[PYTHON/LANGCHAIN] 비스트리밍 구성 요소 구성 LCEL 체인에서 마지막 비스트리밍 단계 이후 부분 출력 스트리밍하기

■ 비스트리밍 구성 요소로 구성된 LCEL 체인에서 마지막 비스트리밍 단계 이후 부분 출력을 스트리밍하는 방법을 보여준다.

▶ main.py


import os

from langchain_core.prompts           import ChatPromptTemplate
from langchain_openai                 import OpenAIEmbeddings
from langchain_community.vectorstores import FAISS
from langchain_core.runnables         import RunnablePassthrough
from langchain_openai                 import ChatOpenAI
from langchain_core.output_parsers    import StrOutputParser

os.environ["OPENAI_API_KEY"] = "<OPENAI_API_KEY>"

chatPromptTemplateString = """Answer the question based only on the following context :
{context}

Question : {question}
"""

chatPromptTemplate = ChatPromptTemplate.from_template(chatPromptTemplateString)

faiss = FAISS.from_texts(
    ["harrison worked at kensho", "harrison likes spicy food"],
    embedding = OpenAIEmbeddings(),
)

vectorStoreRetriever = faiss.as_retriever()

chatOpenAI = ChatOpenAI(model = "gpt-3.5-turbo-0125")

runnableSequence = (
    {
        "context"  : vectorStoreRetriever.with_config(run_name = "Docs"),
        "question" : RunnablePassthrough()
    }
    | chatPromptTemplate
    | chatOpenAI
    | StrOutputParser()
)

for chunkString in runnableSequence.stream("Where did harrison work? Write 3 made up sentences about this place."):
    print(chunkString, end = "", flush = True)

print()

"""
Harrison worked at a trendy tech start-up called Kensho located in the heart of Silicon Valley. The office had a sleek and modern design, with open workspaces and collaborative areas for brainstorming. At Kensho, employees enjoyed perks like catered lunches, ping pong tables, and regular team-building activities.
"""

import os

from langchain_core.prompts import ChatPromptTemplate

from langchain_openai import OpenAIEmbeddings

from langchain_community.vectorstores import FAISS

from langchain_core.runnables import RunnablePassthrough

from langchain_openai import ChatOpenAI

from langchain_core.output_parsers import StrOutputParser

os.environ["OPENAI_API_KEY"] = "<OPENAI_API_KEY>"

chatPromptTemplateString = """Answer the question based only on the following context :

{context}

Question : {question}

"""

chatPromptTemplate = ChatPromptTemplate.from_template(chatPromptTemplateString)

faiss = FAISS.from_texts(

["harrison worked at kensho", "harrison likes spicy food"],

embedding = OpenAIEmbeddings(),

)

vectorStoreRetriever = faiss.as_retriever()

chatOpenAI = ChatOpenAI(model = "gpt-3.5-turbo-0125")

runnableSequence = (

{

"context" : vectorStoreRetriever.with_config(run_name = "Docs"),

"question" : RunnablePassthrough()

}

| chatPromptTemplate

| chatOpenAI

| StrOutputParser()

)

for chunkString in runnableSequence.stream("Where did harrison work? Write 3 made up sentences about this place."):

print(chunkString, end = "", flush = True)

print()

"""

Harrison worked at a trendy tech start-up called Kensho located in the heart of Silicon Valley. The office had a sleek and modern design, with open workspaces and collaborative areas for brainstorming. At Kensho, employees enjoyed perks like catered lunches, ping pong tables, and regular team-building activities.

"""

▶ requirements.txt


aiohttp==3.9.5
aiosignal==1.3.1
annotated-types==0.7.0
anyio==4.4.0
async-timeout==4.0.3
attrs==23.2.0
certifi==2024.6.2
charset-normalizer==3.3.2
dataclasses-json==0.6.7
distro==1.9.0
exceptiongroup==1.2.1
faiss-gpu==1.7.2
frozenlist==1.4.1
greenlet==3.0.3
h11==0.14.0
httpcore==1.0.5
httpx==0.27.0
idna==3.7
jsonpatch==1.33
jsonpointer==3.0.0
langchain==0.2.5
langchain-community==0.2.5
langchain-core==0.2.8
langchain-openai==0.1.8
langchain-text-splitters==0.2.1
langsmith==0.1.79
marshmallow==3.21.3
multidict==6.0.5
mypy-extensions==1.0.0
numpy==1.26.4
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
SQLAlchemy==2.0.30
tenacity==8.4.1
tiktoken==0.7.0
tqdm==4.66.4
typing-inspect==0.9.0
typing_extensions==4.12.2
urllib3==2.2.2
yarl==1.9.4

aiohttp==3.9.5

aiosignal==1.3.1

annotated-types==0.7.0

anyio==4.4.0

async-timeout==4.0.3

attrs==23.2.0

certifi==2024.6.2

charset-normalizer==3.3.2

dataclasses-json==0.6.7

distro==1.9.0

exceptiongroup==1.2.1

faiss-gpu==1.7.2

frozenlist==1.4.1

greenlet==3.0.3

h11==0.14.0

httpcore==1.0.5

httpx==0.27.0

idna==3.7

jsonpatch==1.33

jsonpointer==3.0.0

langchain==0.2.5

langchain-community==0.2.5

langchain-core==0.2.8

langchain-openai==0.1.8

langchain-text-splitters==0.2.1

langsmith==0.1.79

marshmallow==3.21.3

multidict==6.0.5

mypy-extensions==1.0.0

numpy==1.26.4

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

SQLAlchemy==2.0.30

tenacity==8.4.1

tiktoken==0.7.0

tqdm==4.66.4

typing-inspect==0.9.0

typing_extensions==4.12.2

urllib3==2.2.2

yarl==1.9.4

※ pip install langchain langchain-community langchain-openai faiss-gpu 명령을 실행했다.