Documentation Index
Fetch the complete documentation index at: https://docs.praison.ai/llms.txt
Use this file to discover all available pages before exploring further.
Overview
Google Vertex AI provides enterprise-grade embedding models including textembedding-gecko and multimodal embeddings.
Quick Start
from praisonaiagents import embedding
result = embedding(
input="Hello world",
model="vertex_ai/textembedding-gecko"
)
print(f"Dimensions: {len(result.embeddings[0])}")
CLI Usage
praisonai embed "Hello world" --model vertex_ai/textembedding-gecko
Setup
export GOOGLE_APPLICATION_CREDENTIALS="/path/to/service-account.json"
export VERTEXAI_PROJECT="your-project-id"
export VERTEXAI_LOCATION="us-central1"
Available Models
| Model | Dimensions | Use Case |
|---|
vertex_ai/textembedding-gecko | 768 | General purpose |
vertex_ai/textembedding-gecko@003 | 768 | Latest version |
vertex_ai/textembedding-gecko-multilingual | 768 | Multilingual |
vertex_ai/text-embedding-004 | 768 | Newest model |
vertex_ai/text-multilingual-embedding-002 | 768 | Multilingual v2 |
With Project Configuration
from praisonaiagents import embedding
result = embedding(
input="Hello world",
model="vertex_ai/textembedding-gecko",
vertex_project="my-project",
vertex_location="us-central1"
)
Batch Embeddings
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")
Multimodal Embeddings
from praisonaiagents import embedding
# Image + text embeddings
result = embedding(
input="A beautiful sunset",
model="vertex_ai/multimodalembedding",
image="base64_encoded_image_data"
)
