[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.

▶ main.py


from dotenv                        import load_dotenv
from langchain_core.tools          import tool
from langchain_openai              import ChatOpenAI
from pydantic                      import BaseModel
from pydantic                      import Field
from langchain_core.output_parsers import PydanticToolsParser

load_dotenv()

@tool
def addModel(a : int, b : int) -> int:
    """Add two integers.

    Args:
        a : First integer
        b : Second integer
    """
    return a + b

@tool
def multiplyModel(a : int, b : int) -> int:
    """Multiply two integers.

    Args:
        a : First integer
        b : Second integer
    """
    return a * b

toolList = [addModel, multiplyModel]

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

runnableBinding = chatOpenAI.bind_tools(toolList)

class addModel(BaseModel):
    """Add two integers."""

    a : int = Field(..., description = "First integer")
    b : int = Field(..., description = "Second integer")

class multiplyModel(BaseModel):
    """Multiply two integers."""

    a : int = Field(..., description = "First integer")
    b : int = Field(..., description = "Second integer")

runnableSequence = runnableBinding | PydanticToolsParser(tools = [addModel, multiplyModel])

resultList = runnableSequence.invoke("What is 3 * 12?")

print(resultList)

"""
[multiplyModel(a=3, b=12)]
"""

from dotenv import load_dotenv

from langchain_core.tools import tool

from langchain_openai import ChatOpenAI

from pydantic import BaseModel

from pydantic import Field

from langchain_core.output_parsers import PydanticToolsParser

load_dotenv()

@tool

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

"""Add two integers.

Args:

a : First integer

b : Second integer

"""

return a + b

@tool

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

"""Multiply two integers.

Args:

a : First integer

b : Second integer

"""

return a * b

toolList = [addModel, multiplyModel]

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

runnableBinding = chatOpenAI.bind_tools(toolList)

class addModel(BaseModel):

"""Add two integers."""

a : int = Field(..., description = "First integer")

b : int = Field(..., description = "Second integer")

class multiplyModel(BaseModel):

"""Multiply two integers."""

a : int = Field(..., description = "First integer")

b : int = Field(..., description = "Second integer")

runnableSequence = runnableBinding | PydanticToolsParser(tools = [addModel, multiplyModel])

resultList = runnableSequence.invoke("What is 3 * 12?")

print(resultList)

"""

[multiplyModel(a=3, b=12)]

"""

▶ requirements.txt


annotated-types==0.7.0
anyio==4.4.0
certifi==2024.8.30
charset-normalizer==3.3.2
colorama==0.4.6
distro==1.9.0
h11==0.14.0
httpcore==1.0.5
httpx==0.27.2
idna==3.10
jiter==0.5.0
jsonpatch==1.33
jsonpointer==3.0.0
langchain-core==0.3.1
langchain-openai==0.2.0
langsmith==0.1.123
openai==1.46.0
orjson==3.10.7
packaging==24.1
pydantic==2.9.2
pydantic_core==2.23.4
python-dotenv==1.0.1
PyYAML==6.0.2
regex==2024.9.11
requests==2.32.3
sniffio==1.3.1
tenacity==8.5.0
tiktoken==0.7.0
tqdm==4.66.5
typing_extensions==4.12.2
urllib3==2.2.3

annotated-types==0.7.0

anyio==4.4.0

certifi==2024.8.30

charset-normalizer==3.3.2

colorama==0.4.6

distro==1.9.0

h11==0.14.0

httpcore==1.0.5

httpx==0.27.2

idna==3.10

jiter==0.5.0

jsonpatch==1.33

jsonpointer==3.0.0

langchain-core==0.3.1

langchain-openai==0.2.0

langsmith==0.1.123

openai==1.46.0

orjson==3.10.7

packaging==24.1

pydantic==2.9.2

pydantic_core==2.23.4

python-dotenv==1.0.1

PyYAML==6.0.2

regex==2024.9.11

requests==2.32.3

sniffio==1.3.1

tenacity==8.5.0

tiktoken==0.7.0

tqdm==4.66.5

typing_extensions==4.12.2

urllib3==2.2.3

※ pip install python-dotenv langchain-openai 명령을 실행했다.