■ PydanticOutputParser 클래스를 사용해 구조화된 데이터를 받는 방법을 보여준다.
▶ main.py
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import os from typing import List from langchain_core.pydantic_v1 import BaseModel, Field from langchain_core.output_parsers import PydanticOutputParser from langchain_core.prompts import ChatPromptTemplate from langchain_openai import ChatOpenAI os.environ["OPENAI_API_KEY"] = "<OPENAI_API_KEY>" 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) instructionFormatString = pydanticOutputParser.get_format_instructions() chatPromptTemplate = ChatPromptTemplate.from_messages( [ ( "system", "Answer the user query. Wrap the output in `json` tags\n{format_instructions}", ), ("human", "{query}"), ] ).partial(format_instructions = instructionFormatString) chatOpenAI = ChatOpenAI(model = "gpt-3.5-turbo-0125") query = "Anna is 23 years old and she is 6 feet tall" runnableSequence = chatPromptTemplate | chatOpenAI | pydanticOutputParser people = runnableSequence.invoke({"query" : query}) print(people) """ people=[Person(name='Anna', height_in_meters=1.83)] """ |
▶ 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==0.2.7 langchain-openai==0.1.8 langsmith==0.1.77 openai==1.34.0 orjson==3.10.5 packaging==24.1 pydantic==2.7.4 pydantic_core==2.18.4 PyYAML==6.0.1 regex==2024.5.15 requests==2.32.3 sniffio==1.3.1 tenacity==8.3.0 tiktoken==0.7.0 tqdm==4.66.4 typing_extensions==4.12.2 urllib3==2.2.1 |
※ pip install langchain-openai 명령을 실행했다.