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In questo blocco note scoprirai come iniziare a utilizzare l'API PaLM, che ti permette di accedere ai più recenti modelli linguistici di grandi dimensioni (LLM) di Google. Imparerai a utilizzare le funzionalità di generazione degli incorporamenti dell'API PaLM e vedrai un esempio di cosa puoi fare con questi incorporamenti.
Configurazione
Innanzitutto, scarica e installa la libreria Python dell'API PaLM.
pip install -U google-generativeai
import numpy as np
import google.generativeai as palm
Procurati una chiave API
Per iniziare, devi creare una chiave API.
palm.configure(api_key='PALM_KEY')
Che cosa sono gli incorporamenti?
Gli incorporamenti sono una tecnica utilizzata per rappresentare il testo (come parole, frasi o interi paragrafi) come elenco di numeri con rappresentazione in virgola mobile in un array. Questi numeri non sono casuali. L'idea chiave è che un testo con significati simili avrà incorporamenti simili. Puoi utilizzare la relazione tra i due prodotti per molte attività importanti.
Generazione degli incorporamenti
In questa sezione, vedrai come generare incorporamenti per una porzione di testo utilizzando la funzione palm.generate_embeddings dell'API PaLM. Ecco un elenco di modelli che supportano questa funzione.
for model in palm.list_models():
if 'embedText' in model.supported_generation_methods:
print(model.name)
models/embedding-gecko-001
Usa la funzione palm.generate_embeddings e inserisci il nome del modello e del testo. Verrà visualizzato un elenco di valori con rappresentazione in virgola mobile. Inizia con la query "Cosa mangiano gli scoiattoli?" e vedremo quanto sono correlate tra loro due diverse stringhe.
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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Ora che hai creato gli incorporamenti, utilizziamo il prodotto scalare per vedere quanto sono correlati close_to_x e different_from_x a x. Il prodotto scalare restituisce un valore compreso tra -1 e 1 e rappresenta il grado di allineamento di due vettori in termini di direzione in cui puntano. Più il valore è vicino a 0, meno simili agli oggetti (in questo caso, due stringhe). Più il valore è vicino a 1, più simili sono.
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
Come mostrato qui, il valore del prodotto scalare più alto tra gli incorporamenti di x e close_to_x dimostra una maggiore correlazione rispetto agli incorporamenti di x e different_from_x.
Cosa puoi fare con gli incorporamenti?
Hai generato il tuo primo set di incorporamenti con l'API PaLM. Che cosa puoi fare con questo elenco di valori con rappresentazione in virgola mobile? Gli incorporamenti possono essere utilizzati per un'ampia varietà di attività di elaborazione del linguaggio naturale (NLP), tra cui:
- Ricerca (documenti, web ecc.)
- Sistemi di consigli
- Clustering
- Analisi del sentiment/classificazione del testo
Puoi trovare alcuni esempi qui.