■ RunnableGenerator 클래스를 사용해 커스텀 출력 파서를 만드는 방법을 보여준다.
※ OPENAI_API_KEY 환경 변수 값은 .env 파일에 정의한다.
▶ main.py
|
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 |
from dotenv import load_dotenv from langchain_openai import ChatOpenAI from langchain_core.messages import AIMessageChunk from typing import Iterable from langchain_core.runnables import RunnableGenerator load_dotenv() chatOpenAI = ChatOpenAI(model_name = "gpt-4o") def streamingParse(tee : Iterable[AIMessageChunk]) -> Iterable[str]: # tee의 데이터 타입은 itertools._tee이다. for chunk in tee: # chunk 데이터 타입은 AIMessage 또는 AIMessageChunk가 된다. yield chunk.content.swapcase() runnableGenerator = RunnableGenerator(streamingParse) runnableSequence = chatOpenAI | runnableGenerator responseString = runnableSequence.invoke("hello") print(responseString) print("-" * 50) for chunkString in runnableSequence.stream("tell me about yourself in one sentence"): print(chunkString, end = "|", flush = True) print() print("-" * 50) """ hELLO! hOW CAN i ASSIST YOU TODAY? -------------------------------------------------- |i'M| A| LANGUAGE| MODEL| DESIGNED| BY| oPEN|ai| TO| ASSIST| WITH| A| WIDE| RANGE| OF| QUESTIONS| AND| TASKS| BY| PROVIDING| INFORMATION| AND| GENERATING| TEXT|-BASED| RESPONSES|.|| -------------------------------------------------- """ |
▶ requirements.txt
|
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 |
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.