <div>
                현재 시간 (웹 폼) : <asp:Label ID="timeLabel" runat="server" />
                <hr />
                현재 시간 (사용자 정의 컨트롤) : <local:TimeControl ID="timeControl" runat="server" />
            </div>
        </form>
    </body>
</html>

<%@ Page Language="C#" AutoEventWireup="true" CodeBehind="MainPage.aspx.cs" Inherits="TestProject.MainPage" %>

<%@ Register TagPrefix="local" Src="~/TimeControl.ascx"

TagName="TimeControl" %>

<!DOCTYPE html>

<title>OutputCache 지시자 : 캐시 사용하기</title>

</head>

<body>

<div>

현재 시간 (웹 폼) : <asp:Label ID="timeLabel" runat="server" />

<hr />

현재 시간 (사용자 정의 컨트롤) : <local:TimeControl ID="timeControl" runat="server" />

</div>

</form>

</body>

</html>

▶ MainPage.aspx.cs


using System;
using System.Web.UI;

namespace TestProject
{
    /// <summary>
    /// 메인 페이지
    /// </summary>
    public partial class MainPage : Page
    {
        //////////////////////////////////////////////////////////////////////////////////////////////////// Method
        ////////////////////////////////////////////////////////////////////////////////////////// Protected

        #region 페이지 로드시 처리하기 - Page_Load(sender, e)

        /// <summary>
        /// 페이지 로드시 처리하기
        /// </summary>
        /// <param name="sender">이벤트 발생자</param>
        /// <param name="e">이벤트 인자</param>
        protected void Page_Load(object sender, EventArgs e)
        {
            this.timeLabel.Text = DateTime.Now.ToString();
        }

        #endregion
    }
}

using System;

using System.Web.UI;

namespace TestProject

{

/// <summary>

/// 메인 페이지

/// </summary>

public partial class MainPage : Page

{

//////////////////////////////////////////////////////////////////////////////////////////////////// Method

////////////////////////////////////////////////////////////////////////////////////////// Protected

#region 페이지 로드시 처리하기 - Page_Load(sender, e)

/// <summary>

/// 페이지 로드시 처리하기

/// </summary>

/// <param name="sender">이벤트 발생자</param>

/// <param name="e">이벤트 인자</param>

protected void Page_Load(object sender, EventArgs e)

{

this.timeLabel.Text = DateTime.Now.ToString();

}

#endregion

}

TestProject.zip