Leveraging Google Cloud GenAI Python API using service account key file.

genai-text.py

# Create a service account and associate a "Vertex AI User" role
import google.auth
import google.auth.transport.requests
from google.oauth2 import service_account
import vertexai
from vertexai.preview.language_models import TextGenerationModel
key_path = "<YOUR_KEY_PATH>"
credentials = service_account.Credentials.from_service_account_file(key_path,scopes=['https://www.googleapis.com/auth/cloud-platform'])
auth_req = google.auth.transport.requests.Request()
credentials.refresh(auth_req)

def predict_large_language_model_sample(
    project_id: str,
    model_name: str,
    temperature: float,
    max_decode_steps: int,
    top_p: float,
    top_k: int,
    content: str,
    location: str = "us-central1",
    tuned_model_name: str = "",
    ) :
    """Predict using a Large Language Model."""
    vertexai.init(project=project_id, location=location, credentials=credentials)
    model = TextGenerationModel.from_pretrained(model_name)
    if tuned_model_name:
      model = model.get_tuned_model(tuned_model_name)
    response = model.predict(
        content,
        temperature=temperature,
        max_output_tokens=max_decode_steps,
        top_k=top_k,
        top_p=top_p,)
    print(f"Response from Model: {response.text}")


predict_large_language_model_sample("<PROJECT_ID>", "text-bison@001", 0.2, 768, 0.8, 40, '<YOUR_PROMPT_HERE>', "us-central1")