Google Generative AI API 인 Google PaLM 2 API를 사용하기 위해 API Key를 우선 발급한다.
https://aistudio.google.com/app
위 링크를 클릭하여 사이트로 이동 후
Sign In 버튼 선택 후 본인의 Google 계정으로 로그인 한다.
약관 동의 후 계속 입력
좌측 상단 Get API Key 선택
API 키 만들기 -> 새 프로젝트에서 API 키 만들기 선택
API Key 생성 후 복사 버튼으로 복사해서 간직한다.
Python JupyterLab 에서 Google LLM 동작 시키기
[1]:
위에서 생성한 API key가 제대로 생성되었는지 확인을 하기 위해 JupyterLab에서 아래 API를 호출해 본다.
호출 후 응답이 오면 정상적으로 API key가 동작하는것이다.
!curl \
-H 'Content-Type: application/json' \
-d '{"contents":[{"parts":[{"text":"Write a story about a magic backpack"}]}]}' \
-X POST https://generativelanguage.googleapis.com/v1beta/models/gemini-pro:generateContent?key=AIzaSyBtalyoCQDZ7BLVdp4rauxxxxxxxxxxxxx{
"candidates": [
{
"content": {
"parts": [
{
"text": "In the quaint town of Willow Creek, amidst the whispering willows and blooming wildflowers, there lived a peculiar young girl named Anya. Unknown to her, her life was about to take an extraordinary turn when she stumbled upon a hidden treasure—a backpack unlike any other.\n\nAs she rummaged through her grandmother's attic, Anya's eyes sparkled at the sight of an old, dusty backpack tucked away in a forgotten corner. Curiosity got the better of her, and she reached for it. As her fingers brushed against the leather, a faint hum echoed through the room.\n\nSuddenly, the backpack began to glow with an ethereal light. Anya gasped in amazement as it unzipped itself, revealing a kaleidoscopic swirl of colors and symbols. Intrigued, she gingerly slipped it onto her shoulders, and that was when the magic began.\n\nWith each step she took, the backpack seemed to adapt to her needs. Its compartments expanded and contracted magically, holding all her belongings effortlessly. But that wasn't all. As Anya walked through the town, she noticed that people subconsciously made way for her, as if drawn to an inexplicable force emanating from the enchanted backpack.\n\nOne afternoon, as Anya explored the nearby forest, she stumbled upon a group of hikers who had lost their way. Without hesitation, she unzipped a hidden compartment in the backpack that contained a compass and a map. The hikers gasped in gratitude as Anya expertly navigated them back to safety.\n\nWord of Anya's extraordinary backpack spread like wildfire. Soon, people from far and wide sought her help with their troubles. The backpack healed the sick, provided shelter during storms, and even extinguished fires with a mysterious water spout.\n\nBut with great power came great responsibility. As Anya realized the true extent of her backpack's magic, she dedicated herself to using it for good. She became known as \"Anya the Backpack Keeper,\" a symbol of hope and kindness that touched the hearts of all who encountered her.\n\nAnd so, the magic backpack became an integral part of the legend of Willow Creek, a tale whispered throughout generations, reminding people that even the most ordinary of objects can hold extraordinary power when guided by a heart filled with compassion."
}
],
"role": "model"
},
"finishReason": "STOP",
"index": 0,
"safetyRatings": [
{
"category": "HARM_CATEGORY_SEXUALLY_EXPLICIT",
"probability": "NEGLIGIBLE"
},
{
"category": "HARM_CATEGORY_HATE_SPEECH",
"probability": "NEGLIGIBLE"
},
{
"category": "HARM_CATEGORY_HARASSMENT",
"probability": "NEGLIGIBLE"
},
{
"category": "HARM_CATEGORY_DANGEROUS_CONTENT",
"probability": "NEGLIGIBLE"
}
]
}
],
"usageMetadata": {
"promptTokenCount": 7,
"candidatesTokenCount": 447,
"totalTokenCount": 454
}
}
필요한 packages를 설치한다.
!pip install langchain==0.1.9
!pip install langchain_google_genai==0.0.9
!pip install langchain-community==0.0.24
!pip install langchainhub==0.1.15
!pip install tiktoken==0.6.0
!pip install chromadb==0.4.24
실행 결과
더보기
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Installing collected packages: tenacity, pydantic-core, packaging, orjson, mypy-extensions, multidict, jsonpatch, greenlet, frozenlist, annotated-types, yarl, typing-inspect, SQLAlchemy, pydantic, marshmallow, aiosignal, langsmith, dataclasses-json, aiohttp, langchain-core, langchain-community, langchain
Attempting uninstall: packaging
Found existing installation: packaging 24.0
Uninstalling packaging-24.0:
Successfully uninstalled packaging-24.0
Successfully installed SQLAlchemy-2.0.30 aiohttp-3.9.5 aiosignal-1.3.1 annotated-types-0.6.0 dataclasses-json-0.6.6 frozenlist-1.4.1 greenlet-3.0.3 jsonpatch-1.33 langchain-0.1.9 langchain-community-0.0.38 langchain-core-0.1.52 langsmith-0.1.59 marshmallow-3.21.2 multidict-6.0.5 mypy-extensions-1.0.0 orjson-3.10.3 packaging-23.2 pydantic-2.7.1 pydantic-core-2.18.2 tenacity-8.3.0 typing-inspect-0.9.0 yarl-1.9.4
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Installing collected packages: pypika, mpmath, monotonic, mmh3, zipp, websockets, uvloop, ujson, sympy, shellingham, python-multipart, python-dotenv, pyproject_hooks, pulsar-client, opentelemetry-util-http, opentelemetry-semantic-conventions, opentelemetry-proto, mdurl, importlib-resources, humanfriendly, httptools, fsspec, filelock, dnspython, deprecated, click, chroma-hnswlib, bcrypt, backoff, asgiref, watchfiles, uvicorn, starlette, posthog, opentelemetry-exporter-otlp-proto-common, markdown-it-py, importlib-metadata, huggingface-hub, email_validator, coloredlogs, build, tokenizers, rich, opentelemetry-api, onnxruntime, kubernetes, typer, opentelemetry-sdk, opentelemetry-instrumentation, opentelemetry-instrumentation-asgi, opentelemetry-exporter-otlp-proto-grpc, opentelemetry-instrumentation-fastapi, fastapi-cli, fastapi, chromadb
Successfully installed asgiref-3.8.1 backoff-2.2.1 bcrypt-4.1.3 build-1.2.1 chroma-hnswlib-0.7.3 chromadb-0.4.24 click-8.1.7 coloredlogs-15.0.1 deprecated-1.2.14 dnspython-2.6.1 email_validator-2.1.1 fastapi-0.111.0 fastapi-cli-0.0.3 filelock-3.14.0 fsspec-2024.5.0 httptools-0.6.1 huggingface-hub-0.23.0 humanfriendly-10.0 importlib-metadata-7.0.0 importlib-resources-6.4.0 kubernetes-29.0.0 markdown-it-py-3.0.0 mdurl-0.1.2 mmh3-4.1.0 monotonic-1.6 mpmath-1.3.0 onnxruntime-1.18.0 opentelemetry-api-1.24.0 opentelemetry-exporter-otlp-proto-common-1.24.0 opentelemetry-exporter-otlp-proto-grpc-1.24.0 opentelemetry-instrumentation-0.45b0 opentelemetry-instrumentation-asgi-0.45b0 opentelemetry-instrumentation-fastapi-0.45b0 opentelemetry-proto-1.24.0 opentelemetry-sdk-1.24.0 opentelemetry-semantic-conventions-0.45b0 opentelemetry-util-http-0.45b0 posthog-3.5.0 pulsar-client-3.5.0 pypika-0.48.9 pyproject_hooks-1.1.0 python-dotenv-1.0.1 python-multipart-0.0.9 rich-13.7.1 shellingham-1.5.4 starlette-0.37.2 sympy-1.12 tokenizers-0.19.1 typer-0.12.3 ujson-5.10.0 uvicorn-0.29.0 uvloop-0.19.0 watchfiles-0.21.0 websockets-12.0 zipp-3.18.2
Python 변수에 전역으로 발급받은 API Key를 저장한다.
GOOGLE_API_KEY = "AIzaSyBtalyoCQDZ7BLVdpxxxxxxxxxxxxxxxxxxxxxxxxx"
LangChaing 라이브러리를 import하고 GoogleGenerative AI llm 객체를 생성한다.
모델은 text-bison-001 모델을 선택해본다.
from langchain_google_genai import GoogleGenerativeAI
from langchain.prompts import PromptTemplate
from langchain.chains.llm import LLMChain
from langchain.chains.combine_documents.stuff import StuffDocumentsChain
# 현재(24.02.29) 사용 가능 모델: 'models/text-bison-001'(요청 90/분), 'models/gemini-pro' (요청 60/분)
llm = GoogleGenerativeAI(model='models/text-bison-001', google_api_key=GOOGLE_API_KEY)
간단히 긴 글을 주고 2줄로 요약을 하는 시나리오를 돌려보자. 우선 요약할 대상 문단을 변수에 저장한다.
content = """
TEXT:
The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.
It is characterized by its bold black-and-white coat and rotund body.
The name "giant panda" is sometimes used to distinguish it from the red panda, a neighboring musteloid.
Though it belongs to the order Carnivora, the giant panda is a folivore, with bamboo shoots and leaves making up more than 99% of its diet.
Giant pandas in the wild occasionally eat other grasses, wild tubers, or even meat in the form of birds, rodents, or carrion.
In captivity, they may receive honey, eggs, fish, yams, shrub leaves, oranges, or bananas along with specially prepared food."""
String 프롬프트를 위한 템플릿(template)을 생성한다. llm에게 2줄로 요약을 요청하는 것이므로 프롬프트는 아래와 같이 영문으로 2줄 요약 명령을 입력한다.
# 1. PromptTemplate.from_template()을 사용하는 방법 (recommended)
prompt = PromptTemplate.from_template("Please provide a summary of the following text in 2 lines. {content}")
# 2. PromptTemplate 생성자를 사용하는 방법
# prompt = PromptTemplate(
# input_variables=["content"],
# template="Please provide a summary of the following text in 2 lines. {content}"
# )
LLM에 질의 후 결과 받기
최종적으로 위에서 생성한 prompt와 llm으로 체인을 만들고 LangChaing의 invoke를 호출하여 Sync 응답을 받는다.
# LLM chain을 설정
# 1. LLMChain 방법을 사용
# llm_chain = LLMChain(llm=llm, prompt=prompt)
# llm_chain.invoke({'content': content})
# 2. LCEL을 사용 (recommended)
llm_chain = prompt | llm
llm_chain.invoke({'content': content})
실행결과는 아래와 같다. 두개의 문장으로 요약 결과를 출력해준다.
'The giant panda is a bear species found in China. It is characterized by its black and white fur and eats bamboo.'
-- The End --