Azure OpenAI Embeddings - optional#
Similar to the OpenAI Completions endpoint, PyRIT also allows to get embeddings. The embedding response is a wrapper for the OpenAI embedding API.
from pprint import pprint
from pyrit.embedding.azure_text_embedding import AzureTextEmbedding
from pyrit.common import default_values
default_values.load_environment_files()
input_text = "hello"
ada_embedding_engine = AzureTextEmbedding()
embedding_response = ada_embedding_engine.generate_text_embedding(text=input_text)
pprint(embedding_response, width=280, compact=True)
EmbeddingResponse(model='ada', object='list', usage=EmbeddingUsageInformation(prompt_tokens=1, total_tokens=1), data=[EmbeddingData(embedding=[-0.02504645101726055, -0.01940273866057396, -0.027782395482063293, -0.03103380836546421, -0.024649936705827713, 0.027438750490546227, -0.012470357120037079, -0.00849861092865467, -0.01743338815867901, -0.008465568535029888, 0.03254055976867676, 0.004275739658623934, -0.024583851918578148, -0.0006298786029219627, 0.01412910595536232, -0.0015034478856250644, 0.03938703238964081, 0.002009002957493067, 0.026843979954719543, -0.012569485232234001, -0.02101522870361805, 0.008881907910108566, 0.008445742540061474, -0.0030630684923380613, -0.005362848285585642, -0.00950311217457056, 0.01106934156268835, -0.0016967483097687364, 0.003452973673120141, -0.023235704749822617, 0.006730820517987013, -0.007903840392827988, -0.02392299473285675, -0.008901732973754406, 0.00683986209332943, -0.01367972418665886, 0.00950311217457056, -0.014115888625383377, 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Embeddings Serialization#
All the PyRIT’s embeddings are easily serializable. This allows you to easily save and load embeddings for later use, and be able to inspect the value of the embeddings offline (since embeddings are stored as JSON objects).
To view the json of an embedding
embedding_response.to_json()
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To save an embedding to disk
from pyrit.common.path import RESULTS_PATH
saved_embedding_path = embedding_response.save_to_file(directory_path=RESULTS_PATH)
saved_embedding_path
'C:/Users/Roman/AppData/Local/pyrit/results/5d302e1e54b730ffcd68e161fbbb2fc7a153452477e881af412acb3c84a813b1.json'
To load an embedding from disk
from pyrit.common.path import RESULTS_PATH
saved_embedding_path = embedding_response.save_to_file(directory_path=RESULTS_PATH)
saved_embedding_path
'C:/Users/Roman/AppData/Local/pyrit/results/5d302e1e54b730ffcd68e161fbbb2fc7a153452477e881af412acb3c84a813b1.json'