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Specify Embedding dimension for multimodal input
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This code sample shows how to specify a lower embedding dimension for text and image inputs.
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For detailed documentation that includes this code sample, see the following:
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[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Hard to understand","hardToUnderstand","thumb-down"],["Incorrect information or sample code","incorrectInformationOrSampleCode","thumb-down"],["Missing the information/samples I need","missingTheInformationSamplesINeed","thumb-down"],["Other","otherDown","thumb-down"]],[],[],[],null,["# Specify Embedding dimension for multimodal input\n\nThis code sample shows how to specify a lower embedding dimension for text and image inputs.\n\nExplore further\n---------------\n\n\nFor detailed documentation that includes this code sample, see the following:\n\n- [Get multimodal embeddings](/vertex-ai/generative-ai/docs/embeddings/get-multimodal-embeddings)\n\nCode sample\n-----------\n\n### Go\n\n\nBefore trying this sample, follow the Go setup instructions in the\n[Vertex AI quickstart using\nclient libraries](/vertex-ai/docs/start/client-libraries).\n\n\nFor more information, see the\n[Vertex AI Go API\nreference documentation](/go/docs/reference/cloud.google.com/go/aiplatform/latest/apiv1).\n\n\nTo authenticate to Vertex AI, set up Application Default Credentials.\nFor more information, see\n\n[Set up authentication for a local development environment](/docs/authentication/set-up-adc-local-dev-environment).\n\n import (\n \t\"context\"\n \t\"encoding/json\"\n \t\"fmt\"\n \t\"io\"\n\n \taiplatform \"cloud.google.com/go/aiplatform/apiv1beta1\"\n \taiplatformpb \"cloud.google.com/go/aiplatform/apiv1beta1/aiplatformpb\"\n \t\"google.golang.org/api/option\"\n \t\"google.golang.org/protobuf/encoding/protojson\"\n \t\"google.golang.org/protobuf/types/known/structpb\"\n )\n\n // generateWithLowerDimension shows how to generate lower-dimensional embeddings for text and image inputs.\n func generateWithLowerDimension(w io.Writer, project, location string) error {\n \t// location = \"us-central1\"\n \tctx := context.Background()\n \tapiEndpoint := fmt.Sprintf(\"%s-aiplatform.googleapis.com:443\", location)\n \tclient, err := aiplatform.https://cloud.google.com/go/docs/reference/cloud.google.com/go/aiplatform/latest/apiv1beta1.html#cloud_google_com_go_aiplatform_apiv1beta1_PredictionClient_NewPredictionClient(ctx, option.WithEndpoint(apiEndpoint))\n \tif err != nil {\n \t\treturn fmt.Errorf(\"failed to construct API client: %w\", err)\n \t}\n \tdefer client.Close()\n\n \tmodel := \"multimodalembedding@001\"\n \tendpoint := fmt.Sprintf(\"projects/%s/locations/%s/publishers/google/models/%s\", project, location, model)\n\n \t// This is the input to the model's prediction call. For schema, see:\n \t// https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/multimodal-embeddings-api#request_body\n \tinstance, err := structpb.NewValue(map[string]any{\n \t\t\"image\": map[string]any{\n \t\t\t// Image input can be provided either as a Google Cloud Storage URI or as\n \t\t\t// base64-encoded bytes using the \"bytesBase64Encoded\" field.\n \t\t\t\"gcsUri\": \"gs://cloud-samples-data/vertex-ai/llm/prompts/landmark1.png\",\n \t\t},\n \t\t\"text\": \"Colosseum\",\n \t})\n \tif err != nil {\n \t\treturn fmt.Errorf(\"failed to construct request payload: %w\", err)\n \t}\n\n \t// TODO(developer): Try different dimenions: 128, 256, 512, 1408\n \toutputDimensionality := 128\n \tparams, err := structpb.NewValue(map[string]any{\n \t\t\"dimension\": outputDimensionality,\n \t})\n \tif err != nil {\n \t\treturn fmt.Errorf(\"failed to construct request params: %w\", err)\n \t}\n\n \treq := &aiplatformpb.PredictRequest{\n \t\tEndpoint: endpoint,\n \t\t// The model supports only 1 instance per request.\n \t\tInstances: []*structpb.Value{instance},\n \t\tParameters: params,\n \t}\n\n \tresp, err := client.Predict(ctx, req)\n \tif err != nil {\n \t\treturn fmt.Errorf(\"failed to generate embeddings: %w\", err)\n \t}\n\n \tinstanceEmbeddingsJson, err := protojson.Marshal(resp.GetPredictions()[0])\n \tif err != nil {\n \t\treturn fmt.Errorf(\"failed to convert protobuf value to JSON: %w\", err)\n \t}\n \t// For response schema, see:\n \t// https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/multimodal-embeddings-api#response-body\n \tvar instanceEmbeddings struct {\n \t\tImageEmbeddings []float32 `json:\"imageEmbedding\"`\n \t\tTextEmbeddings []float32 `json:\"textEmbedding\"`\n \t}\n \tif err := json.Unmarshal(instanceEmbeddingsJson, &instanceEmbeddings); err != nil {\n \t\treturn fmt.Errorf(\"failed to unmarshal JSON: %w\", err)\n \t}\n\n \timageEmbedding := instanceEmbeddings.ImageEmbeddings\n \ttextEmbedding := instanceEmbeddings.TextEmbeddings\n\n \tfmt.Fprintf(w, \"Text embedding (length=%d): %v\\n\", len(textEmbedding), textEmbedding)\n \tfmt.Fprintf(w, \"Image embedding (length=%d): %v\\n\", len(imageEmbedding), imageEmbedding)\n \t// Example response:\n \t// Text Embedding (length=128): [0.27469793 -0.14625867 0.022280363 ... ]\n \t// Image Embedding (length=128): [0.06225733 -0.040650766 0.02604402 ... ]\n\n \treturn nil\n }\n\n### Python\n\n\nBefore trying this sample, follow the Python setup instructions in the\n[Vertex AI quickstart using\nclient libraries](/vertex-ai/docs/start/client-libraries).\n\n\nFor more information, see the\n[Vertex AI Python API\nreference documentation](/python/docs/reference/aiplatform/latest).\n\n\nTo authenticate to Vertex AI, set up Application Default Credentials.\nFor more information, see\n\n[Set up authentication for a local development environment](/docs/authentication/set-up-adc-local-dev-environment).\n\n import https://cloud.google.com/python/docs/reference/vertexai/latest/\n\n from vertexai.vision_models import https://cloud.google.com/python/docs/reference/vertexai/latest/vertexai.preview.generative_models.Image.html, https://cloud.google.com/python/docs/reference/vertexai/latest/vertexai.preview.vision_models.MultiModalEmbeddingModel.html\n\n # TODO(developer): Update & uncomment line below\n # PROJECT_ID = \"your-project-id\"\n https://cloud.google.com/python/docs/reference/vertexai/latest/.init(project=PROJECT_ID, location=\"us-central1\")\n\n # TODO(developer): Try different dimenions: 128, 256, 512, 1408\n embedding_dimension = 128\n\n model = https://cloud.google.com/python/docs/reference/vertexai/latest/vertexai.preview.vision_models.MultiModalEmbeddingModel.html.https://cloud.google.com/python/docs/reference/vertexai/latest/vertexai.preview.vision_models.MultiModalEmbeddingModel.html#vertexai_preview_vision_models_MultiModalEmbeddingModel_from_pretrained(\"multimodalembedding@001\")\n image = https://cloud.google.com/python/docs/reference/vertexai/latest/vertexai.preview.generative_models.Image.html.https://cloud.google.com/python/docs/reference/vertexai/latest/vertexai.preview.generative_models.Image.html#vertexai_preview_generative_models_Image_load_from_file(\n \"gs://cloud-samples-data/vertex-ai/llm/prompts/landmark1.png\"\n )\n\n embeddings = model.https://cloud.google.com/python/docs/reference/vertexai/latest/vertexai.preview.vision_models.MultiModalEmbeddingModel.html#vertexai_preview_vision_models_MultiModalEmbeddingModel_get_embeddings(\n image=image,\n contextual_text=\"Colosseum\",\n dimension=embedding_dimension,\n )\n\n print(f\"Image Embedding: {embeddings.image_embedding}\")\n print(f\"Text Embedding: {embeddings.text_embedding}\")\n\n # Example response:\n # Image Embedding: [0.0622573346, -0.0406507477, 0.0260440577, ...]\n # Text Embedding: [0.27469793, -0.146258667, 0.0222803634, ...]\n\nWhat's next\n-----------\n\n\nTo search and filter code samples for other Google Cloud products, see the\n[Google Cloud sample browser](/docs/samples?product=generativeaionvertexai)."]]