from langchain_core.prompts import ChatPromptTemplate

prompt = ChatPromptTemplate.from_template("1부터 {input}까지의 숫자를 모두 더한 값을 대답하세요.")

from langchain_openai import ChatOpenAI
llm = ChatOpenAI(model="gpt-3.5-turbo-0125")

# chain 연결
chain = prompt | llm

# chain 호출
output = chain.invoke({"input": "20"})
print(output)

1부터 20까지의 숫자를 모두 더한 값을 대답하세요.

content='210입니다.' response_metadata={'token_usage': {'completion_tokens': 3, 'prompt_tokens': 32, 'total_tokens': 35}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': 'fp_b28b39ffa8', 'finish_reason': 'stop', 'logprobs': None}

from langchain_core.output_parsers import StrOutputParser
output_parser = StrOutputParser()

chain = prompt | llm | output_parser

210입니다.

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

llm = ChatOpenAI(model="gpt-3.5-turbo-0125")
output_parser = StrOutputParser()

prompt_1 = ChatPromptTemplate.from_template("\"{input}\" 이 단어를 영어로 번역하세요.")
prompt_2 = ChatPromptTemplate.from_template("\"{word}\" 이 단어가 포함된 영어 문장을 하나 만들어보세요.")

chain_1 = prompt_1 | llm | output_parser
output_1 = chain_1.invoke({"input": "벚꽃"})
print("[chain_1] : " + output_1)

chain_2 = {"word": chain_1} | prompt_2 | llm | output_parser
output_2 = chain_2.invoke({"input": "벚꽃"})
print("[chain_2] : " + output_2)

[chain_1] : cherry blossom
[chain_2] : Every spring, the cherry blossom trees in our neighborhood bloom in beautiful shades of pink and white.