Google Embedding Provider

The Google embedding provider implements the embedding.Embedder interface using the Google AI Gemini embedding API. It uses the batch batchEmbedContents endpoint for efficient multi-text embedding.

Choose Google embeddings when you are already using the Gemini LLM provider and want to unify your API key and billing, or when you need multilingual embeddings through Google’s text-multilingual-embedding-002 model. The batch endpoint provides efficient processing for large document sets.

Installation

go get github.com/lookatitude/beluga-ai/rag/embedding/providers/google

Quick Start

package main

import (
    "context"
    "fmt"
    "log"
    "os"

    "github.com/lookatitude/beluga-ai/config"
    "github.com/lookatitude/beluga-ai/rag/embedding"
    _ "github.com/lookatitude/beluga-ai/rag/embedding/providers/google"
)

func main() {
    emb, err := embedding.New("google", config.ProviderConfig{
        APIKey: os.Getenv("GOOGLE_API_KEY"),
    })
    if err != nil {
        log.Fatal(err)
    }

    ctx := context.Background()
    vec, err := emb.EmbedSingle(ctx, "Beluga AI is a Go framework for agentic systems")
    if err != nil {
        log.Fatal(err)
    }

    fmt.Printf("Vector length: %d\n", len(vec))
    fmt.Printf("Dimensions: %d\n", emb.Dimensions())
}

Configuration

Parameter	Type	Default	Description
APIKey	string	(required)	Google AI API key
Model	string	text-embedding-004	Embedding model name
BaseURL	string	https://generativelanguage.googleapis.com/v1beta	API base URL
Timeout	time.Duration	0 (no timeout)	Request timeout
Options["dimensions"]	float64	Model-dependent	Override vector dimensions

Supported Models

Model	Default Dimensions
text-embedding-004	768
embedding-001	768
text-multilingual-embedding-002	768

Direct Construction

import (
    googleemb "github.com/lookatitude/beluga-ai/rag/embedding/providers/google"
)

emb, err := googleemb.New(config.ProviderConfig{
    APIKey: os.Getenv("GOOGLE_API_KEY"),
    Model:  "text-embedding-004",
})
if err != nil {
    log.Fatal(err)
}

Batch Embedding

The Google provider uses the batchEmbedContents API for efficient batch processing:

texts := []string{
    "First document about Go programming",
    "Second document about vector databases",
    "Third document about machine learning",
}

vectors, err := emb.Embed(ctx, texts)
if err != nil {
    log.Fatal(err)
}

for i, vec := range vectors {
    fmt.Printf("Text %d: %d dimensions\n", i, len(vec))
}

Vertex AI

To use Vertex AI instead of the public Gemini API, configure a custom base URL:

emb, err := embedding.New("google", config.ProviderConfig{
    APIKey:  os.Getenv("GOOGLE_API_KEY"),
    BaseURL: "https://us-central1-aiplatform.googleapis.com/v1",
    Model:   "text-embedding-004",
})