[PYTHON/LANGCHAIN] ChatPromptTemplate 클래스 : 프롬프트 문자열을 직접 사용해 모델 응답 구조에서 문서 ID와 인묭문 구하기

■ ChatPromptTemplate 클래스에서 프롬프트 문자열을 직접 사용해 모델 응답 구조에서 문서 ID와 인묭문을 구하는 방법을 보여준다.

※ OPENAI_API_KEY 환경 변수 값은 .env 파일에 정의한다.

▶ main.py


from dotenv                         import load_dotenv
from langchain_community.retrievers import WikipediaRetriever
from typing                         import List
from langchain_core.documents       import Document
from langchain_core.prompts         import ChatPromptTemplate
from langchain_openai               import ChatOpenAI
from langchain_core.runnables       import RunnablePassthrough
from langchain_core.output_parsers  import XMLOutputParser

load_dotenv()

wikipediaRetriever = WikipediaRetriever(top_k_results = 6, doc_content_chars_max = 2000)

runnableSequence1 = (lambda x: x["input"]) | wikipediaRetriever

def getStringFromDocumentList(documentList : List[Document]) -> str:
    targetList = []
    for index, document in enumerate(documentList):
        documentXML = f"""\
    <source id=\"{index}\">
        <title>{document.metadata['title']}</title>
        <article_snippet>{document.page_content}</article_snippet>
    </source>"""
        targetList.append(documentXML)
    return "\n\n<sources>" + "\n".join(targetList) + "</sources>"

systemString = """You're a helpful AI assistant. Given a user question and some Wikipedia article snippets, answer the user question and provide citations. If none of the articles answer the question, just say you don't know.

Remember, you must return both an answer and citations. A citation consists of a VERBATIM quote that justifies the answer and the ID of the quote article. Return a citation for every quote across all articles that justify the answer. Use the following format for your final output :

<cited_answer>
    <answer></answer>
    <citations>
        <citation><source_id></source_id><quote></quote></citation>
        <citation><source_id></source_id><quote></quote></citation>
        ...
    </citations>
</cited_answer>

Here are the Wikipedia articles : {context}"""

chatPromptTemplate = ChatPromptTemplate.from_messages(
    [
        ("system", systemString),
        ("human" , "{input}"   )
    ]
)

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

runnableSequence2 = (
    RunnablePassthrough.assign(context = (lambda x : getStringFromDocumentList(x["context"])))
    | chatPromptTemplate
    | chatOpenAI
    | XMLOutputParser()
)

runnableSequence3 = RunnablePassthrough.assign(context = runnableSequence1).assign(answer = runnableSequence2)

responseDictionary = runnableSequence3.invoke({"input" : "How fast are cheetahs?"})

print(responseDictionary["answer"])

"""
{
    'cited_answer' : [
        {'answer' : 'Cheetahs are capable of running at speeds of 93 to 104 km/h (58 to 65 mph).'},
        {
            'citations' : [
                {
                    'citation' : [
                        {'source_id' : '0'},
                        {'quote'     : 'The cheetah is capable of running at 93 to 104 km/h (58 to 65 mph); it has evolved specialized adaptations for speed, including a light build, long thin legs and a long tail.'}
                    ]
                },
                {
                    'citation' : [
                        {'source_id' : '3'},
                        {'quote'     : 'The fastest land animal is the cheetah.'}
                    ]
                }
            ]
        }
    ]
}
"""

from dotenv import load_dotenv

from langchain_community.retrievers import WikipediaRetriever

from typing import List

from langchain_core.documents import Document

from langchain_core.prompts import ChatPromptTemplate

from langchain_openai import ChatOpenAI

from langchain_core.runnables import RunnablePassthrough

from langchain_core.output_parsers import XMLOutputParser

load_dotenv()

wikipediaRetriever = WikipediaRetriever(top_k_results = 6, doc_content_chars_max = 2000)

runnableSequence1 = (lambda x: x["input"]) | wikipediaRetriever

def getStringFromDocumentList(documentList : List[Document]) -> str:

targetList = []

for index, document in enumerate(documentList):

documentXML = f"""\

<title>{document.metadata['title']}</title>

<article_snippet>{document.page_content}</article_snippet>

</source>"""

targetList.append(documentXML)

return "\n\n<sources>" + "\n".join(targetList) + "</sources>"

systemString = """You're a helpful AI assistant. Given a user question and some Wikipedia article snippets, answer the user question and provide citations. If none of the articles answer the question, just say you don't know.

Remember, you must return both an answer and citations. A citation consists of a VERBATIM quote that justifies the answer and the ID of the quote article. Return a citation for every quote across all articles that justify the answer. Use the following format for your final output :

<cited_answer>

...

</citations>

</cited_answer>

Here are the Wikipedia articles : {context}"""

chatPromptTemplate = ChatPromptTemplate.from_messages(

[

("system", systemString),

("human" , "{input}" )

]

)

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

runnableSequence2 = (

RunnablePassthrough.assign(context = (lambda x : getStringFromDocumentList(x["context"])))

| chatPromptTemplate

| chatOpenAI

| XMLOutputParser()

)

runnableSequence3 = RunnablePassthrough.assign(context = runnableSequence1).assign(answer = runnableSequence2)

responseDictionary = runnableSequence3.invoke({"input" : "How fast are cheetahs?"})

print(responseDictionary["answer"])

"""

{

'cited_answer' : [

{'answer' : 'Cheetahs are capable of running at speeds of 93 to 104 km/h (58 to 65 mph).'},

{

'citations' : [

{

'citation' : [

{'source_id' : '0'},

{'quote' : 'The cheetah is capable of running at 93 to 104 km/h (58 to 65 mph); it has evolved specialized adaptations for speed, including a light build, long thin legs and a long tail.'}

]

{

'citation' : [

{'source_id' : '3'},

{'quote' : 'The fastest land animal is the cheetah.'}

]

}

]

}

]

}

"""

▶ requirements.txt


aiohappyeyeballs==2.4.4
aiohttp==3.11.9
aiosignal==1.3.1
annotated-types==0.7.0
anyio==4.6.2.post1
attrs==24.2.0
beautifulsoup4==4.12.3
certifi==2024.8.30
charset-normalizer==3.4.0
colorama==0.4.6
dataclasses-json==0.6.7
defusedxml==0.7.1
distro==1.9.0
frozenlist==1.5.0
greenlet==3.1.1
h11==0.14.0
httpcore==1.0.7
httpx==0.28.0
httpx-sse==0.4.0
idna==3.10
jiter==0.8.0
jsonpatch==1.33
jsonpointer==3.0.0
langchain==0.3.9
langchain-community==0.3.9
langchain-core==0.3.21
langchain-openai==0.2.10
langchain-text-splitters==0.3.2
langsmith==0.1.147
marshmallow==3.23.1
multidict==6.1.0
mypy-extensions==1.0.0
numpy==2.1.3
openai==1.56.0
orjson==3.10.12
packaging==24.2
propcache==0.2.1
pydantic==2.10.2
pydantic-settings==2.6.1
pydantic_core==2.27.1
python-dotenv==1.0.1
PyYAML==6.0.2
regex==2024.11.6
requests==2.32.3
requests-toolbelt==1.0.0
sniffio==1.3.1
soupsieve==2.6
SQLAlchemy==2.0.36
tenacity==9.0.0
tiktoken==0.8.0
tqdm==4.67.1
typing-inspect==0.9.0
typing_extensions==4.12.2
urllib3==2.2.3
wikipedia==1.4.0
yarl==1.18.3

aiohappyeyeballs==2.4.4

aiohttp==3.11.9

aiosignal==1.3.1

annotated-types==0.7.0

anyio==4.6.2.post1

attrs==24.2.0

beautifulsoup4==4.12.3

certifi==2024.8.30

charset-normalizer==3.4.0

colorama==0.4.6

dataclasses-json==0.6.7

defusedxml==0.7.1

distro==1.9.0

frozenlist==1.5.0

greenlet==3.1.1

h11==0.14.0

httpcore==1.0.7

httpx==0.28.0

httpx-sse==0.4.0

idna==3.10

jiter==0.8.0

jsonpatch==1.33

jsonpointer==3.0.0

langchain==0.3.9

langchain-community==0.3.9

langchain-core==0.3.21

langchain-openai==0.2.10

langchain-text-splitters==0.3.2

langsmith==0.1.147

marshmallow==3.23.1

multidict==6.1.0

mypy-extensions==1.0.0

numpy==2.1.3

openai==1.56.0

orjson==3.10.12

packaging==24.2

propcache==0.2.1

pydantic==2.10.2

pydantic-settings==2.6.1

pydantic_core==2.27.1

python-dotenv==1.0.1

PyYAML==6.0.2

regex==2024.11.6

requests==2.32.3

requests-toolbelt==1.0.0

sniffio==1.3.1

soupsieve==2.6

SQLAlchemy==2.0.36

tenacity==9.0.0

tiktoken==0.8.0

tqdm==4.67.1

typing-inspect==0.9.0

typing_extensions==4.12.2

urllib3==2.2.3

wikipedia==1.4.0

yarl==1.18.3

※ pip install python-dotenv langchain langchain-community langchain-openai wikipedia defusedxml 명령을 실행했다.