이제 Gemini 1.5 Pro 2M 컨텍스트 윈도우, 코드 실행 기능, Gemma 2를 사용할 수 있습니다. 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 예시에서는 문서 검색을 수행할 수 있도록 Gemini API를 사용하여 임베딩을 만드는 방법을 보여줍니다. Python 클라이언트 라이브러리를 사용하여 검색 문자열이나 질문을 문서 콘텐츠와 비교할 수 있는 단어 임베딩을 빌드합니다.

이 튜토리얼에서는 임베딩을 사용하여 문서 집합에 대한 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
노트북 실행을 위한 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

# 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 받아야 합니다. 아직 키가 없다면 Google AI Studio에서 클릭 한 번으로 키를 만듭니다.

API 키 가져오기

Colab에서 'recommended' 아래의 보안 비밀 관리자에 키를 추가하세요. 왼쪽 패널에 표시됩니다. 이름을 API_KEY로 지정합니다.

API 키를 확보하면 이를 SDK에 전달합니다. 여기에는 두 가지 방법이 있습니다.

키를 GOOGLE_API_KEY 환경 변수에 입력합니다. SDK가 이 환경 변수에서 키를 자동으로 선택합니다.
키를 genai.configure(api_key=...)에 전달

를 통해 개인정보처리방침을 정의할 수 있습니다.

# 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 텍스트에 대한 임베딩을 생성하는 방법을 알아봅니다.

모델 Embeddings에 대한 API 변경사항

새 임베딩 모델인 embedding-001에는 새로운 작업 유형 매개변수와 선택적 제목이 있습니다 (task_type=RETRIEVAL_DOCUMENT인 경우에만 유효함).

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

할 일 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 샘플 텍스트 세 가지입니다. Gemini API를 사용하여 각 문서의 임베딩을 만들어 보겠습니다. 더 나은 시각화를 위해 데이터 프레임으로 전환하세요.

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 사전의 내용을 DataFrame으로 구성합니다.

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

이러한 각 텍스트 본문의 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 생성한 후 DataFrame의 임베딩 컬렉션과 비교합니다.

문제의 임베딩은 벡터 (부동 소수점 수 값 목록)가 되며 내적을 사용하는 문서의 벡터와 비교됩니다. API에서 반환된 이 벡터는 이미 정규화되었습니다. 내적은 두 벡터 간 방향의 유사성을 나타냅니다.

내적 값의 범위는 -1 이상 1 이하입니다. 두 벡터 간의 내적이 1이면 벡터의 방향도 같습니다. 내적 값이 0이면 이러한 벡터는 직각이거나 서로 관련이 없습니다. 마지막으로 내적이 -1이면 벡터는 반대 방향을 가리키며 서로 비슷하지 않습니다.

새 임베딩 모델 (embedding-001)을 사용하는 경우 사용자 쿼리의 경우 태스크 유형을 QUERY로 지정하고 문서 텍스트를 임베딩할 때는 DOCUMENT로 지정합니다.

할 일 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 DataFrame을 가장 큰 내적 값에서 가장 작은 내적 값 순으로 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 관련성 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.'

질문 및 답변 신청서

텍스트 생성 API를 사용하여 Q&A를 만들어 보겠습니다. 시스템입니다. 아래에 나만의 커스텀 데이터를 입력하여 간단한 질문과 답변 예시를 만들어 보세요. 내적은 유사성의 측정항목으로 계속 사용합니다.

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:

질문에 대한 답을 찾으려면 Gemini 콘텐츠 생성 모델 중 하나를 선택하세요.

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

models/gemini-pro
models/gemini-ultra

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

Markdown(answer.text)

제공된 문구에는 Google 자동차에서 기어를 변속하는 방법에 관한 정보가 포함되어 있지 않으므로 이 출처에서는 질문에 답변해 드릴 수 없습니다.

다음 단계

임베딩을 사용하는 방법을 자세히 알아보려면 다음 튜토리얼을 참조하세요.