Google Vertex AI Provider

Embedding generation using Google Vertex AI's embedding API. Provides access to Google's embedding models including the Gecko family.

Requirements

• Google Cloud project with Vertex AI API enabled
• Authentication via access token or API key

Configuration

Option 1: Access Token (Recommended for development)

Get a token with gcloud auth print-access-token:

{ok, State} = barrel_embed:init(#{
    embedder => {vertex, #{
        access_token => <<"ya29...">>,           % or use env var
        project => <<"my-project">>,
        region => <<"us-central1">>,
        model => <<"text-embedding-004">>
    }}
}).

Option 2: API Key

{ok, State} = barrel_embed:init(#{
    embedder => {vertex, #{
        api_key => <<"...">>,                    % or use env var
        project => <<"my-project">>,
        region => <<"us-central1">>,
        model => <<"text-embedding-004">>
    }}
}).

Options

Using Environment Variables

# Get access token
export GOOGLE_ACCESS_TOKEN=$(gcloud auth print-access-token)
export GOOGLE_CLOUD_PROJECT=my-project

{ok, State} = barrel_embed:init(#{
    embedder => {vertex, #{}}
}).

Supported Models

Setup Steps

1. Enable Vertex AI API

   • Go to GCP Console
   • Enable "Vertex AI API"

2. Get Authentication

   For development (access token):

   gcloud auth login
   gcloud auth print-access-token
   

   For production (service account):

   • Create service account with Vertex AI User role
   • Download JSON key
   • Use GOOGLE_APPLICATION_CREDENTIALS

3. Note Project ID

   gcloud config get-value project
   

Example

%% Initialize
{ok, State} = barrel_embed:init(#{
    embedder => {vertex, #{
        access_token => <<"ya29...">>,
        project => <<"my-project">>,
        region => <<"us-central1">>,
        model => <<"text-embedding-004">>
    }}
}).

%% Generate embeddings
{ok, Vec} = barrel_embed:embed(<<"Machine learning is fascinating">>, State).

%% Batch
{ok, Vecs} = barrel_embed:embed_batch([
    <<"Document about AI">>,
    <<"Document about databases">>,
    <<"Document about networking">>
], State).

Regions

Vertex AI is available in many regions. Common choices:

• us-central1 (Iowa) - Default
• us-east1 (South Carolina)
• europe-west1 (Belgium)
• asia-northeast1 (Tokyo)

Access Token Refresh

Access tokens expire after 1 hour. For long-running applications:

1. Use service account authentication
2. Or refresh token periodically:

%% Refresh token
NewToken = os:cmd("gcloud auth print-access-token"),
{ok, NewState} = barrel_embed:init(#{
    embedder => {vertex, #{
        access_token => list_to_binary(string:trim(NewToken)),
        project => <<"my-project">>
    }}
}).

Why Vertex AI?

• Google's latest embedding models
• Tight integration with GCP services
• BigQuery, Cloud Storage integration
• Enterprise security (VPC-SC, CMEK)

Pricing

See cloud.google.com/vertex-ai/pricing for current rates.