EmbeddingModel

Kotlin |Java

public interface EmbeddingModel

Provides an interface for generating embeddings for text and multimodal Content.

Obtain instances via EmbeddingGeneration.getEmbeddingModels, and call close when the model is no longer needed to release underlying resources.

Typical usage:

val models = EmbeddingGeneration.getEmbeddingModels()
val model = models.firstOrNull() ?: return
models.drop(1).forEach { it.close() }
try {
  if (model.checkStatus() == FeatureStatus.DOWNLOADABLE) {
    model.download().collect()
  }
  val request = EmbeddingRequest(
    Content(listOf(TextPart("What is machine learning?"))),
    TaskConfig.RetrievalQuery(),
  )
  val result = model.generateEmbedding(request)
  val vector = result.embedding.vector
} finally {
  model.close()
}

Summary

Public methods
`abstract @FeatureStatus int`	`checkStatus()` Checks the current availability status of the embedding feature.
`abstract void`	`close()` Releases resources associated with the underlying embedding engine.
`abstract @NonNull Flow<@NonNull DownloadStatus>`	`download()` Downloads the required model assets for the embedding feature if they are not already available.
`abstract @NonNull EmbeddingResult`	`generateEmbedding(@NonNull EmbeddingRequest request)` Performs asynchronous content embedding on the provided input request.
`abstract EmbeddingMetadata`	`getMetadata()` Returns the `EmbeddingMetadata` of the embedding model, or `null` if metadata retrieval fails or is unavailable.
`abstract @NonNull String`	`getModelSignature()` Returns the model signature uniquely identifying the underlying embedding model and version.
`abstract int`	`getTokenLimit()` Returns the maximum number of input tokens supported by this model in a single request.
`abstract boolean`	`isDeprecated()` Returns `true` if the model is currently deprecated.
`abstract void`	`warmup()` Warms up the inference engine for use by loading necessary models and initializing runtime components, reducing latency for the first `generateEmbedding` call.

Public methods

checkStatus

abstract @FeatureStatus int checkStatus()

Checks the current availability status of the embedding feature.

Returns
`@FeatureStatus int`	a `FeatureStatus` constant indicating the feature's readiness.

close

abstract void close()

Releases resources associated with the underlying embedding engine.

Callers should invoke this method when the model is no longer needed, including on any unused EmbeddingModel instances returned by EmbeddingGeneration.getEmbeddingModels.

download

abstract @NonNull Flow<@NonNull DownloadStatus> download()

Downloads the required model assets for the embedding feature if they are not already available.

The download starts when the returned cold Flow is collected. If the download fails, the flow emits DownloadStatus.DownloadFailed and terminates with the corresponding com.google.mlkit.genai.common.GenAiException.

Returns
`@NonNull Flow<@NonNull DownloadStatus>`	a cold `Flow` that emits `DownloadStatus` updates for download progress.

generateEmbedding

abstract @NonNull EmbeddingResult generateEmbedding(@NonNull EmbeddingRequest request)

Performs asynchronous content embedding on the provided input request.

Parameters
`@NonNull EmbeddingRequest request`	the `EmbeddingRequest` containing input `Content` and optional `TaskConfig`.

Returns
`@NonNull EmbeddingResult`	an `EmbeddingResult` containing the generated embedding vector and model signature.

Throws
`com.google.mlkit.genai.common.GenAiException`	if embedding generation fails or the request content is invalid.

getMetadata

abstract EmbeddingMetadata getMetadata()

Returns the EmbeddingMetadata of the embedding model, or null if metadata retrieval fails or is unavailable.

getModelSignature

abstract @NonNull String getModelSignature()

Returns the model signature uniquely identifying the underlying embedding model and version.

Embeddings generated by models with different signatures are incompatible and should not be directly compared. Callers that persist embeddings should store this signature alongside the vectors to detect model updates and re-index when the signature changes.

Returns
`@NonNull String`	the model signature string.

Throws
`com.google.mlkit.genai.common.GenAiException`	if retrieving the model signature fails.

getTokenLimit

abstract int getTokenLimit()

Returns the maximum number of input tokens supported by this model in a single request.

Returns
`int`	the maximum input token capacity, `-1` if the token limit is unknown, or `Int.MAX_VALUE` if there is no token limit restriction.

Throws
`com.google.mlkit.genai.common.GenAiException`	if retrieving the token limit fails.

isDeprecated

abstract boolean isDeprecated()

Returns true if the model is currently deprecated.

A model is considered deprecated if the current UTC date is on or after EmbeddingMetadata.deprecationDate. Returns false if model metadata or the deprecation date is unavailable.

warmup

abstract void warmup()

Warms up the inference engine for use by loading necessary models and initializing runtime components, reducing latency for the first generateEmbedding call.

Throws
`com.google.mlkit.genai.common.GenAiException`	if initializing the inference engine fails.