PaLM API, Google Yapay Zeka hizmetleri ve araçlarıyla kullanım için kullanımdan kaldırılmıştır (ancak Vertex AI için değildir). Kullanımdan kaldırma süreci, bu sürecin zaman çizelgesi ve Gemini'a geçiş süreci hakkında daha fazla bilgiyi PaLM API kullanımdan kaldırma kılavuzundan edinebilirsiniz.

Bu sayfa, Cloud Translation API ile çevrilmiştir.

PaLM API: Python ile yerleştirme hızlı başlangıç kılavuzu

ai.google.dev'de görüntüleyin

Google Colab'de çalıştır

Kaynağı GitHub'da görüntüleyin

Bu not defterinde, Google'ın en yeni büyük dil modellerine erişmenizi sağlayan PaLM API'yi kullanmaya nasıl başlayacağınızı öğreneceksiniz. Burada, PaLM API'nin yerleştirme oluşturma özelliklerini nasıl kullanacağınızı öğrenip bu yerleştirmelerle neler yapabileceğinize dair bir örnek göreceksiniz.

Kurulum

Öncelikle PaLM API Python kitaplığını indirip yükleyin.

pip install -U google-generativeai

import numpy as np
import google.generativeai as palm

API Anahtarı Alma

Başlamak için bir API anahtarı oluşturmanız gerekir.

palm.configure(api_key='PALM_KEY')

Gömme nedir?

Yerleştirme, bir dizideki kayan nokta sayıları listesi olarak metinleri (kelimeler, cümleler veya paragrafların tamamı gibi) temsil etmek için kullanılan bir tekniktir. Bu sayılar rastgele değildir. Buradaki temel fikir, benzer anlamlara sahip metinlerin benzer yerlere sahip olmasıdır. Aralarındaki ilişkiyi birçok önemli görev için kullanabilirsiniz.

Yerleştirme oluşturma

Bu bölümde, PaLM API'nin palm.generate_embeddings işlevini kullanarak bir metin parçası için nasıl yerleştirilmiş öğeler oluşturacağınız açıklanmaktadır. Bu işlevi destekleyen modellerin listesini aşağıda bulabilirsiniz.

for model in palm.list_models():
  if 'embedText' in model.supported_generation_methods:
    print(model.name)

models/embedding-gecko-001

palm.generate_embeddings işlevini kullanın ve modelin adını ve metni iletin. Kayan nokta değerlerinin listesi görüntülenir. "Sincaplar ne yer?" bir sorguyla başlayın ve iki farklı dizenin kendisiyle ne kadar alakalı olduğunu görün.

x = 'What do squirrels eat?'

close_to_x = 'nuts and acorns'

different_from_x = 'This morning I woke up in San Francisco, and took a walk to the Bay Bridge. It was a good, sunny morning with no fog.'

model = "models/embedding-gecko-001"

# Create an embedding
embedding_x = palm.generate_embeddings(model=model, text=x)
embedding_close_to_x = palm.generate_embeddings(model=model, text=close_to_x)
embedding_different_from_x = palm.generate_embeddings(model=model, text=different_from_x)

print(embedding_x)

{'embedding': [-0.025894878, -0.02103396, 0.003574992, 0.00822288, 0.03276648, -0.10068223, -0.037702546, 0.01079403, 0.0001406235, -0.029412385, 0.01919925, 0.0048481044, 0.070619866, -0.013349887, 0.028378602, -0.018658886, -0.038629908, 0.056883123, 0.06332366, 0.039849922, -0.085393265, -0.016251814, -0.025535949, 0.0049480307, 0.048581485, -0.11295683, 0.033869933, 0.015498774, -0.07306243, 0.000857902, -0.022031788, -0.005298939, -0.08311722, -0.027091762, 0.042790364, 0.023175264, 0.011238991, -0.02432924, -0.0044626957, 0.05167071, 0.023430848, 0.027325166, -0.01492389, -0.018770715, -0.003783692, 0.040971957, -0.044652887, 0.033220302, -0.05659744, -0.055191413, -0.0023204528, -0.043687623, 0.030044463, -0.015966717, -0.04318426, 0.015735775, -0.038352676, -0.005009736, -0.03289721, 0.016246213, -0.005696393, -0.0010992853, -0.02768714, -0.03534994, -0.045970507, 0.05784305, -0.026696421, -0.013302212, 0.007055761, -0.05885901, 0.03330113, 0.04399591, 0.020755561, 0.0028288597, 0.037333105, 0.0103595415, -0.01942964, 0.033088185, 0.009558319, -0.06524442, -0.07101354, -0.053975347, -0.003952934, -0.11641813, -0.039488368, -0.0033782825, -0.017735159, 0.03198736, 0.014555729, 0.050724585, -0.07849815, -0.0070436746, 0.017992217, -0.003975652, -0.0039650565, 0.08063971, -0.011685766, -0.018323965, 0.007763516, 0.012011537, 0.028457757, -0.099603206, 0.0328822, 0.0063217366, 0.051288057, 0.060445003, -0.007725884, -0.0033487668, -0.02697037, -0.04471915, 0.014793467, 0.0029390613, -0.04365732, -0.036976494, 0.05571355, -0.034228597, 0.05610819, 0.0016565409, 0.06461147, 0.012197695, -0.029221235, 0.015400638, 0.009992722, -0.0126949195, 0.027302667, 0.04309881, 0.013308768, -0.034253325, -0.028620966, 0.0032988666, 0.008901495, 0.0051033413, 0.08693829, -0.035939537, -0.00014025549, -0.0021354076, 0.043875773, -0.057092454, 0.0048032254, 0.04456835, -0.01337361, 0.018620204, -0.0037525205, 0.018113593, -0.0024051766, -0.006519982, 0.043426506, 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Artık yerleştirmeleri oluşturduğunuza göre close_to_x ve different_from_x öğesinin x ile ne kadar alakalı olduğunu görmek için nokta çarpımını kullanalım. Nokta çarpımı, -1 ile 1 arasında bir değer döndürür ve iki vektörün, gösterdikleri yön açısından ne kadar yakın hizalandığını gösterir. Değer 0'a ne kadar yakınsa nesnelere o kadar az benzer (bu durumda, iki dize). Değer 1'e ne kadar yakın olursa o kadar benzer olur.

similar_measure = np.dot(embedding_x['embedding'], embedding_close_to_x['embedding'])

print(similar_measure)

0.7314063252924405

different_measure = np.dot(embedding_x['embedding'], embedding_different_from_x['embedding'])

print(different_measure)

0.43560702838194704

Burada gösterildiği gibi, x ve close_to_x yerleştirmeleri arasında daha yüksek nokta ürün değeri, x ve different_from_x yerleştirmelerine göre daha fazla alaka düzeyi gösterir.

Gömmelerle neler yapabilirsiniz?

PaLM API ile ilk yerleştirme grubunuzu oluşturdunuz. Peki, bu kayan nokta değerleri listesiyle ne yapabilirsiniz? Yerleştirmeler, aşağıdakiler de dahil olmak üzere çok çeşitli doğal dil işleme (NLP) görevlerinde kullanılabilir:

Arama (dokümanlar, web vb.)
Öneri sistemleri
Kümeleme
Yaklaşım analizi/metin sınıflandırma

Örnekleri burada bulabilirsiniz.