Sascha Heyer SaschaHeyer

Question: How can I create text embeddings with google cloud?

Answer: You can create text embeddings with Google Cloud using the Text Embeddings API on Vertex AI. This API allows you to generate numerical representations of text, which can be used for various tasks like:

Semantic Search: Finding documents similar in meaning to a given query. Recommendation Systems: Suggesting relevant content based on user preferences. Clustering: Grouping similar documents together. Question Answering: Finding answers to questions based on a knowledge base.

	import vertexai
	from vertexai.language_models import TextGenerationModel
	from google.cloud import translate_v2 as translate

	vertexai.init(project="sascha-playground-doit", location="us-central1")
	parameters = {
	"candidate_count": 1,
	"max_output_tokens": 1024,
	"temperature": 0.9,
	"top_p": 1

	response = index_endpoint.match(
	deployed_index_id="similar_text_deployed",
	queries=[embedding[0]],
	num_neighbors=5
	)

	training_data_artifact = aiplatform.Artifact.create(
	schema_title='system.Dataset',
	uri='gs://doit-vertex-demo/higgs/training.csv',
	display_name='data')

	with aiplatform.start_execution(
	schema_title="system.ContainerExecution",
	display_name='training'
	) as execution:

	experiment_run = aiplatform.ExperimentRun(
	run_name='run-1668456760',
	experiment='experiment-demo',
	)

	print(experiment_run.get_artifacts())
	print(experiment_run.get_metrics())
	print(experiment_run.get_params())
	print(experiment_run.get_time_series_data_frame())
	print(experiment_run.get_classification_metrics())

	aiplatform.init(
	project='sascha-playground-doit',
	location='us-central1',
	experiment='experiment-sample',
	experiment_tensorboard='projects/sascha-playground-doit/locations/us-central1/tensorboards/6382621018774568960'
	)

	{
	"predictions": [
	{
	"confidence": 0.9409326314926147,
	"sentiment": "POSITIVE"
	}
	]
	}

	experiment_df = aiplatform.get_experiment_df()
	experiment_df = experiment_df[experiment_df.experiment_name == 'experiment-demo']
	experiment_df

	experiment_tensorboard = vertex_ai.Tensorboard.create()