Gemini API 개발자 대회에 참여하세요. 자세히 알아보기

이 페이지는 Cloud Translation API를 통해 번역되었습니다.

임베딩을 사용한 문서 검색

ai.google.dev에서 보기

Google Colab에서 실행

GitHub에서 소스 보기

개요

이 예에서는 문서 검색을 수행할 수 있도록 Gemini API를 사용하여 임베딩을 만드는 방법을 보여줍니다. Python 클라이언트 라이브러리를 사용하여 검색 문자열이나 질문을 문서 내용과 비교할 수 있는 단어 임베딩을 빌드합니다.

이 튜토리얼에서는 임베딩을 사용하여 문서 집합에 대해 문서 검색을 수행하여 Google 자동차와 관련된 질문을 합니다.

기본 요건

이 빠른 시작은 Google Colab에서 실행할 수 있습니다.

자체 개발 환경에서 이 빠른 시작을 완료하려면 환경이 다음 요구사항을 충족하는지 확인하세요.

Python 3.9 이상
노트북을 실행할 jupyter 설치

설정

먼저 Gemini API Python 라이브러리를 다운로드하고 설치합니다.

pip install -U -q google.generativeai

import textwrap
import numpy as np
import pandas as pd

import google.generativeai as genai
import google.ai.generativelanguage as glm

# Used to securely store your API key
from google.colab import userdata

from IPython.display import Markdown

API 키 가져오기

Gemini API를 사용하려면 먼저 API 키를 가져와야 합니다. 아직 키가 없으면 Google AI Studio에서 클릭 한 번으로 키를 만듭니다.

API 키 가져오기

Colab에서 왼쪽 패널의 'boot' 아래에 있는 보안 비밀 관리자에 키를 추가합니다. 이름을 API_KEY로 지정합니다.

API 키가 있으면 SDK에 전달합니다. 여기에는 두 가지 방법이 있습니다.

GOOGLE_API_KEY 환경 변수에 키를 배치합니다 (SDK가 자동으로 거기에서 가져옴).
genai.configure(api_key=...)에 키 전달

# Or use `os.getenv('API_KEY')` to fetch an environment variable.
API_KEY=userdata.get('API_KEY')

genai.configure(api_key=API_KEY)

for m in genai.list_models():
  if 'embedContent' in m.supported_generation_methods:
    print(m.name)

models/embedding-001
models/embedding-001

임베딩 생성

이 섹션에서는 Gemini API의 임베딩을 사용하여 텍스트 조각의 임베딩을 생성하는 방법을 알아봅니다.

모델 Embedding-001을 사용한 Embeddings의 API 변경사항

새 임베딩 모델인 embedding-001에는 새로운 작업 유형 매개변수와 선택적 제목이 있습니다 (task_type=RETRIEVAL_DOCUMENT에서만 유효).

이러한 새 매개변수는 최신 임베딩 모델에만 적용됩니다. 작업 유형은 다음과 같습니다.

작업 유형	설명
RETRIEVAL_QUERY	지정된 텍스트가 검색/가져오기 설정의 쿼리임을 지정합니다.
RETRIEVAL_DOCUMENT	지정된 텍스트가 검색/가져오기 설정의 문서임을 지정합니다.
SEMANTIC_SIMILARITY	지정된 텍스트를 시맨틱 텍스트 유사성(STS)에 사용하도록 지정합니다.
분류	임베딩이 분류에 사용되도록 지정합니다.
클러스터링	클러스터링에 임베딩을 사용하도록 지정합니다.

title = "The next generation of AI for developers and Google Workspace"
sample_text = ("Title: The next generation of AI for developers and Google Workspace"
    "\n"
    "Full article:\n"
    "\n"
    "Gemini API & Google AI Studio: An approachable way to explore and prototype with generative AI applications")

model = 'models/embedding-001'
embedding = genai.embed_content(model=model,
                                content=sample_text,
                                task_type="retrieval_document",
                                title=title)

print(embedding)

{'embedding': [0.034585103, -0.044509504, -0.027291223, 0.0072681927, 0.061689284, 0.03362112, 0.028627988, 0.022681564, 0.04958079, 0.07274552, 0.011150464, 0.04200501, -0.029782884, -0.0041767005, 0.05074771, -0.056339227, 0.051204756, 0.04734613, -0.022025354, 0.025162602, 0.046016376, -0.003416976, -0.024010269, -0.044340927, -0.01520864, -0.013577372, -0.009918958, -0.028144406, -0.00024770075, 0.031201784, -0.072506696, 0.022366496, -0.032672316, -0.0025522006, -0.0019957912, -0.023193765, -0.020633291, -0.014031609, -0.00071676675, -0.0073200124, 0.014770645, -0.09390713, -0.017846372, 0.032825496, 0.017616265, -0.046674345, 0.03469292, 0.03386835, 0.0028274113, -0.07737739, 0.023789782, 0.025950644, 0.06952142, -0.029875675, -0.018693604, 0.007266584, -0.0067282487, 0.000802912, 0.020609016, 0.012406181, -0.018825717, 0.051171597, -0.0080359895, 0.008457639, 0.01197146, -0.080320396, -0.040698495, 0.0018266322, 0.042915005, 0.021464704, 0.022519842, 0.0059912056, 0.050887667, 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임베딩 데이터베이스 빌드

다음은 임베딩 데이터베이스를 빌드하는 데 사용할 수 있는 세 가지 샘플 텍스트입니다. Gemini API를 사용하여 각 문서의 임베딩을 만듭니다. 더 나은 시각화를 위해 데이터 프레임으로 바꿉니다.

DOCUMENT1 = {
    "title": "Operating the Climate Control System",
    "content": "Your Googlecar has a climate control system that allows you to adjust the temperature and airflow in the car. To operate the climate control system, use the buttons and knobs located on the center console.  Temperature: The temperature knob controls the temperature inside the car. Turn the knob clockwise to increase the temperature or counterclockwise to decrease the temperature. Airflow: The airflow knob controls the amount of airflow inside the car. Turn the knob clockwise to increase the airflow or counterclockwise to decrease the airflow. Fan speed: The fan speed knob controls the speed of the fan. Turn the knob clockwise to increase the fan speed or counterclockwise to decrease the fan speed. Mode: The mode button allows you to select the desired mode. The available modes are: Auto: The car will automatically adjust the temperature and airflow to maintain a comfortable level. Cool: The car will blow cool air into the car. Heat: The car will blow warm air into the car. Defrost: The car will blow warm air onto the windshield to defrost it."}
DOCUMENT2 = {
    "title": "Touchscreen",
    "content": "Your Googlecar has a large touchscreen display that provides access to a variety of features, including navigation, entertainment, and climate control. To use the touchscreen display, simply touch the desired icon.  For example, you can touch the \"Navigation\" icon to get directions to your destination or touch the \"Music\" icon to play your favorite songs."}
DOCUMENT3 = {
    "title": "Shifting Gears",
    "content": "Your Googlecar has an automatic transmission. To shift gears, simply move the shift lever to the desired position.  Park: This position is used when you are parked. The wheels are locked and the car cannot move. Reverse: This position is used to back up. Neutral: This position is used when you are stopped at a light or in traffic. The car is not in gear and will not move unless you press the gas pedal. Drive: This position is used to drive forward. Low: This position is used for driving in snow or other slippery conditions."}

documents = [DOCUMENT1, DOCUMENT2, DOCUMENT3]

더 나은 시각화를 위해 사전 콘텐츠를 DataFrame으로 구성합니다.

df = pd.DataFrame(documents)
df.columns = ['Title', 'Text']
df

각 텍스트 본문에 대한 임베딩을 가져옵니다. 이 정보를 DataFrame에 추가합니다.

# Get the embeddings of each text and add to an embeddings column in the dataframe
def embed_fn(title, text):
  return genai.embed_content(model=model,
                             content=text,
                             task_type="retrieval_document",
                             title=title)["embedding"]

df['Embeddings'] = df.apply(lambda row: embed_fn(row['Title'], row['Text']), axis=1)
df

Q&A로 문서 검색

이제 임베딩이 생성되었으므로 이러한 문서를 검색하는 Q&A 시스템을 만들어 보겠습니다. 초매개변수 조정에 대해 질문하고, 질문의 임베딩을 만든 다음, 이를 DataFrame의 임베딩 컬렉션과 비교합니다.

질문의 임베딩은 내적을 사용하여 문서의 벡터와 비교되는 벡터 (부동 소수점 값 목록)가 됩니다. API에서 반환된 벡터는 이미 정규화되었습니다. 내적은 두 벡터 간 방향의 유사성을 나타냅니다.

내적의 값은 -1 이상 1 이하여야 합니다. 두 벡터 사이의 내적이 1이면 벡터의 방향은 같습니다. 내적 값이 0이면 이러한 벡터는 서로 직각을 이루거나 서로 관련이 없습니다. 마지막으로, 내적이 -1이면 벡터는 반대 방향을 가리키며 서로 유사하지 않습니다.

새 임베딩 모델 (embedding-001)을 사용하면 작업 유형을 사용자 쿼리의 경우 QUERY로, 문서 텍스트를 임베딩할 때는 DOCUMENT로 지정합니다.

작업 유형	설명
RETRIEVAL_QUERY	지정된 텍스트가 검색/가져오기 설정의 쿼리임을 지정합니다.
RETRIEVAL_DOCUMENT	지정된 텍스트가 검색/가져오기 설정의 문서임을 지정합니다.

query = "How do you shift gears in the Google car?"
model = 'models/embedding-001'

request = genai.embed_content(model=model,
                              content=query,
                              task_type="retrieval_query")

find_best_passage 함수를 사용하여 내적을 계산한 다음 데이터 프레임을 가장 큰 내적 값에서 가장 작은 내적 값으로 정렬하여 데이터베이스에서 관련 문구를 가져옵니다.

def find_best_passage(query, dataframe):
  """
  Compute the distances between the query and each document in the dataframe
  using the dot product.
  """
  query_embedding = genai.embed_content(model=model,
                                        content=query,
                                        task_type="retrieval_query")
  dot_products = np.dot(np.stack(dataframe['Embeddings']), query_embedding["embedding"])
  idx = np.argmax(dot_products)
  return dataframe.iloc[idx]['Text'] # Return text from index with max value

데이터베이스에서 가장 관련성이 높은 문서를 확인합니다.

passage = find_best_passage(query, df)
passage

'Shifting Gears  Your Googlecar has an automatic transmission. To shift gears, simply move the shift lever to the desired position.  Park: This position is used when you are parked. The wheels are locked and the car cannot move. Reverse: This position is used to back up. Neutral: This position is used when you are stopped at a light or in traffic. The car is not in gear and will not move unless you press the gas pedal. Drive: This position is used to drive forward. Low: This position is used for driving in snow or other slippery conditions.'

질의 응답 신청

텍스트 생성 API를 사용하여 Q&A 시스템을 만들어 보겠습니다. 아래에 맞춤 데이터를 입력하여 간단한 질문과 답변 예시를 만들어 보세요. 내적은 유사성의 측정항목으로 계속 사용됩니다.

def make_prompt(query, relevant_passage):
  escaped = relevant_passage.replace("'", "").replace('"', "").replace("\n", " ")
  prompt = textwrap.dedent("""You are a helpful and informative bot that answers questions using text from the reference passage included below. \
  Be sure to respond in a complete sentence, being comprehensive, including all relevant background information. \
  However, you are talking to a non-technical audience, so be sure to break down complicated concepts and \
  strike a friendly and converstional tone. \
  If the passage is irrelevant to the answer, you may ignore it.
  QUESTION: '{query}'
  PASSAGE: '{relevant_passage}'

    ANSWER:
  """).format(query=query, relevant_passage=escaped)

  return prompt

prompt = make_prompt(query, passage)
print(prompt)

You are a helpful and informative bot that answers questions using text from the reference passage included below.   Be sure to respond in a complete sentence, being comprehensive, including all relevant background information.   However, you are talking to a non-technical audience, so be sure to break down complicated concepts and   strike a friendly and converstional tone.   If the passage is irrelevant to the answer, you may ignore it.
  QUESTION: 'How do you shift gears in the Google car?'
  PASSAGE: 'Shifting Gears  Your Googlecar has an automatic transmission. To shift gears, simply move the shift lever to the desired position.  Park: This position is used when you are parked. The wheels are locked and the car cannot move. Reverse: This position is used to back up. Neutral: This position is used when you are stopped at a light or in traffic. The car is not in gear and will not move unless you press the gas pedal. Drive: This position is used to drive forward. Low: This position is used for driving in snow or other slippery conditions.'

    ANSWER:

Gemini 콘텐츠 생성 모델 중 하나를 선택하여 질문에 대한 답변을 찾습니다.

for m in genai.list_models():
  if 'generateContent' in m.supported_generation_methods:
    print(m.name)

models/gemini-pro
models/gemini-pro-vision
models/gemini-ultra

model = genai.GenerativeModel('gemini-1.5-pro-latest')
answer = model.generate_content(prompt)

Markdown(answer.text)

제공하신 문구에는 Google 자동차의 기어 변속 방법에 대한 정보가 포함되어 있지 않으므로 이 출처의 질문에 답변할 수 없습니다.

다음 단계

Gemini API에서 다른 서비스를 사용하는 방법을 알아보려면 Python 빠른 시작을 방문하세요.