public class BgeSmallEnEmbeddingModel extends AbstractInProcessEmbeddingModel
Maximum length of text (in tokens) that can be embedded at once: unlimited. However, while you can embed very long texts, the quality of the embedding degrades as the text lengthens. It is recommended to embed segments of no more than 512 tokens long.
Embedding dimensions: 384
It is recommended to add "Represent this sentence for searching relevant passages:" prefix to a query.
Uses an Executor to parallelize the embedding process.
By default, uses a cached thread pool with the number of threads equal to the number of available processors.
Threads are cached for 1 second.
More details here
| Constructor and Description |
|---|
BgeSmallEnEmbeddingModel()
Creates an instance of an
BgeSmallEnEmbeddingModel. |
BgeSmallEnEmbeddingModel(Executor executor)
Creates an instance of an
BgeSmallEnEmbeddingModel. |
| Modifier and Type | Method and Description |
|---|---|
protected Integer |
knownDimension() |
protected OnnxBertBiEncoder |
model() |
embedAll, estimateTokenCount, loadFromJarclone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, waitpublic BgeSmallEnEmbeddingModel()
BgeSmallEnEmbeddingModel.
Uses a cached thread pool with the number of threads equal to the number of available processors.
Threads are cached for 1 second.public BgeSmallEnEmbeddingModel(Executor executor)
BgeSmallEnEmbeddingModel.executor - The executor to use to parallelize the embedding process.protected OnnxBertBiEncoder model()
model in class AbstractInProcessEmbeddingModelprotected Integer knownDimension()
knownDimension in class dev.langchain4j.model.embedding.DimensionAwareEmbeddingModelCopyright © 2024. All rights reserved.