■ PydanticOutputParser 클래스를 체인에서 사용해 구조화된 데이터를 구하는 방법을 보여준다.
※ OPENAI_API_KEY 환경 변수 값은 .env 파일에 정의한다.
▶ main.py
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from dotenv import load_dotenv from langchain_core.pydantic_v1 import BaseModel from langchain_core.pydantic_v1 import Field from langchain_core.pydantic_v1 import validator from langchain_core.prompts import PromptTemplate from langchain_openai import OpenAI from langchain_core.output_parsers import PydanticOutputParser load_dotenv() class Joke(BaseModel): setup : str = Field(description = "question to set up a joke" ) punchline : str = Field(description = "answer to resolve the joke") @validator("setup") def valiedateSetup(cls, field): if field[-1] != "?": raise ValueError("Badly formed question!") return field pydanticOutputParser = PydanticOutputParser(pydantic_object = Joke) promptTemplate = PromptTemplate( template = "Answer the user query.\n{format_instructions}\n{query}\n", input_variables = ["query"], partial_variables = {"format_instructions" : pydanticOutputParser.get_format_instructions()}, ) openAI = OpenAI(model_name = "gpt-3.5-turbo-instruct", temperature = 0.0) runnableSequence = promptTemplate | openAI | pydanticOutputParser joke = runnableSequence.invoke({"query" : "Tell me a joke."}) print(joke) """ setup='Why did the tomato turn red?' punchline='Because it saw the salad dressing!' """ |
▶ requirements.txt
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annotated-types==0.7.0 anyio==4.4.0 certifi==2024.6.2 charset-normalizer==3.3.2 distro==1.9.0 exceptiongroup==1.2.1 h11==0.14.0 httpcore==1.0.5 httpx==0.27.0 idna==3.7 jsonpatch==1.33 jsonpointer==3.0.0 langchain-core==0.2.10 langchain-openai==0.1.10 langsmith==0.1.82 openai==1.35.4 orjson==3.10.5 packaging==24.1 pydantic==2.7.4 pydantic_core==2.18.4 python-dotenv==1.0.1 PyYAML==6.0.1 regex==2024.5.15 requests==2.32.3 sniffio==1.3.1 tenacity==8.4.2 tiktoken==0.7.0 tqdm==4.66.4 typing_extensions==4.12.2 urllib3==2.2.2 |
※ pip install python-dotenv langchain-openai 명령을 실행했다.