API de PaLM: Guía de inicio rápido de incorporaciones con Python

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En este notebook, aprenderás a comenzar a usar la API de PaLM, que te da acceso a los modelos grandes de lenguaje más recientes de Google. Aquí, aprenderás a usar las funciones de generación de incorporaciones de la API de PaLM y verás un ejemplo de lo que puedes hacer con esas incorporaciones.

Configuración

Primero, descarga e instala la biblioteca de Python de la API de PaLM.

pip install -U google-generativeai
import numpy as np
import google.generativeai as palm

Obtén una clave de API

Para comenzar, deberás crear una clave de API.

palm.configure(api_key='PALM_KEY')

¿Qué son las incorporaciones?

Las incorporaciones son una técnica que se usa para representar texto (como palabras, oraciones o párrafos enteros) como una lista de números de punto flotante en un array. Estos números no son aleatorios. La idea clave es que el texto con significados parecidos tenga incorporaciones similares. Puedes usar la relación entre ellos para muchas tareas importantes.

Generación de incorporaciones

En esta sección, verás cómo generar incorporaciones para un fragmento de texto con la función palm.generate_embeddings de la API de PaLM. Aquí hay una lista de modelos que admiten esta función.

for model in palm.list_models():
  if 'embedText' in model.supported_generation_methods:
    print(model.name)
models/embedding-gecko-001

Usa la función palm.generate_embeddings y pasa el nombre del modelo y texto. Se mostrará una lista de valores de punto flotante. Comienza con la consulta "¿Qué comen las ardillas?". y veamos qué tan relacionadas están dos cadenas diferentes con él.

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, -0.028869089, -0.07003764, -0.027043046, -0.047674373, -0.036566455, -0.029664699, 0.054604772, 0.056459025, 0.016209831, 0.06588335, 0.07294827, -0.07351654, -0.050157, 0.05211485, -0.02302033, 0.022877783, 0.013553745, -0.019406103, -0.0058154585, 0.0373227, 0.0052685454, 0.02164789, -0.019631775, -0.015719362, -0.06862338, 0.021698158, -0.013781832, 0.06955018, -0.023942512, -0.018029014, -0.018007774, -0.0059923544, -0.02771734, -0.0019507131, -0.069619514, 0.054189045, 0.0021985532, -0.01132558, 0.015128105, 0.015424623, -0.038302787, -0.038970694, 0.044268098, 0.015156813, 0.030262465, -0.0010455108, -0.032175235, -0.03357542, -9.529959e-05, 0.062028274, -0.10134925, -0.009874221, 0.051682726, -0.022124732, 0.010147164, -0.012185555, 0.03731382, -0.00059438165, -0.017981028, -0.070909515, 0.02605233, 0.06992509, 0.026033426, -0.023944097, -0.047794044, 0.0204043, 0.025562089, -0.01985736, -0.027300185, 0.029983355, -0.0821883, -0.018791717, -0.004772287, -0.02490102, -0.010111937, 0.050968856, 0.029660473, 3.4716293e-05, -0.017517656, 0.023977743, 0.022549666, 0.04181301, 0.007500569, -0.0019229053, 0.023285722, -0.010899088, -0.004949611, -0.012531907, 0.041027624, -0.004620342, -0.013926477, -0.020054528, 0.026111232, -0.06232942, 0.09978252, -0.044156674, 0.061204664, 0.007044644, -0.0027112814, 0.04620226, 0.006134901, 0.03983195, -0.009853767, 0.0137631735, -0.07085734, 0.009606741, -0.008636412, 0.050337072, 0.045284208, -0.0032710661, -0.016086245, 0.008386805, -0.007903436, 0.0350885, 0.0025110857, 0.04684593, 0.12780859, -0.038998656, -0.029157333, -0.029113598, 0.0074333544, 0.05532698, -0.034412585, -0.00013683736, -0.020530468, 0.06506163, 0.0019480588, 0.0030335467, -0.018495142, -0.054084025, 0.023021378, -0.010500294, -0.007759436, -0.020039978, -0.017755102, 0.0006766737, 0.014525485, -0.026014434, 0.002474586, -0.027173916, 0.0093613025, 0.0058087856, 0.0006998545, 0.04791365, -0.04368597, -0.015235596, 0.0069595333, 0.009612967, 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0.037658524, 0.010072951, 0.0064869304, 0.019048406, -0.06919818, -0.017083945, -0.016801478, 0.0027415873, 0.008172279, 0.0019755305, -0.057162683, -0.0053946367, 0.0014972482, -0.033361986, -0.0033606717, 0.03242665, 0.072544955, 0.02279949, -0.046871353, -0.06308129, 0.029209439, 0.011341486, 0.032790348, -0.020073028, -0.0044093695, 0.08292041, -0.03140556, 0.009308279, -0.004211382, -0.052444175, 0.0180874, 0.008575959, -0.0013550716, -0.07186043, 0.028372435, 0.024996122, 0.027749002, 0.016944503, -0.014632978, -0.06674174, -0.043031745, -0.044137582, 0.03530514, 0.030504197, 0.060496386, -0.06423886, 0.012235539, -0.05830343, -0.015868725, 0.041861057, 0.027080601, -0.014182999, -0.028095996, 0.0016349283, 0.010679886, 0.048808616, -0.058294244, -0.010633062, -0.056791265, -0.027161647, -0.030019993, -0.010299281, -0.03821823, -0.016588321, -0.0059704296, -0.053497788, 0.05661912, 0.005010262, -0.020186698, -0.03151958, -0.07490499, 0.045715272, -0.03747153, 0.02902543, 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-0.013629232, -0.03840216, 0.06655019, 0.009643849, 0.025085986, -0.018909356, -0.011246176, -0.05254555, -0.06776485, -0.02931862, 0.014850466, 0.029691922, -0.04090594, 0.0544204, 0.01552631, 0.02912549, -0.0020693596, 0.038805272, -0.009980787, 0.031122748, -0.05562063, 0.021108221, 0.0103203785, 0.044171233, 0.009732269, -0.0011330071]}

Ahora que creaste las incorporaciones, usemos el producto punto para ver qué tan relacionados están close_to_x y different_from_x con x. El producto punto devuelve un valor entre -1 y 1, y representa el nivel de precisión con el que dos vectores se alinean en términos de la dirección hacia la que apuntan. Cuanto más se acerque el valor a 0, menos similar serán los objetos (en este caso, dos cadenas). Cuanto más cercano sea el valor a 1, más similares serán.

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

Como se muestra aquí, el valor más alto del producto de punto entre las incorporaciones de x y close_to_x demuestra una mayor relación que las incorporaciones de x y different_from_x.

¿Qué se puede hacer con las incorporaciones?

Generaste tu primer conjunto de incorporaciones con la API de PaLM. Pero ¿qué puedes hacer con esta lista de valores de punto flotante? Las incorporaciones se pueden utilizar para una amplia variedad de tareas de procesamiento de lenguaje natural (PLN), incluidas las siguientes:

  • Búsqueda (documentos, web, etc.)
  • Sistemas de recomendación
  • Agrupamiento en clústeres
  • Análisis de opiniones/clasificación de texto

Aquí encontrarás ejemplos.