■ BaseOutputParser 클래스를 사용해 커스텀 출력 파서를 체인에서 사용하는 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.output_parsers import BaseOutputParser from langchain_core.exceptions import OutputParserException from langchain_openai import ChatOpenAI load_dotenv() class CustomOutputParser(BaseOutputParser[bool]): """Custom boolean parser.""" trueString : str = "YES" falseString : str = "NO" def parse(self, text : str) -> bool: textCleaned = text.strip().upper() if textCleaned not in (self.trueString.upper(), self.falseString.upper()): raise OutputParserException( f"BooleanOutputParser expected output value to either be " f"{self.trueString} or {self.falseString} (case-insensitive). " f"Received {textCleaned}." ) return textCleaned == self.trueString.upper() @property def _type(self) -> str: return "custom_output_parser" chatOpenAI = ChatOpenAI(model_name = "gpt-4o") customOutputParser = CustomOutputParser() runnableSequence = chatOpenAI | customOutputParser responseString = runnableSequence.invoke("say YES or NO") print(responseString) """ True """ |
▶ requirements.txt
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annotated-types==0.7.0 anyio==4.6.2.post1 certifi==2024.8.30 charset-normalizer==3.4.0 colorama==0.4.6 distro==1.9.0 h11==0.14.0 httpcore==1.0.7 httpx==0.27.2 idna==3.10 jiter==0.7.1 jsonpatch==1.33 jsonpointer==3.0.0 langchain-core==0.3.21 langchain-openai==0.2.9 langsmith==0.1.145 openai==1.55.0 orjson==3.10.12 packaging==24.2 pydantic==2.10.1 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 tenacity==9.0.0 tiktoken==0.8.0 tqdm==4.67.0 typing_extensions==4.12.2 urllib3==2.2.3 |
※ pip install python-dotenv langchain-openai 명령을 실행했다.