Azure OpenAI Embeddings - optional

PyRIT also allows to get embeddings. The embedding response is a wrapper for the OpenAI embedding API.

from pprint import pprint

from pyrit.common import IN_MEMORY, initialize_pyrit
from pyrit.embedding.azure_text_embedding import AzureTextEmbedding

initialize_pyrit(memory_db_type=IN_MEMORY)

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='text-embedding-ada-002', object='list', usage=EmbeddingUsageInformation(prompt_tokens=1, total_tokens=1), data=[EmbeddingData(embedding=[-0.025122925639152527, -0.019487135112285614, -0.02802019938826561, -0.03106299601495266, -0.024752499535679817, 0.027543935924768448, -0.012554848566651344, -0.008420612663030624, -0.017608538269996643, -0.008347849361598492, 0.03251824900507927, 0.004187154583632946, -0.024712810292840004, -0.0006296441424638033, 0.014129165560007095, -0.0015941614983603358, 0.03939761593937874, 0.00207538646645844, 0.02704121172428131, -0.012409322895109653, -0.021048223599791527, 0.008923335000872612, 0.008394152857363224, -0.00326770031824708, -0.005410888232290745, -0.009624501690268517, 0.011165744625031948, -0.0015660487115383148, 0.00355544313788414, -0.023297248408198357, 0.006866139359772205, -0.007818667218089104, -0.023905808106064796, -0.008910105563700199, 0.006846295204013586, -0.013705819845199585, 0.00959804281592369, 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index=0, object='embedding')])

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 DB_DATA_PATH

saved_embedding_path = embedding_response.save_to_file(directory_path=DB_DATA_PATH)
saved_embedding_path

'/home/bjagdagdorj/tools/PyRIT/dbdata/16b89acd0bf3edce72697ac99f23682f46a604af57a6e57d2c42052fc87dc023.json'

To load an embedding from disk

from pyrit.common.path import DB_DATA_PATH

saved_embedding_path = embedding_response.save_to_file(directory_path=DB_DATA_PATH)
saved_embedding_path

'/home/bjagdagdorj/tools/PyRIT/dbdata/16b89acd0bf3edce72697ac99f23682f46a604af57a6e57d2c42052fc87dc023.json'