LLM Archives - 29 중 6 번째 페이지

[PYTHON/LANGCHAIN] StructuredTool 클래스 : tool_call_schema 변수를 사용해 ModelMetadata 객체 구하기

■ StructuredTool 클래스의 tool_call_schema 변수를 사용해 ModelMetadata 객체를 구하는 방법을 보여준다. ▶ main.py


from langchain_core.tools import tool
from typing               import List
from typing_extensions    import Annotated
from langchain_core.tools import InjectedToolArg

userDictionary = {}

@tool(parse_docstring = True)
def updateFavoritePets(petList : List[str], userID : Annotated[str, InjectedToolArg]) -> None:
    """Add the list of favorite pets.

    Args:
        petList : List of favorite pets to set.
        userID  : User's ID.
    """
    userDictionary[userID] = petList

modelClass = updateFavoritePets.tool_call_schema

toolCallSchemaDictionary = modelClass.schema()

print(toolCallSchemaDictionary)

"""
{
    'description' : 'Add the list of favorite pets.',
    'properties'  : {
        'petList' : {
            'description' : 'List of favorite pets to set.',
            'items'       : {'type' : 'string'},
            'title'       : 'Petlist',
            'type'        : 'array'
        }
    },
    'required' : ['petList'],
    'title'    : 'updateFavoritePets',
    'type'     : 'object'
}
"""

from langchain_core.tools import tool

from typing import List

from typing_extensions import Annotated

from langchain_core.tools import InjectedToolArg

userDictionary = {}

@tool(parse_docstring = True)

def updateFavoritePets(petList : List[str], userID : Annotated[str, InjectedToolArg]) -> None:

"""Add the list of favorite pets.

Args:

petList : List of favorite pets to set.

userID : User's ID.

"""

userDictionary[userID] = petList

modelClass = updateFavoritePets.tool_call_schema

toolCallSchemaDictionary = modelClass.schema()

print(toolCallSchemaDictionary)

"""

{

'description' : 'Add the list of favorite pets.',

'properties' : {

'petList' : {

'description' : 'List of favorite pets to set.',

'items' : {'type' : 'string'},

'title' : 'Petlist',

'type' : 'array'

}

'required' : ['petList'],

'title' : 'updateFavoritePets',

'type' : 'object'

}

"""

▶ requirements.txt


aiohappyeyeballs==2.4.0
aiohttp==3.10.5
aiosignal==1.3.1
annotated-types==0.7.0
anyio==4.5.0
attrs==24.2.0
certifi==2024.8.30
charset-normalizer==3.3.2
frozenlist==1.4.1
greenlet==3.1.0
h11==0.14.0
httpcore==1.0.5
httpx==0.27.2
idna==3.10
jsonpatch==1.33
jsonpointer==3.0.0
langchain==0.3.0
langchain-core==0.3.2
langchain-text-splitters==0.3.0
langsmith==0.1.124
multidict==6.1.0
numpy==1.26.4
orjson==3.10.7
packaging==24.1
pydantic==2.9.2
pydantic_core==2.23.4
PyYAML==6.0.2
requests==2.32.3
sniffio==1.3.1
SQLAlchemy==2.0.35
tenacity==8.5.0
typing_extensions==4.12.2
urllib3==2.2.3
yarl==1.11.1

aiohappyeyeballs==2.4.0

aiohttp==3.10.5

aiosignal==1.3.1

annotated-types==0.7.0

anyio==4.5.0

attrs==24.2.0

certifi==2024.8.30

charset-normalizer==3.3.2

frozenlist==1.4.1

greenlet==3.1.0

h11==0.14.0

httpcore==1.0.5

httpx==0.27.2

idna==3.10

jsonpatch==1.33

jsonpointer==3.0.0

langchain==0.3.0

langchain-core==0.3.2

langchain-text-splitters==0.3.0

langsmith==0.1.124

multidict==6.1.0

numpy==1.26.4

orjson==3.10.7

packaging==24.1

pydantic==2.9.2

pydantic_core==2.23.4

PyYAML==6.0.2

requests==2.32.3

sniffio==1.3.1

SQLAlchemy==2.0.35

tenacity==8.5.0

typing_extensions==4.12.2

urllib3==2.2.3

yarl==1.11.1

※ pip install langchain 명령을

[PYTHON/LANGCHAIN] ChatOpenAI 클래스 : bind_tools 메소드를 사용해 도구 출력을 채팅 모델에 전달하기

■ ChatOpenAI 클래스의 bind_tools 메소드를 사용해 도구 출력을 채팅 모델에 전달하는 방법을 보여준다. ※ OPENAI_API_KEY 환경 변수 값은 .env 파일에 정의한다. ▶

[PYTHON/LANGCHAIN] ModelMetaclass 클래스 : schema 메소드를 사용해 스키마 딕셔너리 구하기

■ ModelMetaclass 클래스의 schema 메소드를 사용해 스키마 딕셔너리를 구하는 방법을 보여준다. ▶ main.py


from langchain_core.tools import tool
from typing               import List
from typing_extensions    import Annotated
from langchain_core.tools import InjectedToolArg

userDictionary = {}

@tool(parse_docstring = True)
def updateFavoritePets(petList : List[str], userID : Annotated[str, InjectedToolArg]) -> None:
    """Add the list of favorite pets.

    Args:
        petList : List of favorite pets to set.
        userID  : User's ID.
    """
    userDictionary[userID] = petList

modelMetaclass = updateFavoritePets.get_input_schema()

inputSchemaDictionary = modelMetaclass.schema()

print(inputSchemaDictionary)

"""
{
    'description' : 'Add the list of favorite pets.',
    'properties'  : {
        'petList' : {
            'description' : 'List of favorite pets to set.',
            'items'       : {'type': 'string'},
            'title'       : 'Petlist',
            'type'        : 'array'
        },
        'userID' : {
            'description' : "User's ID.",
            'title'       : 'Userid',
            'type'        : 'string'
        }
    },
    'required' : ['petList', 'userID'],
    'title'    : 'updateFavoritePets',
    'type'     : 'object'
}
"""

from langchain_core.tools import tool

from typing import List

from typing_extensions import Annotated

from langchain_core.tools import InjectedToolArg

userDictionary = {}

@tool(parse_docstring = True)

def updateFavoritePets(petList : List[str], userID : Annotated[str, InjectedToolArg]) -> None:

"""Add the list of favorite pets.

Args:

petList : List of favorite pets to set.

userID : User's ID.

"""

userDictionary[userID] = petList

modelMetaclass = updateFavoritePets.get_input_schema()

inputSchemaDictionary = modelMetaclass.schema()

print(inputSchemaDictionary)

"""

{

'description' : 'Add the list of favorite pets.',

'properties' : {

'petList' : {

'description' : 'List of favorite pets to set.',

'items' : {'type': 'string'},

'title' : 'Petlist',

'type' : 'array'

'userID' : {

'description' : "User's ID.",

'title' : 'Userid',

'type' : 'string'

}

'required' : ['petList', 'userID'],

'title' : 'updateFavoritePets',

'type' : 'object'

}

"""

▶ requirements.txt


aiohappyeyeballs==2.4.0
aiohttp==3.10.5
aiosignal==1.3.1
annotated-types==0.7.0
anyio==4.5.0
attrs==24.2.0
certifi==2024.8.30
charset-normalizer==3.3.2
frozenlist==1.4.1
greenlet==3.1.0
h11==0.14.0
httpcore==1.0.5
httpx==0.27.2
idna==3.10
jsonpatch==1.33
jsonpointer==3.0.0
langchain==0.3.0
langchain-core==0.3.2
langchain-text-splitters==0.3.0
langsmith==0.1.124
multidict==6.1.0
numpy==1.26.4
orjson==3.10.7
packaging==24.1
pydantic==2.9.2
pydantic_core==2.23.4
PyYAML==6.0.2
requests==2.32.3
sniffio==1.3.1
SQLAlchemy==2.0.35
tenacity==8.5.0
typing_extensions==4.12.2
urllib3==2.2.3
yarl==1.11.1

aiohappyeyeballs==2.4.0

aiohttp==3.10.5

aiosignal==1.3.1

annotated-types==0.7.0

anyio==4.5.0

attrs==24.2.0

certifi==2024.8.30

charset-normalizer==3.3.2

frozenlist==1.4.1

greenlet==3.1.0

h11==0.14.0

httpcore==1.0.5

httpx==0.27.2

idna==3.10

jsonpatch==1.33

jsonpointer==3.0.0

langchain==0.3.0

langchain-core==0.3.2

langchain-text-splitters==0.3.0

langsmith==0.1.124

multidict==6.1.0

numpy==1.26.4

orjson==3.10.7

packaging==24.1

pydantic==2.9.2

pydantic_core==2.23.4

PyYAML==6.0.2

requests==2.32.3

sniffio==1.3.1

SQLAlchemy==2.0.35

tenacity==8.5.0

typing_extensions==4.12.2

urllib3==2.2.3

yarl==1.11.1

※ pip install langchain 명령을

[PYTHON/LANGCHAIN] StructuredTool 클래스 : get_input_schema 메소드를 사용해 ModelMetaclass 객체 구하기

■ StructuredTool 클래스의 get_input_schema 메소드를 사용해 ModelMetaclass 객체를 구하는 방법을 보여준다. ▶ main.py


from langchain_core.tools import tool
from typing               import List
from typing_extensions    import Annotated
from langchain_core.tools import InjectedToolArg

userDictionary = {}

@tool(parse_docstring = True)
def updateFavoritePets(petList : List[str], userID : Annotated[str, InjectedToolArg]) -> None:
    """Add the list of favorite pets.

    Args:
        petList : List of favorite pets to set.
        userID  : User's ID.
    """
    userDictionary[userID] = petList

modelMetaclass = updateFavoritePets.get_input_schema()

from langchain_core.tools import tool

from typing import List

from typing_extensions import Annotated

from langchain_core.tools import InjectedToolArg

userDictionary = {}

@tool(parse_docstring = True)

def updateFavoritePets(petList : List[str], userID : Annotated[str, InjectedToolArg]) -> None:

"""Add the list of favorite pets.

Args:

petList : List of favorite pets to set.

userID : User's ID.

"""

userDictionary[userID] = petList

modelMetaclass = updateFavoritePets.get_input_schema()

▶ requirements.txt


aiohappyeyeballs==2.4.0
aiohttp==3.10.5
aiosignal==1.3.1
annotated-types==0.7.0
anyio==4.5.0
attrs==24.2.0
certifi==2024.8.30
charset-normalizer==3.3.2
frozenlist==1.4.1
greenlet==3.1.0
h11==0.14.0
httpcore==1.0.5
httpx==0.27.2
idna==3.10
jsonpatch==1.33
jsonpointer==3.0.0
langchain==0.3.0
langchain-core==0.3.2
langchain-text-splitters==0.3.0
langsmith==0.1.124
multidict==6.1.0
numpy==1.26.4
orjson==3.10.7
packaging==24.1
pydantic==2.9.2
pydantic_core==2.23.4
PyYAML==6.0.2
requests==2.32.3
sniffio==1.3.1
SQLAlchemy==2.0.35
tenacity==8.5.0
typing_extensions==4.12.2
urllib3==2.2.3
yarl==1.11.1

aiohappyeyeballs==2.4.0

aiohttp==3.10.5

aiosignal==1.3.1

annotated-types==0.7.0

anyio==4.5.0

attrs==24.2.0

certifi==2024.8.30

charset-normalizer==3.3.2

frozenlist==1.4.1

greenlet==3.1.0

h11==0.14.0

httpcore==1.0.5

httpx==0.27.2

idna==3.10

jsonpatch==1.33

jsonpointer==3.0.0

langchain==0.3.0

langchain-core==0.3.2

langchain-text-splitters==0.3.0

langsmith==0.1.124

multidict==6.1.0

numpy==1.26.4

orjson==3.10.7

packaging==24.1

pydantic==2.9.2

pydantic_core==2.23.4

PyYAML==6.0.2

requests==2.32.3

sniffio==1.3.1

SQLAlchemy==2.0.35

tenacity==8.5.0

typing_extensions==4.12.2

urllib3==2.2.3

yarl==1.11.1

※ pip install langchain 명령을

[PYTHON/LANGCHAIN] PydanticToolsParser 클래스 : 생성자에서 tools 인자를 사용해 도구 호출 정보 반환하기

■ PydanticToolsParser 클래스의 생성자에서 tools 인자를 사용해 도구 호출 정보를 반환하는 방법을 보여준다. ※ OPENAI_API_KEY 환경 변수 값은 .env 파일에 정의한다. ▶

[PYTHON/LANGCHAIN] ChatOpenAI 클래스 : bind_tools 메소드를 사용해 도구 호출 메시지 반환하기

■ ChatOpenAI 클래스dml bind_tools 메소드를 사용해 도구 호출 메시지를 반환하는 방법을 보여준다. ※ OPENAI_API_KEY 환경 변수 값은 .env 파일에 정의한다. ▶ main.py

[PYTHON/LANGCHAIN] WikipediaQueryRun 클래스 : 생성자에서 description 인자를 사용해 도구 설명 재정의하기

■ WikipediaQueryRun 클래스의 생성자에서 description 인자를 사용해 도구 설명을 재정의하는 방법을 보여준다. ▶ main.py


from pydantic                      import BaseModel
from pydantic                      import Field
from langchain_community.utilities import WikipediaAPIWrapper
from langchain_community.tools     import WikipediaQueryRun

class WIKIModel(BaseModel):
    """Inputs to the wikipedia tool."""

    query : str = Field(description = "query to look up in Wikipedia, should be 3 or less words")

wikipediaAPIWrapper = WikipediaAPIWrapper(top_k_results = 1, doc_content_chars_max = 100)

wikipediaQueryRun = WikipediaQueryRun(
    name          = "wiki-tool",
    description   = "look up things in wikipedia",
    args_schema   = WIKIModel,
    api_wrapper   = wikipediaAPIWrapper,
    return_direct = True,
)

print(wikipediaQueryRun.run("langchain"))
print()

print(f"Name              : {wikipediaQueryRun.name         }")
print(f"Description       : {wikipediaQueryRun.description  }")
print(f"args schema       : {wikipediaQueryRun.args         }")
print(f"returns directly? : {wikipediaQueryRun.return_direct}")

"""
Page: LangChain
Summary: LangChain is a framework designed to simplify the creation of applications

Name              : wiki-tool
Description       : look up things in wikipedia
args schema       : {'query': {'description': 'query to look up in Wikipedia, should be 3 or less words', 'title': 'Query', 'type': 'string'}}
returns directly? : True
"""

from pydantic import BaseModel

from pydantic import Field

from langchain_community.utilities import WikipediaAPIWrapper

from langchain_community.tools import WikipediaQueryRun

class WIKIModel(BaseModel):

"""Inputs to the wikipedia tool."""

query : str = Field(description = "query to look up in Wikipedia, should be 3 or less words")

wikipediaAPIWrapper = WikipediaAPIWrapper(top_k_results = 1, doc_content_chars_max = 100)

wikipediaQueryRun = WikipediaQueryRun(

name = "wiki-tool",

description = "look up things in wikipedia",

args_schema = WIKIModel,

api_wrapper = wikipediaAPIWrapper,

return_direct = True,

)

print(wikipediaQueryRun.run("langchain"))

print()

print(f"Name : {wikipediaQueryRun.name }")

print(f"Description : {wikipediaQueryRun.description }")

print(f"args schema : {wikipediaQueryRun.args }")

print(f"returns directly? : {wikipediaQueryRun.return_direct}")

"""

Page: LangChain

Summary: LangChain is a framework designed to simplify the creation of applications

Name : wiki-tool

Description : look up things in wikipedia

args schema : {'query': {'description': 'query to look up in Wikipedia, should be 3 or less words', 'title': 'Query', 'type': 'string'}}

returns directly? : True

"""

▶ requirements.txt


aiohappyeyeballs==2.4.0
aiohttp==3.10.5
aiosignal==1.3.1
annotated-types==0.7.0
anyio==4.4.0
attrs==24.2.0
beautifulsoup4==4.12.3
certifi==2024.8.30
charset-normalizer==3.3.2
dataclasses-json==0.6.7
frozenlist==1.4.1
greenlet==3.1.0
h11==0.14.0
httpcore==1.0.5
httpx==0.27.2
idna==3.10
jsonpatch==1.33
jsonpointer==3.0.0
langchain==0.3.0
langchain-community==0.3.0
langchain-core==0.3.1
langchain-text-splitters==0.3.0
langsmith==0.1.122
marshmallow==3.22.0
multidict==6.1.0
mypy-extensions==1.0.0
numpy==1.26.4
orjson==3.10.7
packaging==24.1
pydantic==2.9.2
pydantic-settings==2.5.2
pydantic_core==2.23.4
python-dotenv==1.0.1
PyYAML==6.0.2
requests==2.32.3
sniffio==1.3.1
soupsieve==2.6
SQLAlchemy==2.0.35
tenacity==8.5.0
typing-inspect==0.9.0
typing_extensions==4.12.2
urllib3==2.2.3
wikipedia==1.4.0
yarl==1.11.1

aiohappyeyeballs==2.4.0

aiohttp==3.10.5

aiosignal==1.3.1

annotated-types==0.7.0

anyio==4.4.0

attrs==24.2.0

beautifulsoup4==4.12.3

certifi==2024.8.30

charset-normalizer==3.3.2

dataclasses-json==0.6.7

frozenlist==1.4.1

greenlet==3.1.0

h11==0.14.0

httpcore==1.0.5

httpx==0.27.2

idna==3.10

jsonpatch==1.33

jsonpointer==3.0.0

langchain==0.3.0

langchain-community==0.3.0

langchain-core==0.3.1

langchain-text-splitters==0.3.0

langsmith==0.1.122

marshmallow==3.22.0

multidict==6.1.0

mypy-extensions==1.0.0

numpy==1.26.4

orjson==3.10.7

packaging==24.1

pydantic==2.9.2

pydantic-settings==2.5.2

pydantic_core==2.23.4

python-dotenv==1.0.1

PyYAML==6.0.2

requests==2.32.3

sniffio==1.3.1

soupsieve==2.6

SQLAlchemy==2.0.35

tenacity==8.5.0

typing-inspect==0.9.0

typing_extensions==4.12.2

urllib3==2.2.3

wikipedia==1.4.0

yarl==1.11.1

※ pip install langchain-community

[PYTHON/LANGCHAIN] WikipediaQueryRun 클래스 : name/description/args/return_direct 변수 사용하기

■ WikipediaQueryRun 클래스에서 name/description/args/return_direct 변수를 사용하는 방법을 보여준다. ▶ main.py


from langchain_community.utilities import WikipediaAPIWrapper
from langchain_community.tools     import WikipediaQueryRun

wikipediaAPIWrapper = WikipediaAPIWrapper(top_k_results = 1, doc_content_chars_max = 100)

wikipediaQueryRun = WikipediaQueryRun(api_wrapper = wikipediaAPIWrapper)

resultString = wikipediaQueryRun.invoke({"query" : "langchain"})

print(resultString)
print()

print(f"Name              : {wikipediaQueryRun.name         }")
print(f"Description       : {wikipediaQueryRun.description  }")
print(f"args schema       : {wikipediaQueryRun.args         }")
print(f"returns directly? : {wikipediaQueryRun.return_direct}")

"""
Page: LangChain
Summary: LangChain is a framework designed to simplify the creation of applications

Name              : wikipedia
Description       : A wrapper around Wikipedia. Useful for when you need to answer general questions about people, places, companies, facts, historical events, or other subjects. Input should be a search query.
args schema       : {'query': {'description': 'query to look up on wikipedia', 'title': 'Query', 'type': 'string'}}
returns directly? : False
"""

from langchain_community.utilities import WikipediaAPIWrapper

from langchain_community.tools import WikipediaQueryRun

wikipediaAPIWrapper = WikipediaAPIWrapper(top_k_results = 1, doc_content_chars_max = 100)

wikipediaQueryRun = WikipediaQueryRun(api_wrapper = wikipediaAPIWrapper)

resultString = wikipediaQueryRun.invoke({"query" : "langchain"})

print(resultString)

print()

print(f"Name : {wikipediaQueryRun.name }")

print(f"Description : {wikipediaQueryRun.description }")

print(f"args schema : {wikipediaQueryRun.args }")

print(f"returns directly? : {wikipediaQueryRun.return_direct}")

"""

Page: LangChain

Summary: LangChain is a framework designed to simplify the creation of applications

Name : wikipedia

Description : A wrapper around Wikipedia. Useful for when you need to answer general questions about people, places, companies, facts, historical events, or other subjects. Input should be a search query.

args schema : {'query': {'description': 'query to look up on wikipedia', 'title': 'Query', 'type': 'string'}}

returns directly? : False

"""

▶ requirements.txt


aiohappyeyeballs==2.4.0
aiohttp==3.10.5
aiosignal==1.3.1
annotated-types==0.7.0
anyio==4.4.0
attrs==24.2.0
beautifulsoup4==4.12.3
certifi==2024.8.30
charset-normalizer==3.3.2
dataclasses-json==0.6.7
frozenlist==1.4.1
greenlet==3.1.0
h11==0.14.0
httpcore==1.0.5
httpx==0.27.2
idna==3.10
jsonpatch==1.33
jsonpointer==3.0.0
langchain==0.3.0
langchain-community==0.3.0
langchain-core==0.3.1
langchain-text-splitters==0.3.0
langsmith==0.1.122
marshmallow==3.22.0
multidict==6.1.0
mypy-extensions==1.0.0
numpy==1.26.4
orjson==3.10.7
packaging==24.1
pydantic==2.9.2
pydantic-settings==2.5.2
pydantic_core==2.23.4
python-dotenv==1.0.1
PyYAML==6.0.2
requests==2.32.3
sniffio==1.3.1
soupsieve==2.6
SQLAlchemy==2.0.35
tenacity==8.5.0
typing-inspect==0.9.0
typing_extensions==4.12.2
urllib3==2.2.3
wikipedia==1.4.0
yarl==1.11.1

aiohappyeyeballs==2.4.0

aiohttp==3.10.5

aiosignal==1.3.1

annotated-types==0.7.0

anyio==4.4.0

attrs==24.2.0

beautifulsoup4==4.12.3

certifi==2024.8.30

charset-normalizer==3.3.2

dataclasses-json==0.6.7

frozenlist==1.4.1

greenlet==3.1.0

h11==0.14.0

httpcore==1.0.5

httpx==0.27.2

idna==3.10

jsonpatch==1.33

jsonpointer==3.0.0

langchain==0.3.0

langchain-community==0.3.0

langchain-core==0.3.1

langchain-text-splitters==0.3.0

langsmith==0.1.122

marshmallow==3.22.0

multidict==6.1.0

mypy-extensions==1.0.0

numpy==1.26.4

orjson==3.10.7

packaging==24.1

pydantic==2.9.2

pydantic-settings==2.5.2

pydantic_core==2.23.4

python-dotenv==1.0.1

PyYAML==6.0.2

requests==2.32.3

sniffio==1.3.1

soupsieve==2.6

SQLAlchemy==2.0.35

tenacity==8.5.0

typing-inspect==0.9.0

typing_extensions==4.12.2

urllib3==2.2.3

wikipedia==1.4.0

yarl==1.11.1

※ pip install langchain-community wikipedia 명령을 실행했다.

[PYTHON/LANGCHAIN] WikipediaQueryRun 클래스 : invoke 메소드를 사용해 질의하기

■ WikipediaQueryRun 클래스의 invoke 메소드를 사용해 질의하는 방법을 보여준다. ▶ main.py


from langchain_community.utilities import WikipediaAPIWrapper
from langchain_community.tools     import WikipediaQueryRun

wikipediaAPIWrapper = WikipediaAPIWrapper(top_k_results = 1, doc_content_chars_max = 100)

wikipediaQueryRun = WikipediaQueryRun(api_wrapper = wikipediaAPIWrapper)

resultString = wikipediaQueryRun.invoke({"query" : "langchain"})

print(resultString)

from langchain_community.utilities import WikipediaAPIWrapper

from langchain_community.tools import WikipediaQueryRun

wikipediaAPIWrapper = WikipediaAPIWrapper(top_k_results = 1, doc_content_chars_max = 100)

wikipediaQueryRun = WikipediaQueryRun(api_wrapper = wikipediaAPIWrapper)

resultString = wikipediaQueryRun.invoke({"query" : "langchain"})

print(resultString)

▶ requirements.txt


aiohappyeyeballs==2.4.0
aiohttp==3.10.5
aiosignal==1.3.1
annotated-types==0.7.0
anyio==4.4.0
attrs==24.2.0
beautifulsoup4==4.12.3
certifi==2024.8.30
charset-normalizer==3.3.2
dataclasses-json==0.6.7
frozenlist==1.4.1
greenlet==3.1.0
h11==0.14.0
httpcore==1.0.5
httpx==0.27.2
idna==3.10
jsonpatch==1.33
jsonpointer==3.0.0
langchain==0.3.0
langchain-community==0.3.0
langchain-core==0.3.1
langchain-text-splitters==0.3.0
langsmith==0.1.122
marshmallow==3.22.0
multidict==6.1.0
mypy-extensions==1.0.0
numpy==1.26.4
orjson==3.10.7
packaging==24.1
pydantic==2.9.2
pydantic-settings==2.5.2
pydantic_core==2.23.4
python-dotenv==1.0.1
PyYAML==6.0.2
requests==2.32.3
sniffio==1.3.1
soupsieve==2.6
SQLAlchemy==2.0.35
tenacity==8.5.0
typing-inspect==0.9.0
typing_extensions==4.12.2
urllib3==2.2.3
wikipedia==1.4.0
yarl==1.11.1

aiohappyeyeballs==2.4.0

aiohttp==3.10.5

aiosignal==1.3.1

annotated-types==0.7.0

anyio==4.4.0

attrs==24.2.0

beautifulsoup4==4.12.3

certifi==2024.8.30

charset-normalizer==3.3.2

dataclasses-json==0.6.7

frozenlist==1.4.1

greenlet==3.1.0

h11==0.14.0

httpcore==1.0.5

httpx==0.27.2

idna==3.10

jsonpatch==1.33

jsonpointer==3.0.0

langchain==0.3.0

langchain-community==0.3.0

langchain-core==0.3.1

langchain-text-splitters==0.3.0

langsmith==0.1.122

marshmallow==3.22.0

multidict==6.1.0

mypy-extensions==1.0.0

numpy==1.26.4

orjson==3.10.7

packaging==24.1

pydantic==2.9.2

pydantic-settings==2.5.2

pydantic_core==2.23.4

python-dotenv==1.0.1

PyYAML==6.0.2

requests==2.32.3

sniffio==1.3.1

soupsieve==2.6

SQLAlchemy==2.0.35

tenacity==8.5.0

typing-inspect==0.9.0

typing_extensions==4.12.2

urllib3==2.2.3

wikipedia==1.4.0

yarl==1.11.1

※ pip install langchain-community wikipedia 명령을 실행했다.

[PYTHON/LANGCHAIN] WikipediaQueryRun 클래스 : 생성자에서 api_wrapper 인자를 사용해 WikipediaQueryRun 객체 만들기

■ WikipediaQueryRun 클래스의 생성자에서 api_wrapper 인자를 사용해 WikipediaQueryRun 객체를 만드는 방법을 보여준다. ▶ main.py


from langchain_community.utilities import WikipediaAPIWrapper
from langchain_community.tools     import WikipediaQueryRun

wikipediaAPIWrapper = WikipediaAPIWrapper(top_k_results = 1, doc_content_chars_max = 100)

wikipediaQueryRun = WikipediaQueryRun(api_wrapper = wikipediaAPIWrapper)

from langchain_community.utilities import WikipediaAPIWrapper

from langchain_community.tools import WikipediaQueryRun

wikipediaAPIWrapper = WikipediaAPIWrapper(top_k_results = 1, doc_content_chars_max = 100)

wikipediaQueryRun = WikipediaQueryRun(api_wrapper = wikipediaAPIWrapper)

▶ requirements.txt


aiohappyeyeballs==2.4.0
aiohttp==3.10.5
aiosignal==1.3.1
annotated-types==0.7.0
anyio==4.4.0
attrs==24.2.0
beautifulsoup4==4.12.3
certifi==2024.8.30
charset-normalizer==3.3.2
dataclasses-json==0.6.7
frozenlist==1.4.1
greenlet==3.1.0
h11==0.14.0
httpcore==1.0.5
httpx==0.27.2
idna==3.10
jsonpatch==1.33
jsonpointer==3.0.0
langchain==0.3.0
langchain-community==0.3.0
langchain-core==0.3.1
langchain-text-splitters==0.3.0
langsmith==0.1.122
marshmallow==3.22.0
multidict==6.1.0
mypy-extensions==1.0.0
numpy==1.26.4
orjson==3.10.7
packaging==24.1
pydantic==2.9.2
pydantic-settings==2.5.2
pydantic_core==2.23.4
python-dotenv==1.0.1
PyYAML==6.0.2
requests==2.32.3
sniffio==1.3.1
soupsieve==2.6
SQLAlchemy==2.0.35
tenacity==8.5.0
typing-inspect==0.9.0
typing_extensions==4.12.2
urllib3==2.2.3
wikipedia==1.4.0
yarl==1.11.1

aiohappyeyeballs==2.4.0

aiohttp==3.10.5

aiosignal==1.3.1

annotated-types==0.7.0

anyio==4.4.0

attrs==24.2.0

beautifulsoup4==4.12.3

certifi==2024.8.30

charset-normalizer==3.3.2

dataclasses-json==0.6.7

frozenlist==1.4.1

greenlet==3.1.0

h11==0.14.0

httpcore==1.0.5

httpx==0.27.2

idna==3.10

jsonpatch==1.33

jsonpointer==3.0.0

langchain==0.3.0

langchain-community==0.3.0

langchain-core==0.3.1

langchain-text-splitters==0.3.0

langsmith==0.1.122

marshmallow==3.22.0

multidict==6.1.0

mypy-extensions==1.0.0

numpy==1.26.4

orjson==3.10.7

packaging==24.1

pydantic==2.9.2

pydantic-settings==2.5.2

pydantic_core==2.23.4

python-dotenv==1.0.1

PyYAML==6.0.2

requests==2.32.3

sniffio==1.3.1

soupsieve==2.6

SQLAlchemy==2.0.35

tenacity==8.5.0

typing-inspect==0.9.0

typing_extensions==4.12.2

urllib3==2.2.3

wikipedia==1.4.0

yarl==1.11.1

※ pip install langchain-community

[PYTHON/LANGCHAIN] WikipediaAPIWrapper 클래스 : 생성자에서 top_k_results/doc_content_chars_max 인자를 사용해 WikipediaAPIWrapper 객체 만들기

■ WikipediaAPIWrapper 클래스의 생성자에서 top_k_results/doc_content_chars_max 인자를 사용해 WikipediaAPIWrapper 객체를 만드는 방법을 보여준다. ▶ main.py


from langchain_community.utilities import WikipediaAPIWrapper

wikipediaAPIWrapper = WikipediaAPIWrapper(top_k_results = 1, doc_content_chars_max = 100)

from langchain_community.utilities import WikipediaAPIWrapper

wikipediaAPIWrapper = WikipediaAPIWrapper(top_k_results = 1, doc_content_chars_max = 100)

▶ requirements.txt


aiohappyeyeballs==2.4.0
aiohttp==3.10.5
aiosignal==1.3.1
annotated-types==0.7.0
anyio==4.4.0
attrs==24.2.0
beautifulsoup4==4.12.3
certifi==2024.8.30
charset-normalizer==3.3.2
dataclasses-json==0.6.7
frozenlist==1.4.1
greenlet==3.1.0
h11==0.14.0
httpcore==1.0.5
httpx==0.27.2
idna==3.10
jsonpatch==1.33
jsonpointer==3.0.0
langchain==0.3.0
langchain-community==0.3.0
langchain-core==0.3.1
langchain-text-splitters==0.3.0
langsmith==0.1.122
marshmallow==3.22.0
multidict==6.1.0
mypy-extensions==1.0.0
numpy==1.26.4
orjson==3.10.7
packaging==24.1
pydantic==2.9.2
pydantic-settings==2.5.2
pydantic_core==2.23.4
python-dotenv==1.0.1
PyYAML==6.0.2
requests==2.32.3
sniffio==1.3.1
soupsieve==2.6
SQLAlchemy==2.0.35
tenacity==8.5.0
typing-inspect==0.9.0
typing_extensions==4.12.2
urllib3==2.2.3
wikipedia==1.4.0
yarl==1.11.1

aiohappyeyeballs==2.4.0

aiohttp==3.10.5

aiosignal==1.3.1

annotated-types==0.7.0

anyio==4.4.0

attrs==24.2.0

beautifulsoup4==4.12.3

certifi==2024.8.30

charset-normalizer==3.3.2

dataclasses-json==0.6.7

frozenlist==1.4.1

greenlet==3.1.0

h11==0.14.0

httpcore==1.0.5

httpx==0.27.2

idna==3.10

jsonpatch==1.33

jsonpointer==3.0.0

langchain==0.3.0

langchain-community==0.3.0

langchain-core==0.3.1

langchain-text-splitters==0.3.0

langsmith==0.1.122

marshmallow==3.22.0

multidict==6.1.0

mypy-extensions==1.0.0

numpy==1.26.4

orjson==3.10.7

packaging==24.1

pydantic==2.9.2

pydantic-settings==2.5.2

pydantic_core==2.23.4

python-dotenv==1.0.1

PyYAML==6.0.2

requests==2.32.3

sniffio==1.3.1

soupsieve==2.6

SQLAlchemy==2.0.35

tenacity==8.5.0

typing-inspect==0.9.0

typing_extensions==4.12.2

urllib3==2.2.3

wikipedia==1.4.0

yarl==1.11.1

※ pip install langchain-community

[PYTHON/LANGCHAIN] BaseTool 클래스 : invoke 메소드에서 ToolCall 사용해 도구에서 생성된 컨텐츠와 아티펙트가 모두 포함된 ToolMessage 객체 반환하기

■ BaseTool 클래스의 invoke 메소드에서 ToolCall 사용해 도구에서 생성된 컨텐츠와 아티펙트가 모두 포함된 ToolMessage 객체를 반환하는 방법을 보여준다. ▶ main.py


import random

from langchain_core.tools import BaseTool
from typing               import Tuple
from typing               import List

class GenerateRandomValueTool(BaseTool):
    name            : str = "GenerateRandomValueTool"
    description     : str = "Generate size random floats in the range [min, max]."
    response_format : str = "content_and_artifact"

    decimalPointCount : int = 2

    def _run(self, min : float, max : float, size : int) -> Tuple[str, List[float]]:
        rangeValue = max - min
        randomValueList = [round(min + (rangeValue * random.random()), ndigits = self.decimalPointCount) for _ in range(size)]
        content         = f"Generated {size} floats in [{min}, {max}], rounded to {self.decimalPointCount} decimals."
        return content, randomValueList

generateRandomValueTool = GenerateRandomValueTool(decimalPointCount = 4)

resultToolMessage = generateRandomValueTool.invoke(
    {
        "name" : "generate_random_floats",
        "args" : {"min" : 0.1, "max" : 3.3333, "size" : 3},
        "id"   : "123",
        "type" : "tool_call"
    }
)

print(resultToolMessage)

import random

from langchain_core.tools import BaseTool

from typing import Tuple

from typing import List

class GenerateRandomValueTool(BaseTool):

name : str = "GenerateRandomValueTool"

description : str = "Generate size random floats in the range [min, max]."

response_format : str = "content_and_artifact"

decimalPointCount : int = 2

def _run(self, min : float, max : float, size : int) -> Tuple[str, List[float]]:

rangeValue = max - min

randomValueList = [round(min + (rangeValue * random.random()), ndigits = self.decimalPointCount) for _ in range(size)]

content = f"Generated {size} floats in [{min}, {max}], rounded to {self.decimalPointCount} decimals."

return content, randomValueList

generateRandomValueTool = GenerateRandomValueTool(decimalPointCount = 4)

resultToolMessage = generateRandomValueTool.invoke(

{

"name" : "generate_random_floats",

"args" : {"min" : 0.1, "max" : 3.3333, "size" : 3},

"id" : "123",

"type" : "tool_call"

}

)

print(resultToolMessage)

▶

[PYTHON/LANGCHAIN] StructuredTool 클래스 : invoke 메소드에서 ToolCall 사용해 도구에서 생성된 콘텐츠와 아티팩트가 모두 포함된 ToolMessage 객체 반환하기

■ StructuredTool 클래스의 invoke 메소드에서 ToolCall 사용해 도구에서 생성된 콘텐츠와 아티팩트가 모두 포함된 ToolMessage 객체를 반환하는 방법을 보여준다. ▶ main.py


import random

from langchain_core.tools import tool
from typing               import Tuple
from typing               import List

@tool(response_format = "content_and_artifact")
def generateRandomInteger(min : int, max : int, size : int) -> Tuple[str, List[int]]:
    """Generate size random ints in the range [min, max]."""
    rangeList = [random.randint(min, max) for _ in range(size)]
    content   = f"Successfully generated array of {size} random ints in [{min}, {max}]."
    return content, rangeList

resultToolMessage = generateRandomInteger.invoke(
    {
        "name" : "generateRandomInteger",
        "args" : {"min" : 0, "max" : 9, "size" : 10},
        "id"   : "123",      # required
        "type" : "tool_call" # required
    }
)

print(resultToolMessage)

import random

from langchain_core.tools import tool

from typing import Tuple

from typing import List

@tool(response_format = "content_and_artifact")

def generateRandomInteger(min : int, max : int, size : int) -> Tuple[str, List[int]]:

"""Generate size random ints in the range [min, max]."""

rangeList = [random.randint(min, max) for _ in range(size)]

content = f"Successfully generated array of {size} random ints in [{min}, {max}]."

return content, rangeList

resultToolMessage = generateRandomInteger.invoke(

{

"name" : "generateRandomInteger",

"args" : {"min" : 0, "max" : 9, "size" : 10},

"id" : "123", # required

"type" : "tool_call" # required

}

)

print(resultToolMessage)

▶

[PYTHON/LANGCHAIN] @tool 데코레이터 : response_format 인자를 사용해 컨텐트 반환하기

■ @tool 데코레이터에서 response_format 인자를 사용해 컨텐트를 반환하는 방법을 보여준다. ▶ main.py


import random

from langchain_core.tools import tool
from typing               import Tuple
from typing               import List

@tool(response_format = "content_and_artifact")
def generateRandomIntegerValues(minimumValue : int, maximumValue : int, count : int) -> Tuple[str, List[int]]:
    """Generate size random ints in the range [minimumValue, maximumValue]."""
    array = [random.randint(minimumValue, maximumValue) for _ in range(count)]
    content = f"Successfully generated array of {count} random ints in [{minimumValue}, {maximumValue}]."
    return content, array

responseString = generateRandomIntegerValues.invoke({"minimumValue" : 0, "maximumValue" : 9, "count" : 10})

print(responseString)

"""
Successfully generated array of 10 random ints in [0, 9].
"""

import random

from langchain_core.tools import tool

from typing import Tuple

from typing import List

@tool(response_format = "content_and_artifact")

def generateRandomIntegerValues(minimumValue : int, maximumValue : int, count : int) -> Tuple[str, List[int]]:

"""Generate size random ints in the range [minimumValue, maximumValue]."""

array = [random.randint(minimumValue, maximumValue) for _ in range(count)]

content = f"Successfully generated array of {count} random ints in [{minimumValue}, {maximumValue}]."

return content, array

responseString = generateRandomIntegerValues.invoke({"minimumValue" : 0, "maximumValue" : 9, "count" : 10})

print(responseString)

"""

Successfully generated array of 10 random ints in [0, 9].

"""

▶ requirements.txt


aiohappyeyeballs==2.4.0
aiohttp==3.10.5
aiosignal==1.3.1
annotated-types==0.7.0
anyio==4.4.0
attrs==24.2.0
certifi==2024.8.30
charset-normalizer==3.3.2
frozenlist==1.4.1
greenlet==3.1.0
h11==0.14.0
httpcore==1.0.5
httpx==0.27.2
idna==3.10
jsonpatch==1.33
jsonpointer==3.0.0
langchain==0.3.0
langchain-core==0.3.1
langchain-text-splitters==0.3.0
langsmith==0.1.122
multidict==6.1.0
numpy==1.26.4
orjson==3.10.7
packaging==24.1
pydantic==2.9.2
pydantic_core==2.23.4
PyYAML==6.0.2
requests==2.32.3
sniffio==1.3.1
SQLAlchemy==2.0.35
tenacity==8.5.0
typing_extensions==4.12.2
urllib3==2.2.3
yarl==1.11.1

aiohappyeyeballs==2.4.0

aiohttp==3.10.5

aiosignal==1.3.1

annotated-types==0.7.0

anyio==4.4.0

attrs==24.2.0

certifi==2024.8.30

charset-normalizer==3.3.2

frozenlist==1.4.1

greenlet==3.1.0

h11==0.14.0

httpcore==1.0.5

httpx==0.27.2

idna==3.10

jsonpatch==1.33

jsonpointer==3.0.0

langchain==0.3.0

langchain-core==0.3.1

langchain-text-splitters==0.3.0

langsmith==0.1.122

multidict==6.1.0

numpy==1.26.4

orjson==3.10.7

packaging==24.1

pydantic==2.9.2

pydantic_core==2.23.4

PyYAML==6.0.2

requests==2.32.3

sniffio==1.3.1

SQLAlchemy==2.0.35

tenacity==8.5.0

typing_extensions==4.12.2

urllib3==2.2.3

yarl==1.11.1

※ pip install langchain 명령을 실행했다.

[PYTHON/LANGCHAIN] StructuredTool 클래스 : from_function 정적 메소드에서 handle_tool_error 인자를 사용해 도구 오류 처리하기 3

■ StructuredTool 클래스의 from_function 정적 메소드에서 handle_tool_error 인자를 사용해 도구 오류를 처리하는 방법을 보여준다. ▶ main.py


from langchain_core.tools import ToolException
from langchain_core.tools import StructuredTool

def getWeather(city : str) -> int:
    """Get weather for the given city."""
    raise ToolException(f"Error : There is no city by the name of {city}.")

def _handleError(toolException : ToolException) -> str:
    return f"The following errors occurred during tool execution : `{toolException.args[0]}`"

getWeatherTool = StructuredTool.from_function(
    func              = getWeather,
    handle_tool_error = _handleError,
)

resultString = getWeatherTool.invoke({"city" : "foobar"})

print(resultString)

from langchain_core.tools import ToolException

from langchain_core.tools import StructuredTool

def getWeather(city : str) -> int:

"""Get weather for the given city."""

raise ToolException(f"Error : There is no city by the name of {city}.")

def _handleError(toolException : ToolException) -> str:

return f"The following errors occurred during tool execution : `{toolException.args[0]}`"

getWeatherTool = StructuredTool.from_function(

func = getWeather,

handle_tool_error = _handleError,

)

resultString = getWeatherTool.invoke({"city" : "foobar"})

print(resultString)

▶ requirements.txt


aiohappyeyeballs==2.4.0
aiohttp==3.10.5
aiosignal==1.3.1
annotated-types==0.7.0
anyio==4.4.0
attrs==24.2.0
certifi==2024.8.30
charset-normalizer==3.3.2
frozenlist==1.4.1
greenlet==3.1.0
h11==0.14.0
httpcore==1.0.5
httpx==0.27.2
idna==3.10
jsonpatch==1.33
jsonpointer==3.0.0
langchain==0.3.0
langchain-core==0.3.1
langchain-text-splitters==0.3.0
langsmith==0.1.122
multidict==6.1.0
numpy==1.26.4
orjson==3.10.7
packaging==24.1
pydantic==2.9.2
pydantic_core==2.23.4
PyYAML==6.0.2
requests==2.32.3
sniffio==1.3.1
SQLAlchemy==2.0.35
tenacity==8.5.0
typing_extensions==4.12.2
urllib3==2.2.3
yarl==1.11.1

aiohappyeyeballs==2.4.0

aiohttp==3.10.5

aiosignal==1.3.1

annotated-types==0.7.0

anyio==4.4.0

attrs==24.2.0

certifi==2024.8.30

charset-normalizer==3.3.2

frozenlist==1.4.1

greenlet==3.1.0

h11==0.14.0

httpcore==1.0.5

httpx==0.27.2

idna==3.10

jsonpatch==1.33

jsonpointer==3.0.0

langchain==0.3.0

langchain-core==0.3.1

langchain-text-splitters==0.3.0

langsmith==0.1.122

multidict==6.1.0

numpy==1.26.4

orjson==3.10.7

packaging==24.1

pydantic==2.9.2

pydantic_core==2.23.4

PyYAML==6.0.2

requests==2.32.3

sniffio==1.3.1

SQLAlchemy==2.0.35

tenacity==8.5.0

typing_extensions==4.12.2

urllib3==2.2.3

yarl==1.11.1

※ pip

[PYTHON/LANGCHAIN] StructuredTool 클래스 : from_function 정적 메소드에서 handle_tool_error 인자를 사용해 도구 오류 처리하기 2

■ StructuredTool 클래스의 from_function 정적 메소드에서 handle_tool_error 인자를 사용해 도구 오류를 처리하는 방법을 보여준다. ▶ main.py


from langchain_core.tools import ToolException
from langchain_core.tools import StructuredTool

def getWeather(city : str) -> int:
    """Get weather for the given city."""
    raise ToolException(f"Error : There is no city by the name of {city}.")

getWeatherTool = StructuredTool.from_function(
    func              = getWeather,
    handle_tool_error = "There is no such city, but it's probably above 0K there!"
)

resultString = getWeatherTool.invoke({"city" : "foobar"})

print(resultString)

from langchain_core.tools import ToolException

from langchain_core.tools import StructuredTool

def getWeather(city : str) -> int:

"""Get weather for the given city."""

raise ToolException(f"Error : There is no city by the name of {city}.")

getWeatherTool = StructuredTool.from_function(

func = getWeather,

handle_tool_error = "There is no such city, but it's probably above 0K there!"

)

resultString = getWeatherTool.invoke({"city" : "foobar"})

print(resultString)

▶ requirements.txt


aiohappyeyeballs==2.4.0
aiohttp==3.10.5
aiosignal==1.3.1
annotated-types==0.7.0
anyio==4.4.0
attrs==24.2.0
certifi==2024.8.30
charset-normalizer==3.3.2
frozenlist==1.4.1
greenlet==3.1.0
h11==0.14.0
httpcore==1.0.5
httpx==0.27.2
idna==3.10
jsonpatch==1.33
jsonpointer==3.0.0
langchain==0.3.0
langchain-core==0.3.1
langchain-text-splitters==0.3.0
langsmith==0.1.122
multidict==6.1.0
numpy==1.26.4
orjson==3.10.7
packaging==24.1
pydantic==2.9.2
pydantic_core==2.23.4
PyYAML==6.0.2
requests==2.32.3
sniffio==1.3.1
SQLAlchemy==2.0.35
tenacity==8.5.0
typing_extensions==4.12.2
urllib3==2.2.3
yarl==1.11.1

aiohappyeyeballs==2.4.0

aiohttp==3.10.5

aiosignal==1.3.1

annotated-types==0.7.0

anyio==4.4.0

attrs==24.2.0

certifi==2024.8.30

charset-normalizer==3.3.2

frozenlist==1.4.1

greenlet==3.1.0

h11==0.14.0

httpcore==1.0.5

httpx==0.27.2

idna==3.10

jsonpatch==1.33

jsonpointer==3.0.0

langchain==0.3.0

langchain-core==0.3.1

langchain-text-splitters==0.3.0

langsmith==0.1.122

multidict==6.1.0

numpy==1.26.4

orjson==3.10.7

packaging==24.1

pydantic==2.9.2

pydantic_core==2.23.4

PyYAML==6.0.2

requests==2.32.3

sniffio==1.3.1

SQLAlchemy==2.0.35

tenacity==8.5.0

typing_extensions==4.12.2

urllib3==2.2.3

yarl==1.11.1

※ pip

[PYTHON/LANGCHAIN] StructuredTool 클래스 : from_function 정적 메소드에서 handle_tool_error 인자를 사용해 도구 오류 처리하기

■ StructuredTool 클래스의 from_function 정적 메소드에서 handle_tool_error 인자를 사용해 도구 오류를 처리하는 방법을 보여준다. ▶ main.py


from langchain_core.tools import ToolException
from langchain_core.tools import StructuredTool

def getWeather(city : str) -> int:
    """Get weather for the given city."""
    raise ToolException(f"Error : There is no city by the name of {city}.")

getWeatherTool = StructuredTool.from_function(
    func              = getWeather,
    handle_tool_error = True,
)

resultString = getWeatherTool.invoke({"city" : "foobar"})

print(resultString)

from langchain_core.tools import ToolException

from langchain_core.tools import StructuredTool

def getWeather(city : str) -> int:

"""Get weather for the given city."""

raise ToolException(f"Error : There is no city by the name of {city}.")

getWeatherTool = StructuredTool.from_function(

func = getWeather,

handle_tool_error = True,

)

resultString = getWeatherTool.invoke({"city" : "foobar"})

print(resultString)

▶ requirements.txt


aiohappyeyeballs==2.4.0
aiohttp==3.10.5
aiosignal==1.3.1
annotated-types==0.7.0
anyio==4.4.0
attrs==24.2.0
certifi==2024.8.30
charset-normalizer==3.3.2
frozenlist==1.4.1
greenlet==3.1.0
h11==0.14.0
httpcore==1.0.5
httpx==0.27.2
idna==3.10
jsonpatch==1.33
jsonpointer==3.0.0
langchain==0.3.0
langchain-core==0.3.1
langchain-text-splitters==0.3.0
langsmith==0.1.122
multidict==6.1.0
numpy==1.26.4
orjson==3.10.7
packaging==24.1
pydantic==2.9.2
pydantic_core==2.23.4
PyYAML==6.0.2
requests==2.32.3
sniffio==1.3.1
SQLAlchemy==2.0.35
tenacity==8.5.0
typing_extensions==4.12.2
urllib3==2.2.3
yarl==1.11.1

aiohappyeyeballs==2.4.0

aiohttp==3.10.5

aiosignal==1.3.1

annotated-types==0.7.0

anyio==4.4.0

attrs==24.2.0

certifi==2024.8.30

charset-normalizer==3.3.2

frozenlist==1.4.1

greenlet==3.1.0

h11==0.14.0

httpcore==1.0.5

httpx==0.27.2

idna==3.10

jsonpatch==1.33

jsonpointer==3.0.0

langchain==0.3.0

langchain-core==0.3.1

langchain-text-splitters==0.3.0

langsmith==0.1.122

multidict==6.1.0

numpy==1.26.4

orjson==3.10.7

packaging==24.1

pydantic==2.9.2

pydantic_core==2.23.4

PyYAML==6.0.2

requests==2.32.3

sniffio==1.3.1

SQLAlchemy==2.0.35

tenacity==8.5.0

typing_extensions==4.12.2

urllib3==2.2.3

yarl==1.11.1

※ pip

[PYTHON/LANGCHAIN] StructuredTool 클래스 : from_function 정적 메소드에서 func/coroutine 인자를 사용해 도구 함수 동기/비동기 호출하기

■ StructuredTool 클래스의 from_function 정적 메소드에서 func/coroutine 인자를 사용해 도구 함수를 동기/비동기 호출하는 방법을 보여준다. ▶ main.py


import asyncio

from langchain_core.tools import StructuredTool

def multiply(a : int, b : int) -> int:
    """Multiply two numbers."""
    return a * b

async def multiplyAsync(a : int, b : int) -> int:
    """Multiply two numbers."""
    return a * b

calculatorTool = StructuredTool.from_function(func = multiply, coroutine = multiplyAsync)

async def main():
    print(calculatorTool.invoke({"a" : 2, "b" : 3}))
    print(await calculatorTool.ainvoke({"a": 2, "b": 5}))

asyncio.run(main())

import asyncio

from langchain_core.tools import StructuredTool

def multiply(a : int, b : int) -> int:

"""Multiply two numbers."""

return a * b

async def multiplyAsync(a : int, b : int) -> int:

"""Multiply two numbers."""

return a * b

calculatorTool = StructuredTool.from_function(func = multiply, coroutine = multiplyAsync)

async def main():

print(calculatorTool.invoke({"a" : 2, "b" : 3}))

print(await calculatorTool.ainvoke({"a": 2, "b": 5}))

asyncio.run(main())

▶ requirements.txt


aiohappyeyeballs==2.4.0
aiohttp==3.10.5
aiosignal==1.3.1
annotated-types==0.7.0
anyio==4.4.0
attrs==24.2.0
certifi==2024.8.30
charset-normalizer==3.3.2
frozenlist==1.4.1
greenlet==3.1.0
h11==0.14.0
httpcore==1.0.5
httpx==0.27.2
idna==3.10
jsonpatch==1.33
jsonpointer==3.0.0
langchain==0.3.0
langchain-core==0.3.1
langchain-text-splitters==0.3.0
langsmith==0.1.122
multidict==6.1.0
numpy==1.26.4
orjson==3.10.7
packaging==24.1
pydantic==2.9.2
pydantic_core==2.23.4
PyYAML==6.0.2
requests==2.32.3
sniffio==1.3.1
SQLAlchemy==2.0.35
tenacity==8.5.0
typing_extensions==4.12.2
urllib3==2.2.3
yarl==1.11.1

aiohappyeyeballs==2.4.0

aiohttp==3.10.5

aiosignal==1.3.1

annotated-types==0.7.0

anyio==4.4.0

attrs==24.2.0

certifi==2024.8.30

charset-normalizer==3.3.2

frozenlist==1.4.1

greenlet==3.1.0

h11==0.14.0

httpcore==1.0.5

httpx==0.27.2

idna==3.10

jsonpatch==1.33

jsonpointer==3.0.0

langchain==0.3.0

langchain-core==0.3.1

langchain-text-splitters==0.3.0

langsmith==0.1.122

multidict==6.1.0

numpy==1.26.4

orjson==3.10.7

packaging==24.1

pydantic==2.9.2

pydantic_core==2.23.4

PyYAML==6.0.2

requests==2.32.3

sniffio==1.3.1

SQLAlchemy==2.0.35

tenacity==8.5.0

typing_extensions==4.12.2

urllib3==2.2.3

yarl==1.11.1

※

[PYTHON/LANGCHAIN] StructuredTool 클래스 : from_function 정적 메소드에서 func 인자를 사용해 도구 함수 동기/비동기 호출하기

■ StructuredTool 클래스의 from_function 정적 메소드에서 func 인자를 사용해 도구 함수를 동기/비동기 호출하는 방법을 보여준다. ▶ main.py


import asyncio

from langchain_core.tools import StructuredTool

def multiply(a : int, b : int) -> int:
    """Multiply two numbers."""
    return a * b

calculatorTool = StructuredTool.from_function(func = multiply)

async def main():
    print(calculatorTool.invoke({"a" : 2, "b" : 3}))
    print(await calculatorTool.ainvoke({"a" : 2, "b" : 5}))

asyncio.run(main())

import asyncio

from langchain_core.tools import StructuredTool

def multiply(a : int, b : int) -> int:

"""Multiply two numbers."""

return a * b

calculatorTool = StructuredTool.from_function(func = multiply)

async def main():

print(calculatorTool.invoke({"a" : 2, "b" : 3}))

print(await calculatorTool.ainvoke({"a" : 2, "b" : 5}))

asyncio.run(main())

▶ requirements.txt


aiohappyeyeballs==2.4.0
aiohttp==3.10.5
aiosignal==1.3.1
annotated-types==0.7.0
anyio==4.4.0
attrs==24.2.0
certifi==2024.8.30
charset-normalizer==3.3.2
frozenlist==1.4.1
greenlet==3.1.0
h11==0.14.0
httpcore==1.0.5
httpx==0.27.2
idna==3.10
jsonpatch==1.33
jsonpointer==3.0.0
langchain==0.3.0
langchain-core==0.3.1
langchain-text-splitters==0.3.0
langsmith==0.1.122
multidict==6.1.0
numpy==1.26.4
orjson==3.10.7
packaging==24.1
pydantic==2.9.2
pydantic_core==2.23.4
PyYAML==6.0.2
requests==2.32.3
sniffio==1.3.1
SQLAlchemy==2.0.35
tenacity==8.5.0
typing_extensions==4.12.2
urllib3==2.2.3
yarl==1.11.1

aiohappyeyeballs==2.4.0

aiohttp==3.10.5

aiosignal==1.3.1

annotated-types==0.7.0

anyio==4.4.0

attrs==24.2.0

certifi==2024.8.30

charset-normalizer==3.3.2

frozenlist==1.4.1

greenlet==3.1.0

h11==0.14.0

httpcore==1.0.5

httpx==0.27.2

idna==3.10

jsonpatch==1.33

jsonpointer==3.0.0

langchain==0.3.0

langchain-core==0.3.1

langchain-text-splitters==0.3.0

langsmith==0.1.122

multidict==6.1.0

numpy==1.26.4

orjson==3.10.7

packaging==24.1

pydantic==2.9.2

pydantic_core==2.23.4

PyYAML==6.0.2

requests==2.32.3

sniffio==1.3.1

SQLAlchemy==2.0.35

tenacity==8.5.0

typing_extensions==4.12.2

urllib3==2.2.3

yarl==1.11.1

※

[PYTHON/LANGCHAIN] BaseTool 클래스 : 도구 만들기 2

■ BaseTool 클래스를 사용해 도구를 만드는 방법을 보여준다. ▶ main.py


import asyncio

from pydantic                 import BaseModel
from pydantic                 import Field
from langchain_core.tools     import BaseTool
from typing                   import Type
from typing                   import Optional
from langchain_core.callbacks import CallbackManagerForToolRun
from langchain_core.callbacks import AsyncCallbackManagerForToolRun

class CalculatorModel(BaseModel):
    a : int = Field(description = "first number")
    b : int = Field(description = "second number")

class CalculatorTool(BaseTool):
    name          : str = Field(default = "Calculator"                    )
    description   : str = Field(default = "유용한 수학 계산을 수행합니다.")
    args_schema   : Type[BaseModel] = CalculatorModel
    return_direct : bool = True

    def _run(self, a : int, b : int, run_manager : Optional[CallbackManagerForToolRun] = None) -> str:
        """Use the tool."""
        return a * b

    async def _arun(self, a : int, b : int, run_manager : Optional[AsyncCallbackManagerForToolRun] = None) -> str:
        """Use the tool asynchronously."""
        # 계산이 저렴하다면, 아래에 표시된 것처럼 동기화 구현에 위임하면 된다.
        # 동기화 계산이 비싸다면, _arun 메서드 전체를 삭제해야 한다.
        # LangChain은 자동으로 더 나은 구현을 제공하여 다른 비동기 코드를 차단하지 않도록 스레드에서 작업을 시작한다.
        return self._run(a, b, run_manager = run_manager.get_sync())

async def main():
    calculatorTool = CalculatorTool()

    print(calculatorTool.name         )
    print(calculatorTool.description  )
    print(calculatorTool.args         )
    print(calculatorTool.return_direct)

    print(calculatorTool.invoke({"a" : 2, "b" : 3}))
    print(await calculatorTool.ainvoke({"a" : 2, "b" : 3}))

asyncio.run(main())

"""
Calculator
유용한 수학 계산을 수행합니다.
{'a': {'description': 'first number', 'title': 'A', 'type': 'integer'}, 'b': {'description': 'second number', 'title': 'B', 'type': 'integer'}}
True
6
6
"""

import asyncio

from pydantic import BaseModel

from pydantic import Field

from langchain_core.tools import BaseTool

from typing import Type

from typing import Optional

from langchain_core.callbacks import CallbackManagerForToolRun

from langchain_core.callbacks import AsyncCallbackManagerForToolRun

class CalculatorModel(BaseModel):

a : int = Field(description = "first number")

b : int = Field(description = "second number")

class CalculatorTool(BaseTool):

name : str = Field(default = "Calculator" )

description : str = Field(default = "유용한 수학 계산을 수행합니다.")

args_schema : Type[BaseModel] = CalculatorModel

return_direct : bool = True

def _run(self, a : int, b : int, run_manager : Optional[CallbackManagerForToolRun] = None) -> str:

"""Use the tool."""

return a * b

async def _arun(self, a : int, b : int, run_manager : Optional[AsyncCallbackManagerForToolRun] = None) -> str:

"""Use the tool asynchronously."""

# 계산이 저렴하다면, 아래에 표시된 것처럼 동기화 구현에 위임하면 된다.

# 동기화 계산이 비싸다면, _arun 메서드 전체를 삭제해야 한다.

# LangChain은 자동으로 더 나은 구현을 제공하여 다른 비동기 코드를 차단하지 않도록 스레드에서 작업을 시작한다.

return self._run(a, b, run_manager = run_manager.get_sync())

async def main():

calculatorTool = CalculatorTool()

print(calculatorTool.name )

print(calculatorTool.description )

print(calculatorTool.args )

print(calculatorTool.return_direct)

print(calculatorTool.invoke({"a" : 2, "b" : 3}))

print(await calculatorTool.ainvoke({"a" : 2, "b" : 3}))

asyncio.run(main())

"""

Calculator

유용한 수학 계산을 수행합니다.

{'a': {'description': 'first number', 'title': 'A', 'type': 'integer'}, 'b': {'description': 'second number', 'title': 'B', 'type': 'integer'}}

True

"""

▶ requirements.txt


aiohappyeyeballs==2.4.0
aiohttp==3.10.5
aiosignal==1.3.1
annotated-types==0.7.0
anyio==4.4.0
attrs==24.2.0
certifi==2024.8.30
charset-normalizer==3.3.2
frozenlist==1.4.1
greenlet==3.1.0
h11==0.14.0
httpcore==1.0.5
httpx==0.27.2
idna==3.10
jsonpatch==1.33
jsonpointer==3.0.0
langchain==0.3.0
langchain-core==0.3.1
langchain-text-splitters==0.3.0
langsmith==0.1.122
multidict==6.1.0
numpy==1.26.4
orjson==3.10.7
packaging==24.1
pydantic==2.9.2
pydantic_core==2.23.4
PyYAML==6.0.2
requests==2.32.3
sniffio==1.3.1
SQLAlchemy==2.0.35
tenacity==8.5.0
typing_extensions==4.12.2
urllib3==2.2.3
yarl==1.11.1

aiohappyeyeballs==2.4.0

aiohttp==3.10.5

aiosignal==1.3.1

annotated-types==0.7.0

anyio==4.4.0

attrs==24.2.0

certifi==2024.8.30

charset-normalizer==3.3.2

frozenlist==1.4.1

greenlet==3.1.0

h11==0.14.0

httpcore==1.0.5

httpx==0.27.2

idna==3.10

jsonpatch==1.33

jsonpointer==3.0.0

langchain==0.3.0

langchain-core==0.3.1

langchain-text-splitters==0.3.0

langsmith==0.1.122

multidict==6.1.0

numpy==1.26.4

orjson==3.10.7

packaging==24.1

pydantic==2.9.2

pydantic_core==2.23.4

PyYAML==6.0.2

requests==2.32.3

sniffio==1.3.1

SQLAlchemy==2.0.35

tenacity==8.5.0

typing_extensions==4.12.2

urllib3==2.2.3

yarl==1.11.1

※ pip install langchain 명령을 실행했다.

[PYTHON/LANGCHAIN] BaseTool 클래스 : 도구 만들기 1

■ BaseTool 클래스를 사용해 도구를 만드는 방법을 보여준다. ▶ main.py


from langchain.tools import BaseTool
from pydantic        import Field

class CalculatorTool(BaseTool):
    name        : str = Field(default = "Calculator"                    )
    description : str = Field(default = "유용한 수학 계산을 수행합니다.")

    def _run(self, query : str) -> str:
        """주어진 수학 표현식을 계산합니다."""
        try:
            result = eval(query)
            return f"계산 결과 : {result}"
        except:
            return "올바른 수학 표현식이 아닙니다. 다시 시도해주세요."

    async def _arun(self, query : str) -> str:
        """비동기 실행을 위한 메서드입니다."""
        return self._run(query)

calculatorTool = CalculatorTool()

result = calculatorTool.run("2 + 2")

print(result)

from langchain.tools import BaseTool

from pydantic import Field

class CalculatorTool(BaseTool):

name : str = Field(default = "Calculator" )

description : str = Field(default = "유용한 수학 계산을 수행합니다.")

def _run(self, query : str) -> str:

"""주어진 수학 표현식을 계산합니다."""

try:

result = eval(query)

return f"계산 결과 : {result}"

except:

return "올바른 수학 표현식이 아닙니다. 다시 시도해주세요."

async def _arun(self, query : str) -> str:

"""비동기 실행을 위한 메서드입니다."""

return self._run(query)

calculatorTool = CalculatorTool()

result = calculatorTool.run("2 + 2")

print(result)

▶ requirements.txt


aiohappyeyeballs==2.4.0
aiohttp==3.10.5
aiosignal==1.3.1
annotated-types==0.7.0
anyio==4.4.0
attrs==24.2.0
certifi==2024.8.30
charset-normalizer==3.3.2
frozenlist==1.4.1
greenlet==3.1.0
h11==0.14.0
httpcore==1.0.5
httpx==0.27.2
idna==3.10
jsonpatch==1.33
jsonpointer==3.0.0
langchain==0.3.0
langchain-core==0.3.1
langchain-text-splitters==0.3.0
langsmith==0.1.122
multidict==6.1.0
numpy==1.26.4
orjson==3.10.7
packaging==24.1
pydantic==2.9.2
pydantic_core==2.23.4
PyYAML==6.0.2
requests==2.32.3
sniffio==1.3.1
SQLAlchemy==2.0.35
tenacity==8.5.0
typing_extensions==4.12.2
urllib3==2.2.3
yarl==1.11.1

aiohappyeyeballs==2.4.0

aiohttp==3.10.5

aiosignal==1.3.1

annotated-types==0.7.0

anyio==4.4.0

attrs==24.2.0

certifi==2024.8.30

charset-normalizer==3.3.2

frozenlist==1.4.1

greenlet==3.1.0

h11==0.14.0

httpcore==1.0.5

httpx==0.27.2

idna==3.10

jsonpatch==1.33

jsonpointer==3.0.0

langchain==0.3.0

langchain-core==0.3.1

langchain-text-splitters==0.3.0

langsmith==0.1.122

multidict==6.1.0

numpy==1.26.4

orjson==3.10.7

packaging==24.1

pydantic==2.9.2

pydantic_core==2.23.4

PyYAML==6.0.2

requests==2.32.3

sniffio==1.3.1

SQLAlchemy==2.0.35

tenacity==8.5.0

typing_extensions==4.12.2

urllib3==2.2.3

yarl==1.11.1

※ pip install langchain 명령을 실행했다.

[PYTHON/LANGCHAIN] GenericFakeChatModel 클래스 : 생성자에서 messages 인자를 사용해 GenericFakeChatModel 객체 만들기

■ GenericFakeChatModel 클래스의 생성자에서 messages 인자를 사용해 GenericFakeChatModel 객체를 만드는 방법을 보여준다. ▶ 예제 코드 (PY)


from langchain_core.language_models import GenericFakeChatModel

genericFakeChatModel = GenericFakeChatModel(messages = iter(["hello matey"]))

from langchain_core.language_models import GenericFakeChatModel

genericFakeChatModel = GenericFakeChatModel(messages = iter(["hello matey"]))

※ pip install langchain 명령을

[PYTHON/LANGCHAIN] RunnableSequence 클래스 : as_tool 메소드를 사용해 도구 만들기

■ RunnableSequence 클래스의 as_tool 메소드를 사용해 도구를 만드는 방법을 보여준다. ▶ main.py


from langchain_core.prompts         import ChatPromptTemplate
from langchain_core.language_models import GenericFakeChatModel
from langchain_core.output_parsers  import StrOutputParser

chatPromptTemplate = ChatPromptTemplate.from_messages([("human", "Hello. Please respond in the style of {answer_style}.")])

genericFakeChatModel = GenericFakeChatModel(messages = iter(["hello matey"]))

runnableSequence = chatPromptTemplate | genericFakeChatModel | StrOutputParser()

structuredTool = runnableSequence.as_tool(name = "Style responder", description = "Description of when to use tool.")

print(structuredTool.args)

"""
{'answer_style': {'title': 'Answer Style', 'type': 'string'}}
"""

from langchain_core.prompts import ChatPromptTemplate

from langchain_core.language_models import GenericFakeChatModel

from langchain_core.output_parsers import StrOutputParser

chatPromptTemplate = ChatPromptTemplate.from_messages([("human", "Hello. Please respond in the style of {answer_style}.")])

genericFakeChatModel = GenericFakeChatModel(messages = iter(["hello matey"]))

runnableSequence = chatPromptTemplate | genericFakeChatModel | StrOutputParser()

structuredTool = runnableSequence.as_tool(name = "Style responder", description = "Description of when to use tool.")

print(structuredTool.args)

"""

{'answer_style': {'title': 'Answer Style', 'type': 'string'}}

"""

▶ requirements.txt


aiohappyeyeballs==2.4.0
aiohttp==3.10.5
aiosignal==1.3.1
annotated-types==0.7.0
anyio==4.4.0
attrs==24.2.0
certifi==2024.8.30
charset-normalizer==3.3.2
frozenlist==1.4.1
greenlet==3.1.0
h11==0.14.0
httpcore==1.0.5
httpx==0.27.2
idna==3.10
jsonpatch==1.33
jsonpointer==3.0.0
langchain==0.3.0
langchain-core==0.3.1
langchain-text-splitters==0.3.0
langsmith==0.1.122
multidict==6.1.0
numpy==1.26.4
orjson==3.10.7
packaging==24.1
pydantic==2.9.2
pydantic_core==2.23.4
PyYAML==6.0.2
requests==2.32.3
sniffio==1.3.1
SQLAlchemy==2.0.35
tenacity==8.5.0
typing_extensions==4.12.2
urllib3==2.2.3
yarl==1.11.1

aiohappyeyeballs==2.4.0

aiohttp==3.10.5

aiosignal==1.3.1

annotated-types==0.7.0

anyio==4.4.0

attrs==24.2.0

certifi==2024.8.30

charset-normalizer==3.3.2

frozenlist==1.4.1

greenlet==3.1.0

h11==0.14.0

httpcore==1.0.5

httpx==0.27.2

idna==3.10

jsonpatch==1.33

jsonpointer==3.0.0

langchain==0.3.0

langchain-core==0.3.1

langchain-text-splitters==0.3.0

langsmith==0.1.122

multidict==6.1.0

numpy==1.26.4

orjson==3.10.7

packaging==24.1

pydantic==2.9.2

pydantic_core==2.23.4

PyYAML==6.0.2

requests==2.32.3

sniffio==1.3.1

SQLAlchemy==2.0.35

tenacity==8.5.0

typing_extensions==4.12.2

urllib3==2.2.3

yarl==1.11.1

※ pip install langchain 명령을 실행했다.

[PYTHON/LANGCHAIN] StructuredTool 클래스 : from_function 정적 메소드에서 args_schema 인자를 사용해 BaseModel 객체 설정하기

■ StructuredTool 클래스의 from_function 정적 메소드에서 args_schema 인자를 사용해 BaseModel 객체를 설정하는 방법을 보여준다. ▶ main.py


from pydantic             import BaseModel
from pydantic             import Field
from langchain_core.tools import StructuredTool

class CalculatorModel(BaseModel):
    a : int = Field(description = "first number" )
    b : int = Field(description = "second number")

def multiply(a : int, b : int) -> int:
    """Multiply two numbers."""
    return a * b

calculatorTool = StructuredTool.from_function(
    func          = multiply,
    name          = "Calculator",
    description   = "multiply numbers",
    args_schema   = CalculatorModel,
    return_direct = True,
)

print(calculatorTool.invoke({"a" : 2, "b" : 3}))

print(calculatorTool.name       )
print(calculatorTool.description)
print(calculatorTool.args       )

"""
6
Calculator
multiply numbers
{'a': {'description': 'first number', 'title': 'A', 'type': 'integer'}, 'b': {'description': 'second number', 'title': 'B', 'type': 'integer'}}
"""

from pydantic import BaseModel

from pydantic import Field

from langchain_core.tools import StructuredTool

class CalculatorModel(BaseModel):

a : int = Field(description = "first number" )

b : int = Field(description = "second number")

def multiply(a : int, b : int) -> int:

"""Multiply two numbers."""

return a * b

calculatorTool = StructuredTool.from_function(

func = multiply,

name = "Calculator",

description = "multiply numbers",

args_schema = CalculatorModel,

return_direct = True,

)

print(calculatorTool.invoke({"a" : 2, "b" : 3}))

print(calculatorTool.name )

print(calculatorTool.description)

print(calculatorTool.args )

"""

Calculator

multiply numbers

{'a': {'description': 'first number', 'title': 'A', 'type': 'integer'}, 'b': {'description': 'second number', 'title': 'B', 'type': 'integer'}}

"""

▶ requirements.txt


aiohappyeyeballs==2.4.0
aiohttp==3.10.5
aiosignal==1.3.1
annotated-types==0.7.0
anyio==4.4.0
attrs==24.2.0
certifi==2024.8.30
charset-normalizer==3.3.2
frozenlist==1.4.1
greenlet==3.1.0
h11==0.14.0
httpcore==1.0.5
httpx==0.27.2
idna==3.10
jsonpatch==1.33
jsonpointer==3.0.0
langchain==0.3.0
langchain-core==0.3.1
langchain-text-splitters==0.3.0
langsmith==0.1.122
multidict==6.1.0
numpy==1.26.4
orjson==3.10.7
packaging==24.1
pydantic==2.9.2
pydantic_core==2.23.4
PyYAML==6.0.2
requests==2.32.3
sniffio==1.3.1
SQLAlchemy==2.0.35
tenacity==8.5.0
typing_extensions==4.12.2
urllib3==2.2.3
yarl==1.11.1

aiohappyeyeballs==2.4.0

aiohttp==3.10.5

aiosignal==1.3.1

annotated-types==0.7.0

anyio==4.4.0

attrs==24.2.0

certifi==2024.8.30

charset-normalizer==3.3.2

frozenlist==1.4.1

greenlet==3.1.0

h11==0.14.0

httpcore==1.0.5

httpx==0.27.2

idna==3.10

jsonpatch==1.33

jsonpointer==3.0.0

langchain==0.3.0

langchain-core==0.3.1

langchain-text-splitters==0.3.0

langsmith==0.1.122

multidict==6.1.0

numpy==1.26.4

orjson==3.10.7

packaging==24.1

pydantic==2.9.2

pydantic_core==2.23.4

PyYAML==6.0.2

requests==2.32.3

sniffio==1.3.1

SQLAlchemy==2.0.35

tenacity==8.5.0

typing_extensions==4.12.2

urllib3==2.2.3

yarl==1.11.1

※ pip

[PYTHON/LANGCHAIN] StructuredTool 클래스 : from_function 정적 메소드에서 func/coroutine 인자를 사용해 동기/비동기 함수 설정하기

■ StructuredTool 클래스의 from_function 정적 메소드에서 func/coroutine 인자를 사용해 동기/비동기 함수를 설정하는 방법을 보여준다. ▶ main.py


import asyncio

from langchain_core.tools import StructuredTool

def multiply(a : int, b : int) -> int:
    """Multiply two numbers."""
    return a * b

async def multiplyAsync(a: int, b: int) -> int:
    """Multiply two numbers."""
    return a * b

async def main():
    calculatorTool = StructuredTool.from_function(func = multiply, coroutine = multiplyAsync)

    print(calculatorTool.invoke({"a" : 2, "b" : 3}))
    print(await calculatorTool.ainvoke({"a" : 2, "b" : 5}))

asyncio.run(main())

import asyncio

from langchain_core.tools import StructuredTool

def multiply(a : int, b : int) -> int:

"""Multiply two numbers."""

return a * b

async def multiplyAsync(a: int, b: int) -> int:

"""Multiply two numbers."""

return a * b

async def main():

calculatorTool = StructuredTool.from_function(func = multiply, coroutine = multiplyAsync)

print(calculatorTool.invoke({"a" : 2, "b" : 3}))

print(await calculatorTool.ainvoke({"a" : 2, "b" : 5}))

asyncio.run(main())

▶ requirements.txt


aiohappyeyeballs==2.4.0
aiohttp==3.10.5
aiosignal==1.3.1
annotated-types==0.7.0
anyio==4.4.0
attrs==24.2.0
certifi==2024.8.30
charset-normalizer==3.3.2
frozenlist==1.4.1
greenlet==3.1.0
h11==0.14.0
httpcore==1.0.5
httpx==0.27.2
idna==3.10
jsonpatch==1.33
jsonpointer==3.0.0
langchain==0.3.0
langchain-core==0.3.1
langchain-text-splitters==0.3.0
langsmith==0.1.122
multidict==6.1.0
numpy==1.26.4
orjson==3.10.7
packaging==24.1
pydantic==2.9.2
pydantic_core==2.23.4
PyYAML==6.0.2
requests==2.32.3
sniffio==1.3.1
SQLAlchemy==2.0.35
tenacity==8.5.0
typing_extensions==4.12.2
urllib3==2.2.3
yarl==1.11.1

aiohappyeyeballs==2.4.0

aiohttp==3.10.5

aiosignal==1.3.1

annotated-types==0.7.0

anyio==4.4.0

attrs==24.2.0

certifi==2024.8.30

charset-normalizer==3.3.2

frozenlist==1.4.1

greenlet==3.1.0

h11==0.14.0

httpcore==1.0.5

httpx==0.27.2

idna==3.10

jsonpatch==1.33

jsonpointer==3.0.0

langchain==0.3.0

langchain-core==0.3.1

langchain-text-splitters==0.3.0

langsmith==0.1.122

multidict==6.1.0

numpy==1.26.4

orjson==3.10.7

packaging==24.1

pydantic==2.9.2

pydantic_core==2.23.4

PyYAML==6.0.2

requests==2.32.3

sniffio==1.3.1

SQLAlchemy==2.0.35

tenacity==8.5.0

typing_extensions==4.12.2

urllib3==2.2.3

yarl==1.11.1

※ pip