Gömme ile doküman arama

ai.google.dev'de görüntüleyin Google Colab'de çalıştır Kaynağı GitHub'da görüntüle

Genel Bakış

Bu örnekte, doküman araması yapabilmeniz için Gemini API'nin yerleştirilmiş öğeler oluşturmak amacıyla nasıl kullanılacağı gösterilmektedir. Arama dizelerini veya soruları doküman içeriklerini karşılaştırmanıza olanak tanıyan bir kelime yerleştirme işlemi oluşturmak için Python istemci kitaplığını kullanacaksınız.

Bu eğiticide, Google Araba ile ilgili sorular sormak amacıyla bir dizi doküman üzerinde doküman araması gerçekleştirmek 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+
  • 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

# 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. Henüz yoksa Google AI Studio'da tek tıklamayla bir anahtar oluşturun.

API anahtarı alma

Colab'de, anahtarı "🔑" bölümünün altında gizli anahtar yöneticisine ekleyin tıklayın. API_KEY adını verin.

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

  • Anahtarı GOOGLE_API_KEY ortam değişkenine yerleştirin (SDK, değişkeni otomatik olarak oradan alır).
  • Anahtarı genai.configure(api_key=...) adlı cihaza geçirin
ziyaret edin. 
# 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 için nasıl yerleştirme oluşturacağınızı öğreneceksiniz.

Model yerleştirme-001 ile yerleştirmelerde yapılan API değişiklikleri

Yeni yerleştirme modeli (embed-001) için yeni bir görev türü parametresi ve isteğe bağlı başlık (yalnızca task_type=RETRIEVAL_DOCUMENT ile geçerlidir).

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

Görev Türü Açıklama
RETRIEVAL_QUERY Belirtilen metnin, arama/alma ayarındaki bir sorgu olduğunu belirtir.
RETRIEVAL_DOCUMENT Belirtilen metnin, arama/alma ayarındaki bir 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

Aşağıda, yerleştirilmiş veritabanını oluştururken kullanabileceğiniz üç örnek metin verilmiştir. Dokümanların her birini yerleştirmek için Gemini API'yi kullanırsınız. Daha iyi görselleştirme için bunları bir 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 olarak düzenleyin.

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

Bu metin gövdelerinin her biri için yerleştirmeleri 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 ile doküman arama

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

Sorunun yerleştirilmesi bir vektör (kayan noktalı değerler listesi) olacaktır. Bu liste, nokta çarpımının kullanıldığı dokümanların vektörüyle karşılaştırılacaktır. API'den döndürülen bu vektör zaten normalleştirilmiş. Nokta çarpımı, iki vektör arasındaki yöndeki benzerliği gösterir.

Nokta çarpımı değerleri -1 ile 1 arasında (bu değerler dahil) olabilir. İki vektör arasındaki nokta çarpımı 1 ise vektörler aynı yöndedir. Nokta çarpımı değeri 0 ise bu vektörler dikeydir veya birbiriyle alakasızdır. Son olarak, nokta çarpımı -1 ise vektörler ters yönü işaret eder ve birbirlerine benzemezler.

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

Görev Türü Açıklama
RETRIEVAL_QUERY Belirtilen metnin, arama/alma ayarındaki bir sorgu olduğunu belirtir.
RETRIEVAL_DOCUMENT Belirtilen metnin, arama/alma ayarındaki bir 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")

Noktasal çarpımları hesaplamak için find_best_passage işlevini kullanın. Ardından, ilgili pasajı veritabanından almak için veri çerçevesini en büyük nokta çarpımı değerinden en küçük noktaya 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 dokümanı 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 oluşturmak için metin oluşturma API'sini Sistem. Basit bir soru ve cevap örneği oluşturmak için aşağıya kendi özel verilerinizi 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-ultra

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

Markdown(answer.text)

Sağlanan pasaj, bir Google arabasında vites değiştirmeyle ilgili bilgi içermediğinden sorunuzu bu kaynaktan yanıtlayamıyorum.

Sonraki adımlar

Yerleştirmeleri nasıl kullanabileceğiniz hakkında daha fazla bilgi edinmek için şu eğiticilere göz atın: