A API PaLM foi descontinuada para uso com serviços e ferramentas de IA do Google (mas não para a Vertex AI). Saiba mais sobre essa descontinuação, o cronograma dela e como migrar para usar o Gemini no guia de descontinuação da API PaLM.

Esta página foi traduzida pela API Cloud Translation.

API PaLM: guia de início rápido de embeddings com Python

Ver em ai.google.dev

Executar no Google Colab

Consulte o código-fonte no GitHub

Neste notebook, você vai aprender a usar a API PaLM, que dá acesso aos modelos de linguagem grandes mais recentes do Google. Aqui, você vai aprender a usar os recursos de geração de embeddings da API PaLM e ver um exemplo do que é possível fazer com eles.

Configuração

Primeiro, faça o download e instale a biblioteca Python da API PaLM.

pip install -U google-generativeai

import numpy as np
import google.generativeai as palm

Obter uma chave de API

Para começar, crie uma chave de API.

palm.configure(api_key='PALM_KEY')

O que são embeddings?

Embeddings são uma técnica usada para representar texto (como palavras, frases ou parágrafos inteiros) como uma lista de números de ponto flutuante em uma matriz. Esses números não são aleatórios. A ideia principal é que o texto com significados parecidos tenha embeddings parecidos. Você pode usar a relação entre elas para muitas tarefas importantes.

Geração de embeddings

Nesta seção, você vai aprender a gerar embeddings para um texto usando a função palm.generate_embeddings da API PaLM. Veja uma lista de modelos compatíveis com essa função.

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

models/embedding-gecko-001

Use a função palm.generate_embeddings e transmita o nome do modelo e um texto. Você receberá uma lista de valores de ponto flutuante. Comece com uma consulta "O que os esquilos comem?" e veja como duas strings diferentes estão relacionadas a ele.

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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-0.013629232, -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]}

Agora que você criou os embeddings, vamos usar o produto escalar para conferir a relação entre close_to_x e different_from_x e x. O produto escalar retorna um valor entre -1 e 1 e representa o quanto dois vetores se alinham em termos de direção para onde apontam. Quanto mais próximo o valor estiver de 0, menos semelhantes aos objetos (nesse caso, duas strings) são. Quanto mais próximo o valor estiver de 1, mais semelhantes eles são.

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 mostrado aqui, o valor do produto escalar maior entre os embeddings de x e close_to_x demonstra mais relação do que os embeddings de x e different_from_x.

O que é possível fazer com embeddings?

Você gerou seu primeiro conjunto de embeddings com a API PaLM. Mas o que é possível fazer com essa lista de valores de ponto flutuante? Os embeddings podem ser usados para uma ampla variedade de tarefas de processamento de linguagem natural (PLN), incluindo:

Pesquisa (documentos, web etc.)
Sistemas de recomendação
Clustering
Análise de sentimento/classificação de texto

Confira exemplos aqui.