LANGCHAIN Archives - 26 중 26 번째 페이지

[PYTHON/LANGCHAIN] Chroma 클래스 : similarity_search_with_score 메소드를 사용해 검색 문자열로 검색 결과 문서 리스트 구하기 (점수 포함)

■ Chroma 클래스의 similarity_search_with_score 메소드를 사용해 검색 문자열로 검색 결과 문서 리스트를 구하는 방법을 보여준다. (점수 포함) ▶ 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(),
)

searchResultList = chroma.similarity_search_with_score("cat")

print(searchResultList)

"""
[
    (
        Document(
            page_content = 'Cats are independent pets that often enjoy their own space.',
            metadata     = {'source' : 'mammal-pets-doc'}
        ),
        0.375326931476593
    ),
    (
        Document(
            page_content = 'Dogs are great companions, known for their loyalty and friendliness.',
            metadata     = {'source' : 'mammal-pets-doc'}
        ),
        0.4833090305328369
    ),
    (
        Document(
            page_content = 'Rabbits are social animals that need plenty of space to hop around.',
            metadata     = {'source' : 'mammal-pets-doc'}
        ),
        0.4958883225917816
    ),
    (
        Document(
            page_content = 'Parrots are intelligent birds capable of mimicking human speech.',
            metadata     = {'source' : 'bird-pets-doc'}
        ),
        0.4974174499511719
    )
]
"""

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(),

)

searchResultList = chroma.similarity_search_with_score("cat")

print(searchResultList)

"""

[

(

Document(

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

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

0.375326931476593

(

Document(

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

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

0.4833090305328369

(

Document(

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

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

0.4958883225917816

(

Document(

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

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

0.4974174499511719

)

]

"""

▶ requirements.txt

[PYTHON/LANGCHAIN] Chroma 클래스 : asimilarity_search 메소드를 사용해 검색 문자열로 검색 결과 문서 리스트 구하기 (비동기)

■ Chroma 클래스의 asimilarity_search 메소드를 사용해 검색 문자열로 검색 결과 문서 리스트를 구하는 방법을 보여준다. (비동기) ▶ main.py


import asyncio
import os

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

async def main():
    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(),
    )

    searchResultList = await chroma.asimilarity_search("cat")

    print(searchResultList)

asyncio.run(main())

"""
[
    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 asyncio

import os

from langchain_core.documents import Document

from langchain_chroma import Chroma

from langchain_openai import OpenAIEmbeddings

async def main():

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(),

)

searchResultList = await chroma.asimilarity_search("cat")

print(searchResultList)

asyncio.run(main())

"""

[

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


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

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103

104

105

106

107

108

109

110

111

112

113

114

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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 : similarity_search 메소드를 사용해 검색 문자열로 검색 결과 문서 리스트 구하기

■ Chroma 클래스의 similarity_search 메소드를 사용해 검색 문자열로 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"},
    ),
]

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

searchResultList = chroma.similarity_search("cat")

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"},

]

chroma = Chroma.from_documents(

)

documentList,

embedding = OpenAIEmbeddings(),

searchResultList = chroma.similarity_search("cat")

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


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] from_documents 함수 : 메모리 내 벡터 저장소를 위한 Chroma 객체 만들기

■ from_documents 함수를 사용해 메모리 내 벡터 저장소를 위한 Chroma 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(),
)

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(),

)

▶ 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/COMMON] langchain-chroma 패키지 설치하기

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


pip install langchain-chroma

pip install langchain-chroma

[PYTHON/LANGCHAIN] RunnableWithMessageHistory 클래스 : stream 메소드를 사용해 응답 스트리밍 처리하기

■ RunnableWithMessageHistory 클래스의 stream 메소드를 사용해 응답 스트리밍을 처리하는 방법을 보여준다. • LLM이 응답하는 데 시간이 걸릴 수 있으므로 사용자 경험을 개선하기

[PYTHON/LANGCHAIN] RunnablePassthrough 클래스 : assign 메소드를 사용해 대화 기록 관리하기

■ RunnablePassthrough 클래스의 assign 메소드를 사용해 대화 기록을 관리하는 방법을 보여준다. • 챗봇을 구축할 때 중요한 점은 대화 기록을 관리하는 방법이다. •

[PYTHON/LANGCHAIN] RunnableWithMessageHistory 클래스 : 챗봇 만들기

■ RunnableWithMessageHistory 클래스를 사용해 챗봇을 만드는 방법을 보여준다. ▶ main.py


import os

from langchain_core.prompts                     import ChatPromptTemplate
from langchain_core.prompts                     import MessagesPlaceholder
from langchain_openai                           import ChatOpenAI
from langchain_core.chat_history                import BaseChatMessageHistory
from langchain_community.chat_message_histories import ChatMessageHistory
from langchain_core.runnables.history           import RunnableWithMessageHistory
from langchain_core.messages                    import HumanMessage

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

chatPromptTemplate = ChatPromptTemplate.from_messages(
    [
        ("system", "You are a helpful assistant. Answer all questions to the best of your ability in {language}."),
        MessagesPlaceholder(variable_name = "messages"),
    ]
)

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

runnableSequence = chatPromptTemplate | chatOpenAI

storeDictionary = {}

def GetChatMessageHistory(sessionID : str) -> BaseChatMessageHistory:
    if sessionID not in storeDictionary:
        storeDictionary[sessionID] = ChatMessageHistory()
    return storeDictionary[sessionID]

runnableWithMessageHistory = RunnableWithMessageHistory(
    runnableSequence,
    GetChatMessageHistory,
    input_messages_key = "messages",
)

configuratonDictionary = {"configurable" : {"session_id" : "session1"}}

aiMessage1 = runnableWithMessageHistory.invoke(
    {
        "messages" : [HumanMessage(content = "hi! I'm todd")],
        "language" : "Korean"
    },
    config = configuratonDictionary,
)

aiMessage2 = runnableWithMessageHistory.invoke(
    {
        "messages" : [HumanMessage(content = "whats my name?")],
        "language" : "Korean"
    },
    config = configuratonDictionary,
)

print(aiMessage1.content)
print(aiMessage2.content)

"""
안녕하세요, 토드님! 어떻게 도와드릴까요?
당신의 이름은 토드입니다.
"""

import os

from langchain_core.prompts import ChatPromptTemplate

from langchain_core.prompts import MessagesPlaceholder

from langchain_openai import ChatOpenAI

from langchain_core.chat_history import BaseChatMessageHistory

from langchain_community.chat_message_histories import ChatMessageHistory

from langchain_core.runnables.history import RunnableWithMessageHistory

from langchain_core.messages import HumanMessage

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

chatPromptTemplate = ChatPromptTemplate.from_messages(

[

("system", "You are a helpful assistant. Answer all questions to the best of your ability in {language}."),

MessagesPlaceholder(variable_name = "messages"),

]

)

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

runnableSequence = chatPromptTemplate | chatOpenAI

storeDictionary = {}

def GetChatMessageHistory(sessionID : str) -> BaseChatMessageHistory:

if sessionID not in storeDictionary:

storeDictionary[sessionID] = ChatMessageHistory()

return storeDictionary[sessionID]

runnableWithMessageHistory = RunnableWithMessageHistory(

runnableSequence,

GetChatMessageHistory,

input_messages_key = "messages",

)

configuratonDictionary = {"configurable" : {"session_id" : "session1"}}

aiMessage1 = runnableWithMessageHistory.invoke(

{

"messages" : [HumanMessage(content = "hi! I'm todd")],

"language" : "Korean"

config = configuratonDictionary,

)

aiMessage2 = runnableWithMessageHistory.invoke(

{

"messages" : [HumanMessage(content = "whats my name?")],

"language" : "Korean"

config = configuratonDictionary,

)

print(aiMessage1.content)

print(aiMessage2.content)

"""

안녕하세요, 토드님! 어떻게 도와드릴까요?

당신의 이름은 토드입니다.

"""

▶ 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==0.6.6
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
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
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==0.6.6

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

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

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 langchain langchain-openai langchain_community 명령을 실행했다.

[PYTHON/COMMON] langchain_community 패키지 설치하기

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


pip install langchain_community

pip install langchain_community

[PYTHON/LANGCHAIN] | 연산자 : 프롬프트 템플리트, LLM 모델과 출력 파서 체인 만들기

■ | 연산자를 사용해 프롬프트 템플리트, LLM 모델과 출력 파서의 체인을 만드는 방법을 보여준다. ▶ main.py


import os

from langchain_core.prompts        import ChatPromptTemplate
from langchain_openai              import ChatOpenAI
from langchain_core.output_parsers import StrOutputParser

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

promptTemplate = ChatPromptTemplate.from_messages(
    [
        ("system", "Translate the following into {language} : "),
        ("user"  , "{text}"                                    )
    ]
)

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

strOutputParser = StrOutputParser()

runnableSequence = promptTemplate | chatOpenAI | strOutputParser

resultString = runnableSequence.invoke({"language" : "korean", "text" : "hi"})

print(resultString)

"""
안녕하세요
"""

import os

from langchain_core.prompts import ChatPromptTemplate

from langchain_openai import ChatOpenAI

from langchain_core.output_parsers import StrOutputParser

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

promptTemplate = ChatPromptTemplate.from_messages(

[

("system", "Translate the following into {language} : "),

("user" , "{text}" )

]

)

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

strOutputParser = StrOutputParser()

runnableSequence = promptTemplate | chatOpenAI | strOutputParser

resultString = runnableSequence.invoke({"language" : "korean", "text" : "hi"})

print(resultString)

"""

안녕하세요

"""

▶ 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

[PYTHON/LANGCHAIN] langserve 모듈 : langchain 모듈 체인을 REST API 서버로 배포하기

■ langserve 모듈을 사용해 langchain 모듈의 체인을 REST API 서버로 배포하는 방법을 보여준다. ▶ server.py


import os

import uvicorn

from langchain_core.prompts        import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
from langchain_openai              import ChatOpenAI
from fastapi                       import FastAPI
from langserve                     import add_routes

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

promptTemplate = ChatPromptTemplate.from_messages(
    [
        ("system", "Translate the following into {language} : "),
        ("user"  , "{text}"                                    )
    ]
)

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

strOutputParser = StrOutputParser()

runnableSequence = promptTemplate | chatOpenAI | strOutputParser

fastAPI = FastAPI(
  title       = "LangChain Server",
  version     = "1.0",
  description = "A simple API server using LangChain's Runnable interfaces",
)

add_routes(
    fastAPI,
    runnableSequence,
    path = "/chain",
)

if __name__ == "__main__":
    uvicorn.run(fastAPI, host = "0.0.0.0", port = 8000)

import os

import uvicorn

from langchain_core.prompts import ChatPromptTemplate

from langchain_core.output_parsers import StrOutputParser

from langchain_openai import ChatOpenAI

from fastapi import FastAPI

from langserve import add_routes

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

promptTemplate = ChatPromptTemplate.from_messages(

[

("system", "Translate the following into {language} : "),

("user" , "{text}" )

]

)

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

strOutputParser = StrOutputParser()

runnableSequence = promptTemplate | chatOpenAI | strOutputParser

fastAPI = FastAPI(

title = "LangChain Server",

version = "1.0",

description = "A simple API server using LangChain's Runnable interfaces",

)

add_routes(

fastAPI,

runnableSequence,

path = "/chain",

)

if __name__ == "__main__":

uvicorn.run(fastAPI, host = "0.0.0.0", port = 8000)

▶ client.py


from langserve import RemoteRunnable

remoteRunnable = RemoteRunnable("http://localhost:8000/chain/")

resultString = remoteRunnable.invoke({"language" : "korean", "text" : "hi"})

print(resultString)

from langserve import RemoteRunnable

remoteRunnable = RemoteRunnable("http://localhost:8000/chain/")

resultString = remoteRunnable.invoke({"language" : "korean", "text" : "hi"})

print(resultString)

▶ 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

[PYTHON/COMMON] langserve 패키지 설치하기

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


pip install "langserve[all]"

pip install "langserve[all]"

[PYTHON/LANGCHAIN] ChatPromptValue 클래스 : to_messages 메소드를 사용해 ChatPromptTemplate 객체에서 메시지 리스트 구하기

■ ChatPromptValue 클래스의 to_messages 메소드를 사용해 ChatPromptTemplate 객체에서 메시지 리스트를 구하는 방법을 배운다. ▶ main.py


from langchain_core.prompts import ChatPromptTemplate

chatPromptTemplate = ChatPromptTemplate.from_messages(
    [
        ("system", "Translate the following into {language} : "),
        ("user"  , "{text}"                                    )
    ]
)

chatPromptValue = chatPromptTemplate.invoke({"language" : "Korean", "text" : "Good moring!"})

resultMessageList = chatPromptValue.to_messages()

print(resultMessageList)

"""
[SystemMessage(content='Translate the following into Korean : '), HumanMessage(content='Good moring!')]
"""

from langchain_core.prompts import ChatPromptTemplate

chatPromptTemplate = ChatPromptTemplate.from_messages(

[

("system", "Translate the following into {language} : "),

("user" , "{text}" )

]

)

chatPromptValue = chatPromptTemplate.invoke({"language" : "Korean", "text" : "Good moring!"})

resultMessageList = chatPromptValue.to_messages()

print(resultMessageList)

"""

[SystemMessage(content='Translate the following into Korean : '), HumanMessage(content='Good moring!')]

"""

▶ 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", "Translate the following into {language} : "), ("user", "{text}")])

chatPromptValue = chatPromptTemplate.invoke({"language" : "korean", "text" : "Good morning!"})

print(chatPromptValue)

"""
messages=[SystemMessage(content='Translate the following into korean : '), HumanMessage(content='Good morning')]
"""

from langchain_core.prompts import ChatPromptTemplate

chatPromptTemplate = ChatPromptTemplate.from_messages([("system", "Translate the following into {language} : "), ("user", "{text}")])

chatPromptValue = chatPromptTemplate.invoke({"language" : "korean", "text" : "Good morning!"})

print(chatPromptValue)

"""

messages=[SystemMessage(content='Translate the following into korean : '), HumanMessage(content='Good morning')]

"""

▶ 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] | 연산자 : LLM 모델과 출력 파서 체인 만들기

■ | 연산자를 사용해 LLM 모델과 출력 파서의 체인을 만드는 방법을 보여준다. ▶ main.py


import os

from langchain_core.messages       import HumanMessage, SystemMessage
from langchain_core.output_parsers import StrOutputParser
from langchain_openai              import ChatOpenAI

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

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

strOutputParser = StrOutputParser()

runnableSequence = chatOpenAI | strOutputParser

messageList = [
    SystemMessage(content = "Translate the following from English into Korean"),
    HumanMessage (content = "hi!")
]

resultString = runnableSequence.invoke(messageList)

print(resultString)

"""
안녕!
"""

import os

from langchain_core.messages import HumanMessage, SystemMessage

from langchain_core.output_parsers import StrOutputParser

from langchain_openai import ChatOpenAI

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

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

strOutputParser = StrOutputParser()

runnableSequence = chatOpenAI | strOutputParser

messageList = [

SystemMessage(content = "Translate the following from English into Korean"),

HumanMessage (content = "hi!")

]

resultString = runnableSequence.invoke(messageList)

print(resultString)

"""

안녕!

"""

▶ 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

[PYTHON/LANGCHAIN] StrOutputParser 클래스 : invoke 메소드를 사용해 출력 결과 파싱하기

■ StrOutputParser 클래스의 invoke 메소드를 사용해 출력 결과를 파싱하는 방법을 보여준다. ※ StrOutputParser 클래스는 AIMessageChunk에서 content 필드를 추출하여 모델이 반환한 토큰을 제공하는

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

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


import os

from langchain_core.messages import HumanMessage, SystemMessage
from langchain_openai        import ChatOpenAI

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

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

messageList = [
    SystemMessage(content = "Translate the following from English into Korean"),
    HumanMessage (content = "hi!")
]

aiMessage = chatOpenAI.invoke(messageList)

print(aiMessage.content)

"""
안녕!
"""

import os

from langchain_core.messages import HumanMessage, SystemMessage

from langchain_openai import ChatOpenAI

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

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

messageList = [

SystemMessage(content = "Translate the following from English into Korean"),

HumanMessage (content = "hi!")

]

aiMessage = chatOpenAI.invoke(messageList)

print(aiMessage.content)

"""

안녕!

"""

▶ 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] | 연산자 : 프롬프트 템플리트와 LLM 모델 체인 만들기

■ | 연산자를 사용해 프롬프트 템플리트와 LLM 모델의 체인을 만드는 방법을 보여준다. ▶ main.py


import os

from langchain.prompts import PromptTemplate
from langchain_openai  import OpenAI

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

promptTemplate = PromptTemplate(
    input_variables = ["product"],
    template        = "{product}을 만드는 새로운 한국어 회사명을 하나 제안해 주세요."
)

openAI = OpenAI(temperature = 0.9)

runnableSequence = promptTemplate | openAI

resultString = runnableSequence.invoke("가정용 로봇")

print(resultString)

"""

"홈로봇즈"
"""

import os

from langchain.prompts import PromptTemplate

from langchain_openai import OpenAI

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

promptTemplate = PromptTemplate(

input_variables = ["product"],

template = "{product}을 만드는 새로운 한국어 회사명을 하나 제안해 주세요."

)

openAI = OpenAI(temperature = 0.9)

runnableSequence = promptTemplate | openAI

resultString = runnableSequence.invoke("가정용 로봇")

print(resultString)

"""

"홈로봇즈"

"""

▶ 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 openai

[PYTHON/LANGCHAIN] PromptTemplate 클래스 : 생성자에서 input_variables/template 인자를 사용해 PromptTemplate 객체 만들기 1

■ PromptTemplate 클래스의 생성자에서 input_variables/template 인자를 사용해 PromptTemplate 객체를 만드는 방법을 보여준다. • 프롬프트 템플리트는 사용자 입력으로 프롬프트를 생성하기 위한 템플리트이다. •

[PYTHON/COMMON] langchain-openai 패키지 설치하기

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


pip install langchain-openai

pip install langchain-openai

[PYTHON/LANGCHAIN] OpenAI 클래스 : 질의 응답하기

■ OpenAI 클래스를 사용해 질의 응답하는 방법을 보여준다. ▶ main.py


import os

from langchain_openai import OpenAI

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

openAI = OpenAI(temperature = 0.9)

resultString = openAI("컴퓨터 게임을 만드는 새로운 한국어 회사명을 하나 제안해 주세요.")

print(resultString)

"""
"비트월드 게임즈"
"""

import os

from langchain_openai import OpenAI

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

openAI = OpenAI(temperature = 0.9)

resultString = openAI("컴퓨터 게임을 만드는 새로운 한국어 회사명을 하나 제안해 주세요.")

print(resultString)

"""

"비트월드 게임즈"

"""

▶ 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 openai langchain langchain-openai 명령을 실행했다.

[PYTHON/COMMON] langchain-huggingface 패키지 설치하기

■ langchain-huggingface 패키지를 업그레이드하는 방법을 보여준다. 1. 명령 프롬프트를 실행한다. 2. 명령 프롬프트에서 아래 명령을 실행한다. ▶ 실행 명령


pip install langchain-huggingface

pip install langchain-huggingface

[PYTHON/COMMON] langchain-community 패키지 설치하기

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


pip install langchain-community

pip install langchain-community

[PYTHON/COMMON] langchain 패키지 설치하기

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


pip install langchain

pip install langchain