קבלת הטמעות טקסט

from google import genai
from google.genai.types import EmbedContentConfig

client = genai.Client()
response = client.models.embed_content(
    model="gemini-embedding-001",
    contents=[
        "How do I get a driver's license/learner's permit?",
        "How long is my driver's license valid for?",
        "Driver's knowledge test study guide",
    ],
    config=EmbedContentConfig(
        task_type="RETRIEVAL_DOCUMENT",  # Optional
        output_dimensionality=3072,  # Optional
        title="Driver's License",  # Optional
    ),
)
print(response)
# Example response:
# embeddings=[ContentEmbedding(values=[-0.06302902102470398, 0.00928034819662571, 0.014716853387653828, -0.028747491538524628, ... ],
# statistics=ContentEmbeddingStatistics(truncated=False, token_count=13.0))]
# metadata=EmbedContentMetadata(billable_character_count=112)

import (
	"context"
	"fmt"
	"io"

	"google.golang.org/genai"
)

// generateEmbedContentWithText shows how to embed content with text.
func generateEmbedContentWithText(w io.Writer) error {
	ctx := context.Background()

	client, err := genai.NewClient(ctx, &genai.ClientConfig{
		HTTPOptions: genai.HTTPOptions{APIVersion: "v1"},
	})
	if err != nil {
		return fmt.Errorf("failed to create genai client: %w", err)
	}

	outputDimensionality := int32(3072)
	config := &genai.EmbedContentConfig{
		TaskType:             "RETRIEVAL_DOCUMENT",  //optional
		Title:                "Driver's License",    //optional
		OutputDimensionality: &outputDimensionality, //optional
	}

	contents := []*genai.Content{
		{
			Parts: []*genai.Part{
				{
					Text: "How do I get a driver's license/learner's permit?",
				},
				{
					Text: "How long is my driver's license valid for?",
				},
				{
					Text: "Driver's knowledge test study guide",
				},
			},
			Role: genai.RoleUser,
		},
	}

	modelName := "gemini-embedding-001"
	resp, err := client.Models.EmbedContent(ctx, modelName, contents, config)
	if err != nil {
		return fmt.Errorf("failed to generate content: %w", err)
	}

	fmt.Fprintln(w, resp)

	// Example response:
	// embeddings=[ContentEmbedding(values=[-0.06302902102470398, 0.00928034819662571, 0.014716853387653828, -0.028747491538524628, ... ],
	// statistics=ContentEmbeddingStatistics(truncated=False, token_count=13.0))]
	// metadata=EmbedContentMetadata(billable_character_count=112)

	return nil
}

const {GoogleGenAI} = require('@google/genai');

const GOOGLE_CLOUD_PROJECT = process.env.GOOGLE_CLOUD_PROJECT;

async function generateEmbeddingsForRetrieval(
  projectId = GOOGLE_CLOUD_PROJECT
) {
  const client = new GoogleGenAI({
    vertexai: true,
    project: projectId,
  });

  const prompt = [
    "How do I get a driver's license/learner's permit?",
    "How long is my driver's license valid for?",
    "Driver's knowledge test study guide",
  ];

  const response = await client.models.embedContent({
    model: 'gemini-embedding-001',
    contents: prompt,
    config: {
      taskType: 'RETRIEVAL_DOCUMENT', // Optional
      outputDimensionality: 3072, // Optional
      title: "Driver's License", // Optional
    },
  });

  console.log(response);

  // Example response:
  //  embeddings=[ContentEmbedding(values=[-0.06302902102470398, 0.00928034819662571, 0.014716853387653828, -0.028747491538524628, ... ],
  //  statistics=ContentEmbeddingStatistics(truncated=False, token_count=13.0))]
  //  metadata=EmbedContentMetadata(billable_character_count=112)

  return response;
}

import com.google.genai.Client;
import com.google.genai.types.EmbedContentConfig;
import com.google.genai.types.EmbedContentResponse;
import java.util.List;

public class EmbeddingsDocRetrievalWithTxt {

  public static void main(String[] args) {
    // TODO(developer): Replace these variables before running the sample.
    String modelId = "gemini-embedding-001";
    embedContent(modelId);
  }

  // Shows how to embed content with text.
  public static EmbedContentResponse embedContent(String modelId) {
    // Client Initialization. Once created, it can be reused for multiple requests.
    try (Client client = Client.builder().location("global").vertexAI(true).build()) {

      EmbedContentResponse response =
          client.models.embedContent(
              modelId,
              List.of(
                  "How do I get a driver's license/learner's permit?",
                  "How long is my driver's license valid for?",
                  "Driver's knowledge test study guide"),
              EmbedContentConfig.builder()
                  .taskType("RETRIEVAL_DOCUMENT")
                  .outputDimensionality(3072)
                  .title("Driver's License")
                  .build());

      System.out.println(response);
      // Example response:
      // embeddings=Optional[[ContentEmbedding{values=Optional[[-0.035855383, 0.008127963, ... ]]
      // statistics=Optional[ContentEmbeddingStatistics{truncated=Optional[false],
      // tokenCount=Optional[11.0]}]}]],
      // metadata=Optional[EmbedContentMetadata{billableCharacterCount=Optional[153]}]}
      return response;
    }
  }
}