تم إيقاف PaLM API نهائيًا لاستخدامها مع خدمات وأدوات تكنولوجيات الذكاء الاصطناعي من Google (ولكنها ليس مع Vertex AI). تعرَّف على مزيد من المعلومات حول عملية الإيقاف النهائي هذه ومخططها الزمني وكيفية الانتقال لاستخدام Gemini في دليل إيقاف PaLM API.

تمت ترجمة هذه الصفحة بواسطة Cloud Translation API‏.

PaLM API: البدء السريع للتضمينات باستخدام لغة بايثون

العرض على ai.google.dev

التشغيل في Google Colab

الاطّلاع على المصدر على GitHub

ستتعرف في هذا الدفتر على طريقة بدء استخدام واجهة برمجة التطبيقات PaLM التي تتيح لك الوصول إلى أحدث النماذج اللغوية الكبيرة من Google. ستتعرّف هنا على كيفية استخدام ميزات إنشاء التضمين في PaLM API، واطّلِع على مثال حول الإجراءات التي يمكنك اتّخاذها باستخدام هذه التضمينات.

ضبط إعدادات الجهاز

أولاً، عليك تنزيل مكتبة PaLM API Python وتثبيتها.

pip install -U google-generativeai

import numpy as np
import google.generativeai as palm

الحصول على مفتاح واجهة برمجة التطبيقات

للبدء، عليك إنشاء مفتاح واجهة برمجة التطبيقات.

palm.configure(api_key='PALM_KEY')

ما المقصود بالتضمين؟

عمليات التضمين هي أسلوب يُستخدَم لتمثيل النص (مثل الكلمات أو الجمل أو الفقرات الكاملة) كقائمة بأرقام النقاط العائمة في مصفوفة. هذه الأرقام ليست عشوائية. الفكرة الأساسية هي أن النص الذي له معانٍ متشابهة سيكون له التضمينات المتشابهة. يمكنك استخدام العلاقة بينهما للعديد من المهام المهمة.

إنشاء تضمين

في هذا القسم، ستطّلع على كيفية إنشاء عمليات تضمين لجزء من النص باستخدام دالة palm.generate_embeddings في PaLM API. فيما يلي قائمة بالنماذج التي تدعم هذه الدالة.

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

models/embedding-gecko-001

استخدِم الدالة palm.generate_embeddings ومرِّر اسم النموذج بالإضافة إلى بعض النصوص. ستظهر لك قائمة بقيم النقاط العائمة. ابدأ بالاستعلام "ماذا تأكل السناجب؟" وشاهد مدى ارتباط سلسلتين مختلفتين بها.

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, 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الآن وبعد أن أنشأت التضمينات، لنستخدم المنتج النقطي لمعرفة مدى ارتباط close_to_x وdifferent_from_x بـ x. ينتج ناتج الضرب النقطي قيمة بين -1 و1، ويمثل مدى تقارب الخطين المتجهين في الاتجاه الذي يشيران إليهما. كلما اقتربت القيمة من 0، كان أقل تشابهًا مع الكائنات (في هذه الحالة، سلسلتان). كلما اقتربت القيمة من 1، زاد تشابهها.

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

كما هو موضّح هنا، إنّ القيمة الأعلى للنقطة بين تضمينات x وclose_to_x توضح ارتباطًا أكبر من تضمينات x وdifferent_from_x.

ما الذي يمكنك فعله مع التضمينات؟

لقد أنشأت المجموعة الأولى من عمليات التضمين باستخدام PaLM API. ولكن ماذا يمكنك أن تفعل بهذه القائمة من قيم النقاط العائمة؟ يمكن استخدام عمليات التضمين لمجموعة كبيرة من مهام معالجة اللغات الطبيعية، بما في ذلك:

البحث (المستندات والويب وما إلى ذلك)
أنظمة الاقتراح
مجمّع
تحليل المشاعر/تصنيف النصوص

يمكنك الاطّلاع على أمثلة هنا.