[PYTHON/LANGCHAIN] ChatPromptTemplate 클래스 : partial 메소드를 사용해 매개 변수 값을 설정하고 새로운 ChatPromptTemplate 객체 만들기

■ ChatPromptTemplate 클래스의 partial 메소드를 사용해 매개 변수 값을 설정하고 새로운 ChatPromptTemplate 객체를 만드는 방법을 보여준다.

▶ main.py


from pydantic                      import BaseModel
from pydantic                      import Field
from typing                        import List
from langchain_core.output_parsers import PydanticOutputParser
from langchain_core.prompts        import ChatPromptTemplate

class Person(BaseModel):
    """Information about a person."""

    name             : str   = Field(..., description = "The name of the person"                       )
    height_in_meters : float = Field(..., description = "The height of the person expressed in meters.")

class People(BaseModel):
    """Identifying information about all people in a text."""

    people : List[Person]

pydanticOutputParser = PydanticOutputParser(pydantic_object = People)

chatPromptTemplate1 = ChatPromptTemplate.from_messages(
    [
        ("system", "Answer the user query. Wrap the output in `json` tags\n{format_instructions}"),
        ("human" , "{query}")
    ]
)

chatPromptTemplate2 = chatPromptTemplate1.partial(format_instructions = pydanticOutputParser.get_format_instructions())

from pydantic import BaseModel

from pydantic import Field

from typing import List

from langchain_core.output_parsers import PydanticOutputParser

from langchain_core.prompts import ChatPromptTemplate

class Person(BaseModel):

"""Information about a person."""

name : str = Field(..., description = "The name of the person" )

height_in_meters : float = Field(..., description = "The height of the person expressed in meters.")

class People(BaseModel):

"""Identifying information about all people in a text."""

people : List[Person]

pydanticOutputParser = PydanticOutputParser(pydantic_object = People)

chatPromptTemplate1 = ChatPromptTemplate.from_messages(

[

("system", "Answer the user query. Wrap the output in `json` tags\n{format_instructions}"),

("human" , "{query}")

]

)

chatPromptTemplate2 = chatPromptTemplate1.partial(format_instructions = pydanticOutputParser.get_format_instructions())

▶ requirements.txt


aiohappyeyeballs==2.4.4
aiohttp==3.11.10
aiosignal==1.3.1
annotated-types==0.7.0
anyio==4.7.0
attrs==24.2.0
certifi==2024.8.30
charset-normalizer==3.4.0
frozenlist==1.5.0
greenlet==3.1.1
h11==0.14.0
httpcore==1.0.7
httpx==0.28.1
idna==3.10
jsonpatch==1.33
jsonpointer==3.0.0
langchain==0.3.10
langchain-core==0.3.22
langchain-text-splitters==0.3.2
langsmith==0.1.147
multidict==6.1.0
numpy==2.1.3
orjson==3.10.12
packaging==24.2
propcache==0.2.1
pydantic==2.10.3
pydantic_core==2.27.1
PyYAML==6.0.2
requests==2.32.3
requests-toolbelt==1.0.0
sniffio==1.3.1
SQLAlchemy==2.0.36
tenacity==9.0.0
typing_extensions==4.12.2
urllib3==2.2.3
yarl==1.18.3

aiohappyeyeballs==2.4.4

aiohttp==3.11.10

aiosignal==1.3.1

annotated-types==0.7.0

anyio==4.7.0

attrs==24.2.0

certifi==2024.8.30

charset-normalizer==3.4.0

frozenlist==1.5.0

greenlet==3.1.1

h11==0.14.0

httpcore==1.0.7

httpx==0.28.1

idna==3.10

jsonpatch==1.33

jsonpointer==3.0.0

langchain==0.3.10

langchain-core==0.3.22

langchain-text-splitters==0.3.2

langsmith==0.1.147

multidict==6.1.0

numpy==2.1.3

orjson==3.10.12

packaging==24.2

propcache==0.2.1

pydantic==2.10.3

pydantic_core==2.27.1

PyYAML==6.0.2

requests==2.32.3

requests-toolbelt==1.0.0

sniffio==1.3.1

SQLAlchemy==2.0.36

tenacity==9.0.0

typing_extensions==4.12.2

urllib3==2.2.3

yarl==1.18.3

※ pip install langchain 명령을 실행했다.