Documentation IndexFetch the complete documentation index at: /llms.txtUse this file to discover all available pages before exploring further.
Fetch the complete documentation index at: /llms.txt
Use this file to discover all available pages before exploring further.
Generate embeddings using Google Cloud Vertex AI
from praisonaiagents import embedding result = embedding( input="Hello world", model="vertex_ai/textembedding-gecko" ) print(f"Dimensions: {len(result.embeddings[0])}")
praisonai embed "Hello world" --model vertex_ai/textembedding-gecko
export GOOGLE_APPLICATION_CREDENTIALS="/path/to/service-account.json" export VERTEXAI_PROJECT="your-project-id" export VERTEXAI_LOCATION="us-central1"
vertex_ai/textembedding-gecko
vertex_ai/textembedding-gecko@003
vertex_ai/textembedding-gecko-multilingual
vertex_ai/text-embedding-004
vertex_ai/text-multilingual-embedding-002
from praisonaiagents import embedding result = embedding( input="Hello world", model="vertex_ai/textembedding-gecko", vertex_project="my-project", vertex_location="us-central1" )
from praisonaiagents import embedding texts = ["Document 1", "Document 2", "Document 3"] result = embedding( input=texts, model="vertex_ai/textembedding-gecko" ) print(f"Generated {len(result.embeddings)} embeddings")
from praisonaiagents import embedding # Image + text embeddings result = embedding( input="A beautiful sunset", model="vertex_ai/multimodalembedding", image="base64_encoded_image_data" )