LANGCHAIN Archives - 26 중 25 번째 페이지

[PYTHON/LANGCHAIN] pull 함수 : 랭체인 허브에서 RAG 프롬프트 관련 ChatPromptTempalte 객체 만들기

■ pull 함수를 사용해 랭체인 허브에서 RAG 프롬프트 관련 ChatPromptTempalte 객체를 만드는 방법을 보여준다. ▶ main.py


from langchain import hub

chatPromptTemplate = hub.pull("rlm/rag-prompt")

print(chatPromptTemplate)

"""
input_variables = ['context', 'question']
metadata = {'lc_hub_owner' : 'rlm', 'lc_hub_repo' : 'rag-prompt', 'lc_hub_commit_hash' : '50442af133e61576e74536c6556cefe1fac147cad032f4377b60c436e6cdcb6e'}
messages = [
    HumanMessagePromptTemplate(
        prompt = PromptTemplate
        (
            input_variables = ['context', 'question'],
            template        = "You are an assistant for question-answering tasks. Use the following pieces of retrieved context to answer the question. If you don't know the answer, just say that you don't know. Use three sentences maximum and keep the answer concise.\nQuestion: {question} \nContext: {context} \nAnswer:"
        )
    )
]
"""

from langchain import hub

chatPromptTemplate = hub.pull("rlm/rag-prompt")

print(chatPromptTemplate)

"""

input_variables = ['context', 'question']

metadata = {'lc_hub_owner' : 'rlm', 'lc_hub_repo' : 'rag-prompt', 'lc_hub_commit_hash' : '50442af133e61576e74536c6556cefe1fac147cad032f4377b60c436e6cdcb6e'}

messages = [

HumanMessagePromptTemplate(

prompt = PromptTemplate

(

input_variables = ['context', 'question'],

template = "You are an assistant for question-answering tasks. Use the following pieces of retrieved context to answer the question. If you don't know the answer, just say that you don't know. Use three sentences maximum and keep the answer concise.\nQuestion: {question} \nContext: {context} \nAnswer:"

)

]

"""

▶ requirements.txt


aiohappyeyeballs==2.4.3
aiohttp==3.10.9
aiosignal==1.3.1
annotated-types==0.7.0
anyio==4.6.0
async-timeout==4.0.3
attrs==24.2.0
certifi==2024.8.30
charset-normalizer==3.4.0
exceptiongroup==1.2.2
frozenlist==1.4.1
greenlet==3.1.1
h11==0.14.0
httpcore==1.0.6
httpx==0.27.2
idna==3.10
jsonpatch==1.33
jsonpointer==3.0.0
langchain==0.3.3
langchain-core==0.3.10
langchain-text-splitters==0.3.0
langsmith==0.1.132
multidict==6.1.0
numpy==1.26.4
orjson==3.10.7
packaging==24.1
propcache==0.2.0
pydantic==2.9.2
pydantic_core==2.23.4
PyYAML==6.0.2
requests==2.32.3
requests-toolbelt==1.0.0
sniffio==1.3.1
SQLAlchemy==2.0.35
tenacity==8.5.0
typing_extensions==4.12.2
urllib3==2.2.3
yarl==1.14.0

aiohappyeyeballs==2.4.3

aiohttp==3.10.9

aiosignal==1.3.1

annotated-types==0.7.0

anyio==4.6.0

async-timeout==4.0.3

attrs==24.2.0

certifi==2024.8.30

charset-normalizer==3.4.0

exceptiongroup==1.2.2

frozenlist==1.4.1

greenlet==3.1.1

h11==0.14.0

httpcore==1.0.6

httpx==0.27.2

idna==3.10

jsonpatch==1.33

jsonpointer==3.0.0

langchain==0.3.3

langchain-core==0.3.10

langchain-text-splitters==0.3.0

langsmith==0.1.132

multidict==6.1.0

numpy==1.26.4

orjson==3.10.7

packaging==24.1

propcache==0.2.0

pydantic==2.9.2

pydantic_core==2.23.4

PyYAML==6.0.2

requests==2.32.3

requests-toolbelt==1.0.0

sniffio==1.3.1

SQLAlchemy==2.0.35

tenacity==8.5.0

typing_extensions==4.12.2

urllib3==2.2.3

yarl==1.14.0

※ pip

[PYTHON/LANGCHAIN] RecursiveCharacterTextSplitter 클래스 : split_documents 메소드를 사용해 문서 분할하기

■ RecursiveCharacterTextSplitter 클래스의 split_documents 메소드를 사용해 문서를 분할하는 방법을 보여준다. ▶ main.py


import bs4

from langchain_community.document_loaders import WebBaseLoader
from langchain_text_splitters             import RecursiveCharacterTextSplitter

webBaseLoader = WebBaseLoader(
    web_paths = ("https://lilianweng.github.io/posts/2023-06-23-agent/",),
    bs_kwargs = dict(parse_only = bs4.SoupStrainer(class_ = ("post-content", "post-title", "post-header")))
)

documentList = webBaseLoader.load()

recursiveCharacterTextSplitter = RecursiveCharacterTextSplitter(chunk_size = 1000, chunk_overlap = 200)

splitDocumentList = recursiveCharacterTextSplitter.split_documents(documentList)

print(f"문서 리스트 요소 수      : {len(documentList     )}")
print(f"분할 문서 리스트 요소 수 : {len(splitDocumentList)}")

"""
문서 리스트 요소 수      : 1
분할 문서 리스트 요소 수 : 66
"""

import bs4

from langchain_community.document_loaders import WebBaseLoader

from langchain_text_splitters import RecursiveCharacterTextSplitter

webBaseLoader = WebBaseLoader(

web_paths = ("https://lilianweng.github.io/posts/2023-06-23-agent/",),

bs_kwargs = dict(parse_only = bs4.SoupStrainer(class_ = ("post-content", "post-title", "post-header")))

)

documentList = webBaseLoader.load()

recursiveCharacterTextSplitter = RecursiveCharacterTextSplitter(chunk_size = 1000, chunk_overlap = 200)

splitDocumentList = recursiveCharacterTextSplitter.split_documents(documentList)

print(f"문서 리스트 요소 수 : {len(documentList )}")

print(f"분할 문서 리스트 요소 수 : {len(splitDocumentList)}")

"""

문서 리스트 요소 수 : 1

분할 문서 리스트 요소 수 : 66

"""

▶ requirements.txt


aiohttp==3.9.5
aiosignal==1.3.1
annotated-types==0.7.0
async-timeout==4.0.3
attrs==23.2.0
beautifulsoup4==4.12.3
bs4==0.0.2
certifi==2024.6.2
charset-normalizer==3.3.2
dataclasses-json==0.6.7
frozenlist==1.4.1
greenlet==3.0.3
idna==3.7
jsonpatch==1.33
jsonpointer==3.0.0
langchain==0.2.3
langchain-community==0.2.4
langchain-core==0.2.5
langchain-text-splitters==0.2.1
langsmith==0.1.76
marshmallow==3.21.3
multidict==6.0.5
mypy-extensions==1.0.0
numpy==1.26.4
orjson==3.10.4
packaging==23.2
pydantic==2.7.3
pydantic_core==2.18.4
PyYAML==6.0.1
requests==2.32.3
soupsieve==2.5
SQLAlchemy==2.0.30
tenacity==8.3.0
typing-inspect==0.9.0
typing_extensions==4.12.2
urllib3==2.2.1
yarl==1.9.4

aiohttp==3.9.5

aiosignal==1.3.1

annotated-types==0.7.0

async-timeout==4.0.3

attrs==23.2.0

beautifulsoup4==4.12.3

bs4==0.0.2

certifi==2024.6.2

charset-normalizer==3.3.2

dataclasses-json==0.6.7

frozenlist==1.4.1

greenlet==3.0.3

idna==3.7

jsonpatch==1.33

jsonpointer==3.0.0

langchain==0.2.3

langchain-community==0.2.4

langchain-core==0.2.5

langchain-text-splitters==0.2.1

langsmith==0.1.76

marshmallow==3.21.3

multidict==6.0.5

mypy-extensions==1.0.0

numpy==1.26.4

orjson==3.10.4

packaging==23.2

pydantic==2.7.3

pydantic_core==2.18.4

PyYAML==6.0.1

requests==2.32.3

soupsieve==2.5

SQLAlchemy==2.0.30

tenacity==8.3.0

typing-inspect==0.9.0

typing_extensions==4.12.2

urllib3==2.2.1

yarl==1.9.4

※ pip install bs4 langchain_community 명령을

[PYTHON/LANGCHAIN] WebBaseLoader 클래스 : load 메소드를 사용해 웹 문서 로드하기

■ WebBaseLoader 클래스의 load 메소드를 사용해 웹 문서를 로드하는 방법을 보여준다. ▶ main.py


import bs4

from langchain_community.document_loaders import WebBaseLoader

webBaseLoader = WebBaseLoader(
    web_paths = ("https://lilianweng.github.io/posts/2023-06-23-agent/",),
    bs_kwargs = dict(parse_only = bs4.SoupStrainer(class_ = ("post-content", "post-title", "post-header")))
)

documentList = webBaseLoader.load()

import bs4

from langchain_community.document_loaders import WebBaseLoader

webBaseLoader = WebBaseLoader(

web_paths = ("https://lilianweng.github.io/posts/2023-06-23-agent/",),

bs_kwargs = dict(parse_only = bs4.SoupStrainer(class_ = ("post-content", "post-title", "post-header")))

)

documentList = webBaseLoader.load()

▶ requirements.txt


aiohttp==3.9.5
aiosignal==1.3.1
annotated-types==0.7.0
async-timeout==4.0.3
attrs==23.2.0
beautifulsoup4==4.12.3
bs4==0.0.2
certifi==2024.6.2
charset-normalizer==3.3.2
dataclasses-json==0.6.7
frozenlist==1.4.1
greenlet==3.0.3
idna==3.7
jsonpatch==1.33
jsonpointer==3.0.0
langchain==0.2.3
langchain-community==0.2.4
langchain-core==0.2.5
langchain-text-splitters==0.2.1
langsmith==0.1.76
marshmallow==3.21.3
multidict==6.0.5
mypy-extensions==1.0.0
numpy==1.26.4
orjson==3.10.4
packaging==23.2
pydantic==2.7.3
pydantic_core==2.18.4
PyYAML==6.0.1
requests==2.32.3
soupsieve==2.5
SQLAlchemy==2.0.30
tenacity==8.3.0
typing-inspect==0.9.0
typing_extensions==4.12.2
urllib3==2.2.1
yarl==1.9.4

aiohttp==3.9.5

aiosignal==1.3.1

annotated-types==0.7.0

async-timeout==4.0.3

attrs==23.2.0

beautifulsoup4==4.12.3

bs4==0.0.2

certifi==2024.6.2

charset-normalizer==3.3.2

dataclasses-json==0.6.7

frozenlist==1.4.1

greenlet==3.0.3

idna==3.7

jsonpatch==1.33

jsonpointer==3.0.0

langchain==0.2.3

langchain-community==0.2.4

langchain-core==0.2.5

langchain-text-splitters==0.2.1

langsmith==0.1.76

marshmallow==3.21.3

multidict==6.0.5

mypy-extensions==1.0.0

numpy==1.26.4

orjson==3.10.4

packaging==23.2

pydantic==2.7.3

pydantic_core==2.18.4

PyYAML==6.0.1

requests==2.32.3

soupsieve==2.5

SQLAlchemy==2.0.30

tenacity==8.3.0

typing-inspect==0.9.0

typing_extensions==4.12.2

urllib3==2.2.1

yarl==1.9.4

※ pip install bs4 langchain_community

[PYTHON/LANGCHAIN] 단순 검색 증강 생성(RAG, Retrieval Augmented Generation) 애플리케이션 만들기

■ 단순 검색 증강 생성(RAG, Retrieval Augmented Generation) 애플리케이션을 만드는 방법을 보여준다. ▶ main.py


import bs4
import os

from langchain_community.document_loaders import WebBaseLoader
from langchain_text_splitters             import RecursiveCharacterTextSplitter
from langchain_chroma                     import Chroma
from langchain_openai                     import OpenAIEmbeddings
from langchain                            import hub
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>"

# 웹 문서를 로드한다.
webBaseLoader = WebBaseLoader(
    web_paths = ("https://lilianweng.github.io/posts/2023-06-23-agent/",),
    bs_kwargs = dict(parse_only = bs4.SoupStrainer(class_ = ("post-content", "post-title", "post-header")))
)

documentList = webBaseLoader.load() # 문서 리스트 : 1건

# 문서를 분할한다.
recursiveCharacterTextSplitter = RecursiveCharacterTextSplitter(chunk_size = 1000, chunk_overlap = 200)

splitDocumentList = recursiveCharacterTextSplitter.split_documents(documentList) # 분할 문서 리스트 : 66건

# 분할한 문서를 벡터 저장소에 저장한다.
chroma = Chroma.from_documents(documents = splitDocumentList, embedding = OpenAIEmbeddings())

vectorStoreRetriever = chroma.as_retriever() # 벡터 저장소 검색기

# 문서 내용 병합 함수를 정의한다.
def mergeDocumentContent(documentList):
    return "\n\n".join(document.page_content for document in documentList)

# 랭체인 허브에서 프롬프트 템플리트를 가져온다.
chatPromptTemplate = hub.pull("rlm/rag-prompt")

# 질의응답 모델을 설정한다.
chatOpenAI = ChatOpenAI(model = "gpt-3.5-turbo-0125")

# 질의응답 체인을 설정한다.
runnableSequence = (
    {"context" : vectorStoreRetriever | mergeDocumentContent, "question" : RunnablePassthrough()}
    | chatPromptTemplate
    | chatOpenAI
    | StrOutputParser()
)

# 질의응답을 실행한다.
resultString = runnableSequence.invoke("What is Task Decomposition?")

print(resultString)

"""
Task Decomposition is a technique that breaks down complex tasks into smaller and simpler steps to make them more manageable.
It involves transforming big tasks into multiple smaller tasks to enhance model performance.
This process can be done using prompting techniques, task-specific instructions, or with human inputs.
"""

import bs4

import os

from langchain_community.document_loaders import WebBaseLoader

from langchain_text_splitters import RecursiveCharacterTextSplitter

from langchain_chroma import Chroma

from langchain_openai import OpenAIEmbeddings

from langchain import hub

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>"

# 웹 문서를 로드한다.

webBaseLoader = WebBaseLoader(

web_paths = ("https://lilianweng.github.io/posts/2023-06-23-agent/",),

bs_kwargs = dict(parse_only = bs4.SoupStrainer(class_ = ("post-content", "post-title", "post-header")))

)

documentList = webBaseLoader.load() # 문서 리스트 : 1건

# 문서를 분할한다.

recursiveCharacterTextSplitter = RecursiveCharacterTextSplitter(chunk_size = 1000, chunk_overlap = 200)

splitDocumentList = recursiveCharacterTextSplitter.split_documents(documentList) # 분할 문서 리스트 : 66건

# 분할한 문서를 벡터 저장소에 저장한다.

chroma = Chroma.from_documents(documents = splitDocumentList, embedding = OpenAIEmbeddings())

vectorStoreRetriever = chroma.as_retriever() # 벡터 저장소 검색기

# 문서 내용 병합 함수를 정의한다.

def mergeDocumentContent(documentList):

return "\n\n".join(document.page_content for document in documentList)

# 랭체인 허브에서 프롬프트 템플리트를 가져온다.

chatPromptTemplate = hub.pull("rlm/rag-prompt")

# 질의응답 모델을 설정한다.

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

# 질의응답 체인을 설정한다.

runnableSequence = (

{"context" : vectorStoreRetriever | mergeDocumentContent, "question" : RunnablePassthrough()}

| chatPromptTemplate

| chatOpenAI

| StrOutputParser()

)

# 질의응답을 실행한다.

resultString = runnableSequence.invoke("What is Task Decomposition?")

print(resultString)

"""

Task Decomposition is a technique that breaks down complex tasks into smaller and simpler steps to make them more manageable.

It involves transforming big tasks into multiple smaller tasks to enhance model performance.

This process can be done using prompting techniques, task-specific instructions, or with human inputs.

"""

▶ requirements.txt


aiohttp==3.9.5
aiosignal==1.3.1
annotated-types==0.7.0
anyio==4.4.0
asgiref==3.8.1
async-timeout==4.0.3
attrs==23.2.0
backoff==2.2.1
bcrypt==4.1.3
beautifulsoup4==4.12.3
bs4==0.0.2
build==1.2.1
cachetools==5.3.3
certifi==2024.6.2
charset-normalizer==3.3.2
chroma-hnswlib==0.7.3
chromadb==0.5.0
click==8.1.7
coloredlogs==15.0.1
dataclasses-json==0.6.7
Deprecated==1.2.14
distro==1.9.0
dnspython==2.6.1
email_validator==2.1.1
exceptiongroup==1.2.1
fastapi==0.111.0
fastapi-cli==0.0.4
filelock==3.14.0
flatbuffers==24.3.25
frozenlist==1.4.1
fsspec==2024.6.0
google-auth==2.30.0
googleapis-common-protos==1.63.1
greenlet==3.0.3
grpcio==1.64.1
h11==0.14.0
httpcore==1.0.5
httptools==0.6.1
httpx==0.27.0
huggingface-hub==0.23.3
humanfriendly==10.0
idna==3.7
importlib_metadata==7.1.0
importlib_resources==6.4.0
Jinja2==3.1.4
jsonpatch==1.33
jsonpointer==3.0.0
kubernetes==30.1.0
langchain==0.2.3
langchain-chroma==0.1.1
langchain-community==0.2.4
langchain-core==0.2.5
langchain-openai==0.1.8
langchain-text-splitters==0.2.1
langchainhub==0.1.18
langsmith==0.1.76
markdown-it-py==3.0.0
MarkupSafe==2.1.5
marshmallow==3.21.3
mdurl==0.1.2
mmh3==4.1.0
monotonic==1.6
mpmath==1.3.0
multidict==6.0.5
mypy-extensions==1.0.0
numpy==1.26.4
oauthlib==3.2.2
onnxruntime==1.18.0
openai==1.33.0
opentelemetry-api==1.25.0
opentelemetry-exporter-otlp-proto-common==1.25.0
opentelemetry-exporter-otlp-proto-grpc==1.25.0
opentelemetry-instrumentation==0.46b0
opentelemetry-instrumentation-asgi==0.46b0
opentelemetry-instrumentation-fastapi==0.46b0
opentelemetry-proto==1.25.0
opentelemetry-sdk==1.25.0
opentelemetry-semantic-conventions==0.46b0
opentelemetry-util-http==0.46b0
orjson==3.10.4
overrides==7.7.0
packaging==23.2
posthog==3.5.0
protobuf==4.25.3
pyasn1==0.6.0
pyasn1_modules==0.4.0
pydantic==2.7.3
pydantic_core==2.18.4
Pygments==2.18.0
PyPika==0.48.9
pyproject_hooks==1.1.0
python-dateutil==2.9.0.post0
python-dotenv==1.0.1
python-multipart==0.0.9
PyYAML==6.0.1
regex==2024.5.15
requests==2.32.3
requests-oauthlib==2.0.0
rich==13.7.1
rsa==4.9
shellingham==1.5.4
six==1.16.0
sniffio==1.3.1
soupsieve==2.5
SQLAlchemy==2.0.30
starlette==0.37.2
sympy==1.12.1
tenacity==8.3.0
tiktoken==0.7.0
tokenizers==0.19.1
tomli==2.0.1
tqdm==4.66.4
typer==0.12.3
types-requests==2.32.0.20240602
typing-inspect==0.9.0
typing_extensions==4.12.2
ujson==5.10.0
urllib3==2.2.1
uvicorn==0.30.1
uvloop==0.19.0
watchfiles==0.22.0
websocket-client==1.8.0
websockets==12.0
wrapt==1.16.0
yarl==1.9.4
zipp==3.19.2

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aiohttp==3.9.5

aiosignal==1.3.1

annotated-types==0.7.0

anyio==4.4.0

asgiref==3.8.1

async-timeout==4.0.3

attrs==23.2.0

backoff==2.2.1

bcrypt==4.1.3

beautifulsoup4==4.12.3

bs4==0.0.2

build==1.2.1

cachetools==5.3.3

certifi==2024.6.2

charset-normalizer==3.3.2

chroma-hnswlib==0.7.3

chromadb==0.5.0

click==8.1.7

coloredlogs==15.0.1

dataclasses-json==0.6.7

Deprecated==1.2.14

distro==1.9.0

dnspython==2.6.1

email_validator==2.1.1

exceptiongroup==1.2.1

fastapi==0.111.0

fastapi-cli==0.0.4

filelock==3.14.0

flatbuffers==24.3.25

frozenlist==1.4.1

fsspec==2024.6.0

google-auth==2.30.0

googleapis-common-protos==1.63.1

greenlet==3.0.3

grpcio==1.64.1

h11==0.14.0

httpcore==1.0.5

httptools==0.6.1

httpx==0.27.0

huggingface-hub==0.23.3

humanfriendly==10.0

idna==3.7

importlib_metadata==7.1.0

importlib_resources==6.4.0

Jinja2==3.1.4

jsonpatch==1.33

jsonpointer==3.0.0

kubernetes==30.1.0

langchain==0.2.3

langchain-chroma==0.1.1

langchain-community==0.2.4

langchain-core==0.2.5

langchain-openai==0.1.8

langchain-text-splitters==0.2.1

langchainhub==0.1.18

langsmith==0.1.76

markdown-it-py==3.0.0

MarkupSafe==2.1.5

marshmallow==3.21.3

mdurl==0.1.2

mmh3==4.1.0

monotonic==1.6

mpmath==1.3.0

multidict==6.0.5

mypy-extensions==1.0.0

numpy==1.26.4

oauthlib==3.2.2

onnxruntime==1.18.0

openai==1.33.0

opentelemetry-api==1.25.0

opentelemetry-exporter-otlp-proto-common==1.25.0

opentelemetry-exporter-otlp-proto-grpc==1.25.0

opentelemetry-instrumentation==0.46b0

opentelemetry-instrumentation-asgi==0.46b0

opentelemetry-instrumentation-fastapi==0.46b0

opentelemetry-proto==1.25.0

opentelemetry-sdk==1.25.0

opentelemetry-semantic-conventions==0.46b0

opentelemetry-util-http==0.46b0

orjson==3.10.4

overrides==7.7.0

packaging==23.2

posthog==3.5.0

protobuf==4.25.3

pyasn1==0.6.0

pyasn1_modules==0.4.0

pydantic==2.7.3

pydantic_core==2.18.4

Pygments==2.18.0

PyPika==0.48.9

pyproject_hooks==1.1.0

python-dateutil==2.9.0.post0

python-dotenv==1.0.1

python-multipart==0.0.9

PyYAML==6.0.1

regex==2024.5.15

requests==2.32.3

requests-oauthlib==2.0.0

rich==13.7.1

rsa==4.9

shellingham==1.5.4

six==1.16.0

sniffio==1.3.1

soupsieve==2.5

SQLAlchemy==2.0.30

starlette==0.37.2

sympy==1.12.1

tenacity==8.3.0

tiktoken==0.7.0

tokenizers==0.19.1

tomli==2.0.1

tqdm==4.66.4

typer==0.12.3

types-requests==2.32.0.20240602

typing-inspect==0.9.0

typing_extensions==4.12.2

ujson==5.10.0

urllib3==2.2.1

uvicorn==0.30.1

uvloop==0.19.0

watchfiles==0.22.0

websocket-client==1.8.0

websockets==12.0

wrapt==1.16.0

yarl==1.9.4

zipp==3.19.2

※ pip install bs4

[PYTHON/COMMON] langchainhub 패키지 설치하기

■ langchainhub 패키지를 설치하는 방법을 보여준다. 1. 명령 프롬프트를 실행한다. 2. 명령 프롬프트에서 아래 명령을 실행한다. ▶ 실행 명령


pip install langchainhub

pip install langchainhub

[PYTHON/LANGCHAIN] MessagesPlaceholder 클래스 : 암묵적으로 프롬프트 템플리트의 특정 위치에 메시지 목록 추가하기

■ MessagePlaceholder 클래스를 직접적으로 사용하지 않고 암묵적으로 프롬프트 템플리트의 특정 위치에 메시지 목록을 추가하는 방법을 보여준다. ▶ main.py


from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_core.messages import HumanMessage

chatPromptTemplate = ChatPromptTemplate.from_messages(
    [
        ("system", "You are a helpful assistant"),
        ("placeholder", "{message1}")
    ]
)

chatPromptValue = chatPromptTemplate.invoke({"message1" : [HumanMessage(content = "hi!")]})

print(chatPromptValue)

"""
messages=[SystemMessage(content='You are a helpful assistant'), HumanMessage(content='hi!')]
"""

from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder

from langchain_core.messages import HumanMessage

chatPromptTemplate = ChatPromptTemplate.from_messages(

[

("system", "You are a helpful assistant"),

("placeholder", "{message1}")

]

)

chatPromptValue = chatPromptTemplate.invoke({"message1" : [HumanMessage(content = "hi!")]})

print(chatPromptValue)

"""

messages=[SystemMessage(content='You are a helpful assistant'), HumanMessage(content='hi!')]

"""

▶ requirements.txt


aiohttp==3.9.5
aiosignal==1.3.1
annotated-types==0.7.0
async-timeout==4.0.3
attrs==23.2.0
certifi==2024.6.2
charset-normalizer==3.3.2
frozenlist==1.4.1
greenlet==3.0.3
idna==3.7
jsonpatch==1.33
jsonpointer==2.4
langchain==0.2.3
langchain-core==0.2.5
langchain-text-splitters==0.2.1
langsmith==0.1.75
multidict==6.0.5
numpy==1.26.4
orjson==3.10.3
packaging==23.2
pydantic==2.7.3
pydantic_core==2.18.4
PyYAML==6.0.1
requests==2.32.3
SQLAlchemy==2.0.30
tenacity==8.3.0
typing_extensions==4.12.2
urllib3==2.2.1
yarl==1.9.4

aiohttp==3.9.5

aiosignal==1.3.1

annotated-types==0.7.0

async-timeout==4.0.3

attrs==23.2.0

certifi==2024.6.2

charset-normalizer==3.3.2

frozenlist==1.4.1

greenlet==3.0.3

idna==3.7

jsonpatch==1.33

jsonpointer==2.4

langchain==0.2.3

langchain-core==0.2.5

langchain-text-splitters==0.2.1

langsmith==0.1.75

multidict==6.0.5

numpy==1.26.4

orjson==3.10.3

packaging==23.2

pydantic==2.7.3

pydantic_core==2.18.4

PyYAML==6.0.1

requests==2.32.3

SQLAlchemy==2.0.30

tenacity==8.3.0

typing_extensions==4.12.2

urllib3==2.2.1

yarl==1.9.4

[PYTHON/LANGCHAIN] MessagesPlaceholder 클래스 : 프롬프트 템플리트의 특정 위치에 메시지 목록 추가하기

■ MessagesPlaceholder 클래스를 사용해 프롬프트 템플리트의 특정 위치에 메시지 목록을 추가하는 방법을 보여준다. ▶ main.py


from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_core.messages import HumanMessage

chatPromptTemplate = ChatPromptTemplate.from_messages(
    [
        ("system", "You are a helpful assistant"),
        MessagesPlaceholder("message1")
    ]
)

chatPromptValue = chatPromptTemplate.invoke({"message1" : [HumanMessage(content = "hi!")]})

print(chatPromptValue)

"""
messages=[SystemMessage(content='You are a helpful assistant'), HumanMessage(content='hi!')]
"""

from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder

from langchain_core.messages import HumanMessage

chatPromptTemplate = ChatPromptTemplate.from_messages(

[

("system", "You are a helpful assistant"),

MessagesPlaceholder("message1")

]

)

chatPromptValue = chatPromptTemplate.invoke({"message1" : [HumanMessage(content = "hi!")]})

print(chatPromptValue)

"""

messages=[SystemMessage(content='You are a helpful assistant'), HumanMessage(content='hi!')]

"""

▶ requirements.txt


aiohttp==3.9.5
aiosignal==1.3.1
annotated-types==0.7.0
async-timeout==4.0.3
attrs==23.2.0
certifi==2024.6.2
charset-normalizer==3.3.2
frozenlist==1.4.1
greenlet==3.0.3
idna==3.7
jsonpatch==1.33
jsonpointer==2.4
langchain==0.2.3
langchain-core==0.2.5
langchain-text-splitters==0.2.1
langsmith==0.1.75
multidict==6.0.5
numpy==1.26.4
orjson==3.10.3
packaging==23.2
pydantic==2.7.3
pydantic_core==2.18.4
PyYAML==6.0.1
requests==2.32.3
SQLAlchemy==2.0.30
tenacity==8.3.0
typing_extensions==4.12.2
urllib3==2.2.1
yarl==1.9.4

aiohttp==3.9.5

aiosignal==1.3.1

annotated-types==0.7.0

async-timeout==4.0.3

attrs==23.2.0

certifi==2024.6.2

charset-normalizer==3.3.2

frozenlist==1.4.1

greenlet==3.0.3

idna==3.7

jsonpatch==1.33

jsonpointer==2.4

langchain==0.2.3

langchain-core==0.2.5

langchain-text-splitters==0.2.1

langsmith==0.1.75

multidict==6.0.5

numpy==1.26.4

orjson==3.10.3

packaging==23.2

pydantic==2.7.3

pydantic_core==2.18.4

PyYAML==6.0.1

requests==2.32.3

SQLAlchemy==2.0.30

tenacity==8.3.0

typing_extensions==4.12.2

urllib3==2.2.1

yarl==1.9.4

※ pip install

[PYTHON/LANGCHAIN] ChatPromptTemplate 클래스 : from_messages 정적 메소드를 사용해 채팅 프롬프트 템플리트 만들기 (ChatPromptTemplate 객체)

■ ChatPromptTemplate 클래스의 from_messages 정적 메소드를 사용해 채팅 프롬프트 템플리트를 만드는 방법을 보여준다. (ChatPromptTemplate 객체) ▶ main.py


from langchain_core.prompts import ChatPromptTemplate

chatPromptTemplate = ChatPromptTemplate.from_messages(
    [
        ("system", "You are a helpful assistant" ),
        ("user"  , "Tell me a joke about {topic}")
    ]
)

chatPromptValue = chatPromptTemplate.invoke({"topic" : "cats"})

print(chatPromptValue)

"""
messages = [
    SystemMessage(content = 'You are a helpful assistant'),
    HumanMessage (content = 'Tell me a joke about cats'  )
]
"""

from langchain_core.prompts import ChatPromptTemplate

chatPromptTemplate = ChatPromptTemplate.from_messages(

[

("system", "You are a helpful assistant" ),

("user" , "Tell me a joke about {topic}")

]

)

chatPromptValue = chatPromptTemplate.invoke({"topic" : "cats"})

print(chatPromptValue)

"""

messages = [

SystemMessage(content = 'You are a helpful assistant'),

HumanMessage (content = 'Tell me a joke about cats' )

]

"""

▶ requirements.txt


aiohttp==3.9.5
aiosignal==1.3.1
annotated-types==0.7.0
async-timeout==4.0.3
attrs==23.2.0
certifi==2024.6.2
charset-normalizer==3.3.2
frozenlist==1.4.1
greenlet==3.0.3
idna==3.7
jsonpatch==1.33
jsonpointer==2.4
langchain==0.2.3
langchain-core==0.2.5
langchain-text-splitters==0.2.1
langsmith==0.1.75
multidict==6.0.5
numpy==1.26.4
orjson==3.10.3
packaging==23.2
pydantic==2.7.3
pydantic_core==2.18.4
PyYAML==6.0.1
requests==2.32.3
SQLAlchemy==2.0.30
tenacity==8.3.0
typing_extensions==4.12.2
urllib3==2.2.1
yarl==1.9.4

aiohttp==3.9.5

aiosignal==1.3.1

annotated-types==0.7.0

async-timeout==4.0.3

attrs==23.2.0

certifi==2024.6.2

charset-normalizer==3.3.2

frozenlist==1.4.1

greenlet==3.0.3

idna==3.7

jsonpatch==1.33

jsonpointer==2.4

langchain==0.2.3

langchain-core==0.2.5

langchain-text-splitters==0.2.1

langsmith==0.1.75

multidict==6.0.5

numpy==1.26.4

orjson==3.10.3

packaging==23.2

pydantic==2.7.3

pydantic_core==2.18.4

PyYAML==6.0.1

requests==2.32.3

SQLAlchemy==2.0.30

tenacity==8.3.0

typing_extensions==4.12.2

urllib3==2.2.1

yarl==1.9.4

※

[PYTHON/LANGCHAIN] PromptTemplate 클래스 : from_template 정적 메소드를 사용해 PromptTemplate 객체 만들기 1

■ PromptTemplate 클래스의 from_template 정적 메소드를 사용해 PromptTemplate 객체를 만드는 방법을 보여준다. ▶ main.py


from langchain_core.prompts import PromptTemplate

promptTemplate = PromptTemplate.from_template("Tell me a joke about {topic}")

stringPromptValue = promptTemplate.invoke({"topic": "cats"})

print(stringPromptValue)

"""
text='Tell me a joke about cats'
"""

from langchain_core.prompts import PromptTemplate

promptTemplate = PromptTemplate.from_template("Tell me a joke about {topic}")

stringPromptValue = promptTemplate.invoke({"topic": "cats"})

print(stringPromptValue)

"""

text='Tell me a joke about cats'

"""

▶ requirements.txt


aiohttp==3.9.5
aiosignal==1.3.1
annotated-types==0.7.0
async-timeout==4.0.3
attrs==23.2.0
certifi==2024.6.2
charset-normalizer==3.3.2
frozenlist==1.4.1
greenlet==3.0.3
idna==3.7
jsonpatch==1.33
jsonpointer==2.4
langchain==0.2.3
langchain-core==0.2.5
langchain-text-splitters==0.2.1
langsmith==0.1.75
multidict==6.0.5
numpy==1.26.4
orjson==3.10.3
packaging==23.2
pydantic==2.7.3
pydantic_core==2.18.4
PyYAML==6.0.1
requests==2.32.3
SQLAlchemy==2.0.30
tenacity==8.3.0
typing_extensions==4.12.2
urllib3==2.2.1
yarl==1.9.4

aiohttp==3.9.5

aiosignal==1.3.1

annotated-types==0.7.0

async-timeout==4.0.3

attrs==23.2.0

certifi==2024.6.2

charset-normalizer==3.3.2

frozenlist==1.4.1

greenlet==3.0.3

idna==3.7

jsonpatch==1.33

jsonpointer==2.4

langchain==0.2.3

langchain-core==0.2.5

langchain-text-splitters==0.2.1

langsmith==0.1.75

multidict==6.0.5

numpy==1.26.4

orjson==3.10.3

packaging==23.2

pydantic==2.7.3

pydantic_core==2.18.4

PyYAML==6.0.1

requests==2.32.3

SQLAlchemy==2.0.30

tenacity==8.3.0

typing_extensions==4.12.2

urllib3==2.2.1

yarl==1.9.4

※ pip install langchain

[PYTHON/LANGCHAIN] ChatOpenAI 클래스 : generate 메소드를 사용해 LLM 호출하기

■ ChatOpenAI 클래스의 generate 메소드를 사용해 LLM을 호출하는 방법을 보여준다. ▶ main.py


import os

from langchain_core.messages import HumanMessage
from langchain_openai        import ChatOpenAI

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

chatOpenAI = ChatOpenAI(model_name = "gpt-4", temperature = 0)

messageList = [
    [HumanMessage(content = "고양이 울음소리는?")],
    [HumanMessage(content = "까마귀 울음소리는?")]
]

llmResult = chatOpenAI.generate(messageList)

print(llmResult)

"""
generations = [
    [
        ChatGeneration(
            text = '"야옹"',
            generation_info = {'finish_reason' : 'stop', 'logprobs' : None},
            message         = AIMessage
            (
                content           = '"야옹"',
                response_metadata = {
                    'token_usage'        : {'completion_tokens' : 5, 'prompt_tokens' : 18, 'total_tokens' : 23},
                    'model_name'         : 'gpt-4',
                    'system_fingerprint' : None,
                    'finish_reason'      : 'stop',
                    'logprobs'           : None
                },
                id                = 'run-a0df7fb5-9cf3-4896-87bc-61c22ee6af1c-0',
                usage_metadata    = {'input_tokens' : 18, 'output_tokens' : 5, 'total_tokens' : 23}
            )
        )
    ],
    [
        ChatGeneration(
            text            = '까마귀의 울음소리는 "까악 까악" 또는 "크아크 크아크"라고 표현합니다.',
            generation_info = {'finish_reason' : 'stop', 'logprobs' : None},
            message         = AIMessage(
                content           = '까마귀의 울음소리는 "까악 까악" 또는 "크아크 크아크"라고 표현합니다.',
                response_metadata = {
                    'token_usage'        : {'completion_tokens' : 45, 'prompt_tokens' : 20, 'total_tokens' : 65},
                    'model_name'         : 'gpt-4',
                    'system_fingerprint' : None,
                    'finish_reason'      : 'stop',
                    'logprobs'           : None
                },
                id                = 'run-52077b57-5a7a-4634-a599-dbb2b1d22583-0',
                usage_metadata    = {'input_tokens' : 20, 'output_tokens' : 45, 'total_tokens' : 65}
            )
        )
    ]
]

llm_output = {
    'token_usage' : {'completion_tokens' : 50, 'prompt_tokens' : 38, 'total_tokens' : 88},
    'model_name'  : 'gpt-4'
}

run = [
    RunInfo(run_id = UUID('a0df7fb5-9cf3-4896-87bc-61c22ee6af1c')),
    RunInfo(run_id = UUID('52077b57-5a7a-4634-a599-dbb2b1d22583'))
]
"""

import os

from langchain_core.messages import HumanMessage

from langchain_openai import ChatOpenAI

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

chatOpenAI = ChatOpenAI(model_name = "gpt-4", temperature = 0)

messageList = [

[HumanMessage(content = "고양이 울음소리는?")],

[HumanMessage(content = "까마귀 울음소리는?")]

]

llmResult = chatOpenAI.generate(messageList)

print(llmResult)

"""

generations = [

[

ChatGeneration(

text = '"야옹"',

generation_info = {'finish_reason' : 'stop', 'logprobs' : None},

message = AIMessage

(

content = '"야옹"',

response_metadata = {

'token_usage' : {'completion_tokens' : 5, 'prompt_tokens' : 18, 'total_tokens' : 23},

'model_name' : 'gpt-4',

'system_fingerprint' : None,

'finish_reason' : 'stop',

'logprobs' : None

id = 'run-a0df7fb5-9cf3-4896-87bc-61c22ee6af1c-0',

usage_metadata = {'input_tokens' : 18, 'output_tokens' : 5, 'total_tokens' : 23}

)

[

ChatGeneration(

text = '까마귀의 울음소리는 "까악 까악" 또는 "크아크 크아크"라고 표현합니다.',

generation_info = {'finish_reason' : 'stop', 'logprobs' : None},

message = AIMessage(

content = '까마귀의 울음소리는 "까악 까악" 또는 "크아크 크아크"라고 표현합니다.',

response_metadata = {

'token_usage' : {'completion_tokens' : 45, 'prompt_tokens' : 20, 'total_tokens' : 65},

'model_name' : 'gpt-4',

'system_fingerprint' : None,

'finish_reason' : 'stop',

'logprobs' : None

id = 'run-52077b57-5a7a-4634-a599-dbb2b1d22583-0',

usage_metadata = {'input_tokens' : 20, 'output_tokens' : 45, 'total_tokens' : 65}

)

]

llm_output = {

'token_usage' : {'completion_tokens' : 50, 'prompt_tokens' : 38, 'total_tokens' : 88},

'model_name' : 'gpt-4'

}

run = [

RunInfo(run_id = UUID('a0df7fb5-9cf3-4896-87bc-61c22ee6af1c')),

RunInfo(run_id = UUID('52077b57-5a7a-4634-a599-dbb2b1d22583'))

]

"""

▶ 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
distro==1.9.0
exceptiongroup==1.2.1
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==2.4
langchain==0.2.3
langchain-core==0.2.5
langchain-openai==0.1.8
langchain-text-splitters==0.2.1
langsmith==0.1.75
multidict==6.0.5
numpy==1.26.4
openai==1.33.0
orjson==3.10.3
packaging==23.2
pydantic==2.7.3
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==8.3.0
tiktoken==0.7.0
tqdm==4.66.4
typing_extensions==4.12.2
urllib3==2.2.1
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

distro==1.9.0

exceptiongroup==1.2.1

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==2.4

langchain==0.2.3

langchain-core==0.2.5

langchain-openai==0.1.8

langchain-text-splitters==0.2.1

langsmith==0.1.75

multidict==6.0.5

numpy==1.26.4

openai==1.33.0

orjson==3.10.3

packaging==23.2

pydantic==2.7.3

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==8.3.0

tiktoken==0.7.0

tqdm==4.66.4

typing_extensions==4.12.2

urllib3==2.2.1

yarl==1.9.4

※ pip install langchain langchain-openai 명령을

[PYTHON/LANGCHAIN] ChatOpenAI 클래스 : invoke 메소드를 사용해 LLM 호출하기

■ ChatOpenAI 클래스의 invoke 메소드를 사용해 LLM을 호출하는 방법을 보여준다. ▶ main.py


import os

from langchain_core.messages import HumanMessage
from langchain_openai        import ChatOpenAI

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

chatOpenAI = ChatOpenAI(model_name = "gpt-4", temperature = 0)

messageList = [
    HumanMessage(content = "고양이 울음소리는?")
]

aiMessage = chatOpenAI.invoke(messageList)

print(aiMessage)

"""
content = '"야옹"'
response_metadata = {
    'token_usage' : {'completion_tokens' : 5, 'prompt_tokens': 18, 'total_tokens' : 23},
    'model_name'  : 'gpt-4',
    'system_fingerprint' : None,
    'finish_reason'      : 'stop',
    'logprobs'           : None
}
id             = 'run-0b2d6982-0129-4c01-acb4-bf668b6e7f4e-0'
usage_metadata = {'input_tokens' : 18, 'output_tokens' : 5, 'total_tokens' : 23}
"""

import os

from langchain_core.messages import HumanMessage

from langchain_openai import ChatOpenAI

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

chatOpenAI = ChatOpenAI(model_name = "gpt-4", temperature = 0)

messageList = [

HumanMessage(content = "고양이 울음소리는?")

]

aiMessage = chatOpenAI.invoke(messageList)

print(aiMessage)

"""

content = '"야옹"'

response_metadata = {

'token_usage' : {'completion_tokens' : 5, 'prompt_tokens': 18, 'total_tokens' : 23},

'model_name' : 'gpt-4',

'system_fingerprint' : None,

'finish_reason' : 'stop',

'logprobs' : None

}

id = 'run-0b2d6982-0129-4c01-acb4-bf668b6e7f4e-0'

usage_metadata = {'input_tokens' : 18, 'output_tokens' : 5, 'total_tokens' : 23}

"""

▶ 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
distro==1.9.0
exceptiongroup==1.2.1
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==2.4
langchain==0.2.3
langchain-core==0.2.5
langchain-openai==0.1.8
langchain-text-splitters==0.2.1
langsmith==0.1.75
multidict==6.0.5
numpy==1.26.4
openai==1.33.0
orjson==3.10.3
packaging==23.2
pydantic==2.7.3
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==8.3.0
tiktoken==0.7.0
tqdm==4.66.4
typing_extensions==4.12.2
urllib3==2.2.1
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

distro==1.9.0

exceptiongroup==1.2.1

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==2.4

langchain==0.2.3

langchain-core==0.2.5

langchain-openai==0.1.8

langchain-text-splitters==0.2.1

langsmith==0.1.75

multidict==6.0.5

numpy==1.26.4

openai==1.33.0

orjson==3.10.3

packaging==23.2

pydantic==2.7.3

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==8.3.0

tiktoken==0.7.0

tqdm==4.66.4

typing_extensions==4.12.2

urllib3==2.2.1

yarl==1.9.4

※ pip install langchain langchain-openai 명령을

[PYTHON/LANGCHAIN] OpenAI 클래스 : generate 메소드를 사용해 LLM 호출하기

■ OpenAI 클래스의 generate 메소드를 사용해 LLM을 호출하는 방법을 보여준다. • 여러 텍스트를 종합적으로 추론할 수 있으며, 사용한 토큰 수와 같은 정보도

[PYTHON/LANGCHAIN] OpenAI 클래스 : invoke 메소드를 사용해 LLM 호출하기

■ OpenAI 클래스의 invoke 메소드를 사용해 LLM을 호출하는 방법을 보여준다. ▶ main.py


import os

from langchain_openai import OpenAI

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

openAI = OpenAI(model_name = "gpt-3.5-turbo-instruct", temperature = 0)

result = openAI.invoke("고양이 울음소리는?")

print(result)

"""

야옹~
"""

import os

from langchain_openai import OpenAI

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

openAI = OpenAI(model_name = "gpt-3.5-turbo-instruct", temperature = 0)

result = openAI.invoke("고양이 울음소리는?")

print(result)

"""

야옹~

"""

▶ 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
distro==1.9.0
exceptiongroup==1.2.1
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==2.4
langchain==0.2.3
langchain-core==0.2.5
langchain-openai==0.1.8
langchain-text-splitters==0.2.1
langsmith==0.1.75
multidict==6.0.5
numpy==1.26.4
openai==1.33.0
orjson==3.10.3
packaging==23.2
pydantic==2.7.3
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==8.3.0
tiktoken==0.7.0
tqdm==4.66.4
typing_extensions==4.12.2
urllib3==2.2.1
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

distro==1.9.0

exceptiongroup==1.2.1

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==2.4

langchain==0.2.3

langchain-core==0.2.5

langchain-openai==0.1.8

langchain-text-splitters==0.2.1

langsmith==0.1.75

multidict==6.0.5

numpy==1.26.4

openai==1.33.0

orjson==3.10.3

packaging==23.2

pydantic==2.7.3

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==8.3.0

tiktoken==0.7.0

tqdm==4.66.4

typing_extensions==4.12.2

urllib3==2.2.1

yarl==1.9.4

※ pip install langchain langchain-openai 명령을

[PYTHON/LANGCHAIN] SqliteSaver 클래스 : from_conn_string 정적 메소드를 사용해 상호 작용을 위한 SqliteSaver 객체 만들기

■ SqliteSaver 클래스 : from_conn_string 정적 메소드를 사용해 상호 작용을 위한 SqliteSaver 객체를 만드는 방법을 보여준다. ▶ main.py


import os

from langchain_core.messages                 import HumanMessage
from langchain_community.tools.tavily_search import TavilySearchResults
from langchain_openai                        import ChatOpenAI
from langgraph.prebuilt                      import create_react_agent
from langgraph.checkpoint.sqlite             import SqliteSaver

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

tavilySearchResults = TavilySearchResults(max_results = 2) # TAVILY API를 사용하기 위한 도구를 생성한다.

toolList = [tavilySearchResults]

chatOpenAI = ChatOpenAI(model = "gpt-4") # OPEN AI LLM 모델을 생성한다.

chatOpenAI.bind_tools(toolList) # OPEN AI LLM 모델과 도구를 바인딩한다.

sqliteSaver = SqliteSaver.from_conn_string(":memory:") # 상호 작용을 위한 메모리를 생성한다.

compiledStateGraph = create_react_agent(chatOpenAI, toolList, checkpointer = sqliteSaver) # 에이전트를 생성한다.

configurationDictionary = {"configurable" : {"thread_id" : "thread1"}} # thread_id로 세션을 구분한다.

addableValuesDict1 = compiledStateGraph.invoke({"messages" : [HumanMessage(content = "hi!")]}, configurationDictionary)

print(addableValuesDict1["messages"])

"""
[
    HumanMessage(content='hi!', id='e5a65000-7bcd-4a24-bac3-27806cbe8dce'),
    AIMessage(content='Hello! How can I assist you today?', response_metadata={'token_usage': {'completion_tokens': 10, 'prompt_tokens': 83, 'total_tokens': 93}, 'model_name': 'gpt-4', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-8ff9af0d-0586-4222-966b-3a593a46fbc9-0', usage_metadata={'input_tokens': 83, 'output_tokens': 10, 'total_tokens': 93})]
"""

addableValuesDict2 = compiledStateGraph.invoke({"messages" : [HumanMessage(content = "whats the weather in sf?")]}, configurationDictionary)

print(addableValuesDict2["messages"])

"""
[
    HumanMessage(content='hi!', id='e5a65000-7bcd-4a24-bac3-27806cbe8dce'),
    AIMessage(content='Hello! How can I assist you today?', response_metadata={'token_usage': {'completion_tokens': 10, 'prompt_tokens': 83, 'total_tokens': 93}, 'model_name': 'gpt-4', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-8ff9af0d-0586-4222-966b-3a593a46fbc9-0', usage_metadata={'input_tokens': 83, 'output_tokens': 10, 'total_tokens': 93}),
    HumanMessage(content='whats the weather in sf?', id='a3b53bc7-8d50-448c-aa8c-5ca1cd195797'),
    AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_V4h5BFmGfdmSBiHP9rVaRC9p', 'function': {'arguments': '{\n  "query": "current weather in San Francisco"\n}', 'name': 'tavily_search_results_json'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 23, 'prompt_tokens': 107, 'total_tokens': 130}, 'model_name': 'gpt-4', 'system_fingerprint': None, 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-c6e6a2e7-a2b3-40e9-9b25-1563b59cf83a-0', tool_calls=[{'name': 'tavily_search_results_json', 'args': {'query': 'current weather in San Francisco'}, 'id': 'call_V4h5BFmGfdmSBiHP9rVaRC9p'}], usage_metadata={'input_tokens': 107, 'output_tokens': 23, 'total_tokens': 130}), ToolMessage(content='[{"url": "https://www.weatherapi.com/", "content": "{\'location\': {\'name\': \'San Francisco\', \'region\': \'California\', \'country\': \'United States of America\', \'lat\': 37.78, \'lon\': -122.42, \'tz_id\': \'America/Los_Angeles\', \'localtime_epoch\': 1718003828, \'localtime\': \'2024-06-10 0:17\'}, \'current\': {\'last_updated_epoch\': 1718003700, \'last_updated\': \'2024-06-10 00:15\', \'temp_c\': 12.8, \'temp_f\': 55.0, \'is_day\': 0, \'condition\': {\'text\': \'Overcast\', \'icon\': \'//cdn.weatherapi.com/weather/64x64/night/122.png\', \'code\': 1009}, \'wind_mph\': 12.5, \'wind_kph\': 20.2, \'wind_degree\': 250, \'wind_dir\': \'WSW\', \'pressure_mb\': 1015.0, \'pressure_in\': 29.98, \'precip_mm\': 0.01, \'precip_in\': 0.0, \'humidity\': 93, \'cloud\': 100, \'feelslike_c\': 11.3, \'feelslike_f\': 52.3, \'windchill_c\': 10.4, \'windchill_f\': 50.8, \'heatindex_c\': 12.1, \'heatindex_f\': 53.8, \'dewpoint_c\': 10.1, \'dewpoint_f\': 50.2, \'vis_km\': 16.0, \'vis_miles\': 9.0, \'uv\': 1.0, \'gust_mph\': 14.0, \'gust_kph\': 22.6}}"}, {"url": "https://forecast.weather.gov/zipcity.php?inputstring=San+Francisco,CA", "content": "San Francisco CA 37.77\\u00b0N 122.41\\u00b0W (Elev. 131 ft) Last Update: 6:00 pm PDT Jun 7, 2024. Forecast Valid: 7pm PDT Jun 7, 2024-6pm PDT Jun 14, 2024 . Forecast Discussion . Additional Resources. Radar & Satellite Image. Hourly Weather Forecast. ... Severe Weather ; Current Outlook Maps ; Drought ; Fire Weather ; Fronts/Precipitation Maps ; Current ..."}]', name='tavily_search_results_json', id='fb4045ac-3877-4c98-ae9e-1d56267a5f69', tool_call_id='call_V4h5BFmGfdmSBiHP9rVaRC9p'),
    AIMessage(content='The current weather in San Francisco, California is overcast with a temperature of 12.8°C (55°F). The wind is blowing from the west-southwest at a speed of 20.2 kph (12.5 mph). The humidity level is at 93%.', response_metadata={'token_usage': {'completion_tokens': 59, 'prompt_tokens': 691, 'total_tokens': 750}, 'model_name': 'gpt-4', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-e0ebe54c-dc83-4dfa-abfa-d625fbd97f44-0', usage_metadata={'input_tokens': 691, 'output_tokens': 59, 'total_tokens': 750})]
"""

import os

from langchain_core.messages import HumanMessage

from langchain_community.tools.tavily_search import TavilySearchResults

from langchain_openai import ChatOpenAI

from langgraph.prebuilt import create_react_agent

from langgraph.checkpoint.sqlite import SqliteSaver

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

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

tavilySearchResults = TavilySearchResults(max_results = 2) # TAVILY API를 사용하기 위한 도구를 생성한다.

toolList = [tavilySearchResults]

chatOpenAI = ChatOpenAI(model = "gpt-4") # OPEN AI LLM 모델을 생성한다.

chatOpenAI.bind_tools(toolList) # OPEN AI LLM 모델과 도구를 바인딩한다.

sqliteSaver = SqliteSaver.from_conn_string(":memory:") # 상호 작용을 위한 메모리를 생성한다.

compiledStateGraph = create_react_agent(chatOpenAI, toolList, checkpointer = sqliteSaver) # 에이전트를 생성한다.

configurationDictionary = {"configurable" : {"thread_id" : "thread1"}} # thread_id로 세션을 구분한다.

addableValuesDict1 = compiledStateGraph.invoke({"messages" : [HumanMessage(content = "hi!")]}, configurationDictionary)

print(addableValuesDict1["messages"])

"""

[

HumanMessage(content='hi!', id='e5a65000-7bcd-4a24-bac3-27806cbe8dce'),

AIMessage(content='Hello! How can I assist you today?', response_metadata={'token_usage': {'completion_tokens': 10, 'prompt_tokens': 83, 'total_tokens': 93}, 'model_name': 'gpt-4', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-8ff9af0d-0586-4222-966b-3a593a46fbc9-0', usage_metadata={'input_tokens': 83, 'output_tokens': 10, 'total_tokens': 93})]

"""

addableValuesDict2 = compiledStateGraph.invoke({"messages" : [HumanMessage(content = "whats the weather in sf?")]}, configurationDictionary)

print(addableValuesDict2["messages"])

"""

[

HumanMessage(content='hi!', id='e5a65000-7bcd-4a24-bac3-27806cbe8dce'),

HumanMessage(content='whats the weather in sf?', id='a3b53bc7-8d50-448c-aa8c-5ca1cd195797'),

AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_V4h5BFmGfdmSBiHP9rVaRC9p', 'function': {'arguments': '{\n "query": "current weather in San Francisco"\n}', 'name': 'tavily_search_results_json'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 23, 'prompt_tokens': 107, 'total_tokens': 130}, 'model_name': 'gpt-4', 'system_fingerprint': None, 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-c6e6a2e7-a2b3-40e9-9b25-1563b59cf83a-0', tool_calls=[{'name': 'tavily_search_results_json', 'args': {'query': 'current weather in San Francisco'}, 'id': 'call_V4h5BFmGfdmSBiHP9rVaRC9p'}], usage_metadata={'input_tokens': 107, 'output_tokens': 23, 'total_tokens': 130}), ToolMessage(content='[{"url": "https://www.weatherapi.com/", "content": "{\'location\': {\'name\': \'San Francisco\', \'region\': \'California\', \'country\': \'United States of America\', \'lat\': 37.78, \'lon\': -122.42, \'tz_id\': \'America/Los_Angeles\', \'localtime_epoch\': 1718003828, \'localtime\': \'2024-06-10 0:17\'}, \'current\': {\'last_updated_epoch\': 1718003700, \'last_updated\': \'2024-06-10 00:15\', \'temp_c\': 12.8, \'temp_f\': 55.0, \'is_day\': 0, \'condition\': {\'text\': \'Overcast\', \'icon\': \'//cdn.weatherapi.com/weather/64x64/night/122.png\', \'code\': 1009}, \'wind_mph\': 12.5, \'wind_kph\': 20.2, \'wind_degree\': 250, \'wind_dir\': \'WSW\', \'pressure_mb\': 1015.0, \'pressure_in\': 29.98, \'precip_mm\': 0.01, \'precip_in\': 0.0, \'humidity\': 93, \'cloud\': 100, \'feelslike_c\': 11.3, \'feelslike_f\': 52.3, \'windchill_c\': 10.4, \'windchill_f\': 50.8, \'heatindex_c\': 12.1, \'heatindex_f\': 53.8, \'dewpoint_c\': 10.1, \'dewpoint_f\': 50.2, \'vis_km\': 16.0, \'vis_miles\': 9.0, \'uv\': 1.0, \'gust_mph\': 14.0, \'gust_kph\': 22.6}}"}, {"url": "https://forecast.weather.gov/zipcity.php?inputstring=San+Francisco,CA", "content": "San Francisco CA 37.77\\u00b0N 122.41\\u00b0W (Elev. 131 ft) Last Update: 6:00 pm PDT Jun 7, 2024. Forecast Valid: 7pm PDT Jun 7, 2024-6pm PDT Jun 14, 2024 . Forecast Discussion . Additional Resources. Radar & Satellite Image. Hourly Weather Forecast. ... Severe Weather ; Current Outlook Maps ; Drought ; Fire Weather ; Fronts/Precipitation Maps ; Current ..."}]', name='tavily_search_results_json', id='fb4045ac-3877-4c98-ae9e-1d56267a5f69', tool_call_id='call_V4h5BFmGfdmSBiHP9rVaRC9p'),

AIMessage(content='The current weather in San Francisco, California is overcast with a temperature of 12.8°C (55°F). The wind is blowing from the west-southwest at a speed of 20.2 kph (12.5 mph). The humidity level is at 93%.', response_metadata={'token_usage': {'completion_tokens': 59, 'prompt_tokens': 691, 'total_tokens': 750}, 'model_name': 'gpt-4', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-e0ebe54c-dc83-4dfa-abfa-d625fbd97f44-0', usage_metadata={'input_tokens': 691, 'output_tokens': 59, 'total_tokens': 750})]

"""

▶ 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
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==2.4
langchain==0.2.3
langchain-community==0.2.4
langchain-core==0.2.5
langchain-openai==0.1.8
langchain-text-splitters==0.2.1
langgraph==0.0.66
langsmith==0.1.75
marshmallow==3.21.3
multidict==6.0.5
mypy-extensions==1.0.0
numpy==1.26.4
openai==1.33.0
orjson==3.10.3
packaging==23.2
pydantic==2.7.3
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
tavily-python==0.3.3
tenacity==8.3.0
tiktoken==0.7.0
tqdm==4.66.4
typing-inspect==0.9.0
typing_extensions==4.12.2
urllib3==2.2.1
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

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==2.4

langchain==0.2.3

langchain-community==0.2.4

langchain-core==0.2.5

langchain-openai==0.1.8

langchain-text-splitters==0.2.1

langgraph==0.0.66

langsmith==0.1.75

marshmallow==3.21.3

multidict==6.0.5

mypy-extensions==1.0.0

numpy==1.26.4

openai==1.33.0

orjson==3.10.3

packaging==23.2

pydantic==2.7.3

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

tavily-python==0.3.3

tenacity==8.3.0

tiktoken==0.7.0

tqdm==4.66.4

typing-inspect==0.9.0

typing_extensions==4.12.2

urllib3==2.2.1

yarl==1.9.4

[PYTHON/LANGCHAIN] load_tools 함수 : serpapi와 llm-math 도구 로드하기

■ load_tools 함수를 사용해 serpapi와 llm-math 도구를 로드하는 방법을 보여준다. ▶ main.py


import os

from langchain_openai                              import ChatOpenAI
from langchain_community.agent_toolkits.load_tools import load_tools

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

chatOpenAI = ChatOpenAI(model = "gpt-4")

toolList = load_tools(tool_names = ["serpapi", "llm-math"], llm = chatOpenAI) # 도구를 로드한다.

print(toolList)

"""
[
    Tool(name='Search', description='A search engine. Useful for when you need to answer questions about current events. Input should be a search query.', func=<bound method SerpAPIWrapper.run of SerpAPIWrapper(search_engine=<class 'serpapi.google_search.GoogleSearch'>, params={'engine': 'google', 'google_domain': 'google.com', 'gl': 'us', 'hl': 'en'}, serpapi_api_key='83dffd65104b51e264e6f063394f66a3d22a492631a4f438c9b1a71b89cbbd24', aiosession=None)>, coroutine=<bound method SerpAPIWrapper.arun of SerpAPIWrapper(search_engine=<class 'serpapi.google_search.GoogleSearch'>, params={'engine': 'google', 'google_domain': 'google.com', 'gl': 'us', 'hl': 'en'}, serpapi_api_key='83dffd65104b51e264e6f063394f66a3d22a492631a4f438c9b1a71b89cbbd24', aiosession=None)>),
    Tool(name='Calculator', description='Useful for when you need to answer questions about math.', func=<bound method Chain.run of LLMMathChain(llm_chain=LLMChain(prompt=PromptTemplate(input_variables=['question'], template='Translate a math problem into a expression that can be executed using Python\'s numexpr library. Use the output of running this code to answer the question.\n\nQuestion: ${{Question with math problem.}}\n```text\n${{single line mathematical expression that solves the problem}}\n```\n...numexpr.evaluate(text)...\n```output\n${{Output of running the code}}\n```\nAnswer: ${{Answer}}\n\nBegin.\n\nQuestion: What is 37593 * 67?\n```text\n37593 * 67\n```\n...numexpr.evaluate("37593 * 67")...\n```output\n2518731\n```\nAnswer: 2518731\n\nQuestion: 37593^(1/5)\n```text\n37593**(1/5)\n```\n...numexpr.evaluate("37593**(1/5)")...\n```output\n8.222831614237718\n```\nAnswer: 8.222831614237718\n\nQuestion: {question}\n'), llm=ChatOpenAI(client=<openai.resources.chat.completions.Completions object at 0x7fc2c5782170>, async_client=<openai.resources.chat.completions.AsyncCompletions object at 0x7fc2c5783880>, model_name='gpt-4', openai_api_key=SecretStr('**********'), openai_proxy='')))>, coroutine=<bound method Chain.arun of LLMMathChain(llm_chain=LLMChain(prompt=PromptTemplate(input_variables=['question'], template='Translate a math problem into a expression that can be executed using Python\'s numexpr library. Use the output of running this code to answer the question.\n\nQuestion: ${{Question with math problem.}}\n```text\n${{single line mathematical expression that solves the problem}}\n```\n...numexpr.evaluate(text)...\n```output\n${{Output of running the code}}\n```\nAnswer: ${{Answer}}\n\nBegin.\n\nQuestion: What is 37593 * 67?\n```text\n37593 * 67\n```\n...numexpr.evaluate("37593 * 67")...\n```output\n2518731\n```\nAnswer: 2518731\n\nQuestion: 37593^(1/5)\n```text\n37593**(1/5)\n```\n...numexpr.evaluate("37593**(1/5)")...\n```output\n8.222831614237718\n```\nAnswer: 8.222831614237718\n\nQuestion: {question}\n'), llm=ChatOpenAI(client=<openai.resources.chat.completions.Completions object at 0x7fc2c5782170>, async_client=<openai.resources.chat.completions.AsyncCompletions object at 0x7fc2c5783880>, model_name='gpt-4', openai_api_key=SecretStr('**********'), openai_proxy='')))>)
]
"""

import os

from langchain_openai import ChatOpenAI

from langchain_community.agent_toolkits.load_tools import load_tools

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

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

chatOpenAI = ChatOpenAI(model = "gpt-4")

toolList = load_tools(tool_names = ["serpapi", "llm-math"], llm = chatOpenAI) # 도구를 로드한다.

print(toolList)

"""

[

Tool(name='Search', description='A search engine. Useful for when you need to answer questions about current events. Input should be a search query.', func=<bound method SerpAPIWrapper.run of SerpAPIWrapper(search_engine=<class 'serpapi.google_search.GoogleSearch'>, params={'engine': 'google', 'google_domain': 'google.com', 'gl': 'us', 'hl': 'en'}, serpapi_api_key='83dffd65104b51e264e6f063394f66a3d22a492631a4f438c9b1a71b89cbbd24', aiosession=None)>, coroutine=<bound method SerpAPIWrapper.arun of SerpAPIWrapper(search_engine=<class 'serpapi.google_search.GoogleSearch'>, params={'engine': 'google', 'google_domain': 'google.com', 'gl': 'us', 'hl': 'en'}, serpapi_api_key='83dffd65104b51e264e6f063394f66a3d22a492631a4f438c9b1a71b89cbbd24', aiosession=None)>),

Tool(name='Calculator', description='Useful for when you need to answer questions about math.', func=<bound method Chain.run of LLMMathChain(llm_chain=LLMChain(prompt=PromptTemplate(input_variables=['question'], template='Translate a math problem into a expression that can be executed using Python\'s numexpr library. Use the output of running this code to answer the question.\n\nQuestion: ${{Question with math problem.}}\n```text\n${{single line mathematical expression that solves the problem}}\n```\n...numexpr.evaluate(text)...\n```output\n${{Output of running the code}}\n```\nAnswer: ${{Answer}}\n\nBegin.\n\nQuestion: What is 37593 * 67?\n```text\n37593 * 67\n```\n...numexpr.evaluate("37593 * 67")...\n```output\n2518731\n```\nAnswer: 2518731\n\nQuestion: 37593^(1/5)\n```text\n37593**(1/5)\n```\n...numexpr.evaluate("37593**(1/5)")...\n```output\n8.222831614237718\n```\nAnswer: 8.222831614237718\n\nQuestion: {question}\n'), llm=ChatOpenAI(client=<openai.resources.chat.completions.Completions object at 0x7fc2c5782170>, async_client=<openai.resources.chat.completions.AsyncCompletions object at 0x7fc2c5783880>, model_name='gpt-4', openai_api_key=SecretStr('**********'), openai_proxy='')))>, coroutine=<bound method Chain.arun of LLMMathChain(llm_chain=LLMChain(prompt=PromptTemplate(input_variables=['question'], template='Translate a math problem into a expression that can be executed using Python\'s numexpr library. Use the output of running this code to answer the question.\n\nQuestion: ${{Question with math problem.}}\n```text\n${{single line mathematical expression that solves the problem}}\n```\n...numexpr.evaluate(text)...\n```output\n${{Output of running the code}}\n```\nAnswer: ${{Answer}}\n\nBegin.\n\nQuestion: What is 37593 * 67?\n```text\n37593 * 67\n```\n...numexpr.evaluate("37593 * 67")...\n```output\n2518731\n```\nAnswer: 2518731\n\nQuestion: 37593^(1/5)\n```text\n37593**(1/5)\n```\n...numexpr.evaluate("37593**(1/5)")...\n```output\n8.222831614237718\n```\nAnswer: 8.222831614237718\n\nQuestion: {question}\n'), llm=ChatOpenAI(client=<openai.resources.chat.completions.Completions object at 0x7fc2c5782170>, async_client=<openai.resources.chat.completions.AsyncCompletions object at 0x7fc2c5783880>, model_name='gpt-4', openai_api_key=SecretStr('**********'), openai_proxy='')))>)

]

"""

▶ 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
frozenlist==1.4.1
google-search-results==2.4.2
greenlet==3.0.3
h11==0.14.0
httpcore==1.0.5
httpx==0.27.0
idna==3.7
jsonpatch==1.33
jsonpointer==2.4
langchain==0.2.3
langchain-community==0.2.4
langchain-core==0.2.5
langchain-openai==0.1.8
langchain-text-splitters==0.2.1
langsmith==0.1.75
marshmallow==3.21.3
multidict==6.0.5
mypy-extensions==1.0.0
numexpr==2.10.0
numpy==1.26.4
openai==1.33.0
orjson==3.10.3
packaging==23.2
pydantic==2.7.3
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==8.3.0
tiktoken==0.7.0
tqdm==4.66.4
typing-inspect==0.9.0
typing_extensions==4.12.2
urllib3==2.2.1
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

frozenlist==1.4.1

google-search-results==2.4.2

greenlet==3.0.3

h11==0.14.0

httpcore==1.0.5

httpx==0.27.0

idna==3.7

jsonpatch==1.33

jsonpointer==2.4

langchain==0.2.3

langchain-community==0.2.4

langchain-core==0.2.5

langchain-openai==0.1.8

langchain-text-splitters==0.2.1

langsmith==0.1.75

marshmallow==3.21.3

multidict==6.0.5

mypy-extensions==1.0.0

numexpr==2.10.0

numpy==1.26.4

openai==1.33.0

orjson==3.10.3

packaging==23.2

pydantic==2.7.3

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==8.3.0

tiktoken==0.7.0

tqdm==4.66.4

typing-inspect==0.9.0

typing_extensions==4.12.2

urllib3==2.2.1

yarl==1.9.4

※ pip install numexpr google-search-results langchain

[PYTHON/LANGCHAIN] create_react_agent 함수 : OpenAI 모델과 SerpAPI를 사용해 날씨예보 및 계산기 채팅 에이전트 만들기

■ create_react_agent 함수를 사용해 OpenAI 모델과 SerpAPI를 연결해 날씨예보 및 계산기 채팅 에이전트를 만드는 방법을 보여준다. ▶ main.py


import os

from langchain_core.messages                       import HumanMessage
from langchain_openai                              import ChatOpenAI
from langchain_community.agent_toolkits.load_tools import load_tools
from langgraph.checkpoint.sqlite                   import SqliteSaver
from langgraph.prebuilt                            import create_react_agent

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

chatOpenAI = ChatOpenAI(model = "gpt-4")

toolList = load_tools(tool_names = ["serpapi", "llm-math"], llm = chatOpenAI) # 도구를 로드한다.

sqliteSaver = SqliteSaver.from_conn_string(":memory:") # 상호 작용을 위한 메모리를 생성한다.

compiledStateGraph = create_react_agent(chatOpenAI, toolList, checkpointer = sqliteSaver) # 에이전트를 생성한다.

configurationDictionary = {"configurable" : {"thread_id" : "thread1"}} # thread_id로 세션을 구분한다.

addableValuesDict1 = compiledStateGraph.invoke({"messages" : [HumanMessage(content = "안녕하세요, 저는 홍길동입니다.")]}, configurationDictionary)

print(addableValuesDict1["messages"][-1].content)

addableValuesDict2 = compiledStateGraph.invoke({"messages" : [HumanMessage(content = "오늘 한국 수원의 날씨를 웹 검색으로 섭씨로 확인해주세요.")]}, configurationDictionary)

print(addableValuesDict2["messages"][-1].content)

addableValuesDict3 = compiledStateGraph.invoke({"messages" : [HumanMessage(content = "저의 이름은 무엇입니까?")]}, configurationDictionary)

print(addableValuesDict3["messages"][-1].content)

addableValuesDict4 = compiledStateGraph.invoke({"messages" : [HumanMessage(content = "4,567 * 32,324 / 4를 계산기로 계산해줘")]}, configurationDictionary)

print(addableValuesDict4["messages"][-1].content)

"""
안녕하세요, 홍길동님! 어떻게 도와드릴 수 있을까요?
오늘 대한민국 경기도 수원시의 날씨는 대부분 흐립니다. 화씨 86도로, 섭씨로 변환하면 대략 30도 정도 됩니다. 강수 확률은 10%, 습도는 62%, 풍속은 시간당 10마일입니다.
당신의 이름은 홍길동님이라고 말씀하셨습니다.
4,567 * 32,324 / 4의 계산 결과는 36905927입니다.
"""

import os

from langchain_core.messages import HumanMessage

from langchain_openai import ChatOpenAI

from langchain_community.agent_toolkits.load_tools import load_tools

from langgraph.checkpoint.sqlite import SqliteSaver

from langgraph.prebuilt import create_react_agent

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

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

chatOpenAI = ChatOpenAI(model = "gpt-4")

toolList = load_tools(tool_names = ["serpapi", "llm-math"], llm = chatOpenAI) # 도구를 로드한다.

sqliteSaver = SqliteSaver.from_conn_string(":memory:") # 상호 작용을 위한 메모리를 생성한다.

compiledStateGraph = create_react_agent(chatOpenAI, toolList, checkpointer = sqliteSaver) # 에이전트를 생성한다.

configurationDictionary = {"configurable" : {"thread_id" : "thread1"}} # thread_id로 세션을 구분한다.

addableValuesDict1 = compiledStateGraph.invoke({"messages" : [HumanMessage(content = "안녕하세요, 저는 홍길동입니다.")]}, configurationDictionary)

print(addableValuesDict1["messages"][-1].content)

addableValuesDict2 = compiledStateGraph.invoke({"messages" : [HumanMessage(content = "오늘 한국 수원의 날씨를 웹 검색으로 섭씨로 확인해주세요.")]}, configurationDictionary)

print(addableValuesDict2["messages"][-1].content)

addableValuesDict3 = compiledStateGraph.invoke({"messages" : [HumanMessage(content = "저의 이름은 무엇입니까?")]}, configurationDictionary)

print(addableValuesDict3["messages"][-1].content)

addableValuesDict4 = compiledStateGraph.invoke({"messages" : [HumanMessage(content = "4,567 * 32,324 / 4를 계산기로 계산해줘")]}, configurationDictionary)

print(addableValuesDict4["messages"][-1].content)

"""

안녕하세요, 홍길동님! 어떻게 도와드릴 수 있을까요?

오늘 대한민국 경기도 수원시의 날씨는 대부분 흐립니다. 화씨 86도로, 섭씨로 변환하면 대략 30도 정도 됩니다. 강수 확률은 10%, 습도는 62%, 풍속은 시간당 10마일입니다.

당신의 이름은 홍길동님이라고 말씀하셨습니다.

4,567 * 32,324 / 4의 계산 결과는 36905927입니다.

"""

▶ 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
frozenlist==1.4.1
google-search-results==2.4.2
greenlet==3.0.3
h11==0.14.0
httpcore==1.0.5
httpx==0.27.0
idna==3.7
jsonpatch==1.33
jsonpointer==2.4
langchain==0.2.3
langchain-community==0.2.4
langchain-core==0.2.5
langchain-openai==0.1.8
langchain-text-splitters==0.2.1
langgraph==0.0.66
langsmith==0.1.75
marshmallow==3.21.3
multidict==6.0.5
mypy-extensions==1.0.0
numexpr==2.10.0
numpy==1.26.4
openai==1.33.0
orjson==3.10.3
packaging==23.2
pydantic==2.7.3
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==8.3.0
tiktoken==0.7.0
tqdm==4.66.4
typing-inspect==0.9.0
typing_extensions==4.12.2
urllib3==2.2.1
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

frozenlist==1.4.1

google-search-results==2.4.2

greenlet==3.0.3

h11==0.14.0

httpcore==1.0.5

httpx==0.27.0

idna==3.7

jsonpatch==1.33

jsonpointer==2.4

langchain==0.2.3

langchain-community==0.2.4

langchain-core==0.2.5

langchain-openai==0.1.8

langchain-text-splitters==0.2.1

langgraph==0.0.66

langsmith==0.1.75

marshmallow==3.21.3

multidict==6.0.5

mypy-extensions==1.0.0

numexpr==2.10.0

numpy==1.26.4

openai==1.33.0

orjson==3.10.3

packaging==23.2

pydantic==2.7.3

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==8.3.0

tiktoken==0.7.0

tqdm==4.66.4

typing-inspect==0.9.0

typing_extensions==4.12.2

urllib3==2.2.1

yarl==1.9.4

[PYTHON/COMMON] langgraph 패키지 설치하기

■ langgraph 패키지를 설치하는 방법을 보여준다. 1. 명령 프롬프트를 실행한다. 2. 명령 프롬프트에서 아래 명령을 실행한다. ▶ 실행 명령


pip install langgraph

pip install langserve[client]

pip install langserve[server]

pip install "langserve[all]"

pip install langgraph

pip install langserve[client]

pip install langserve[server]

pip install "langserve[all]"

[PYTHON/LANGCHAIN] create_react_agent 함수 : OpenAI 모델과 Tavily API를 연결해 날씨예보 채팅 에이전트 만들기

■ create_react_agent 함수를 사용해 OpenAI 모델과 Tavily API를 연결해 날씨예보 채팅 에이전트를 만드는 방법을 보여준다. ▶ main.py


import os

from langchain_core.messages                 import HumanMessage
from langchain_community.tools.tavily_search import TavilySearchResults
from langchain_openai                        import ChatOpenAI
from langgraph.prebuilt                      import create_react_agent
from langgraph.checkpoint.sqlite             import SqliteSaver

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

tavilySearchResults = TavilySearchResults(max_results = 2) # TAVILY API를 사용하기 위한 도구를 생성한다.

toolList = [tavilySearchResults]

chatOpenAI = ChatOpenAI(model = "gpt-4") # OPEN AI LLM 모델을 생성한다.

chatOpenAI.bind_tools(toolList) # OPEN AI LLM 모델과 도구를 바인딩한다.

sqliteSaver = SqliteSaver.from_conn_string(":memory:") # 상호 작용을 위한 메모리를 생성한다.

compiledStateGraph = create_react_agent(chatOpenAI, toolList, checkpointer = sqliteSaver) # 에이전트를 생성한다.

configurationDictionary = {"configurable" : {"thread_id" : "thread1"}} # thread_id로 세션을 구분한다.

addableValuesDict1 = compiledStateGraph.invoke({"messages" : [HumanMessage(content = "hi!")]}, configurationDictionary)

print(addableValuesDict1["messages"])

"""
[
    HumanMessage(content='hi!', id='e5a65000-7bcd-4a24-bac3-27806cbe8dce'),
    AIMessage(content='Hello! How can I assist you today?', response_metadata={'token_usage': {'completion_tokens': 10, 'prompt_tokens': 83, 'total_tokens': 93}, 'model_name': 'gpt-4', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-8ff9af0d-0586-4222-966b-3a593a46fbc9-0', usage_metadata={'input_tokens': 83, 'output_tokens': 10, 'total_tokens': 93})]
"""

addableValuesDict2 = compiledStateGraph.invoke({"messages" : [HumanMessage(content = "whats the weather in sf?")]}, configurationDictionary)

print(addableValuesDict2["messages"])

"""
[
    HumanMessage(content='hi!', id='e5a65000-7bcd-4a24-bac3-27806cbe8dce'),
    AIMessage(content='Hello! How can I assist you today?', response_metadata={'token_usage': {'completion_tokens': 10, 'prompt_tokens': 83, 'total_tokens': 93}, 'model_name': 'gpt-4', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-8ff9af0d-0586-4222-966b-3a593a46fbc9-0', usage_metadata={'input_tokens': 83, 'output_tokens': 10, 'total_tokens': 93}),
    HumanMessage(content='whats the weather in sf?', id='a3b53bc7-8d50-448c-aa8c-5ca1cd195797'),
    AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_V4h5BFmGfdmSBiHP9rVaRC9p', 'function': {'arguments': '{\n  "query": "current weather in San Francisco"\n}', 'name': 'tavily_search_results_json'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 23, 'prompt_tokens': 107, 'total_tokens': 130}, 'model_name': 'gpt-4', 'system_fingerprint': None, 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-c6e6a2e7-a2b3-40e9-9b25-1563b59cf83a-0', tool_calls=[{'name': 'tavily_search_results_json', 'args': {'query': 'current weather in San Francisco'}, 'id': 'call_V4h5BFmGfdmSBiHP9rVaRC9p'}], usage_metadata={'input_tokens': 107, 'output_tokens': 23, 'total_tokens': 130}), ToolMessage(content='[{"url": "https://www.weatherapi.com/", "content": "{\'location\': {\'name\': \'San Francisco\', \'region\': \'California\', \'country\': \'United States of America\', \'lat\': 37.78, \'lon\': -122.42, \'tz_id\': \'America/Los_Angeles\', \'localtime_epoch\': 1718003828, \'localtime\': \'2024-06-10 0:17\'}, \'current\': {\'last_updated_epoch\': 1718003700, \'last_updated\': \'2024-06-10 00:15\', \'temp_c\': 12.8, \'temp_f\': 55.0, \'is_day\': 0, \'condition\': {\'text\': \'Overcast\', \'icon\': \'//cdn.weatherapi.com/weather/64x64/night/122.png\', \'code\': 1009}, \'wind_mph\': 12.5, \'wind_kph\': 20.2, \'wind_degree\': 250, \'wind_dir\': \'WSW\', \'pressure_mb\': 1015.0, \'pressure_in\': 29.98, \'precip_mm\': 0.01, \'precip_in\': 0.0, \'humidity\': 93, \'cloud\': 100, \'feelslike_c\': 11.3, \'feelslike_f\': 52.3, \'windchill_c\': 10.4, \'windchill_f\': 50.8, \'heatindex_c\': 12.1, \'heatindex_f\': 53.8, \'dewpoint_c\': 10.1, \'dewpoint_f\': 50.2, \'vis_km\': 16.0, \'vis_miles\': 9.0, \'uv\': 1.0, \'gust_mph\': 14.0, \'gust_kph\': 22.6}}"}, {"url": "https://forecast.weather.gov/zipcity.php?inputstring=San+Francisco,CA", "content": "San Francisco CA 37.77\\u00b0N 122.41\\u00b0W (Elev. 131 ft) Last Update: 6:00 pm PDT Jun 7, 2024. Forecast Valid: 7pm PDT Jun 7, 2024-6pm PDT Jun 14, 2024 . Forecast Discussion . Additional Resources. Radar & Satellite Image. Hourly Weather Forecast. ... Severe Weather ; Current Outlook Maps ; Drought ; Fire Weather ; Fronts/Precipitation Maps ; Current ..."}]', name='tavily_search_results_json', id='fb4045ac-3877-4c98-ae9e-1d56267a5f69', tool_call_id='call_V4h5BFmGfdmSBiHP9rVaRC9p'),
    AIMessage(content='The current weather in San Francisco, California is overcast with a temperature of 12.8°C (55°F). The wind is blowing from the west-southwest at a speed of 20.2 kph (12.5 mph). The humidity level is at 93%.', response_metadata={'token_usage': {'completion_tokens': 59, 'prompt_tokens': 691, 'total_tokens': 750}, 'model_name': 'gpt-4', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-e0ebe54c-dc83-4dfa-abfa-d625fbd97f44-0', usage_metadata={'input_tokens': 691, 'output_tokens': 59, 'total_tokens': 750})]
"""

import os

from langchain_core.messages import HumanMessage

from langchain_community.tools.tavily_search import TavilySearchResults

from langchain_openai import ChatOpenAI

from langgraph.prebuilt import create_react_agent

from langgraph.checkpoint.sqlite import SqliteSaver

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

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

tavilySearchResults = TavilySearchResults(max_results = 2) # TAVILY API를 사용하기 위한 도구를 생성한다.

toolList = [tavilySearchResults]

chatOpenAI = ChatOpenAI(model = "gpt-4") # OPEN AI LLM 모델을 생성한다.

chatOpenAI.bind_tools(toolList) # OPEN AI LLM 모델과 도구를 바인딩한다.

sqliteSaver = SqliteSaver.from_conn_string(":memory:") # 상호 작용을 위한 메모리를 생성한다.

compiledStateGraph = create_react_agent(chatOpenAI, toolList, checkpointer = sqliteSaver) # 에이전트를 생성한다.

configurationDictionary = {"configurable" : {"thread_id" : "thread1"}} # thread_id로 세션을 구분한다.

addableValuesDict1 = compiledStateGraph.invoke({"messages" : [HumanMessage(content = "hi!")]}, configurationDictionary)

print(addableValuesDict1["messages"])

"""

[

HumanMessage(content='hi!', id='e5a65000-7bcd-4a24-bac3-27806cbe8dce'),

"""

addableValuesDict2 = compiledStateGraph.invoke({"messages" : [HumanMessage(content = "whats the weather in sf?")]}, configurationDictionary)

print(addableValuesDict2["messages"])

"""

[

HumanMessage(content='hi!', id='e5a65000-7bcd-4a24-bac3-27806cbe8dce'),

HumanMessage(content='whats the weather in sf?', id='a3b53bc7-8d50-448c-aa8c-5ca1cd195797'),

"""

▶ 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
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==2.4
langchain==0.2.3
langchain-community==0.2.4
langchain-core==0.2.5
langchain-openai==0.1.8
langchain-text-splitters==0.2.1
langgraph==0.0.66
langsmith==0.1.75
marshmallow==3.21.3
multidict==6.0.5
mypy-extensions==1.0.0
numpy==1.26.4
openai==1.33.0
orjson==3.10.3
packaging==23.2
pydantic==2.7.3
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
tavily-python==0.3.3
tenacity==8.3.0
tiktoken==0.7.0
tqdm==4.66.4
typing-inspect==0.9.0
typing_extensions==4.12.2
urllib3==2.2.1
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

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==2.4

langchain==0.2.3

langchain-community==0.2.4

langchain-core==0.2.5

langchain-openai==0.1.8

langchain-text-splitters==0.2.1

langgraph==0.0.66

langsmith==0.1.75

marshmallow==3.21.3

multidict==6.0.5

mypy-extensions==1.0.0

numpy==1.26.4

openai==1.33.0

orjson==3.10.3

packaging==23.2

pydantic==2.7.3

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

tavily-python==0.3.3

tenacity==8.3.0

tiktoken==0.7.0

tqdm==4.66.4

typing-inspect==0.9.0

typing_extensions==4.12.2

urllib3==2.2.1

yarl==1.9.4

※

[PYTHON/LANGCHAIN] TavilySearchResults 클래스 : invoke 메소드를 사용해 날씨예보 질의 응답하기

■ TavilySearchResults 클래스의 invoke 메소드를 사용해 날씨예보를 질의 응답하는 방법을 보여준다. ▶ main.py


import os

from langchain_community.tools.tavily_search import TavilySearchResults

os.environ["TAVILY_API_KEY"] = "<TAVILY_API_KEY>" # https://app.tavily.com/home에서 계정 가입 후 발급받는다.

tavilySearchResults = TavilySearchResults(max_results = 2)

searchResultList = tavilySearchResults.invoke("what is the weather in SF")

print(searchResultList)

"""
[
    {
        'url'     : 'https://www.weatherapi.com/',
        'content' :
            "{
                'location' :
                    {
                        'name'            : 'San Francisco',
                        'region'          : 'California',
                        'country'         : 'United States of America',
                        'lat'             : 37.78,
                        'lon'             : -122.42,
                        'tz_id'           : 'America/Los_Angeles',
                        'localtime_epoch' : 1718000793,
                        'localtime'       : '2024-06-09 23:26'
                    },
                'current' :
                    {
                        'last_updated_epoch' : 1718000100,
                        'last_updated'       : '2024-06-09 23:15',
                        'temp_c'             : 13.3,
                        'temp_f'             : 55.9,
                        'is_day'             : 0,
                        'condition'          :
                            {
                                'text' : 'Overcast',
                                'icon' : '//cdn.weatherapi.com/weather/64x64/night/122.png',
                                'code' : 1009
                            },
                        'wind_mph'           : 12.5,
                        'wind_kph'           : 20.2,
                        'wind_degree'        : 240,
                        'wind_dir'           : 'WSW',
                        'pressure_mb'        : 1015.0,
                        'pressure_in'        : 29.98,
                        'precip_mm'          : 0.01,
                        'precip_in'          : 0.0,
                        'humidity'           : 90,
                        'cloud'              : 100,
                        'feelslike_c'        : 11.7,
                        'feelslike_f'        : 53.1,
                        'windchill_c'        : 10.5,
                        'windchill_f'        : 50.9,
                        'heatindex_c'        : 12.3,
                        'heatindex_f'        : 54.2,
                        'dewpoint_c'         : 10.4,
                        'dewpoint_f'         : 50.7,
                        'vis_km'             : 16.0,
                        'vis_miles'          : 9.0,
                        'uv'                 : 1.0,
                        'gust_mph'           : 15.3,
                        'gust_kph'           : 24.6
                    }
            }"
    },
    {
        'url '    : 'https://forecast.weather.gov/zipcity.php?inputstring=San+Francisco,CA',
        'content' : 'San Francisco CA 37.77°N 122.41°W (Elev. 131 ft) Last Update: 6:00 pm PDT Jun 7, 2024. Forecast Valid: 7pm PDT Jun 7, 2024-6pm PDT Jun 14, 2024 . Forecast Discussion . Additional Resources. Radar & Satellite Image. Hourly Weather Forecast. ... Severe Weather ; Current Outlook Maps ; Drought ; Fire Weather ; Fronts/Precipitation Maps ; Current ...'
    }
]
"""

import os

from langchain_community.tools.tavily_search import TavilySearchResults

os.environ["TAVILY_API_KEY"] = "<TAVILY_API_KEY>" # https://app.tavily.com/home에서 계정 가입 후 발급받는다.

tavilySearchResults = TavilySearchResults(max_results = 2)

searchResultList = tavilySearchResults.invoke("what is the weather in SF")

print(searchResultList)

"""

[

{

'url' : 'https://www.weatherapi.com/',

'content' :

'location' :

{

'name' : 'San Francisco',

'region' : 'California',

'country' : 'United States of America',

'lat' : 37.78,

'lon' : -122.42,

'tz_id' : 'America/Los_Angeles',

'localtime_epoch' : 1718000793,

'localtime' : '2024-06-09 23:26'

'current' :

{

'last_updated_epoch' : 1718000100,

'last_updated' : '2024-06-09 23:15',

'temp_c' : 13.3,

'temp_f' : 55.9,

'is_day' : 0,

'condition' :

{

'text' : 'Overcast',

'icon' : '//cdn.weatherapi.com/weather/64x64/night/122.png',

'code' : 1009

'wind_mph' : 12.5,

'wind_kph' : 20.2,

'wind_degree' : 240,

'wind_dir' : 'WSW',

'pressure_mb' : 1015.0,

'pressure_in' : 29.98,

'precip_mm' : 0.01,

'precip_in' : 0.0,

'humidity' : 90,

'cloud' : 100,

'feelslike_c' : 11.7,

'feelslike_f' : 53.1,

'windchill_c' : 10.5,

'windchill_f' : 50.9,

'heatindex_c' : 12.3,

'heatindex_f' : 54.2,

'dewpoint_c' : 10.4,

'dewpoint_f' : 50.7,

'vis_km' : 16.0,

'vis_miles' : 9.0,

'uv' : 1.0,

'gust_mph' : 15.3,

'gust_kph' : 24.6

}

{

'url ' : 'https://forecast.weather.gov/zipcity.php?inputstring=San+Francisco,CA',

'content' : 'San Francisco CA 37.77°N 122.41°W (Elev. 131 ft) Last Update: 6:00 pm PDT Jun 7, 2024. Forecast Valid: 7pm PDT Jun 7, 2024-6pm PDT Jun 14, 2024 . Forecast Discussion . Additional Resources. Radar & Satellite Image. Hourly Weather Forecast. ... Severe Weather ; Current Outlook Maps ; Drought ; Fire Weather ; Fronts/Precipitation Maps ; Current ...'

}

]

"""

▶ requirements.txt


aiohttp==3.9.5
aiosignal==1.3.1
annotated-types==0.7.0
async-timeout==4.0.3
attrs==23.2.0
certifi==2024.6.2
charset-normalizer==3.3.2
dataclasses-json==0.6.7
frozenlist==1.4.1
greenlet==3.0.3
idna==3.7
jsonpatch==1.33
jsonpointer==2.4
langchain==0.2.3
langchain-community==0.2.4
langchain-core==0.2.5
langchain-text-splitters==0.2.1
langsmith==0.1.75
marshmallow==3.21.3
multidict==6.0.5
mypy-extensions==1.0.0
numpy==1.26.4
orjson==3.10.3
packaging==23.2
pydantic==2.7.3
pydantic_core==2.18.4
PyYAML==6.0.1
regex==2024.5.15
requests==2.32.3
SQLAlchemy==2.0.30
tavily-python==0.3.3
tenacity==8.3.0
tiktoken==0.7.0
typing-inspect==0.9.0
typing_extensions==4.12.2
urllib3==2.2.1
yarl==1.9.4

aiohttp==3.9.5

aiosignal==1.3.1

annotated-types==0.7.0

async-timeout==4.0.3

attrs==23.2.0

certifi==2024.6.2

charset-normalizer==3.3.2

dataclasses-json==0.6.7

frozenlist==1.4.1

greenlet==3.0.3

idna==3.7

jsonpatch==1.33

jsonpointer==2.4

langchain==0.2.3

langchain-community==0.2.4

langchain-core==0.2.5

langchain-text-splitters==0.2.1

langsmith==0.1.75

marshmallow==3.21.3

multidict==6.0.5

mypy-extensions==1.0.0

numpy==1.26.4

orjson==3.10.3

packaging==23.2

pydantic==2.7.3

pydantic_core==2.18.4

PyYAML==6.0.1

regex==2024.5.15

requests==2.32.3

SQLAlchemy==2.0.30

tavily-python==0.3.3

tenacity==8.3.0

tiktoken==0.7.0

typing-inspect==0.9.0

typing_extensions==4.12.2

urllib3==2.2.1

yarl==1.9.4

※ pip install langchain langchain-community

[PYTHON/LANGCHAIN] VectorStoreRetriever 클래스 : 검색기로 주어진 질문과 검색된 컨텍스트를 LLM 프롬프트에 결합하기

■ VectorStoreRetriever 클래스를 사용해 검색기로 주어진 질문과 검색된 컨텍스트를 LLM 프롬프트에 결합하는 방법을 보여준다. ▶ main.py


import os

from langchain_core.documents import Document
from langchain_core.prompts   import ChatPromptTemplate
from langchain_core.runnables import RunnablePassthrough
from langchain_openai         import ChatOpenAI
from langchain_openai         import OpenAIEmbeddings
from langchain_chroma         import Chroma

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

documentList = [
    Document(
        page_content = "Dogs are great companions, known for their loyalty and friendliness.",
        metadata     = {"source" : "mammal-pets-doc"}
    ),
    Document(
        page_content = "Cats are independent pets that often enjoy their own space.",
        metadata     = {"source" : "mammal-pets-doc"},
    ),
    Document(
        page_content = "Goldfish are popular pets for beginners, requiring relatively simple care.",
        metadata     = {"source" : "fish-pets-doc"},
    ),
    Document(
        page_content = "Parrots are intelligent birds capable of mimicking human speech.",
        metadata     = {"source" : "bird-pets-doc"},
    ),
    Document(
        page_content = "Rabbits are social animals that need plenty of space to hop around.",
        metadata     = {"source" : "mammal-pets-doc"},
    ),
]

chroma = Chroma.from_documents(
    documentList,
    embedding = OpenAIEmbeddings(),
)

vectorStoreRetriever = chroma.as_retriever(
    search_type   = "similarity", # "similarity"(디폴트), "mmr" (maximum marginal relevance, described above), "similarity_score_threshold"
    search_kwargs = {"k" : 1},
)

message = """
Answer this question using the provided context only.

{question}

Context:
{context}
"""

chatPromptTemplate = ChatPromptTemplate.from_messages([("human", message)])

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

runnableSequence = {"context" : vectorStoreRetriever, "question" : RunnablePassthrough()} | chatPromptTemplate | chatOpenAI

aiMessage = runnableSequence.invoke("tell me about cats")

print(aiMessage.content)

"""
Cats are independent pets that often enjoy their own space.
"""

import os

from langchain_core.documents import Document

from langchain_core.prompts import ChatPromptTemplate

from langchain_core.runnables import RunnablePassthrough

from langchain_openai import ChatOpenAI

from langchain_openai import OpenAIEmbeddings

from langchain_chroma import Chroma

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

documentList = [

Document(

page_content = "Dogs are great companions, known for their loyalty and friendliness.",

metadata = {"source" : "mammal-pets-doc"}

Document(

page_content = "Cats are independent pets that often enjoy their own space.",

metadata = {"source" : "mammal-pets-doc"},

Document(

page_content = "Goldfish are popular pets for beginners, requiring relatively simple care.",

metadata = {"source" : "fish-pets-doc"},

Document(

page_content = "Parrots are intelligent birds capable of mimicking human speech.",

metadata = {"source" : "bird-pets-doc"},

Document(

page_content = "Rabbits are social animals that need plenty of space to hop around.",

metadata = {"source" : "mammal-pets-doc"},

]

chroma = Chroma.from_documents(

documentList,

embedding = OpenAIEmbeddings(),

)

vectorStoreRetriever = chroma.as_retriever(

search_type = "similarity", # "similarity"(디폴트), "mmr" (maximum marginal relevance, described above), "similarity_score_threshold"

search_kwargs = {"k" : 1},

)

message = """

Answer this question using the provided context only.

{question}

Context:

{context}

"""

chatPromptTemplate = ChatPromptTemplate.from_messages([("human", message)])

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

runnableSequence = {"context" : vectorStoreRetriever, "question" : RunnablePassthrough()} | chatPromptTemplate | chatOpenAI

aiMessage = runnableSequence.invoke("tell me about cats")

print(aiMessage.content)

"""

Cats are independent pets that often enjoy their own space.

"""

▶ requirements.txt


aiohttp==3.9.5
aiosignal==1.3.1
annotated-types==0.7.0
anyio==4.4.0
asgiref==3.8.1
async-timeout==4.0.3
attrs==23.2.0
backoff==2.2.1
bcrypt==4.1.3
build==1.2.1
cachetools==5.3.3
certifi==2024.6.2
charset-normalizer==3.3.2
chroma-hnswlib==0.7.3
chromadb==0.5.0
click==8.1.7
coloredlogs==15.0.1
Deprecated==1.2.14
distro==1.9.0
dnspython==2.6.1
email_validator==2.1.1
exceptiongroup==1.2.1
fastapi==0.111.0
fastapi-cli==0.0.4
filelock==3.14.0
flatbuffers==24.3.25
frozenlist==1.4.1
fsspec==2024.6.0
google-auth==2.30.0
googleapis-common-protos==1.63.1
greenlet==3.0.3
grpcio==1.64.1
h11==0.14.0
httpcore==1.0.5
httptools==0.6.1
httpx==0.27.0
huggingface-hub==0.23.3
humanfriendly==10.0
idna==3.7
importlib_metadata==7.1.0
importlib_resources==6.4.0
Jinja2==3.1.4
jsonpatch==1.33
jsonpointer==2.4
kubernetes==30.1.0
langchain==0.2.3
langchain-chroma==0.1.1
langchain-core==0.2.5
langchain-openai==0.1.8
langchain-text-splitters==0.2.1
langsmith==0.1.75
markdown-it-py==3.0.0
MarkupSafe==2.1.5
mdurl==0.1.2
mmh3==4.1.0
monotonic==1.6
mpmath==1.3.0
multidict==6.0.5
numpy==1.26.4
oauthlib==3.2.2
onnxruntime==1.18.0
openai==1.33.0
opentelemetry-api==1.25.0
opentelemetry-exporter-otlp-proto-common==1.25.0
opentelemetry-exporter-otlp-proto-grpc==1.25.0
opentelemetry-instrumentation==0.46b0
opentelemetry-instrumentation-asgi==0.46b0
opentelemetry-instrumentation-fastapi==0.46b0
opentelemetry-proto==1.25.0
opentelemetry-sdk==1.25.0
opentelemetry-semantic-conventions==0.46b0
opentelemetry-util-http==0.46b0
orjson==3.10.3
overrides==7.7.0
packaging==23.2
posthog==3.5.0
protobuf==4.25.3
pyasn1==0.6.0
pyasn1_modules==0.4.0
pydantic==2.7.3
pydantic_core==2.18.4
Pygments==2.18.0
PyPika==0.48.9
pyproject_hooks==1.1.0
python-dateutil==2.9.0.post0
python-dotenv==1.0.1
python-multipart==0.0.9
PyYAML==6.0.1
regex==2024.5.15
requests==2.32.3
requests-oauthlib==2.0.0
rich==13.7.1
rsa==4.9
shellingham==1.5.4
six==1.16.0
sniffio==1.3.1
SQLAlchemy==2.0.30
starlette==0.37.2
sympy==1.12.1
tenacity==8.3.0
tiktoken==0.7.0
tokenizers==0.19.1
tomli==2.0.1
tqdm==4.66.4
typer==0.12.3
typing_extensions==4.12.2
ujson==5.10.0
urllib3==2.2.1
uvicorn==0.30.1
uvloop==0.19.0
watchfiles==0.22.0
websocket-client==1.8.0
websockets==12.0
wrapt==1.16.0
yarl==1.9.4
zipp==3.19.2

100

101

102

103

104

105

106

107

108

109

110

111

112

113

114

115

116

117

118

aiohttp==3.9.5

aiosignal==1.3.1

annotated-types==0.7.0

anyio==4.4.0

asgiref==3.8.1

async-timeout==4.0.3

attrs==23.2.0

backoff==2.2.1

bcrypt==4.1.3

build==1.2.1

cachetools==5.3.3

certifi==2024.6.2

charset-normalizer==3.3.2

chroma-hnswlib==0.7.3

chromadb==0.5.0

click==8.1.7

coloredlogs==15.0.1

Deprecated==1.2.14

distro==1.9.0

dnspython==2.6.1

email_validator==2.1.1

exceptiongroup==1.2.1

fastapi==0.111.0

fastapi-cli==0.0.4

filelock==3.14.0

flatbuffers==24.3.25

frozenlist==1.4.1

fsspec==2024.6.0

google-auth==2.30.0

googleapis-common-protos==1.63.1

greenlet==3.0.3

grpcio==1.64.1

h11==0.14.0

httpcore==1.0.5

httptools==0.6.1

httpx==0.27.0

huggingface-hub==0.23.3

humanfriendly==10.0

idna==3.7

importlib_metadata==7.1.0

importlib_resources==6.4.0

Jinja2==3.1.4

jsonpatch==1.33

jsonpointer==2.4

kubernetes==30.1.0

langchain==0.2.3

langchain-chroma==0.1.1

langchain-core==0.2.5

langchain-openai==0.1.8

langchain-text-splitters==0.2.1

langsmith==0.1.75

markdown-it-py==3.0.0

MarkupSafe==2.1.5

mdurl==0.1.2

mmh3==4.1.0

monotonic==1.6

mpmath==1.3.0

multidict==6.0.5

numpy==1.26.4

oauthlib==3.2.2

onnxruntime==1.18.0

openai==1.33.0

opentelemetry-api==1.25.0

opentelemetry-exporter-otlp-proto-common==1.25.0

opentelemetry-exporter-otlp-proto-grpc==1.25.0

opentelemetry-instrumentation==0.46b0

opentelemetry-instrumentation-asgi==0.46b0

opentelemetry-instrumentation-fastapi==0.46b0

opentelemetry-proto==1.25.0

opentelemetry-sdk==1.25.0

opentelemetry-semantic-conventions==0.46b0

opentelemetry-util-http==0.46b0

orjson==3.10.3

overrides==7.7.0

packaging==23.2

posthog==3.5.0

protobuf==4.25.3

pyasn1==0.6.0

pyasn1_modules==0.4.0

pydantic==2.7.3

pydantic_core==2.18.4

Pygments==2.18.0

PyPika==0.48.9

pyproject_hooks==1.1.0

python-dateutil==2.9.0.post0

python-dotenv==1.0.1

python-multipart==0.0.9

PyYAML==6.0.1

regex==2024.5.15

requests==2.32.3

requests-oauthlib==2.0.0

rich==13.7.1

rsa==4.9

shellingham==1.5.4

six==1.16.0

sniffio==1.3.1

SQLAlchemy==2.0.30

starlette==0.37.2

sympy==1.12.1

tenacity==8.3.0

tiktoken==0.7.0

tokenizers==0.19.1

tomli==2.0.1

tqdm==4.66.4

typer==0.12.3

typing_extensions==4.12.2

ujson==5.10.0

urllib3==2.2.1

uvicorn==0.30.1

uvloop==0.19.0

watchfiles==0.22.0

websocket-client==1.8.0

websockets==12.0

wrapt==1.16.0

yarl==1.9.4

zipp==3.19.2

※ pip

[PYTHON/LANGCHAIN] VectorStoreRetriever 클래스 : batch 메소드를 사용해 복수 문자열 쿼리 유사성 기반으로 문서 리스트 구하기

■ VectorStoreRetriever 클래스의 batch 메소드를 사용해 복수 문자열 쿼리와의 유사성 기반으로 문서 리스트를 구하는 방법을 보여준다. ▶ main.py


import os

from langchain_core.documents import Document
from langchain_chroma         import Chroma
from langchain_openai         import OpenAIEmbeddings

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

documentList = [
    Document(
        page_content = "Dogs are great companions, known for their loyalty and friendliness.",
        metadata     = {"source" : "mammal-pets-doc"}
    ),
    Document(
        page_content = "Cats are independent pets that often enjoy their own space.",
        metadata     = {"source" : "mammal-pets-doc"},
    ),
    Document(
        page_content = "Goldfish are popular pets for beginners, requiring relatively simple care.",
        metadata     = {"source" : "fish-pets-doc"},
    ),
    Document(
        page_content = "Parrots are intelligent birds capable of mimicking human speech.",
        metadata     = {"source" : "bird-pets-doc"},
    ),
    Document(
        page_content = "Rabbits are social animals that need plenty of space to hop around.",
        metadata     = {"source" : "mammal-pets-doc"},
    ),
]

chroma = Chroma.from_documents(
    documentList,
    embedding = OpenAIEmbeddings(),
)

vectorStoreRetriever = chroma.as_retriever(
    search_type   = "similarity", # "similarity"(디폴트), "mmr" (maximum marginal relevance, described above), "similarity_score_threshold"
    search_kwargs = {"k" : 1},
)

searchResultList = vectorStoreRetriever.batch(["cat", "shark"])

print(searchResultList)

"""
[
    [
        Document(
            page_content = 'Cats are independent pets that often enjoy their own space.',
            metadata     = {'source' : 'mammal-pets-doc'}
        )
    ],
    [
        Document(
            page_content = 'Goldfish are popular pets for beginners, requiring relatively simple care.',
            metadata     = {'source' : 'fish-pets-doc'}
        )
    ]
]
"""

import os

from langchain_core.documents import Document

from langchain_chroma import Chroma

from langchain_openai import OpenAIEmbeddings

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

documentList = [

Document(

page_content = "Dogs are great companions, known for their loyalty and friendliness.",

metadata = {"source" : "mammal-pets-doc"}

Document(

page_content = "Cats are independent pets that often enjoy their own space.",

metadata = {"source" : "mammal-pets-doc"},

Document(

page_content = "Goldfish are popular pets for beginners, requiring relatively simple care.",

metadata = {"source" : "fish-pets-doc"},

Document(

page_content = "Parrots are intelligent birds capable of mimicking human speech.",

metadata = {"source" : "bird-pets-doc"},

Document(

page_content = "Rabbits are social animals that need plenty of space to hop around.",

metadata = {"source" : "mammal-pets-doc"},

]

chroma = Chroma.from_documents(

documentList,

embedding = OpenAIEmbeddings(),

)

vectorStoreRetriever = chroma.as_retriever(

search_type = "similarity", # "similarity"(디폴트), "mmr" (maximum marginal relevance, described above), "similarity_score_threshold"

search_kwargs = {"k" : 1},

)

searchResultList = vectorStoreRetriever.batch(["cat", "shark"])

print(searchResultList)

"""

[

Document(

page_content = 'Cats are independent pets that often enjoy their own space.',

metadata = {'source' : 'mammal-pets-doc'}

)

[

Document(

page_content = 'Goldfish are popular pets for beginners, requiring relatively simple care.',

metadata = {'source' : 'fish-pets-doc'}

)

]

"""

▶ requirements.txt


aiohttp==3.9.5
aiosignal==1.3.1
annotated-types==0.7.0
anyio==4.4.0
asgiref==3.8.1
async-timeout==4.0.3
attrs==23.2.0
backoff==2.2.1
bcrypt==4.1.3
build==1.2.1
cachetools==5.3.3
certifi==2024.6.2
charset-normalizer==3.3.2
chroma-hnswlib==0.7.3
chromadb==0.5.0
click==8.1.7
coloredlogs==15.0.1
Deprecated==1.2.14
distro==1.9.0
dnspython==2.6.1
email_validator==2.1.1
exceptiongroup==1.2.1
fastapi==0.111.0
fastapi-cli==0.0.4
filelock==3.14.0
flatbuffers==24.3.25
frozenlist==1.4.1
fsspec==2024.6.0
google-auth==2.30.0
googleapis-common-protos==1.63.1
greenlet==3.0.3
grpcio==1.64.1
h11==0.14.0
httpcore==1.0.5
httptools==0.6.1
httpx==0.27.0
huggingface-hub==0.23.3
humanfriendly==10.0
idna==3.7
importlib_metadata==7.1.0
importlib_resources==6.4.0
Jinja2==3.1.4
jsonpatch==1.33
jsonpointer==2.4
kubernetes==30.1.0
langchain==0.2.3
langchain-chroma==0.1.1
langchain-core==0.2.5
langchain-openai==0.1.8
langchain-text-splitters==0.2.1
langsmith==0.1.75
markdown-it-py==3.0.0
MarkupSafe==2.1.5
mdurl==0.1.2
mmh3==4.1.0
monotonic==1.6
mpmath==1.3.0
multidict==6.0.5
numpy==1.26.4
oauthlib==3.2.2
onnxruntime==1.18.0
openai==1.33.0
opentelemetry-api==1.25.0
opentelemetry-exporter-otlp-proto-common==1.25.0
opentelemetry-exporter-otlp-proto-grpc==1.25.0
opentelemetry-instrumentation==0.46b0
opentelemetry-instrumentation-asgi==0.46b0
opentelemetry-instrumentation-fastapi==0.46b0
opentelemetry-proto==1.25.0
opentelemetry-sdk==1.25.0
opentelemetry-semantic-conventions==0.46b0
opentelemetry-util-http==0.46b0
orjson==3.10.3
overrides==7.7.0
packaging==23.2
posthog==3.5.0
protobuf==4.25.3
pyasn1==0.6.0
pyasn1_modules==0.4.0
pydantic==2.7.3
pydantic_core==2.18.4
Pygments==2.18.0
PyPika==0.48.9
pyproject_hooks==1.1.0
python-dateutil==2.9.0.post0
python-dotenv==1.0.1
python-multipart==0.0.9
PyYAML==6.0.1
regex==2024.5.15
requests==2.32.3
requests-oauthlib==2.0.0
rich==13.7.1
rsa==4.9
shellingham==1.5.4
six==1.16.0
sniffio==1.3.1
SQLAlchemy==2.0.30
starlette==0.37.2
sympy==1.12.1
tenacity==8.3.0
tiktoken==0.7.0
tokenizers==0.19.1
tomli==2.0.1
tqdm==4.66.4
typer==0.12.3
typing_extensions==4.12.2
ujson==5.10.0
urllib3==2.2.1
uvicorn==0.30.1
uvloop==0.19.0
watchfiles==0.22.0
websocket-client==1.8.0
websockets==12.0
wrapt==1.16.0
yarl==1.9.4
zipp==3.19.2

100

101

102

103

104

105

106

107

108

109

110

111

112

113

114

115

116

117

118

aiohttp==3.9.5

aiosignal==1.3.1

annotated-types==0.7.0

anyio==4.4.0

asgiref==3.8.1

async-timeout==4.0.3

attrs==23.2.0

backoff==2.2.1

bcrypt==4.1.3

build==1.2.1

cachetools==5.3.3

certifi==2024.6.2

charset-normalizer==3.3.2

chroma-hnswlib==0.7.3

chromadb==0.5.0

click==8.1.7

coloredlogs==15.0.1

Deprecated==1.2.14

distro==1.9.0

dnspython==2.6.1

email_validator==2.1.1

exceptiongroup==1.2.1

fastapi==0.111.0

fastapi-cli==0.0.4

filelock==3.14.0

flatbuffers==24.3.25

frozenlist==1.4.1

fsspec==2024.6.0

google-auth==2.30.0

googleapis-common-protos==1.63.1

greenlet==3.0.3

grpcio==1.64.1

h11==0.14.0

httpcore==1.0.5

httptools==0.6.1

httpx==0.27.0

huggingface-hub==0.23.3

humanfriendly==10.0

idna==3.7

importlib_metadata==7.1.0

importlib_resources==6.4.0

Jinja2==3.1.4

jsonpatch==1.33

jsonpointer==2.4

kubernetes==30.1.0

langchain==0.2.3

langchain-chroma==0.1.1

langchain-core==0.2.5

langchain-openai==0.1.8

langchain-text-splitters==0.2.1

langsmith==0.1.75

markdown-it-py==3.0.0

MarkupSafe==2.1.5

mdurl==0.1.2

mmh3==4.1.0

monotonic==1.6

mpmath==1.3.0

multidict==6.0.5

numpy==1.26.4

oauthlib==3.2.2

onnxruntime==1.18.0

openai==1.33.0

opentelemetry-api==1.25.0

opentelemetry-exporter-otlp-proto-common==1.25.0

opentelemetry-exporter-otlp-proto-grpc==1.25.0

opentelemetry-instrumentation==0.46b0

opentelemetry-instrumentation-asgi==0.46b0

opentelemetry-instrumentation-fastapi==0.46b0

opentelemetry-proto==1.25.0

opentelemetry-sdk==1.25.0

opentelemetry-semantic-conventions==0.46b0

opentelemetry-util-http==0.46b0

orjson==3.10.3

overrides==7.7.0

packaging==23.2

posthog==3.5.0

protobuf==4.25.3

pyasn1==0.6.0

pyasn1_modules==0.4.0

pydantic==2.7.3

pydantic_core==2.18.4

Pygments==2.18.0

PyPika==0.48.9

pyproject_hooks==1.1.0

python-dateutil==2.9.0.post0

python-dotenv==1.0.1

python-multipart==0.0.9

PyYAML==6.0.1

regex==2024.5.15

requests==2.32.3

requests-oauthlib==2.0.0

rich==13.7.1

rsa==4.9

shellingham==1.5.4

six==1.16.0

sniffio==1.3.1

SQLAlchemy==2.0.30

starlette==0.37.2

sympy==1.12.1

tenacity==8.3.0

tiktoken==0.7.0

tokenizers==0.19.1

tomli==2.0.1

tqdm==4.66.4

typer==0.12.3

typing_extensions==4.12.2

ujson==5.10.0

urllib3==2.2.1

uvicorn==0.30.1

uvloop==0.19.0

watchfiles==0.22.0

websocket-client==1.8.0

websockets==12.0

wrapt==1.16.0

yarl==1.9.4

zipp==3.19.2

[PYTHON/LANGCHAIN] Chroma 클래스 : as_retriever 메소드를 사용해 VectorStoreRetriever 객체 만들기 2

■ Chroma 클래스의 as_retriever 메소드를 사용해 VectorStoreRetriever 객체를 만드는 방법을 보여준다. ▶ main.py


import os

from langchain_core.documents import Document
from langchain_chroma         import Chroma
from langchain_openai         import OpenAIEmbeddings

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

documentList = [
    Document(
        page_content = "Dogs are great companions, known for their loyalty and friendliness.",
        metadata     = {"source" : "mammal-pets-doc"}
    ),
    Document(
        page_content = "Cats are independent pets that often enjoy their own space.",
        metadata     = {"source" : "mammal-pets-doc"},
    ),
    Document(
        page_content = "Goldfish are popular pets for beginners, requiring relatively simple care.",
        metadata     = {"source" : "fish-pets-doc"},
    ),
    Document(
        page_content = "Parrots are intelligent birds capable of mimicking human speech.",
        metadata     = {"source" : "bird-pets-doc"},
    ),
    Document(
        page_content = "Rabbits are social animals that need plenty of space to hop around.",
        metadata     = {"source" : "mammal-pets-doc"},
    ),
]

chroma = Chroma.from_documents(
    documentList,
    embedding = OpenAIEmbeddings(),
)

vectorStoreRetriever = chroma.as_retriever(
    search_type   = "similarity", # "similarity"(디폴트), "mmr" (maximum marginal relevance, described above), "similarity_score_threshold"
    search_kwargs = {"k" : 1},
)

import os

from langchain_core.documents import Document

from langchain_chroma import Chroma

from langchain_openai import OpenAIEmbeddings

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

documentList = [

Document(

page_content = "Dogs are great companions, known for their loyalty and friendliness.",

metadata = {"source" : "mammal-pets-doc"}

Document(

page_content = "Cats are independent pets that often enjoy their own space.",

metadata = {"source" : "mammal-pets-doc"},

Document(

page_content = "Goldfish are popular pets for beginners, requiring relatively simple care.",

metadata = {"source" : "fish-pets-doc"},

Document(

page_content = "Parrots are intelligent birds capable of mimicking human speech.",

metadata = {"source" : "bird-pets-doc"},

Document(

page_content = "Rabbits are social animals that need plenty of space to hop around.",

metadata = {"source" : "mammal-pets-doc"},

]

chroma = Chroma.from_documents(

documentList,

embedding = OpenAIEmbeddings(),

)

vectorStoreRetriever = chroma.as_retriever(

search_type = "similarity", # "similarity"(디폴트), "mmr" (maximum marginal relevance, described above), "similarity_score_threshold"

search_kwargs = {"k" : 1},

)

▶ requirements.txt


aiohttp==3.9.5
aiosignal==1.3.1
annotated-types==0.7.0
anyio==4.4.0
asgiref==3.8.1
async-timeout==4.0.3
attrs==23.2.0
backoff==2.2.1
bcrypt==4.1.3
build==1.2.1
cachetools==5.3.3
certifi==2024.6.2
charset-normalizer==3.3.2
chroma-hnswlib==0.7.3
chromadb==0.5.0
click==8.1.7
coloredlogs==15.0.1
Deprecated==1.2.14
distro==1.9.0
dnspython==2.6.1
email_validator==2.1.1
exceptiongroup==1.2.1
fastapi==0.111.0
fastapi-cli==0.0.4
filelock==3.14.0
flatbuffers==24.3.25
frozenlist==1.4.1
fsspec==2024.6.0
google-auth==2.30.0
googleapis-common-protos==1.63.1
greenlet==3.0.3
grpcio==1.64.1
h11==0.14.0
httpcore==1.0.5
httptools==0.6.1
httpx==0.27.0
huggingface-hub==0.23.3
humanfriendly==10.0
idna==3.7
importlib_metadata==7.1.0
importlib_resources==6.4.0
Jinja2==3.1.4
jsonpatch==1.33
jsonpointer==2.4
kubernetes==30.1.0
langchain==0.2.3
langchain-chroma==0.1.1
langchain-core==0.2.5
langchain-openai==0.1.8
langchain-text-splitters==0.2.1
langsmith==0.1.75
markdown-it-py==3.0.0
MarkupSafe==2.1.5
mdurl==0.1.2
mmh3==4.1.0
monotonic==1.6
mpmath==1.3.0
multidict==6.0.5
numpy==1.26.4
oauthlib==3.2.2
onnxruntime==1.18.0
openai==1.33.0
opentelemetry-api==1.25.0
opentelemetry-exporter-otlp-proto-common==1.25.0
opentelemetry-exporter-otlp-proto-grpc==1.25.0
opentelemetry-instrumentation==0.46b0
opentelemetry-instrumentation-asgi==0.46b0
opentelemetry-instrumentation-fastapi==0.46b0
opentelemetry-proto==1.25.0
opentelemetry-sdk==1.25.0
opentelemetry-semantic-conventions==0.46b0
opentelemetry-util-http==0.46b0
orjson==3.10.3
overrides==7.7.0
packaging==23.2
posthog==3.5.0
protobuf==4.25.3
pyasn1==0.6.0
pyasn1_modules==0.4.0
pydantic==2.7.3
pydantic_core==2.18.4
Pygments==2.18.0
PyPika==0.48.9
pyproject_hooks==1.1.0
python-dateutil==2.9.0.post0
python-dotenv==1.0.1
python-multipart==0.0.9
PyYAML==6.0.1
regex==2024.5.15
requests==2.32.3
requests-oauthlib==2.0.0
rich==13.7.1
rsa==4.9
shellingham==1.5.4
six==1.16.0
sniffio==1.3.1
SQLAlchemy==2.0.30
starlette==0.37.2
sympy==1.12.1
tenacity==8.3.0
tiktoken==0.7.0
tokenizers==0.19.1
tomli==2.0.1
tqdm==4.66.4
typer==0.12.3
typing_extensions==4.12.2
ujson==5.10.0
urllib3==2.2.1
uvicorn==0.30.1
uvloop==0.19.0
watchfiles==0.22.0
websocket-client==1.8.0
websockets==12.0
wrapt==1.16.0
yarl==1.9.4
zipp==3.19.2

100

101

102

103

104

105

106

107

108

109

110

111

112

113

114

115

116

117

118

aiohttp==3.9.5

aiosignal==1.3.1

annotated-types==0.7.0

anyio==4.4.0

asgiref==3.8.1

async-timeout==4.0.3

attrs==23.2.0

backoff==2.2.1

bcrypt==4.1.3

build==1.2.1

cachetools==5.3.3

certifi==2024.6.2

charset-normalizer==3.3.2

chroma-hnswlib==0.7.3

chromadb==0.5.0

click==8.1.7

coloredlogs==15.0.1

Deprecated==1.2.14

distro==1.9.0

dnspython==2.6.1

email_validator==2.1.1

exceptiongroup==1.2.1

fastapi==0.111.0

fastapi-cli==0.0.4

filelock==3.14.0

flatbuffers==24.3.25

frozenlist==1.4.1

fsspec==2024.6.0

google-auth==2.30.0

googleapis-common-protos==1.63.1

greenlet==3.0.3

grpcio==1.64.1

h11==0.14.0

httpcore==1.0.5

httptools==0.6.1

httpx==0.27.0

huggingface-hub==0.23.3

humanfriendly==10.0

idna==3.7

importlib_metadata==7.1.0

importlib_resources==6.4.0

Jinja2==3.1.4

jsonpatch==1.33

jsonpointer==2.4

kubernetes==30.1.0

langchain==0.2.3

langchain-chroma==0.1.1

langchain-core==0.2.5

langchain-openai==0.1.8

langchain-text-splitters==0.2.1

langsmith==0.1.75

markdown-it-py==3.0.0

MarkupSafe==2.1.5

mdurl==0.1.2

mmh3==4.1.0

monotonic==1.6

mpmath==1.3.0

multidict==6.0.5

numpy==1.26.4

oauthlib==3.2.2

onnxruntime==1.18.0

openai==1.33.0

opentelemetry-api==1.25.0

opentelemetry-exporter-otlp-proto-common==1.25.0

opentelemetry-exporter-otlp-proto-grpc==1.25.0

opentelemetry-instrumentation==0.46b0

opentelemetry-instrumentation-asgi==0.46b0

opentelemetry-instrumentation-fastapi==0.46b0

opentelemetry-proto==1.25.0

opentelemetry-sdk==1.25.0

opentelemetry-semantic-conventions==0.46b0

opentelemetry-util-http==0.46b0

orjson==3.10.3

overrides==7.7.0

packaging==23.2

posthog==3.5.0

protobuf==4.25.3

pyasn1==0.6.0

pyasn1_modules==0.4.0

pydantic==2.7.3

pydantic_core==2.18.4

Pygments==2.18.0

PyPika==0.48.9

pyproject_hooks==1.1.0

python-dateutil==2.9.0.post0

python-dotenv==1.0.1

python-multipart==0.0.9

PyYAML==6.0.1

regex==2024.5.15

requests==2.32.3

requests-oauthlib==2.0.0

rich==13.7.1

rsa==4.9

shellingham==1.5.4

six==1.16.0

sniffio==1.3.1

SQLAlchemy==2.0.30

starlette==0.37.2

sympy==1.12.1

tenacity==8.3.0

tiktoken==0.7.0

tokenizers==0.19.1

tomli==2.0.1

tqdm==4.66.4

typer==0.12.3

typing_extensions==4.12.2

ujson==5.10.0

urllib3==2.2.1

uvicorn==0.30.1

uvloop==0.19.0

watchfiles==0.22.0

websocket-client==1.8.0

websockets==12.0

wrapt==1.16.0

yarl==1.9.4

zipp==3.19.2

※ pip install langchain langchain-chroma

[PYTHON/LANGCHAIN] RunnableBinding 클래스 : batch 메소드를 사용해 복수 문자열 쿼리 유사성 기반으로 문서 리스트 구하기

■ RunnableBinding 클래스의 batch 메소드를 사용해 복수 문자열 쿼리와의 유사성 기반으로 문서 리스트를 구하는 방법을 보여준다. ▶ main.py


import os

from langchain_core.documents import Document
from langchain_core.runnables import RunnableLambda
from langchain_chroma         import Chroma
from langchain_openai         import OpenAIEmbeddings

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

documentList = [
    Document(
        page_content = "Dogs are great companions, known for their loyalty and friendliness.",
        metadata     = {"source" : "mammal-pets-doc"}
    ),
    Document(
        page_content = "Cats are independent pets that often enjoy their own space.",
        metadata     = {"source" : "mammal-pets-doc"},
    ),
    Document(
        page_content = "Goldfish are popular pets for beginners, requiring relatively simple care.",
        metadata     = {"source" : "fish-pets-doc"},
    ),
    Document(
        page_content = "Parrots are intelligent birds capable of mimicking human speech.",
        metadata     = {"source" : "bird-pets-doc"},
    ),
    Document(
        page_content = "Rabbits are social animals that need plenty of space to hop around.",
        metadata     = {"source" : "mammal-pets-doc"},
    ),
]

chroma = Chroma.from_documents(
    documentList,
    embedding = OpenAIEmbeddings(),
)

runnableLambda = RunnableLambda(chroma.similarity_search)

runnableBinding = runnableLambda.bind(k = 1)  # 최상위 결과를 선택한다.

searchResultList = runnableBinding.batch(["cat", "shark"])

print(searchResultList)

"""
[
    [
        Document(
            page_content = 'Cats are independent pets that often enjoy their own space.',
            metadata     = {'source' : 'mammal-pets-doc'}
        )
    ],
    [
        Document(
            page_content = 'Goldfish are popular pets for beginners, requiring relatively simple care.',
            metadata     = {'source' : 'fish-pets-doc'}
        )
    ]
]
"""

import os

from langchain_core.documents import Document

from langchain_core.runnables import RunnableLambda

from langchain_chroma import Chroma

from langchain_openai import OpenAIEmbeddings

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

documentList = [

Document(

page_content = "Dogs are great companions, known for their loyalty and friendliness.",

metadata = {"source" : "mammal-pets-doc"}

Document(

page_content = "Cats are independent pets that often enjoy their own space.",

metadata = {"source" : "mammal-pets-doc"},

Document(

page_content = "Goldfish are popular pets for beginners, requiring relatively simple care.",

metadata = {"source" : "fish-pets-doc"},

Document(

page_content = "Parrots are intelligent birds capable of mimicking human speech.",

metadata = {"source" : "bird-pets-doc"},

Document(

page_content = "Rabbits are social animals that need plenty of space to hop around.",

metadata = {"source" : "mammal-pets-doc"},

]

chroma = Chroma.from_documents(

documentList,

embedding = OpenAIEmbeddings(),

)

runnableLambda = RunnableLambda(chroma.similarity_search)

runnableBinding = runnableLambda.bind(k = 1) # 최상위 결과를 선택한다.

searchResultList = runnableBinding.batch(["cat", "shark"])

print(searchResultList)

"""

[

Document(

page_content = 'Cats are independent pets that often enjoy their own space.',

metadata = {'source' : 'mammal-pets-doc'}

)

[

Document(

page_content = 'Goldfish are popular pets for beginners, requiring relatively simple care.',

metadata = {'source' : 'fish-pets-doc'}

)

]

"""

▶ requirements.txt


aiohttp==3.9.5
aiosignal==1.3.1
annotated-types==0.7.0
anyio==4.4.0
asgiref==3.8.1
async-timeout==4.0.3
attrs==23.2.0
backoff==2.2.1
bcrypt==4.1.3
build==1.2.1
cachetools==5.3.3
certifi==2024.6.2
charset-normalizer==3.3.2
chroma-hnswlib==0.7.3
chromadb==0.5.0
click==8.1.7
coloredlogs==15.0.1
Deprecated==1.2.14
distro==1.9.0
dnspython==2.6.1
email_validator==2.1.1
exceptiongroup==1.2.1
fastapi==0.111.0
fastapi-cli==0.0.4
filelock==3.14.0
flatbuffers==24.3.25
frozenlist==1.4.1
fsspec==2024.6.0
google-auth==2.30.0
googleapis-common-protos==1.63.1
greenlet==3.0.3
grpcio==1.64.1
h11==0.14.0
httpcore==1.0.5
httptools==0.6.1
httpx==0.27.0
huggingface-hub==0.23.3
humanfriendly==10.0
idna==3.7
importlib_metadata==7.1.0
importlib_resources==6.4.0
Jinja2==3.1.4
jsonpatch==1.33
jsonpointer==2.4
kubernetes==30.1.0
langchain==0.2.3
langchain-chroma==0.1.1
langchain-core==0.2.5
langchain-openai==0.1.8
langchain-text-splitters==0.2.1
langsmith==0.1.75
markdown-it-py==3.0.0
MarkupSafe==2.1.5
mdurl==0.1.2
mmh3==4.1.0
monotonic==1.6
mpmath==1.3.0
multidict==6.0.5
numpy==1.26.4
oauthlib==3.2.2
onnxruntime==1.18.0
openai==1.33.0
opentelemetry-api==1.25.0
opentelemetry-exporter-otlp-proto-common==1.25.0
opentelemetry-exporter-otlp-proto-grpc==1.25.0
opentelemetry-instrumentation==0.46b0
opentelemetry-instrumentation-asgi==0.46b0
opentelemetry-instrumentation-fastapi==0.46b0
opentelemetry-proto==1.25.0
opentelemetry-sdk==1.25.0
opentelemetry-semantic-conventions==0.46b0
opentelemetry-util-http==0.46b0
orjson==3.10.3
overrides==7.7.0
packaging==23.2
posthog==3.5.0
protobuf==4.25.3
pyasn1==0.6.0
pyasn1_modules==0.4.0
pydantic==2.7.3
pydantic_core==2.18.4
Pygments==2.18.0
PyPika==0.48.9
pyproject_hooks==1.1.0
python-dateutil==2.9.0.post0
python-dotenv==1.0.1
python-multipart==0.0.9
PyYAML==6.0.1
regex==2024.5.15
requests==2.32.3
requests-oauthlib==2.0.0
rich==13.7.1
rsa==4.9
shellingham==1.5.4
six==1.16.0
sniffio==1.3.1
SQLAlchemy==2.0.30
starlette==0.37.2
sympy==1.12.1
tenacity==8.3.0
tiktoken==0.7.0
tokenizers==0.19.1
tomli==2.0.1
tqdm==4.66.4
typer==0.12.3
typing_extensions==4.12.2
ujson==5.10.0
urllib3==2.2.1
uvicorn==0.30.1
uvloop==0.19.0
watchfiles==0.22.0
websocket-client==1.8.0
websockets==12.0
wrapt==1.16.0
yarl==1.9.4
zipp==3.19.2

100

101

102

103

104

105

106

107

108

109

110

111

112

113

114

115

116

117

118

aiohttp==3.9.5

aiosignal==1.3.1

annotated-types==0.7.0

anyio==4.4.0

asgiref==3.8.1

async-timeout==4.0.3

attrs==23.2.0

backoff==2.2.1

bcrypt==4.1.3

build==1.2.1

cachetools==5.3.3

certifi==2024.6.2

charset-normalizer==3.3.2

chroma-hnswlib==0.7.3

chromadb==0.5.0

click==8.1.7

coloredlogs==15.0.1

Deprecated==1.2.14

distro==1.9.0

dnspython==2.6.1

email_validator==2.1.1

exceptiongroup==1.2.1

fastapi==0.111.0

fastapi-cli==0.0.4

filelock==3.14.0

flatbuffers==24.3.25

frozenlist==1.4.1

fsspec==2024.6.0

google-auth==2.30.0

googleapis-common-protos==1.63.1

greenlet==3.0.3

grpcio==1.64.1

h11==0.14.0

httpcore==1.0.5

httptools==0.6.1

httpx==0.27.0

huggingface-hub==0.23.3

humanfriendly==10.0

idna==3.7

importlib_metadata==7.1.0

importlib_resources==6.4.0

Jinja2==3.1.4

jsonpatch==1.33

jsonpointer==2.4

kubernetes==30.1.0

langchain==0.2.3

langchain-chroma==0.1.1

langchain-core==0.2.5

langchain-openai==0.1.8

langchain-text-splitters==0.2.1

langsmith==0.1.75

markdown-it-py==3.0.0

MarkupSafe==2.1.5

mdurl==0.1.2

mmh3==4.1.0

monotonic==1.6

mpmath==1.3.0

multidict==6.0.5

numpy==1.26.4

oauthlib==3.2.2

onnxruntime==1.18.0

openai==1.33.0

opentelemetry-api==1.25.0

opentelemetry-exporter-otlp-proto-common==1.25.0

opentelemetry-exporter-otlp-proto-grpc==1.25.0

opentelemetry-instrumentation==0.46b0

opentelemetry-instrumentation-asgi==0.46b0

opentelemetry-instrumentation-fastapi==0.46b0

opentelemetry-proto==1.25.0

opentelemetry-sdk==1.25.0

opentelemetry-semantic-conventions==0.46b0

opentelemetry-util-http==0.46b0

orjson==3.10.3

overrides==7.7.0

packaging==23.2

posthog==3.5.0

protobuf==4.25.3

pyasn1==0.6.0

pyasn1_modules==0.4.0

pydantic==2.7.3

pydantic_core==2.18.4

Pygments==2.18.0

PyPika==0.48.9

pyproject_hooks==1.1.0

python-dateutil==2.9.0.post0

python-dotenv==1.0.1

python-multipart==0.0.9

PyYAML==6.0.1

regex==2024.5.15

requests==2.32.3

requests-oauthlib==2.0.0

rich==13.7.1

rsa==4.9

shellingham==1.5.4

six==1.16.0

sniffio==1.3.1

SQLAlchemy==2.0.30

starlette==0.37.2

sympy==1.12.1

tenacity==8.3.0

tiktoken==0.7.0

tokenizers==0.19.1

tomli==2.0.1

tqdm==4.66.4

typer==0.12.3

typing_extensions==4.12.2

ujson==5.10.0

urllib3==2.2.1

uvicorn==0.30.1

uvloop==0.19.0

watchfiles==0.22.0

websocket-client==1.8.0

websockets==12.0

wrapt==1.16.0

yarl==1.9.4

zipp==3.19.2

[PYTHON/LANGCHAIN] RunnableLambda 클래스 : bind 메소드를 사용해 RunnableBinding 객체 만들기

■ RunnableLambda 클래스의 bind 메소드를 사용해 RunnableBinding 객체를 만드는 방법을 보여준다. ▶ main.py


import os

from langchain_core.documents import Document
from langchain_core.runnables import RunnableLambda
from langchain_chroma         import Chroma
from langchain_openai         import OpenAIEmbeddings

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

documentList = [
    Document(
        page_content = "Dogs are great companions, known for their loyalty and friendliness.",
        metadata     = {"source" : "mammal-pets-doc"}
    ),
    Document(
        page_content = "Cats are independent pets that often enjoy their own space.",
        metadata     = {"source" : "mammal-pets-doc"},
    ),
    Document(
        page_content = "Goldfish are popular pets for beginners, requiring relatively simple care.",
        metadata     = {"source" : "fish-pets-doc"},
    ),
    Document(
        page_content = "Parrots are intelligent birds capable of mimicking human speech.",
        metadata     = {"source" : "bird-pets-doc"},
    ),
    Document(
        page_content = "Rabbits are social animals that need plenty of space to hop around.",
        metadata     = {"source" : "mammal-pets-doc"},
    ),
]

chroma = Chroma.from_documents(
    documentList,
    embedding = OpenAIEmbeddings(),
)

runnableLambda = RunnableLambda(chroma.similarity_search)

import os

from langchain_core.documents import Document

from langchain_core.runnables import RunnableLambda

from langchain_chroma import Chroma

from langchain_openai import OpenAIEmbeddings

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

documentList = [

Document(

page_content = "Dogs are great companions, known for their loyalty and friendliness.",

metadata = {"source" : "mammal-pets-doc"}

Document(

page_content = "Cats are independent pets that often enjoy their own space.",

metadata = {"source" : "mammal-pets-doc"},

Document(

page_content = "Goldfish are popular pets for beginners, requiring relatively simple care.",

metadata = {"source" : "fish-pets-doc"},

Document(

page_content = "Parrots are intelligent birds capable of mimicking human speech.",

metadata = {"source" : "bird-pets-doc"},

Document(

page_content = "Rabbits are social animals that need plenty of space to hop around.",

metadata = {"source" : "mammal-pets-doc"},

]

chroma = Chroma.from_documents(

documentList,

embedding = OpenAIEmbeddings(),

)

runnableLambda = RunnableLambda(chroma.similarity_search)

▶ requirements.txt


aiohttp==3.9.5
aiosignal==1.3.1
annotated-types==0.7.0
anyio==4.4.0
asgiref==3.8.1
async-timeout==4.0.3
attrs==23.2.0
backoff==2.2.1
bcrypt==4.1.3
build==1.2.1
cachetools==5.3.3
certifi==2024.6.2
charset-normalizer==3.3.2
chroma-hnswlib==0.7.3
chromadb==0.5.0
click==8.1.7
coloredlogs==15.0.1
Deprecated==1.2.14
distro==1.9.0
dnspython==2.6.1
email_validator==2.1.1
exceptiongroup==1.2.1
fastapi==0.111.0
fastapi-cli==0.0.4
filelock==3.14.0
flatbuffers==24.3.25
frozenlist==1.4.1
fsspec==2024.6.0
google-auth==2.30.0
googleapis-common-protos==1.63.1
greenlet==3.0.3
grpcio==1.64.1
h11==0.14.0
httpcore==1.0.5
httptools==0.6.1
httpx==0.27.0
huggingface-hub==0.23.3
humanfriendly==10.0
idna==3.7
importlib_metadata==7.1.0
importlib_resources==6.4.0
Jinja2==3.1.4
jsonpatch==1.33
jsonpointer==2.4
kubernetes==30.1.0
langchain==0.2.3
langchain-chroma==0.1.1
langchain-core==0.2.5
langchain-openai==0.1.8
langchain-text-splitters==0.2.1
langsmith==0.1.75
markdown-it-py==3.0.0
MarkupSafe==2.1.5
mdurl==0.1.2
mmh3==4.1.0
monotonic==1.6
mpmath==1.3.0
multidict==6.0.5
numpy==1.26.4
oauthlib==3.2.2
onnxruntime==1.18.0
openai==1.33.0
opentelemetry-api==1.25.0
opentelemetry-exporter-otlp-proto-common==1.25.0
opentelemetry-exporter-otlp-proto-grpc==1.25.0
opentelemetry-instrumentation==0.46b0
opentelemetry-instrumentation-asgi==0.46b0
opentelemetry-instrumentation-fastapi==0.46b0
opentelemetry-proto==1.25.0
opentelemetry-sdk==1.25.0
opentelemetry-semantic-conventions==0.46b0
opentelemetry-util-http==0.46b0
orjson==3.10.3
overrides==7.7.0
packaging==23.2
posthog==3.5.0
protobuf==4.25.3
pyasn1==0.6.0
pyasn1_modules==0.4.0
pydantic==2.7.3
pydantic_core==2.18.4
Pygments==2.18.0
PyPika==0.48.9
pyproject_hooks==1.1.0
python-dateutil==2.9.0.post0
python-dotenv==1.0.1
python-multipart==0.0.9
PyYAML==6.0.1
regex==2024.5.15
requests==2.32.3
requests-oauthlib==2.0.0
rich==13.7.1
rsa==4.9
shellingham==1.5.4
six==1.16.0
sniffio==1.3.1
SQLAlchemy==2.0.30
starlette==0.37.2
sympy==1.12.1
tenacity==8.3.0
tiktoken==0.7.0
tokenizers==0.19.1
tomli==2.0.1
tqdm==4.66.4
typer==0.12.3
typing_extensions==4.12.2
ujson==5.10.0
urllib3==2.2.1
uvicorn==0.30.1
uvloop==0.19.0
watchfiles==0.22.0
websocket-client==1.8.0
websockets==12.0
wrapt==1.16.0
yarl==1.9.4
zipp==3.19.2

100

101

102

103

104

105

106

107

108

109

110

111

112

113

114

115

116

117

118

aiohttp==3.9.5

aiosignal==1.3.1

annotated-types==0.7.0

anyio==4.4.0

asgiref==3.8.1

async-timeout==4.0.3

attrs==23.2.0

backoff==2.2.1

bcrypt==4.1.3

build==1.2.1

cachetools==5.3.3

certifi==2024.6.2

charset-normalizer==3.3.2

chroma-hnswlib==0.7.3

chromadb==0.5.0

click==8.1.7

coloredlogs==15.0.1

Deprecated==1.2.14

distro==1.9.0

dnspython==2.6.1

email_validator==2.1.1

exceptiongroup==1.2.1

fastapi==0.111.0

fastapi-cli==0.0.4

filelock==3.14.0

flatbuffers==24.3.25

frozenlist==1.4.1

fsspec==2024.6.0

google-auth==2.30.0

googleapis-common-protos==1.63.1

greenlet==3.0.3

grpcio==1.64.1

h11==0.14.0

httpcore==1.0.5

httptools==0.6.1

httpx==0.27.0

huggingface-hub==0.23.3

humanfriendly==10.0

idna==3.7

importlib_metadata==7.1.0

importlib_resources==6.4.0

Jinja2==3.1.4

jsonpatch==1.33

jsonpointer==2.4

kubernetes==30.1.0

langchain==0.2.3

langchain-chroma==0.1.1

langchain-core==0.2.5

langchain-openai==0.1.8

langchain-text-splitters==0.2.1

langsmith==0.1.75

markdown-it-py==3.0.0

MarkupSafe==2.1.5

mdurl==0.1.2

mmh3==4.1.0

monotonic==1.6

mpmath==1.3.0

multidict==6.0.5

numpy==1.26.4

oauthlib==3.2.2

onnxruntime==1.18.0

openai==1.33.0

opentelemetry-api==1.25.0

opentelemetry-exporter-otlp-proto-common==1.25.0

opentelemetry-exporter-otlp-proto-grpc==1.25.0

opentelemetry-instrumentation==0.46b0

opentelemetry-instrumentation-asgi==0.46b0

opentelemetry-instrumentation-fastapi==0.46b0

opentelemetry-proto==1.25.0

opentelemetry-sdk==1.25.0

opentelemetry-semantic-conventions==0.46b0

opentelemetry-util-http==0.46b0

orjson==3.10.3

overrides==7.7.0

packaging==23.2

posthog==3.5.0

protobuf==4.25.3

pyasn1==0.6.0

pyasn1_modules==0.4.0

pydantic==2.7.3

pydantic_core==2.18.4

Pygments==2.18.0

PyPika==0.48.9

pyproject_hooks==1.1.0

python-dateutil==2.9.0.post0

python-dotenv==1.0.1

python-multipart==0.0.9

PyYAML==6.0.1

regex==2024.5.15

requests==2.32.3

requests-oauthlib==2.0.0

rich==13.7.1

rsa==4.9

shellingham==1.5.4

six==1.16.0

sniffio==1.3.1

SQLAlchemy==2.0.30

starlette==0.37.2

sympy==1.12.1

tenacity==8.3.0

tiktoken==0.7.0

tokenizers==0.19.1

tomli==2.0.1

tqdm==4.66.4

typer==0.12.3

typing_extensions==4.12.2

ujson==5.10.0

urllib3==2.2.1

uvicorn==0.30.1

uvloop==0.19.0

watchfiles==0.22.0

websocket-client==1.8.0

websockets==12.0

wrapt==1.16.0

yarl==1.9.4

zipp==3.19.2

※ pip install langchain langchain-chroma

[PYTHON/LANGCHAIN] Chroma 클래스 : similarity_search_by_vector 메소드를 사용해 검색 문자열 벡터 리스트로 검색 결과 문서 리스트 구하기 1

■ Chroma 클래스의 similarity_search_by_vector 메소드를 사용해 검색 문자열의 벡터 리스트로 검색 결과 문서 리스트를 구하는 방법을 보여준다. ▶ main.py


import os

from langchain_core.documents import Document
from langchain_chroma         import Chroma
from langchain_openai         import OpenAIEmbeddings

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

documentList = [
    Document(
        page_content = "Dogs are great companions, known for their loyalty and friendliness.",
        metadata     = {"source" : "mammal-pets-doc"}
    ),
    Document(
        page_content = "Cats are independent pets that often enjoy their own space.",
        metadata     = {"source" : "mammal-pets-doc"},
    ),
    Document(
        page_content = "Goldfish are popular pets for beginners, requiring relatively simple care.",
        metadata     = {"source" : "fish-pets-doc"},
    ),
    Document(
        page_content = "Parrots are intelligent birds capable of mimicking human speech.",
        metadata     = {"source" : "bird-pets-doc"},
    ),
    Document(
        page_content = "Rabbits are social animals that need plenty of space to hop around.",
        metadata     = {"source" : "mammal-pets-doc"},
    ),
]

openAIEmbeddings = OpenAIEmbeddings()

chroma = Chroma.from_documents(
    documentList,
    embedding = openAIEmbeddings,
)

catEnbeddingList = openAIEmbeddings.embed_query("cat") # catEnbeddingList 항목 수 : 1536개

searchResultList = chroma.similarity_search_by_vector(catEnbeddingList)

print(searchResultList)

"""
[
    Document(
        page_content = 'Cats are independent pets that often enjoy their own space.',
        metadata     = {'source' : 'mammal-pets-doc'}
    ),
    Document(
        page_content = 'Dogs are great companions, known for their loyalty and friendliness.',
        metadata = {'source' : 'mammal-pets-doc'}
    ),
    Document(
        page_content = 'Rabbits are social animals that need plenty of space to hop around.',
        metadata     = {'source' : 'mammal-pets-doc'}
    ),
    Document(
        page_content = 'Parrots are intelligent birds capable of mimicking human speech.',
        metadata     = {'source' : 'bird-pets-doc'}
    )
]
"""

import os

from langchain_core.documents import Document

from langchain_chroma import Chroma

from langchain_openai import OpenAIEmbeddings

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

documentList = [

Document(

page_content = "Dogs are great companions, known for their loyalty and friendliness.",

metadata = {"source" : "mammal-pets-doc"}

Document(

page_content = "Cats are independent pets that often enjoy their own space.",

metadata = {"source" : "mammal-pets-doc"},

Document(

page_content = "Goldfish are popular pets for beginners, requiring relatively simple care.",

metadata = {"source" : "fish-pets-doc"},

Document(

page_content = "Parrots are intelligent birds capable of mimicking human speech.",

metadata = {"source" : "bird-pets-doc"},

Document(

page_content = "Rabbits are social animals that need plenty of space to hop around.",

metadata = {"source" : "mammal-pets-doc"},

]

openAIEmbeddings = OpenAIEmbeddings()

chroma = Chroma.from_documents(

documentList,

embedding = openAIEmbeddings,

)

catEnbeddingList = openAIEmbeddings.embed_query("cat") # catEnbeddingList 항목 수 : 1536개

searchResultList = chroma.similarity_search_by_vector(catEnbeddingList)

print(searchResultList)

"""

[

Document(

page_content = 'Cats are independent pets that often enjoy their own space.',

metadata = {'source' : 'mammal-pets-doc'}

Document(

page_content = 'Dogs are great companions, known for their loyalty and friendliness.',

metadata = {'source' : 'mammal-pets-doc'}

Document(

page_content = 'Rabbits are social animals that need plenty of space to hop around.',

metadata = {'source' : 'mammal-pets-doc'}

Document(

page_content = 'Parrots are intelligent birds capable of mimicking human speech.',

metadata = {'source' : 'bird-pets-doc'}

)

]

"""

▶ requirements.txt