GPT4 Archives - icodebroker

[PYTHON/LANGCHAIN] AIMessage 클래스 : response_metadata 속성 사용하기 (ChatOpenAI 객체 사용시)

■ AIMessage 클래스의 response_metadata 속성을 사용하는 방법을 보여준다. (ChatOpenAI 객체 사용시) ▶ 예제 코드 (PY)


from langchain_openai import ChatOpenAI

chatOpenAI = ChatOpenAI(model = "gpt-4-turbo")

aiMessage = chatOpenAI.invoke([("human", "What's the oldest known example of cuneiform")])

print(aiMessage.response_metadata)

"""
{
    'token_usage' : {
        'completion_tokens' : 164,
        'prompt_tokens'     : 17,
        'total_tokens'      : 181
    },
    'model_name'         : 'gpt-4-turbo',
    'system_fingerprint' : 'fp_76f018034d',
    'finish_reason'      : 'stop',
    'logprobs'           : None
}
"""

from langchain_openai import ChatOpenAI

chatOpenAI = ChatOpenAI(model = "gpt-4-turbo")

aiMessage = chatOpenAI.invoke([("human", "What's the oldest known example of cuneiform")])

print(aiMessage.response_metadata)

"""

{

'token_usage' : {

'completion_tokens' : 164,

'prompt_tokens' : 17,

'total_tokens' : 181

'model_name' : 'gpt-4-turbo',

'system_fingerprint' : 'fp_76f018034d',

'finish_reason' : 'stop',

'logprobs' : None

}

"""

[PYTHON/LANGCHAIN] RunnableSequence 클래스 : with_fallbacks 메소드를 사용해 파싱 성능이 더 나은 모델로 대체하는 RunnableWithFallbacks 객체 만들기

■ RunnableSequence 클래스의 with_fallbacks 메소드를 사용해 파싱 성능이 더 나은 모델로 대체하는 RunnableWithFallbacks 객체를 만드는 방법을 보여준다. ※ OPENAI_API_KEY 환경 변수 값은

[PYTHON/LANGCHAIN] 대체 가능한 프롬프트 및 LLM 설정하기

■ 대체 가능한 프롬프트 및 LLM을 설정하는 방법을 보여준다. ※ ANTHROPIC_API_KEY 및 OPENAI_API_KEY 환경 변수 값은 .env 파일에 정의한다. ▶ main.py


from dotenv                   import load_dotenv
from langchain_core.prompts   import PromptTemplate
from langchain_core.runnables import ConfigurableField
from langchain_anthropic      import ChatAnthropic
from langchain_openai         import ChatOpenAI

load_dotenv()

promptTemplate = PromptTemplate.from_template("Tell me a joke about {topic}")

promptRunnableConfigurableAlternatives = promptTemplate.configurable_alternatives(
    ConfigurableField(id = "prompt"),
    default_key = "joke",
    poem        = PromptTemplate.from_template("Write a short poem about {topic}")
)

chatAnthropic = ChatAnthropic(model = "claude-3-haiku-20240307", temperature = 0)

llmRunnableConfigurableAlternatives = chatAnthropic.configurable_alternatives(
    ConfigurableField(id = "llm"),
    default_key = "anthropic",
    openai      = ChatOpenAI(),
    gpt4        = ChatOpenAI(model = "gpt-4")
)

runnableSequence = promptRunnableConfigurableAlternatives | llmRunnableConfigurableAlternatives

runnableBinding = runnableSequence.with_config(configurable = {"prompt" : "poem", "llm" : "openai"})

responseAIMessage = runnableBinding.invoke({"topic" : "bears"})

print(responseAIMessage)

"""
content           = 'In the forest deep and wide,\nWhere the shadows softly glide,\nThere roams a creature strong and grand,\nThe mighty bear, ruler of the land.\n\nWith fur as dark as the night,\nAnd eyes that gleam with ancient might,\nHe wanders through the trees so tall,\nA silent guardian over all.\n\nHis paws leave prints in the snow,\nAs he hunts for food below,\nBut do not fear his fearsome roar,\nFor the bear is gentle at his core.\n\nSo let us admire this noble beast,\nAnd hope that in the wild, he finds his feast,\nFor in the heart of every bear,\nThere lies a beauty beyond compare.'
response_metadata = {
    'token_usage'        : {'completion_tokens' : 132, 'prompt_tokens' : 13, 'total_tokens' : 145},
    'model_name'         : 'gpt-3.5-turbo',
    'system_fingerprint' : None,
    'finish_reason'      : 'stop',
    'logprobs'           : None
}
id                = 'run-c71c2737-3b2d-4460-be10-735f74ac7e7f-0'
usage_metadata    = {'input_tokens' : 13, 'output_tokens' : 132, 'total_tokens' : 145}
"""

from dotenv import load_dotenv

from langchain_core.prompts import PromptTemplate

from langchain_core.runnables import ConfigurableField

from langchain_anthropic import ChatAnthropic

from langchain_openai import ChatOpenAI

load_dotenv()

promptTemplate = PromptTemplate.from_template("Tell me a joke about {topic}")

promptRunnableConfigurableAlternatives = promptTemplate.configurable_alternatives(

ConfigurableField(id = "prompt"),

default_key = "joke",

poem = PromptTemplate.from_template("Write a short poem about {topic}")

)

chatAnthropic = ChatAnthropic(model = "claude-3-haiku-20240307", temperature = 0)

llmRunnableConfigurableAlternatives = chatAnthropic.configurable_alternatives(

ConfigurableField(id = "llm"),

default_key = "anthropic",

openai = ChatOpenAI(),

gpt4 = ChatOpenAI(model = "gpt-4")

)

runnableSequence = promptRunnableConfigurableAlternatives | llmRunnableConfigurableAlternatives

runnableBinding = runnableSequence.with_config(configurable = {"prompt" : "poem", "llm" : "openai"})

responseAIMessage = runnableBinding.invoke({"topic" : "bears"})

print(responseAIMessage)

"""

content = 'In the forest deep and wide,\nWhere the shadows softly glide,\nThere roams a creature strong and grand,\nThe mighty bear, ruler of the land.\n\nWith fur as dark as the night,\nAnd eyes that gleam with ancient might,\nHe wanders through the trees so tall,\nA silent guardian over all.\n\nHis paws leave prints in the snow,\nAs he hunts for food below,\nBut do not fear his fearsome roar,\nFor the bear is gentle at his core.\n\nSo let us admire this noble beast,\nAnd hope that in the wild, he finds his feast,\nFor in the heart of every bear,\nThere lies a beauty beyond compare.'

response_metadata = {

'token_usage' : {'completion_tokens' : 132, 'prompt_tokens' : 13, 'total_tokens' : 145},

'model_name' : 'gpt-3.5-turbo',

'system_fingerprint' : None,

'finish_reason' : 'stop',

'logprobs' : None

}

id = 'run-c71c2737-3b2d-4460-be10-735f74ac7e7f-0'

usage_metadata = {'input_tokens' : 13, 'output_tokens' : 132, 'total_tokens' : 145}

"""

[PYTHON/LANGCHAIN] ChatAnthropic 클래스 : configurable_alternatives 메소드를 사용해 대체 가능한 LLM을 설정하는 RunnableConfigurableAlternatives 객체 만들기

■ ChatAnthropic 클래스의 configurable_alternatives 메소드를 사용해 대체 가능한 LLM을 설정하는 RunnableConfigurableAlternatives 객체를 만드는 방법을 보여준다. ※ ANTHROPIC_API_KEY 및 OPENAI_API_KEY 환경 변수 값은

[PYTHON/LANGCHAIN] ChatOpenAI 클래스 : generate 메소드를 사용해 LLM 호출하기

■ ChatOpenAI 클래스의 generate 메소드를 사용해 LLM을 호출하는 방법을 보여준다. ▶ main.py


import os

from langchain_core.messages import HumanMessage
from langchain_openai        import ChatOpenAI

os.environ["OPENAI_API_KEY"] = "<OPENAI_API_KEY>"

chatOpenAI = ChatOpenAI(model_name = "gpt-4", temperature = 0)

messageList = [
    [HumanMessage(content = "고양이 울음소리는?")],
    [HumanMessage(content = "까마귀 울음소리는?")]
]

llmResult = chatOpenAI.generate(messageList)

print(llmResult)

"""
generations = [
    [
        ChatGeneration(
            text = '"야옹"',
            generation_info = {'finish_reason' : 'stop', 'logprobs' : None},
            message         = AIMessage
            (
                content           = '"야옹"',
                response_metadata = {
                    'token_usage'        : {'completion_tokens' : 5, 'prompt_tokens' : 18, 'total_tokens' : 23},
                    'model_name'         : 'gpt-4',
                    'system_fingerprint' : None,
                    'finish_reason'      : 'stop',
                    'logprobs'           : None
                },
                id                = 'run-a0df7fb5-9cf3-4896-87bc-61c22ee6af1c-0',
                usage_metadata    = {'input_tokens' : 18, 'output_tokens' : 5, 'total_tokens' : 23}
            )
        )
    ],
    [
        ChatGeneration(
            text            = '까마귀의 울음소리는 "까악 까악" 또는 "크아크 크아크"라고 표현합니다.',
            generation_info = {'finish_reason' : 'stop', 'logprobs' : None},
            message         = AIMessage(
                content           = '까마귀의 울음소리는 "까악 까악" 또는 "크아크 크아크"라고 표현합니다.',
                response_metadata = {
                    'token_usage'        : {'completion_tokens' : 45, 'prompt_tokens' : 20, 'total_tokens' : 65},
                    'model_name'         : 'gpt-4',
                    'system_fingerprint' : None,
                    'finish_reason'      : 'stop',
                    'logprobs'           : None
                },
                id                = 'run-52077b57-5a7a-4634-a599-dbb2b1d22583-0',
                usage_metadata    = {'input_tokens' : 20, 'output_tokens' : 45, 'total_tokens' : 65}
            )
        )
    ]
]

llm_output = {
    'token_usage' : {'completion_tokens' : 50, 'prompt_tokens' : 38, 'total_tokens' : 88},
    'model_name'  : 'gpt-4'
}

run = [
    RunInfo(run_id = UUID('a0df7fb5-9cf3-4896-87bc-61c22ee6af1c')),
    RunInfo(run_id = UUID('52077b57-5a7a-4634-a599-dbb2b1d22583'))
]
"""

import os

from langchain_core.messages import HumanMessage

from langchain_openai import ChatOpenAI

os.environ["OPENAI_API_KEY"] = "<OPENAI_API_KEY>"

chatOpenAI = ChatOpenAI(model_name = "gpt-4", temperature = 0)

messageList = [

[HumanMessage(content = "고양이 울음소리는?")],

[HumanMessage(content = "까마귀 울음소리는?")]

]

llmResult = chatOpenAI.generate(messageList)

print(llmResult)

"""

generations = [

[

ChatGeneration(

text = '"야옹"',

generation_info = {'finish_reason' : 'stop', 'logprobs' : None},

message = AIMessage

(

content = '"야옹"',

response_metadata = {

'token_usage' : {'completion_tokens' : 5, 'prompt_tokens' : 18, 'total_tokens' : 23},

'model_name' : 'gpt-4',

'system_fingerprint' : None,

'finish_reason' : 'stop',

'logprobs' : None

id = 'run-a0df7fb5-9cf3-4896-87bc-61c22ee6af1c-0',

usage_metadata = {'input_tokens' : 18, 'output_tokens' : 5, 'total_tokens' : 23}

)

[

ChatGeneration(

text = '까마귀의 울음소리는 "까악 까악" 또는 "크아크 크아크"라고 표현합니다.',

generation_info = {'finish_reason' : 'stop', 'logprobs' : None},

message = AIMessage(

content = '까마귀의 울음소리는 "까악 까악" 또는 "크아크 크아크"라고 표현합니다.',

response_metadata = {

'token_usage' : {'completion_tokens' : 45, 'prompt_tokens' : 20, 'total_tokens' : 65},

'model_name' : 'gpt-4',

'system_fingerprint' : None,

'finish_reason' : 'stop',

'logprobs' : None

id = 'run-52077b57-5a7a-4634-a599-dbb2b1d22583-0',

usage_metadata = {'input_tokens' : 20, 'output_tokens' : 45, 'total_tokens' : 65}

)

]

llm_output = {

'token_usage' : {'completion_tokens' : 50, 'prompt_tokens' : 38, 'total_tokens' : 88},

'model_name' : 'gpt-4'

}

run = [

RunInfo(run_id = UUID('a0df7fb5-9cf3-4896-87bc-61c22ee6af1c')),

RunInfo(run_id = UUID('52077b57-5a7a-4634-a599-dbb2b1d22583'))

]

"""

▶ requirements.txt


aiohttp==3.9.5
aiosignal==1.3.1
annotated-types==0.7.0
anyio==4.4.0
async-timeout==4.0.3
attrs==23.2.0
certifi==2024.6.2
charset-normalizer==3.3.2
distro==1.9.0
exceptiongroup==1.2.1
frozenlist==1.4.1
greenlet==3.0.3
h11==0.14.0
httpcore==1.0.5
httpx==0.27.0
idna==3.7
jsonpatch==1.33
jsonpointer==2.4
langchain==0.2.3
langchain-core==0.2.5
langchain-openai==0.1.8
langchain-text-splitters==0.2.1
langsmith==0.1.75
multidict==6.0.5
numpy==1.26.4
openai==1.33.0
orjson==3.10.3
packaging==23.2
pydantic==2.7.3
pydantic_core==2.18.4
PyYAML==6.0.1
regex==2024.5.15
requests==2.32.3
sniffio==1.3.1
SQLAlchemy==2.0.30
tenacity==8.3.0
tiktoken==0.7.0
tqdm==4.66.4
typing_extensions==4.12.2
urllib3==2.2.1
yarl==1.9.4

aiohttp==3.9.5

aiosignal==1.3.1

annotated-types==0.7.0

anyio==4.4.0

async-timeout==4.0.3

attrs==23.2.0

certifi==2024.6.2

charset-normalizer==3.3.2

distro==1.9.0

exceptiongroup==1.2.1

frozenlist==1.4.1

greenlet==3.0.3

h11==0.14.0

httpcore==1.0.5

httpx==0.27.0

idna==3.7

jsonpatch==1.33

jsonpointer==2.4

langchain==0.2.3

langchain-core==0.2.5

langchain-openai==0.1.8

langchain-text-splitters==0.2.1

langsmith==0.1.75

multidict==6.0.5

numpy==1.26.4

openai==1.33.0

orjson==3.10.3

packaging==23.2

pydantic==2.7.3

pydantic_core==2.18.4

PyYAML==6.0.1

regex==2024.5.15

requests==2.32.3

sniffio==1.3.1

SQLAlchemy==2.0.30

tenacity==8.3.0

tiktoken==0.7.0

tqdm==4.66.4

typing_extensions==4.12.2

urllib3==2.2.1

yarl==1.9.4

※ pip install langchain langchain-openai 명령을

[PYTHON/LANGCHAIN] ChatOpenAI 클래스 : invoke 메소드를 사용해 LLM 호출하기

■ ChatOpenAI 클래스의 invoke 메소드를 사용해 LLM을 호출하는 방법을 보여준다. ▶ main.py


import os

from langchain_core.messages import HumanMessage
from langchain_openai        import ChatOpenAI

os.environ["OPENAI_API_KEY"] = "<OPENAI_API_KEY>"

chatOpenAI = ChatOpenAI(model_name = "gpt-4", temperature = 0)

messageList = [
    HumanMessage(content = "고양이 울음소리는?")
]

aiMessage = chatOpenAI.invoke(messageList)

print(aiMessage)

"""
content = '"야옹"'
response_metadata = {
    'token_usage' : {'completion_tokens' : 5, 'prompt_tokens': 18, 'total_tokens' : 23},
    'model_name'  : 'gpt-4',
    'system_fingerprint' : None,
    'finish_reason'      : 'stop',
    'logprobs'           : None
}
id             = 'run-0b2d6982-0129-4c01-acb4-bf668b6e7f4e-0'
usage_metadata = {'input_tokens' : 18, 'output_tokens' : 5, 'total_tokens' : 23}
"""

import os

from langchain_core.messages import HumanMessage

from langchain_openai import ChatOpenAI

os.environ["OPENAI_API_KEY"] = "<OPENAI_API_KEY>"

chatOpenAI = ChatOpenAI(model_name = "gpt-4", temperature = 0)

messageList = [

HumanMessage(content = "고양이 울음소리는?")

]

aiMessage = chatOpenAI.invoke(messageList)

print(aiMessage)

"""

content = '"야옹"'

response_metadata = {

'token_usage' : {'completion_tokens' : 5, 'prompt_tokens': 18, 'total_tokens' : 23},

'model_name' : 'gpt-4',

'system_fingerprint' : None,

'finish_reason' : 'stop',

'logprobs' : None

}

id = 'run-0b2d6982-0129-4c01-acb4-bf668b6e7f4e-0'

usage_metadata = {'input_tokens' : 18, 'output_tokens' : 5, 'total_tokens' : 23}

"""

▶ requirements.txt


aiohttp==3.9.5
aiosignal==1.3.1
annotated-types==0.7.0
anyio==4.4.0
async-timeout==4.0.3
attrs==23.2.0
certifi==2024.6.2
charset-normalizer==3.3.2
distro==1.9.0
exceptiongroup==1.2.1
frozenlist==1.4.1
greenlet==3.0.3
h11==0.14.0
httpcore==1.0.5
httpx==0.27.0
idna==3.7
jsonpatch==1.33
jsonpointer==2.4
langchain==0.2.3
langchain-core==0.2.5
langchain-openai==0.1.8
langchain-text-splitters==0.2.1
langsmith==0.1.75
multidict==6.0.5
numpy==1.26.4
openai==1.33.0
orjson==3.10.3
packaging==23.2
pydantic==2.7.3
pydantic_core==2.18.4
PyYAML==6.0.1
regex==2024.5.15
requests==2.32.3
sniffio==1.3.1
SQLAlchemy==2.0.30
tenacity==8.3.0
tiktoken==0.7.0
tqdm==4.66.4
typing_extensions==4.12.2
urllib3==2.2.1
yarl==1.9.4

aiohttp==3.9.5

aiosignal==1.3.1

annotated-types==0.7.0

anyio==4.4.0

async-timeout==4.0.3

attrs==23.2.0

certifi==2024.6.2

charset-normalizer==3.3.2

distro==1.9.0

exceptiongroup==1.2.1

frozenlist==1.4.1

greenlet==3.0.3

h11==0.14.0

httpcore==1.0.5

httpx==0.27.0

idna==3.7

jsonpatch==1.33

jsonpointer==2.4

langchain==0.2.3

langchain-core==0.2.5

langchain-openai==0.1.8

langchain-text-splitters==0.2.1

langsmith==0.1.75

multidict==6.0.5

numpy==1.26.4

openai==1.33.0

orjson==3.10.3

packaging==23.2

pydantic==2.7.3

pydantic_core==2.18.4

PyYAML==6.0.1

regex==2024.5.15

requests==2.32.3

sniffio==1.3.1

SQLAlchemy==2.0.30

tenacity==8.3.0

tiktoken==0.7.0

tqdm==4.66.4

typing_extensions==4.12.2

urllib3==2.2.1

yarl==1.9.4

※ pip install langchain langchain-openai 명령을

[PYTHON/LANGCHAIN] SqliteSaver 클래스 : from_conn_string 정적 메소드를 사용해 상호 작용을 위한 SqliteSaver 객체 만들기

■ SqliteSaver 클래스 : from_conn_string 정적 메소드를 사용해 상호 작용을 위한 SqliteSaver 객체를 만드는 방법을 보여준다. ▶ main.py


import os

from langchain_core.messages                 import HumanMessage
from langchain_community.tools.tavily_search import TavilySearchResults
from langchain_openai                        import ChatOpenAI
from langgraph.prebuilt                      import create_react_agent
from langgraph.checkpoint.sqlite             import SqliteSaver

os.environ["OPENAI_API_KEY"] = "<OPENAI_API_KEY>"
os.environ["TAVILY_API_KEY"] = "<TAVILY_API_KEY>"

tavilySearchResults = TavilySearchResults(max_results = 2) # TAVILY API를 사용하기 위한 도구를 생성한다.

toolList = [tavilySearchResults]

chatOpenAI = ChatOpenAI(model = "gpt-4") # OPEN AI LLM 모델을 생성한다.

chatOpenAI.bind_tools(toolList) # OPEN AI LLM 모델과 도구를 바인딩한다.

sqliteSaver = SqliteSaver.from_conn_string(":memory:") # 상호 작용을 위한 메모리를 생성한다.

compiledStateGraph = create_react_agent(chatOpenAI, toolList, checkpointer = sqliteSaver) # 에이전트를 생성한다.

configurationDictionary = {"configurable" : {"thread_id" : "thread1"}} # thread_id로 세션을 구분한다.

addableValuesDict1 = compiledStateGraph.invoke({"messages" : [HumanMessage(content = "hi!")]}, configurationDictionary)

print(addableValuesDict1["messages"])

"""
[
    HumanMessage(content='hi!', id='e5a65000-7bcd-4a24-bac3-27806cbe8dce'),
    AIMessage(content='Hello! How can I assist you today?', response_metadata={'token_usage': {'completion_tokens': 10, 'prompt_tokens': 83, 'total_tokens': 93}, 'model_name': 'gpt-4', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-8ff9af0d-0586-4222-966b-3a593a46fbc9-0', usage_metadata={'input_tokens': 83, 'output_tokens': 10, 'total_tokens': 93})]
"""

addableValuesDict2 = compiledStateGraph.invoke({"messages" : [HumanMessage(content = "whats the weather in sf?")]}, configurationDictionary)

print(addableValuesDict2["messages"])

"""
[
    HumanMessage(content='hi!', id='e5a65000-7bcd-4a24-bac3-27806cbe8dce'),
    AIMessage(content='Hello! How can I assist you today?', response_metadata={'token_usage': {'completion_tokens': 10, 'prompt_tokens': 83, 'total_tokens': 93}, 'model_name': 'gpt-4', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-8ff9af0d-0586-4222-966b-3a593a46fbc9-0', usage_metadata={'input_tokens': 83, 'output_tokens': 10, 'total_tokens': 93}),
    HumanMessage(content='whats the weather in sf?', id='a3b53bc7-8d50-448c-aa8c-5ca1cd195797'),
    AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_V4h5BFmGfdmSBiHP9rVaRC9p', 'function': {'arguments': '{\n  "query": "current weather in San Francisco"\n}', 'name': 'tavily_search_results_json'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 23, 'prompt_tokens': 107, 'total_tokens': 130}, 'model_name': 'gpt-4', 'system_fingerprint': None, 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-c6e6a2e7-a2b3-40e9-9b25-1563b59cf83a-0', tool_calls=[{'name': 'tavily_search_results_json', 'args': {'query': 'current weather in San Francisco'}, 'id': 'call_V4h5BFmGfdmSBiHP9rVaRC9p'}], usage_metadata={'input_tokens': 107, 'output_tokens': 23, 'total_tokens': 130}), ToolMessage(content='[{"url": "https://www.weatherapi.com/", "content": "{\'location\': {\'name\': \'San Francisco\', \'region\': \'California\', \'country\': \'United States of America\', \'lat\': 37.78, \'lon\': -122.42, \'tz_id\': \'America/Los_Angeles\', \'localtime_epoch\': 1718003828, \'localtime\': \'2024-06-10 0:17\'}, \'current\': {\'last_updated_epoch\': 1718003700, \'last_updated\': \'2024-06-10 00:15\', \'temp_c\': 12.8, \'temp_f\': 55.0, \'is_day\': 0, \'condition\': {\'text\': \'Overcast\', \'icon\': \'//cdn.weatherapi.com/weather/64x64/night/122.png\', \'code\': 1009}, \'wind_mph\': 12.5, \'wind_kph\': 20.2, \'wind_degree\': 250, \'wind_dir\': \'WSW\', \'pressure_mb\': 1015.0, \'pressure_in\': 29.98, \'precip_mm\': 0.01, \'precip_in\': 0.0, \'humidity\': 93, \'cloud\': 100, \'feelslike_c\': 11.3, \'feelslike_f\': 52.3, \'windchill_c\': 10.4, \'windchill_f\': 50.8, \'heatindex_c\': 12.1, \'heatindex_f\': 53.8, \'dewpoint_c\': 10.1, \'dewpoint_f\': 50.2, \'vis_km\': 16.0, \'vis_miles\': 9.0, \'uv\': 1.0, \'gust_mph\': 14.0, \'gust_kph\': 22.6}}"}, {"url": "https://forecast.weather.gov/zipcity.php?inputstring=San+Francisco,CA", "content": "San Francisco CA 37.77\\u00b0N 122.41\\u00b0W (Elev. 131 ft) Last Update: 6:00 pm PDT Jun 7, 2024. Forecast Valid: 7pm PDT Jun 7, 2024-6pm PDT Jun 14, 2024 . Forecast Discussion . Additional Resources. Radar & Satellite Image. Hourly Weather Forecast. ... Severe Weather ; Current Outlook Maps ; Drought ; Fire Weather ; Fronts/Precipitation Maps ; Current ..."}]', name='tavily_search_results_json', id='fb4045ac-3877-4c98-ae9e-1d56267a5f69', tool_call_id='call_V4h5BFmGfdmSBiHP9rVaRC9p'),
    AIMessage(content='The current weather in San Francisco, California is overcast with a temperature of 12.8°C (55°F). The wind is blowing from the west-southwest at a speed of 20.2 kph (12.5 mph). The humidity level is at 93%.', response_metadata={'token_usage': {'completion_tokens': 59, 'prompt_tokens': 691, 'total_tokens': 750}, 'model_name': 'gpt-4', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-e0ebe54c-dc83-4dfa-abfa-d625fbd97f44-0', usage_metadata={'input_tokens': 691, 'output_tokens': 59, 'total_tokens': 750})]
"""

import os

from langchain_core.messages import HumanMessage

from langchain_community.tools.tavily_search import TavilySearchResults

from langchain_openai import ChatOpenAI

from langgraph.prebuilt import create_react_agent

from langgraph.checkpoint.sqlite import SqliteSaver

os.environ["OPENAI_API_KEY"] = "<OPENAI_API_KEY>"

os.environ["TAVILY_API_KEY"] = "<TAVILY_API_KEY>"

tavilySearchResults = TavilySearchResults(max_results = 2) # TAVILY API를 사용하기 위한 도구를 생성한다.

toolList = [tavilySearchResults]

chatOpenAI = ChatOpenAI(model = "gpt-4") # OPEN AI LLM 모델을 생성한다.

chatOpenAI.bind_tools(toolList) # OPEN AI LLM 모델과 도구를 바인딩한다.

sqliteSaver = SqliteSaver.from_conn_string(":memory:") # 상호 작용을 위한 메모리를 생성한다.

compiledStateGraph = create_react_agent(chatOpenAI, toolList, checkpointer = sqliteSaver) # 에이전트를 생성한다.

configurationDictionary = {"configurable" : {"thread_id" : "thread1"}} # thread_id로 세션을 구분한다.

addableValuesDict1 = compiledStateGraph.invoke({"messages" : [HumanMessage(content = "hi!")]}, configurationDictionary)

print(addableValuesDict1["messages"])

"""

[

HumanMessage(content='hi!', id='e5a65000-7bcd-4a24-bac3-27806cbe8dce'),

AIMessage(content='Hello! How can I assist you today?', response_metadata={'token_usage': {'completion_tokens': 10, 'prompt_tokens': 83, 'total_tokens': 93}, 'model_name': 'gpt-4', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-8ff9af0d-0586-4222-966b-3a593a46fbc9-0', usage_metadata={'input_tokens': 83, 'output_tokens': 10, 'total_tokens': 93})]

"""

addableValuesDict2 = compiledStateGraph.invoke({"messages" : [HumanMessage(content = "whats the weather in sf?")]}, configurationDictionary)

print(addableValuesDict2["messages"])

"""

[

HumanMessage(content='hi!', id='e5a65000-7bcd-4a24-bac3-27806cbe8dce'),

HumanMessage(content='whats the weather in sf?', id='a3b53bc7-8d50-448c-aa8c-5ca1cd195797'),

AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_V4h5BFmGfdmSBiHP9rVaRC9p', 'function': {'arguments': '{\n "query": "current weather in San Francisco"\n}', 'name': 'tavily_search_results_json'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 23, 'prompt_tokens': 107, 'total_tokens': 130}, 'model_name': 'gpt-4', 'system_fingerprint': None, 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-c6e6a2e7-a2b3-40e9-9b25-1563b59cf83a-0', tool_calls=[{'name': 'tavily_search_results_json', 'args': {'query': 'current weather in San Francisco'}, 'id': 'call_V4h5BFmGfdmSBiHP9rVaRC9p'}], usage_metadata={'input_tokens': 107, 'output_tokens': 23, 'total_tokens': 130}), ToolMessage(content='[{"url": "https://www.weatherapi.com/", "content": "{\'location\': {\'name\': \'San Francisco\', \'region\': \'California\', \'country\': \'United States of America\', \'lat\': 37.78, \'lon\': -122.42, \'tz_id\': \'America/Los_Angeles\', \'localtime_epoch\': 1718003828, \'localtime\': \'2024-06-10 0:17\'}, \'current\': {\'last_updated_epoch\': 1718003700, \'last_updated\': \'2024-06-10 00:15\', \'temp_c\': 12.8, \'temp_f\': 55.0, \'is_day\': 0, \'condition\': {\'text\': \'Overcast\', \'icon\': \'//cdn.weatherapi.com/weather/64x64/night/122.png\', \'code\': 1009}, \'wind_mph\': 12.5, \'wind_kph\': 20.2, \'wind_degree\': 250, \'wind_dir\': \'WSW\', \'pressure_mb\': 1015.0, \'pressure_in\': 29.98, \'precip_mm\': 0.01, \'precip_in\': 0.0, \'humidity\': 93, \'cloud\': 100, \'feelslike_c\': 11.3, \'feelslike_f\': 52.3, \'windchill_c\': 10.4, \'windchill_f\': 50.8, \'heatindex_c\': 12.1, \'heatindex_f\': 53.8, \'dewpoint_c\': 10.1, \'dewpoint_f\': 50.2, \'vis_km\': 16.0, \'vis_miles\': 9.0, \'uv\': 1.0, \'gust_mph\': 14.0, \'gust_kph\': 22.6}}"}, {"url": "https://forecast.weather.gov/zipcity.php?inputstring=San+Francisco,CA", "content": "San Francisco CA 37.77\\u00b0N 122.41\\u00b0W (Elev. 131 ft) Last Update: 6:00 pm PDT Jun 7, 2024. Forecast Valid: 7pm PDT Jun 7, 2024-6pm PDT Jun 14, 2024 . Forecast Discussion . Additional Resources. Radar & Satellite Image. Hourly Weather Forecast. ... Severe Weather ; Current Outlook Maps ; Drought ; Fire Weather ; Fronts/Precipitation Maps ; Current ..."}]', name='tavily_search_results_json', id='fb4045ac-3877-4c98-ae9e-1d56267a5f69', tool_call_id='call_V4h5BFmGfdmSBiHP9rVaRC9p'),

AIMessage(content='The current weather in San Francisco, California is overcast with a temperature of 12.8°C (55°F). The wind is blowing from the west-southwest at a speed of 20.2 kph (12.5 mph). The humidity level is at 93%.', response_metadata={'token_usage': {'completion_tokens': 59, 'prompt_tokens': 691, 'total_tokens': 750}, 'model_name': 'gpt-4', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-e0ebe54c-dc83-4dfa-abfa-d625fbd97f44-0', usage_metadata={'input_tokens': 691, 'output_tokens': 59, 'total_tokens': 750})]

"""

▶ requirements.txt


aiohttp==3.9.5
aiosignal==1.3.1
annotated-types==0.7.0
anyio==4.4.0
async-timeout==4.0.3
attrs==23.2.0
certifi==2024.6.2
charset-normalizer==3.3.2
dataclasses-json==0.6.7
distro==1.9.0
exceptiongroup==1.2.1
frozenlist==1.4.1
greenlet==3.0.3
h11==0.14.0
httpcore==1.0.5
httpx==0.27.0
idna==3.7
jsonpatch==1.33
jsonpointer==2.4
langchain==0.2.3
langchain-community==0.2.4
langchain-core==0.2.5
langchain-openai==0.1.8
langchain-text-splitters==0.2.1
langgraph==0.0.66
langsmith==0.1.75
marshmallow==3.21.3
multidict==6.0.5
mypy-extensions==1.0.0
numpy==1.26.4
openai==1.33.0
orjson==3.10.3
packaging==23.2
pydantic==2.7.3
pydantic_core==2.18.4
PyYAML==6.0.1
regex==2024.5.15
requests==2.32.3
sniffio==1.3.1
SQLAlchemy==2.0.30
tavily-python==0.3.3
tenacity==8.3.0
tiktoken==0.7.0
tqdm==4.66.4
typing-inspect==0.9.0
typing_extensions==4.12.2
urllib3==2.2.1
yarl==1.9.4

aiohttp==3.9.5

aiosignal==1.3.1

annotated-types==0.7.0

anyio==4.4.0

async-timeout==4.0.3

attrs==23.2.0

certifi==2024.6.2

charset-normalizer==3.3.2

dataclasses-json==0.6.7

distro==1.9.0

exceptiongroup==1.2.1

frozenlist==1.4.1

greenlet==3.0.3

h11==0.14.0

httpcore==1.0.5

httpx==0.27.0

idna==3.7

jsonpatch==1.33

jsonpointer==2.4

langchain==0.2.3

langchain-community==0.2.4

langchain-core==0.2.5

langchain-openai==0.1.8

langchain-text-splitters==0.2.1

langgraph==0.0.66

langsmith==0.1.75

marshmallow==3.21.3

multidict==6.0.5

mypy-extensions==1.0.0

numpy==1.26.4

openai==1.33.0

orjson==3.10.3

packaging==23.2

pydantic==2.7.3

pydantic_core==2.18.4

PyYAML==6.0.1

regex==2024.5.15

requests==2.32.3

sniffio==1.3.1

SQLAlchemy==2.0.30

tavily-python==0.3.3

tenacity==8.3.0

tiktoken==0.7.0

tqdm==4.66.4

typing-inspect==0.9.0

typing_extensions==4.12.2

urllib3==2.2.1

yarl==1.9.4

[PYTHON/LANGCHAIN] load_tools 함수 : serpapi와 llm-math 도구 로드하기

■ load_tools 함수를 사용해 serpapi와 llm-math 도구를 로드하는 방법을 보여준다. ▶ main.py


import os

from langchain_openai                              import ChatOpenAI
from langchain_community.agent_toolkits.load_tools import load_tools

os.environ["OPENAI_API_KEY" ] = "<OPENAI_API_KEY>"
os.environ["SERPAPI_API_KEY"] = "<SERPAPI_API_KEY>"

chatOpenAI = ChatOpenAI(model = "gpt-4")

toolList = load_tools(tool_names = ["serpapi", "llm-math"], llm = chatOpenAI) # 도구를 로드한다.

print(toolList)

"""
[
    Tool(name='Search', description='A search engine. Useful for when you need to answer questions about current events. Input should be a search query.', func=<bound method SerpAPIWrapper.run of SerpAPIWrapper(search_engine=<class 'serpapi.google_search.GoogleSearch'>, params={'engine': 'google', 'google_domain': 'google.com', 'gl': 'us', 'hl': 'en'}, serpapi_api_key='83dffd65104b51e264e6f063394f66a3d22a492631a4f438c9b1a71b89cbbd24', aiosession=None)>, coroutine=<bound method SerpAPIWrapper.arun of SerpAPIWrapper(search_engine=<class 'serpapi.google_search.GoogleSearch'>, params={'engine': 'google', 'google_domain': 'google.com', 'gl': 'us', 'hl': 'en'}, serpapi_api_key='83dffd65104b51e264e6f063394f66a3d22a492631a4f438c9b1a71b89cbbd24', aiosession=None)>),
    Tool(name='Calculator', description='Useful for when you need to answer questions about math.', func=<bound method Chain.run of LLMMathChain(llm_chain=LLMChain(prompt=PromptTemplate(input_variables=['question'], template='Translate a math problem into a expression that can be executed using Python\'s numexpr library. Use the output of running this code to answer the question.\n\nQuestion: ${{Question with math problem.}}\n```text\n${{single line mathematical expression that solves the problem}}\n```\n...numexpr.evaluate(text)...\n```output\n${{Output of running the code}}\n```\nAnswer: ${{Answer}}\n\nBegin.\n\nQuestion: What is 37593 * 67?\n```text\n37593 * 67\n```\n...numexpr.evaluate("37593 * 67")...\n```output\n2518731\n```\nAnswer: 2518731\n\nQuestion: 37593^(1/5)\n```text\n37593**(1/5)\n```\n...numexpr.evaluate("37593**(1/5)")...\n```output\n8.222831614237718\n```\nAnswer: 8.222831614237718\n\nQuestion: {question}\n'), llm=ChatOpenAI(client=<openai.resources.chat.completions.Completions object at 0x7fc2c5782170>, async_client=<openai.resources.chat.completions.AsyncCompletions object at 0x7fc2c5783880>, model_name='gpt-4', openai_api_key=SecretStr('**********'), openai_proxy='')))>, coroutine=<bound method Chain.arun of LLMMathChain(llm_chain=LLMChain(prompt=PromptTemplate(input_variables=['question'], template='Translate a math problem into a expression that can be executed using Python\'s numexpr library. Use the output of running this code to answer the question.\n\nQuestion: ${{Question with math problem.}}\n```text\n${{single line mathematical expression that solves the problem}}\n```\n...numexpr.evaluate(text)...\n```output\n${{Output of running the code}}\n```\nAnswer: ${{Answer}}\n\nBegin.\n\nQuestion: What is 37593 * 67?\n```text\n37593 * 67\n```\n...numexpr.evaluate("37593 * 67")...\n```output\n2518731\n```\nAnswer: 2518731\n\nQuestion: 37593^(1/5)\n```text\n37593**(1/5)\n```\n...numexpr.evaluate("37593**(1/5)")...\n```output\n8.222831614237718\n```\nAnswer: 8.222831614237718\n\nQuestion: {question}\n'), llm=ChatOpenAI(client=<openai.resources.chat.completions.Completions object at 0x7fc2c5782170>, async_client=<openai.resources.chat.completions.AsyncCompletions object at 0x7fc2c5783880>, model_name='gpt-4', openai_api_key=SecretStr('**********'), openai_proxy='')))>)
]
"""

import os

from langchain_openai import ChatOpenAI

from langchain_community.agent_toolkits.load_tools import load_tools

os.environ["OPENAI_API_KEY" ] = "<OPENAI_API_KEY>"

os.environ["SERPAPI_API_KEY"] = "<SERPAPI_API_KEY>"

chatOpenAI = ChatOpenAI(model = "gpt-4")

toolList = load_tools(tool_names = ["serpapi", "llm-math"], llm = chatOpenAI) # 도구를 로드한다.

print(toolList)

"""

[

Tool(name='Search', description='A search engine. Useful for when you need to answer questions about current events. Input should be a search query.', func=<bound method SerpAPIWrapper.run of SerpAPIWrapper(search_engine=<class 'serpapi.google_search.GoogleSearch'>, params={'engine': 'google', 'google_domain': 'google.com', 'gl': 'us', 'hl': 'en'}, serpapi_api_key='83dffd65104b51e264e6f063394f66a3d22a492631a4f438c9b1a71b89cbbd24', aiosession=None)>, coroutine=<bound method SerpAPIWrapper.arun of SerpAPIWrapper(search_engine=<class 'serpapi.google_search.GoogleSearch'>, params={'engine': 'google', 'google_domain': 'google.com', 'gl': 'us', 'hl': 'en'}, serpapi_api_key='83dffd65104b51e264e6f063394f66a3d22a492631a4f438c9b1a71b89cbbd24', aiosession=None)>),

Tool(name='Calculator', description='Useful for when you need to answer questions about math.', func=<bound method Chain.run of LLMMathChain(llm_chain=LLMChain(prompt=PromptTemplate(input_variables=['question'], template='Translate a math problem into a expression that can be executed using Python\'s numexpr library. Use the output of running this code to answer the question.\n\nQuestion: ${{Question with math problem.}}\n```text\n${{single line mathematical expression that solves the problem}}\n```\n...numexpr.evaluate(text)...\n```output\n${{Output of running the code}}\n```\nAnswer: ${{Answer}}\n\nBegin.\n\nQuestion: What is 37593 * 67?\n```text\n37593 * 67\n```\n...numexpr.evaluate("37593 * 67")...\n```output\n2518731\n```\nAnswer: 2518731\n\nQuestion: 37593^(1/5)\n```text\n37593**(1/5)\n```\n...numexpr.evaluate("37593**(1/5)")...\n```output\n8.222831614237718\n```\nAnswer: 8.222831614237718\n\nQuestion: {question}\n'), llm=ChatOpenAI(client=<openai.resources.chat.completions.Completions object at 0x7fc2c5782170>, async_client=<openai.resources.chat.completions.AsyncCompletions object at 0x7fc2c5783880>, model_name='gpt-4', openai_api_key=SecretStr('**********'), openai_proxy='')))>, coroutine=<bound method Chain.arun of LLMMathChain(llm_chain=LLMChain(prompt=PromptTemplate(input_variables=['question'], template='Translate a math problem into a expression that can be executed using Python\'s numexpr library. Use the output of running this code to answer the question.\n\nQuestion: ${{Question with math problem.}}\n```text\n${{single line mathematical expression that solves the problem}}\n```\n...numexpr.evaluate(text)...\n```output\n${{Output of running the code}}\n```\nAnswer: ${{Answer}}\n\nBegin.\n\nQuestion: What is 37593 * 67?\n```text\n37593 * 67\n```\n...numexpr.evaluate("37593 * 67")...\n```output\n2518731\n```\nAnswer: 2518731\n\nQuestion: 37593^(1/5)\n```text\n37593**(1/5)\n```\n...numexpr.evaluate("37593**(1/5)")...\n```output\n8.222831614237718\n```\nAnswer: 8.222831614237718\n\nQuestion: {question}\n'), llm=ChatOpenAI(client=<openai.resources.chat.completions.Completions object at 0x7fc2c5782170>, async_client=<openai.resources.chat.completions.AsyncCompletions object at 0x7fc2c5783880>, model_name='gpt-4', openai_api_key=SecretStr('**********'), openai_proxy='')))>)

]

"""

▶ requirements.txt


aiohttp==3.9.5
aiosignal==1.3.1
annotated-types==0.7.0
anyio==4.4.0
async-timeout==4.0.3
attrs==23.2.0
certifi==2024.6.2
charset-normalizer==3.3.2
dataclasses-json==0.6.7
distro==1.9.0
exceptiongroup==1.2.1
frozenlist==1.4.1
google-search-results==2.4.2
greenlet==3.0.3
h11==0.14.0
httpcore==1.0.5
httpx==0.27.0
idna==3.7
jsonpatch==1.33
jsonpointer==2.4
langchain==0.2.3
langchain-community==0.2.4
langchain-core==0.2.5
langchain-openai==0.1.8
langchain-text-splitters==0.2.1
langsmith==0.1.75
marshmallow==3.21.3
multidict==6.0.5
mypy-extensions==1.0.0
numexpr==2.10.0
numpy==1.26.4
openai==1.33.0
orjson==3.10.3
packaging==23.2
pydantic==2.7.3
pydantic_core==2.18.4
PyYAML==6.0.1
regex==2024.5.15
requests==2.32.3
sniffio==1.3.1
SQLAlchemy==2.0.30
tenacity==8.3.0
tiktoken==0.7.0
tqdm==4.66.4
typing-inspect==0.9.0
typing_extensions==4.12.2
urllib3==2.2.1
yarl==1.9.4

aiohttp==3.9.5

aiosignal==1.3.1

annotated-types==0.7.0

anyio==4.4.0

async-timeout==4.0.3

attrs==23.2.0

certifi==2024.6.2

charset-normalizer==3.3.2

dataclasses-json==0.6.7

distro==1.9.0

exceptiongroup==1.2.1

frozenlist==1.4.1

google-search-results==2.4.2

greenlet==3.0.3

h11==0.14.0

httpcore==1.0.5

httpx==0.27.0

idna==3.7

jsonpatch==1.33

jsonpointer==2.4

langchain==0.2.3

langchain-community==0.2.4

langchain-core==0.2.5

langchain-openai==0.1.8

langchain-text-splitters==0.2.1

langsmith==0.1.75

marshmallow==3.21.3

multidict==6.0.5

mypy-extensions==1.0.0

numexpr==2.10.0

numpy==1.26.4

openai==1.33.0

orjson==3.10.3

packaging==23.2

pydantic==2.7.3

pydantic_core==2.18.4

PyYAML==6.0.1

regex==2024.5.15

requests==2.32.3

sniffio==1.3.1

SQLAlchemy==2.0.30

tenacity==8.3.0

tiktoken==0.7.0

tqdm==4.66.4

typing-inspect==0.9.0

typing_extensions==4.12.2

urllib3==2.2.1

yarl==1.9.4

※ pip install numexpr google-search-results langchain

[PYTHON/LANGCHAIN] create_react_agent 함수 : OpenAI 모델과 SerpAPI를 사용해 날씨예보 및 계산기 채팅 에이전트 만들기

■ create_react_agent 함수를 사용해 OpenAI 모델과 SerpAPI를 연결해 날씨예보 및 계산기 채팅 에이전트를 만드는 방법을 보여준다. ▶ main.py


import os

from langchain_core.messages                       import HumanMessage
from langchain_openai                              import ChatOpenAI
from langchain_community.agent_toolkits.load_tools import load_tools
from langgraph.checkpoint.sqlite                   import SqliteSaver
from langgraph.prebuilt                            import create_react_agent

os.environ["OPENAI_API_KEY" ] = "<OPENAI_API_KEY>"
os.environ["SERPAPI_API_KEY"] = "<SERPAPI_API_KEY>"

chatOpenAI = ChatOpenAI(model = "gpt-4")

toolList = load_tools(tool_names = ["serpapi", "llm-math"], llm = chatOpenAI) # 도구를 로드한다.

sqliteSaver = SqliteSaver.from_conn_string(":memory:") # 상호 작용을 위한 메모리를 생성한다.

compiledStateGraph = create_react_agent(chatOpenAI, toolList, checkpointer = sqliteSaver) # 에이전트를 생성한다.

configurationDictionary = {"configurable" : {"thread_id" : "thread1"}} # thread_id로 세션을 구분한다.

addableValuesDict1 = compiledStateGraph.invoke({"messages" : [HumanMessage(content = "안녕하세요, 저는 홍길동입니다.")]}, configurationDictionary)

print(addableValuesDict1["messages"][-1].content)

addableValuesDict2 = compiledStateGraph.invoke({"messages" : [HumanMessage(content = "오늘 한국 수원의 날씨를 웹 검색으로 섭씨로 확인해주세요.")]}, configurationDictionary)

print(addableValuesDict2["messages"][-1].content)

addableValuesDict3 = compiledStateGraph.invoke({"messages" : [HumanMessage(content = "저의 이름은 무엇입니까?")]}, configurationDictionary)

print(addableValuesDict3["messages"][-1].content)

addableValuesDict4 = compiledStateGraph.invoke({"messages" : [HumanMessage(content = "4,567 * 32,324 / 4를 계산기로 계산해줘")]}, configurationDictionary)

print(addableValuesDict4["messages"][-1].content)

"""
안녕하세요, 홍길동님! 어떻게 도와드릴 수 있을까요?
오늘 대한민국 경기도 수원시의 날씨는 대부분 흐립니다. 화씨 86도로, 섭씨로 변환하면 대략 30도 정도 됩니다. 강수 확률은 10%, 습도는 62%, 풍속은 시간당 10마일입니다.
당신의 이름은 홍길동님이라고 말씀하셨습니다.
4,567 * 32,324 / 4의 계산 결과는 36905927입니다.
"""

import os

from langchain_core.messages import HumanMessage

from langchain_openai import ChatOpenAI

from langchain_community.agent_toolkits.load_tools import load_tools

from langgraph.checkpoint.sqlite import SqliteSaver

from langgraph.prebuilt import create_react_agent

os.environ["OPENAI_API_KEY" ] = "<OPENAI_API_KEY>"

os.environ["SERPAPI_API_KEY"] = "<SERPAPI_API_KEY>"

chatOpenAI = ChatOpenAI(model = "gpt-4")

toolList = load_tools(tool_names = ["serpapi", "llm-math"], llm = chatOpenAI) # 도구를 로드한다.

sqliteSaver = SqliteSaver.from_conn_string(":memory:") # 상호 작용을 위한 메모리를 생성한다.

compiledStateGraph = create_react_agent(chatOpenAI, toolList, checkpointer = sqliteSaver) # 에이전트를 생성한다.

configurationDictionary = {"configurable" : {"thread_id" : "thread1"}} # thread_id로 세션을 구분한다.

addableValuesDict1 = compiledStateGraph.invoke({"messages" : [HumanMessage(content = "안녕하세요, 저는 홍길동입니다.")]}, configurationDictionary)

print(addableValuesDict1["messages"][-1].content)

addableValuesDict2 = compiledStateGraph.invoke({"messages" : [HumanMessage(content = "오늘 한국 수원의 날씨를 웹 검색으로 섭씨로 확인해주세요.")]}, configurationDictionary)

print(addableValuesDict2["messages"][-1].content)

addableValuesDict3 = compiledStateGraph.invoke({"messages" : [HumanMessage(content = "저의 이름은 무엇입니까?")]}, configurationDictionary)

print(addableValuesDict3["messages"][-1].content)

addableValuesDict4 = compiledStateGraph.invoke({"messages" : [HumanMessage(content = "4,567 * 32,324 / 4를 계산기로 계산해줘")]}, configurationDictionary)

print(addableValuesDict4["messages"][-1].content)

"""

안녕하세요, 홍길동님! 어떻게 도와드릴 수 있을까요?

오늘 대한민국 경기도 수원시의 날씨는 대부분 흐립니다. 화씨 86도로, 섭씨로 변환하면 대략 30도 정도 됩니다. 강수 확률은 10%, 습도는 62%, 풍속은 시간당 10마일입니다.

당신의 이름은 홍길동님이라고 말씀하셨습니다.

4,567 * 32,324 / 4의 계산 결과는 36905927입니다.

"""

▶ requirements.txt


aiohttp==3.9.5
aiosignal==1.3.1
annotated-types==0.7.0
anyio==4.4.0
async-timeout==4.0.3
attrs==23.2.0
certifi==2024.6.2
charset-normalizer==3.3.2
dataclasses-json==0.6.7
distro==1.9.0
exceptiongroup==1.2.1
frozenlist==1.4.1
google-search-results==2.4.2
greenlet==3.0.3
h11==0.14.0
httpcore==1.0.5
httpx==0.27.0
idna==3.7
jsonpatch==1.33
jsonpointer==2.4
langchain==0.2.3
langchain-community==0.2.4
langchain-core==0.2.5
langchain-openai==0.1.8
langchain-text-splitters==0.2.1
langgraph==0.0.66
langsmith==0.1.75
marshmallow==3.21.3
multidict==6.0.5
mypy-extensions==1.0.0
numexpr==2.10.0
numpy==1.26.4
openai==1.33.0
orjson==3.10.3
packaging==23.2
pydantic==2.7.3
pydantic_core==2.18.4
PyYAML==6.0.1
regex==2024.5.15
requests==2.32.3
sniffio==1.3.1
SQLAlchemy==2.0.30
tenacity==8.3.0
tiktoken==0.7.0
tqdm==4.66.4
typing-inspect==0.9.0
typing_extensions==4.12.2
urllib3==2.2.1
yarl==1.9.4

aiohttp==3.9.5

aiosignal==1.3.1

annotated-types==0.7.0

anyio==4.4.0

async-timeout==4.0.3

attrs==23.2.0

certifi==2024.6.2

charset-normalizer==3.3.2

dataclasses-json==0.6.7

distro==1.9.0

exceptiongroup==1.2.1

frozenlist==1.4.1

google-search-results==2.4.2

greenlet==3.0.3

h11==0.14.0

httpcore==1.0.5

httpx==0.27.0

idna==3.7

jsonpatch==1.33

jsonpointer==2.4

langchain==0.2.3

langchain-community==0.2.4

langchain-core==0.2.5

langchain-openai==0.1.8

langchain-text-splitters==0.2.1

langgraph==0.0.66

langsmith==0.1.75

marshmallow==3.21.3

multidict==6.0.5

mypy-extensions==1.0.0

numexpr==2.10.0

numpy==1.26.4

openai==1.33.0

orjson==3.10.3

packaging==23.2

pydantic==2.7.3

pydantic_core==2.18.4

PyYAML==6.0.1

regex==2024.5.15

requests==2.32.3

sniffio==1.3.1

SQLAlchemy==2.0.30

tenacity==8.3.0

tiktoken==0.7.0

tqdm==4.66.4

typing-inspect==0.9.0

typing_extensions==4.12.2

urllib3==2.2.1

yarl==1.9.4

[PYTHON/LANGCHAIN] create_react_agent 함수 : OpenAI 모델과 Tavily API를 연결해 날씨예보 채팅 에이전트 만들기

■ create_react_agent 함수를 사용해 OpenAI 모델과 Tavily API를 연결해 날씨예보 채팅 에이전트를 만드는 방법을 보여준다. ▶ main.py


import os

from langchain_core.messages                 import HumanMessage
from langchain_community.tools.tavily_search import TavilySearchResults
from langchain_openai                        import ChatOpenAI
from langgraph.prebuilt                      import create_react_agent
from langgraph.checkpoint.sqlite             import SqliteSaver

os.environ["OPENAI_API_KEY"] = "<OPENAI_API_KEY>"
os.environ["TAVILY_API_KEY"] = "<TAVILY_API_KEY>"

tavilySearchResults = TavilySearchResults(max_results = 2) # TAVILY API를 사용하기 위한 도구를 생성한다.

toolList = [tavilySearchResults]

chatOpenAI = ChatOpenAI(model = "gpt-4") # OPEN AI LLM 모델을 생성한다.

chatOpenAI.bind_tools(toolList) # OPEN AI LLM 모델과 도구를 바인딩한다.

sqliteSaver = SqliteSaver.from_conn_string(":memory:") # 상호 작용을 위한 메모리를 생성한다.

compiledStateGraph = create_react_agent(chatOpenAI, toolList, checkpointer = sqliteSaver) # 에이전트를 생성한다.

configurationDictionary = {"configurable" : {"thread_id" : "thread1"}} # thread_id로 세션을 구분한다.

addableValuesDict1 = compiledStateGraph.invoke({"messages" : [HumanMessage(content = "hi!")]}, configurationDictionary)

print(addableValuesDict1["messages"])

"""
[
    HumanMessage(content='hi!', id='e5a65000-7bcd-4a24-bac3-27806cbe8dce'),
    AIMessage(content='Hello! How can I assist you today?', response_metadata={'token_usage': {'completion_tokens': 10, 'prompt_tokens': 83, 'total_tokens': 93}, 'model_name': 'gpt-4', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-8ff9af0d-0586-4222-966b-3a593a46fbc9-0', usage_metadata={'input_tokens': 83, 'output_tokens': 10, 'total_tokens': 93})]
"""

addableValuesDict2 = compiledStateGraph.invoke({"messages" : [HumanMessage(content = "whats the weather in sf?")]}, configurationDictionary)

print(addableValuesDict2["messages"])

"""
[
    HumanMessage(content='hi!', id='e5a65000-7bcd-4a24-bac3-27806cbe8dce'),
    AIMessage(content='Hello! How can I assist you today?', response_metadata={'token_usage': {'completion_tokens': 10, 'prompt_tokens': 83, 'total_tokens': 93}, 'model_name': 'gpt-4', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-8ff9af0d-0586-4222-966b-3a593a46fbc9-0', usage_metadata={'input_tokens': 83, 'output_tokens': 10, 'total_tokens': 93}),
    HumanMessage(content='whats the weather in sf?', id='a3b53bc7-8d50-448c-aa8c-5ca1cd195797'),
    AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_V4h5BFmGfdmSBiHP9rVaRC9p', 'function': {'arguments': '{\n  "query": "current weather in San Francisco"\n}', 'name': 'tavily_search_results_json'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 23, 'prompt_tokens': 107, 'total_tokens': 130}, 'model_name': 'gpt-4', 'system_fingerprint': None, 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-c6e6a2e7-a2b3-40e9-9b25-1563b59cf83a-0', tool_calls=[{'name': 'tavily_search_results_json', 'args': {'query': 'current weather in San Francisco'}, 'id': 'call_V4h5BFmGfdmSBiHP9rVaRC9p'}], usage_metadata={'input_tokens': 107, 'output_tokens': 23, 'total_tokens': 130}), ToolMessage(content='[{"url": "https://www.weatherapi.com/", "content": "{\'location\': {\'name\': \'San Francisco\', \'region\': \'California\', \'country\': \'United States of America\', \'lat\': 37.78, \'lon\': -122.42, \'tz_id\': \'America/Los_Angeles\', \'localtime_epoch\': 1718003828, \'localtime\': \'2024-06-10 0:17\'}, \'current\': {\'last_updated_epoch\': 1718003700, \'last_updated\': \'2024-06-10 00:15\', \'temp_c\': 12.8, \'temp_f\': 55.0, \'is_day\': 0, \'condition\': {\'text\': \'Overcast\', \'icon\': \'//cdn.weatherapi.com/weather/64x64/night/122.png\', \'code\': 1009}, \'wind_mph\': 12.5, \'wind_kph\': 20.2, \'wind_degree\': 250, \'wind_dir\': \'WSW\', \'pressure_mb\': 1015.0, \'pressure_in\': 29.98, \'precip_mm\': 0.01, \'precip_in\': 0.0, \'humidity\': 93, \'cloud\': 100, \'feelslike_c\': 11.3, \'feelslike_f\': 52.3, \'windchill_c\': 10.4, \'windchill_f\': 50.8, \'heatindex_c\': 12.1, \'heatindex_f\': 53.8, \'dewpoint_c\': 10.1, \'dewpoint_f\': 50.2, \'vis_km\': 16.0, \'vis_miles\': 9.0, \'uv\': 1.0, \'gust_mph\': 14.0, \'gust_kph\': 22.6}}"}, {"url": "https://forecast.weather.gov/zipcity.php?inputstring=San+Francisco,CA", "content": "San Francisco CA 37.77\\u00b0N 122.41\\u00b0W (Elev. 131 ft) Last Update: 6:00 pm PDT Jun 7, 2024. Forecast Valid: 7pm PDT Jun 7, 2024-6pm PDT Jun 14, 2024 . Forecast Discussion . Additional Resources. Radar & Satellite Image. Hourly Weather Forecast. ... Severe Weather ; Current Outlook Maps ; Drought ; Fire Weather ; Fronts/Precipitation Maps ; Current ..."}]', name='tavily_search_results_json', id='fb4045ac-3877-4c98-ae9e-1d56267a5f69', tool_call_id='call_V4h5BFmGfdmSBiHP9rVaRC9p'),
    AIMessage(content='The current weather in San Francisco, California is overcast with a temperature of 12.8°C (55°F). The wind is blowing from the west-southwest at a speed of 20.2 kph (12.5 mph). The humidity level is at 93%.', response_metadata={'token_usage': {'completion_tokens': 59, 'prompt_tokens': 691, 'total_tokens': 750}, 'model_name': 'gpt-4', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-e0ebe54c-dc83-4dfa-abfa-d625fbd97f44-0', usage_metadata={'input_tokens': 691, 'output_tokens': 59, 'total_tokens': 750})]
"""

import os

from langchain_core.messages import HumanMessage

from langchain_community.tools.tavily_search import TavilySearchResults

from langchain_openai import ChatOpenAI

from langgraph.prebuilt import create_react_agent

from langgraph.checkpoint.sqlite import SqliteSaver

os.environ["OPENAI_API_KEY"] = "<OPENAI_API_KEY>"

os.environ["TAVILY_API_KEY"] = "<TAVILY_API_KEY>"

tavilySearchResults = TavilySearchResults(max_results = 2) # TAVILY API를 사용하기 위한 도구를 생성한다.

toolList = [tavilySearchResults]

chatOpenAI = ChatOpenAI(model = "gpt-4") # OPEN AI LLM 모델을 생성한다.

chatOpenAI.bind_tools(toolList) # OPEN AI LLM 모델과 도구를 바인딩한다.

sqliteSaver = SqliteSaver.from_conn_string(":memory:") # 상호 작용을 위한 메모리를 생성한다.

compiledStateGraph = create_react_agent(chatOpenAI, toolList, checkpointer = sqliteSaver) # 에이전트를 생성한다.

configurationDictionary = {"configurable" : {"thread_id" : "thread1"}} # thread_id로 세션을 구분한다.

addableValuesDict1 = compiledStateGraph.invoke({"messages" : [HumanMessage(content = "hi!")]}, configurationDictionary)

print(addableValuesDict1["messages"])

"""

[

HumanMessage(content='hi!', id='e5a65000-7bcd-4a24-bac3-27806cbe8dce'),

"""

addableValuesDict2 = compiledStateGraph.invoke({"messages" : [HumanMessage(content = "whats the weather in sf?")]}, configurationDictionary)

print(addableValuesDict2["messages"])

"""

[

HumanMessage(content='hi!', id='e5a65000-7bcd-4a24-bac3-27806cbe8dce'),

HumanMessage(content='whats the weather in sf?', id='a3b53bc7-8d50-448c-aa8c-5ca1cd195797'),

"""

▶ requirements.txt


aiohttp==3.9.5
aiosignal==1.3.1
annotated-types==0.7.0
anyio==4.4.0
async-timeout==4.0.3
attrs==23.2.0
certifi==2024.6.2
charset-normalizer==3.3.2
dataclasses-json==0.6.7
distro==1.9.0
exceptiongroup==1.2.1
frozenlist==1.4.1
greenlet==3.0.3
h11==0.14.0
httpcore==1.0.5
httpx==0.27.0
idna==3.7
jsonpatch==1.33
jsonpointer==2.4
langchain==0.2.3
langchain-community==0.2.4
langchain-core==0.2.5
langchain-openai==0.1.8
langchain-text-splitters==0.2.1
langgraph==0.0.66
langsmith==0.1.75
marshmallow==3.21.3
multidict==6.0.5
mypy-extensions==1.0.0
numpy==1.26.4
openai==1.33.0
orjson==3.10.3
packaging==23.2
pydantic==2.7.3
pydantic_core==2.18.4
PyYAML==6.0.1
regex==2024.5.15
requests==2.32.3
sniffio==1.3.1
SQLAlchemy==2.0.30
tavily-python==0.3.3
tenacity==8.3.0
tiktoken==0.7.0
tqdm==4.66.4
typing-inspect==0.9.0
typing_extensions==4.12.2
urllib3==2.2.1
yarl==1.9.4

aiohttp==3.9.5

aiosignal==1.3.1

annotated-types==0.7.0

anyio==4.4.0

async-timeout==4.0.3

attrs==23.2.0

certifi==2024.6.2

charset-normalizer==3.3.2

dataclasses-json==0.6.7

distro==1.9.0

exceptiongroup==1.2.1

frozenlist==1.4.1

greenlet==3.0.3

h11==0.14.0

httpcore==1.0.5

httpx==0.27.0

idna==3.7

jsonpatch==1.33

jsonpointer==2.4

langchain==0.2.3

langchain-community==0.2.4

langchain-core==0.2.5

langchain-openai==0.1.8

langchain-text-splitters==0.2.1

langgraph==0.0.66

langsmith==0.1.75

marshmallow==3.21.3

multidict==6.0.5

mypy-extensions==1.0.0

numpy==1.26.4

openai==1.33.0

orjson==3.10.3

packaging==23.2

pydantic==2.7.3

pydantic_core==2.18.4

PyYAML==6.0.1

regex==2024.5.15

requests==2.32.3

sniffio==1.3.1

SQLAlchemy==2.0.30

tavily-python==0.3.3

tenacity==8.3.0

tiktoken==0.7.0

tqdm==4.66.4

typing-inspect==0.9.0

typing_extensions==4.12.2

urllib3==2.2.1

yarl==1.9.4

※

[PYTHON/LANGCHAIN] | 연산자 : 프롬프트 템플리트, LLM 모델과 출력 파서 체인 만들기

■ | 연산자를 사용해 프롬프트 템플리트, LLM 모델과 출력 파서의 체인을 만드는 방법을 보여준다. ▶ main.py


import os

from langchain_core.prompts        import ChatPromptTemplate
from langchain_openai              import ChatOpenAI
from langchain_core.output_parsers import StrOutputParser

os.environ["OPENAI_API_KEY"] = "<OPENAI_API_KEY>"

promptTemplate = ChatPromptTemplate.from_messages(
    [
        ("system", "Translate the following into {language} : "),
        ("user"  , "{text}"                                    )
    ]
)

chatOpenAI = ChatOpenAI(model = "gpt-4")

strOutputParser = StrOutputParser()

runnableSequence = promptTemplate | chatOpenAI | strOutputParser

resultString = runnableSequence.invoke({"language" : "korean", "text" : "hi"})

print(resultString)

"""
안녕하세요
"""

import os

from langchain_core.prompts import ChatPromptTemplate

from langchain_openai import ChatOpenAI

from langchain_core.output_parsers import StrOutputParser

os.environ["OPENAI_API_KEY"] = "<OPENAI_API_KEY>"

promptTemplate = ChatPromptTemplate.from_messages(

[

("system", "Translate the following into {language} : "),

("user" , "{text}" )

]

)

chatOpenAI = ChatOpenAI(model = "gpt-4")

strOutputParser = StrOutputParser()

runnableSequence = promptTemplate | chatOpenAI | strOutputParser

resultString = runnableSequence.invoke({"language" : "korean", "text" : "hi"})

print(resultString)

"""

안녕하세요

"""

▶ requirements.txt


aiohttp==3.9.5
aiosignal==1.3.1
annotated-types==0.7.0
anyio==4.4.0
async-timeout==4.0.3
attrs==23.2.0
certifi==2024.6.2
charset-normalizer==3.3.2
distro==1.9.0
exceptiongroup==1.2.1
frozenlist==1.4.1
greenlet==3.0.3
h11==0.14.0
httpcore==1.0.5
httpx==0.27.0
idna==3.7
jsonpatch==1.33
jsonpointer==2.4
langchain==0.2.3
langchain-core==0.2.5
langchain-openai==0.1.8
langchain-text-splitters==0.2.1
langsmith==0.1.75
multidict==6.0.5
numpy==1.26.4
openai==1.33.0
orjson==3.10.3
packaging==23.2
pydantic==2.7.3
pydantic_core==2.18.4
PyYAML==6.0.1
regex==2024.5.15
requests==2.32.3
sniffio==1.3.1
SQLAlchemy==2.0.30
tenacity==8.3.0
tiktoken==0.7.0
tqdm==4.66.4
typing_extensions==4.12.2
urllib3==2.2.1
yarl==1.9.4

aiohttp==3.9.5

aiosignal==1.3.1

annotated-types==0.7.0

anyio==4.4.0

async-timeout==4.0.3

attrs==23.2.0

certifi==2024.6.2

charset-normalizer==3.3.2

distro==1.9.0

exceptiongroup==1.2.1

frozenlist==1.4.1

greenlet==3.0.3

h11==0.14.0

httpcore==1.0.5

httpx==0.27.0

idna==3.7

jsonpatch==1.33

jsonpointer==2.4

langchain==0.2.3

langchain-core==0.2.5

langchain-openai==0.1.8

langchain-text-splitters==0.2.1

langsmith==0.1.75

multidict==6.0.5

numpy==1.26.4

openai==1.33.0

orjson==3.10.3

packaging==23.2

pydantic==2.7.3

pydantic_core==2.18.4

PyYAML==6.0.1

regex==2024.5.15

requests==2.32.3

sniffio==1.3.1

SQLAlchemy==2.0.30

tenacity==8.3.0

tiktoken==0.7.0

tqdm==4.66.4

typing_extensions==4.12.2

urllib3==2.2.1

yarl==1.9.4

※ pip

[PYTHON/LANGCHAIN] langserve 모듈 : langchain 모듈 체인을 REST API 서버로 배포하기

■ langserve 모듈을 사용해 langchain 모듈의 체인을 REST API 서버로 배포하는 방법을 보여준다. ▶ server.py


import os

import uvicorn

from langchain_core.prompts        import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
from langchain_openai              import ChatOpenAI
from fastapi                       import FastAPI
from langserve                     import add_routes

os.environ["OPENAI_API_KEY"] = "<OPENAI_API_KEY>"

promptTemplate = ChatPromptTemplate.from_messages(
    [
        ("system", "Translate the following into {language} : "),
        ("user"  , "{text}"                                    )
    ]
)

chatOpenAI = ChatOpenAI(model = "gpt-4")

strOutputParser = StrOutputParser()

runnableSequence = promptTemplate | chatOpenAI | strOutputParser

fastAPI = FastAPI(
  title       = "LangChain Server",
  version     = "1.0",
  description = "A simple API server using LangChain's Runnable interfaces",
)

add_routes(
    fastAPI,
    runnableSequence,
    path = "/chain",
)

if __name__ == "__main__":
    uvicorn.run(fastAPI, host = "0.0.0.0", port = 8000)

import os

import uvicorn

from langchain_core.prompts import ChatPromptTemplate

from langchain_core.output_parsers import StrOutputParser

from langchain_openai import ChatOpenAI

from fastapi import FastAPI

from langserve import add_routes

os.environ["OPENAI_API_KEY"] = "<OPENAI_API_KEY>"

promptTemplate = ChatPromptTemplate.from_messages(

[

("system", "Translate the following into {language} : "),

("user" , "{text}" )

]

)

chatOpenAI = ChatOpenAI(model = "gpt-4")

strOutputParser = StrOutputParser()

runnableSequence = promptTemplate | chatOpenAI | strOutputParser

fastAPI = FastAPI(

title = "LangChain Server",

version = "1.0",

description = "A simple API server using LangChain's Runnable interfaces",

)

add_routes(

fastAPI,

runnableSequence,

path = "/chain",

)

if __name__ == "__main__":

uvicorn.run(fastAPI, host = "0.0.0.0", port = 8000)

▶ client.py


from langserve import RemoteRunnable

remoteRunnable = RemoteRunnable("http://localhost:8000/chain/")

resultString = remoteRunnable.invoke({"language" : "korean", "text" : "hi"})

print(resultString)

from langserve import RemoteRunnable

remoteRunnable = RemoteRunnable("http://localhost:8000/chain/")

resultString = remoteRunnable.invoke({"language" : "korean", "text" : "hi"})

print(resultString)

▶ requirements.txt


aiohttp==3.9.5
aiosignal==1.3.1
annotated-types==0.7.0
anyio==4.4.0
async-timeout==4.0.3
attrs==23.2.0
certifi==2024.6.2
charset-normalizer==3.3.2
distro==1.9.0
exceptiongroup==1.2.1
frozenlist==1.4.1
greenlet==3.0.3
h11==0.14.0
httpcore==1.0.5
httpx==0.27.0
idna==3.7
jsonpatch==1.33
jsonpointer==2.4
langchain==0.2.3
langchain-core==0.2.5
langchain-openai==0.1.8
langchain-text-splitters==0.2.1
langsmith==0.1.75
multidict==6.0.5
numpy==1.26.4
openai==1.33.0
orjson==3.10.3
packaging==23.2
pydantic==2.7.3
pydantic_core==2.18.4
PyYAML==6.0.1
regex==2024.5.15
requests==2.32.3
sniffio==1.3.1
SQLAlchemy==2.0.30
tenacity==8.3.0
tiktoken==0.7.0
tqdm==4.66.4
typing_extensions==4.12.2
urllib3==2.2.1
yarl==1.9.4

aiohttp==3.9.5

aiosignal==1.3.1

annotated-types==0.7.0

anyio==4.4.0

async-timeout==4.0.3

attrs==23.2.0

certifi==2024.6.2

charset-normalizer==3.3.2

distro==1.9.0

exceptiongroup==1.2.1

frozenlist==1.4.1

greenlet==3.0.3

h11==0.14.0

httpcore==1.0.5

httpx==0.27.0

idna==3.7

jsonpatch==1.33

jsonpointer==2.4

langchain==0.2.3

langchain-core==0.2.5

langchain-openai==0.1.8

langchain-text-splitters==0.2.1

langsmith==0.1.75

multidict==6.0.5

numpy==1.26.4

openai==1.33.0

orjson==3.10.3

packaging==23.2

pydantic==2.7.3

pydantic_core==2.18.4

PyYAML==6.0.1

regex==2024.5.15

requests==2.32.3

sniffio==1.3.1

SQLAlchemy==2.0.30

tenacity==8.3.0

tiktoken==0.7.0

tqdm==4.66.4

typing_extensions==4.12.2

urllib3==2.2.1

yarl==1.9.4

[PYTHON/LANGCHAIN] | 연산자 : LLM 모델과 출력 파서 체인 만들기

■ | 연산자를 사용해 LLM 모델과 출력 파서의 체인을 만드는 방법을 보여준다. ▶ main.py


import os

from langchain_core.messages       import HumanMessage, SystemMessage
from langchain_core.output_parsers import StrOutputParser
from langchain_openai              import ChatOpenAI

os.environ["OPENAI_API_KEY"] = "<OPENAI_API_KEY>"

chatOpenAI = ChatOpenAI(model = "gpt-4")

strOutputParser = StrOutputParser()

runnableSequence = chatOpenAI | strOutputParser

messageList = [
    SystemMessage(content = "Translate the following from English into Korean"),
    HumanMessage (content = "hi!")
]

resultString = runnableSequence.invoke(messageList)

print(resultString)

"""
안녕!
"""

import os

from langchain_core.messages import HumanMessage, SystemMessage

from langchain_core.output_parsers import StrOutputParser

from langchain_openai import ChatOpenAI

os.environ["OPENAI_API_KEY"] = "<OPENAI_API_KEY>"

chatOpenAI = ChatOpenAI(model = "gpt-4")

strOutputParser = StrOutputParser()

runnableSequence = chatOpenAI | strOutputParser

messageList = [

SystemMessage(content = "Translate the following from English into Korean"),

HumanMessage (content = "hi!")

]

resultString = runnableSequence.invoke(messageList)

print(resultString)

"""

안녕!

"""

▶ requirements.txt


aiohttp==3.9.5
aiosignal==1.3.1
annotated-types==0.7.0
anyio==4.4.0
async-timeout==4.0.3
attrs==23.2.0
certifi==2024.6.2
charset-normalizer==3.3.2
distro==1.9.0
exceptiongroup==1.2.1
frozenlist==1.4.1
greenlet==3.0.3
h11==0.14.0
httpcore==1.0.5
httpx==0.27.0
idna==3.7
jsonpatch==1.33
jsonpointer==2.4
langchain==0.2.3
langchain-core==0.2.5
langchain-openai==0.1.8
langchain-text-splitters==0.2.1
langsmith==0.1.75
multidict==6.0.5
numpy==1.26.4
openai==1.33.0
orjson==3.10.3
packaging==23.2
pydantic==2.7.3
pydantic_core==2.18.4
PyYAML==6.0.1
regex==2024.5.15
requests==2.32.3
sniffio==1.3.1
SQLAlchemy==2.0.30
tenacity==8.3.0
tiktoken==0.7.0
tqdm==4.66.4
typing_extensions==4.12.2
urllib3==2.2.1
yarl==1.9.4

aiohttp==3.9.5

aiosignal==1.3.1

annotated-types==0.7.0

anyio==4.4.0

async-timeout==4.0.3

attrs==23.2.0

certifi==2024.6.2

charset-normalizer==3.3.2

distro==1.9.0

exceptiongroup==1.2.1

frozenlist==1.4.1

greenlet==3.0.3

h11==0.14.0

httpcore==1.0.5

httpx==0.27.0

idna==3.7

jsonpatch==1.33

jsonpointer==2.4

langchain==0.2.3

langchain-core==0.2.5

langchain-openai==0.1.8

langchain-text-splitters==0.2.1

langsmith==0.1.75

multidict==6.0.5

numpy==1.26.4

openai==1.33.0

orjson==3.10.3

packaging==23.2

pydantic==2.7.3

pydantic_core==2.18.4

PyYAML==6.0.1

regex==2024.5.15

requests==2.32.3

sniffio==1.3.1

SQLAlchemy==2.0.30

tenacity==8.3.0

tiktoken==0.7.0

tqdm==4.66.4

typing_extensions==4.12.2

urllib3==2.2.1

yarl==1.9.4

※ pip install langchain

[PYTHON/LANGCHAIN] StrOutputParser 클래스 : invoke 메소드를 사용해 출력 결과 파싱하기

■ StrOutputParser 클래스의 invoke 메소드를 사용해 출력 결과를 파싱하는 방법을 보여준다. ※ StrOutputParser 클래스는 AIMessageChunk에서 content 필드를 추출하여 모델이 반환한 토큰을 제공하는

[PYTHON/LANGCHAIN] ChatOpenAI 클래스 : invoke 메소드를 사용해 질의 응답하기

■ ChatOpenAI 클래스의 invoke 메소드를 사용해 질의 응답하는 방법을 보여준다. ▶ main.py


import os

from langchain_core.messages import HumanMessage, SystemMessage
from langchain_openai        import ChatOpenAI

os.environ["OPENAI_API_KEY"] = "<OPENAI_API_KEY>"

chatOpenAI = ChatOpenAI(model = "gpt-4")

messageList = [
    SystemMessage(content = "Translate the following from English into Korean"),
    HumanMessage (content = "hi!")
]

aiMessage = chatOpenAI.invoke(messageList)

print(aiMessage.content)

"""
안녕!
"""

import os

from langchain_core.messages import HumanMessage, SystemMessage

from langchain_openai import ChatOpenAI

os.environ["OPENAI_API_KEY"] = "<OPENAI_API_KEY>"

chatOpenAI = ChatOpenAI(model = "gpt-4")

messageList = [

SystemMessage(content = "Translate the following from English into Korean"),

HumanMessage (content = "hi!")

]

aiMessage = chatOpenAI.invoke(messageList)

print(aiMessage.content)

"""

안녕!

"""

▶ requirements.txt


aiohttp==3.9.5
aiosignal==1.3.1
annotated-types==0.7.0
anyio==4.4.0
async-timeout==4.0.3
attrs==23.2.0
certifi==2024.6.2
charset-normalizer==3.3.2
distro==1.9.0
exceptiongroup==1.2.1
frozenlist==1.4.1
greenlet==3.0.3
h11==0.14.0
httpcore==1.0.5
httpx==0.27.0
idna==3.7
jsonpatch==1.33
jsonpointer==2.4
langchain==0.2.3
langchain-core==0.2.5
langchain-openai==0.1.8
langchain-text-splitters==0.2.1
langsmith==0.1.75
multidict==6.0.5
numpy==1.26.4
openai==1.33.0
orjson==3.10.3
packaging==23.2
pydantic==2.7.3
pydantic_core==2.18.4
PyYAML==6.0.1
regex==2024.5.15
requests==2.32.3
sniffio==1.3.1
SQLAlchemy==2.0.30
tenacity==8.3.0
tiktoken==0.7.0
tqdm==4.66.4
typing_extensions==4.12.2
urllib3==2.2.1
yarl==1.9.4

aiohttp==3.9.5

aiosignal==1.3.1

annotated-types==0.7.0

anyio==4.4.0

async-timeout==4.0.3

attrs==23.2.0

certifi==2024.6.2

charset-normalizer==3.3.2

distro==1.9.0

exceptiongroup==1.2.1

frozenlist==1.4.1

greenlet==3.0.3

h11==0.14.0

httpcore==1.0.5

httpx==0.27.0

idna==3.7

jsonpatch==1.33

jsonpointer==2.4

langchain==0.2.3

langchain-core==0.2.5

langchain-openai==0.1.8

langchain-text-splitters==0.2.1

langsmith==0.1.75

multidict==6.0.5

numpy==1.26.4

openai==1.33.0

orjson==3.10.3

packaging==23.2

pydantic==2.7.3

pydantic_core==2.18.4

PyYAML==6.0.1

regex==2024.5.15

requests==2.32.3

sniffio==1.3.1

SQLAlchemy==2.0.30

tenacity==8.3.0

tiktoken==0.7.0

tqdm==4.66.4

typing_extensions==4.12.2

urllib3==2.2.1

yarl==1.9.4

※ pip install langchain langchain-openai 명령을