Gemini API Geliştirici Yarışması'na katılın. Daha fazla bilgi

Bu sayfa, Cloud Translation API ile çevrilmiştir.

Gömme ile doküman arama

ai.google.dev'de görüntüleyin

Google Colab'de çalıştır

Kaynağı GitHub'da görüntüleyin

Genel bakış

Bu örnekte, doküman araması yapabilmek için yerleştirilmiş öğeler oluşturmak üzere Gemini API'nin nasıl kullanılacağı gösterilmektedir. Arama dizelerini veya soruları doküman içeriğiyle karşılaştırmanıza olanak tanıyan bir kelime yerleştirme sistemi oluşturmak için Python istemci kitaplığını kullanacaksınız.

Bu eğiticide, Google Arabası ile ilgili sorular sormak üzere bir dizi doküman üzerinde doküman araması yapmak için yerleştirmeleri kullanacaksınız.

Ön koşullar

Bu hızlı başlangıç kılavuzunu Google Colab'de çalıştırabilirsiniz.

Bu hızlı başlangıç kılavuzunu kendi geliştirme ortamınızda tamamlamak için ortamınızın aşağıdaki gereksinimleri karşıladığından emin olun:

Python 3.9 ve üzeri
Not defterini çalıştırmak için jupyter yüklemesi.

Kurulum

Öncelikle Gemini API Python kitaplığını indirip yükleyin.

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 Anahtarı Alma

Gemini API'yi kullanabilmek için önce bir API anahtarı edinmeniz gerekir. Anahtarınız yoksa Google AI Studio'da tek tıklamayla oluşturun.

API anahtarı alma

Colab'de, anahtarı sol paneldeki "🔑" simgesinin altında gizli anahtar yöneticisine ekleyin. API_KEY adını verin.

API anahtarınızı aldıktan sonra SDK'ya iletin. Bunu iki şekilde yapabilirsiniz:

Anahtarı, GOOGLE_API_KEY ortam değişkenine yerleştirin (SDK otomatik olarak oradan alır).
Anahtarı genai.configure(api_key=...) adlı cihaza verin

# 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

Yerleştirme oluşturma

Bu bölümde, Gemini API'deki yerleştirmeleri kullanarak bir metin parçası için nasıl yerleştirilmiş öğeler oluşturacağınızı göreceksiniz.

Yerleştirme modeli ile yerleştirmede yapılan API değişiklikleri-001

Yerleştirme-001 adlı yeni yerleştirme modeli için yeni bir görev türü parametresi ve isteğe bağlı başlık (yalnızcatask_type=RETRIEVAL_DOCUMENT ile geçerlidir) bulunmaktadır.

Bu yeni parametreler yalnızca en yeni yerleştirme modelleri için geçerlidir.Görev türleri şunlardır:

Görev Türü	Açıklama
RETRIEVAL_QUERY	Belirtilen metnin, arama/alma ayarında sorgu olduğunu belirtir.
RETRIEVAL_DOCUMENT	Belirtilen metnin, arama/alma ayarında doküman olduğunu belirtir.
SEMANTIC_SIMILARITY	Belirtilen metnin Semantik Metin Benzerliği (STS) için kullanılacağını belirtir.
SINIFLANDIRMA	Yerleştirmelerin sınıflandırma için kullanılacağını belirtir.
KÜMELEME	Yerleştirmelerin kümeleme için kullanılacağını belirtir.

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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Yerleştirme veritabanı oluşturma

Burada, yerleştirilmiş öğe veritabanını oluşturmak için kullanılacak üç örnek metin sunulmaktadır. Dokümanların her birinin yerleştirmelerini oluşturmak için Gemini API'yi kullanacaksınız. Daha iyi görselleştirme için bunları veri çerçevesine dönüştürün.

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]

Daha iyi görselleştirme için sözlüğün içeriğini bir veri çerçevesi içinde düzenleyin.

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

Bu metin kütlelerinin her biri için yerleştirilmiş öğeleri alın. Bu bilgileri veri çerçevesine ekleyin.

# 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

Soru-Cevap oturumuyla doküman arama

Artık öğeler oluşturulduğuna göre bu dokümanları aramak için bir Soru-Cevap sistemi oluşturalım. Hiperparametre ayarı hakkında bir soru soracak, sorunun bir gömülmesini oluşturacak ve bunu veri çerçevesindeki yerleştirmelerin koleksiyonuyla karşılaştıracaksınız.

Sorunun gömülmesi bir vektör (kayan değer listesi) olacaktır. Bu değer, nokta çarpımı kullanılarak belgelerin vektörüyle karşılaştırılacaktır. API'den döndürülen bu vektör zaten normalleştirilmiştir. Noktalı çarpım, iki vektör arasındaki yön benzerliğini temsil eder.

Nokta çarpımının değerleri, -1 ile 1 (1 dahil) arasında değişebilir. İki vektör arasındaki nokta çarpımı 1 ise vektörler aynı yöndedir. Nokta çarpım değeri 0 ise bu vektörler dik veya birbiriyle alakasızdır. Son olarak, noktanın çarpımı -1 ise, vektörler ters yönü gösterir ve birbirlerine benzer değildir.

Yeni yerleştirme modelinde (embedding-001) görev türünü, kullanıcı sorgusu için QUERY ve doküman metni yerleştirirken DOCUMENT olarak belirtin.

Görev Türü	Açıklama
RETRIEVAL_QUERY	Belirtilen metnin, arama/alma ayarında sorgu olduğunu belirtir.
RETRIEVAL_DOCUMENT	Belirtilen metnin, arama/alma ayarında doküman olduğunu belirtir.

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")

Nokta çarpımlarını hesaplamak için find_best_passage işlevini kullanın ve ardından ilgili pasajı veritabanından almak için veri çerçevesini en büyükten en küçüğüne doğru sıralayın.

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

Veritabanındaki en alakalı belgeyi görüntüleyin:

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.'

Soru-Cevap Başvurusu

Soru-Cevap sistemi oluşturmak için metin oluşturma API'sını kullanmayı deneyelim. Basit bir soru ve yanıt örneği oluşturmak için kendi özel verilerinizi aşağıya girin. Benzerlik metriği olarak nokta çarpımını kullanmaya devam edersiniz.

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:

Sorgunuzun yanıtını bulmak için Gemini içerik oluşturma modellerinden birini seçin.

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)

Sağlanan pasaj, Google arabasında vites değiştirme hakkında bilgi içermediğinden sorunuzu bu kaynaktan yanıtlayamıyoruz.

Sonraki adımlar

Gemini API'deki diğer hizmetleri nasıl kullanacağınızı öğrenmek için Python hızlı başlangıç kılavuzunu ziyaret edin.