Class BedrockEmbedder
- Namespace
- LMKit.Integrations.Aws.Embeddings
- Assembly
- LM-Kit.NET.Integrations.Aws.dll
An IEmbedder backed by Amazon Bedrock, so Bedrock-hosted embedding models
(Amazon Titan Text Embeddings and Cohere Embed) can be used anywhere LMKit consumes
embeddings, including RagEngine.
public sealed class BedrockEmbedder : EmbedderBase, IEmbedder, IQueryEmbedder
- Inheritance
-
BedrockEmbedder
- Implements
- Inherited Members
Remarks
The request and response shape differs per model family; this class detects the family from the model id (or an explicit override) and formats each call accordingly:
-
Amazon Titan (for example
amazon.titan-embed-text-v2:0) embeds a single text per request, so a batch is issued as sequential calls. The optional output dimension and normalization flag are forwarded when supported. -
Cohere (for example
cohere.embed-english-v3) embeds a batch in one request and distinguishes queries from passages viainput_type, which this class sets when IQueryEmbedder is used.
Credentials and region are resolved by the AWS SDK. Pass an IAmazonBedrockRuntime you have configured, or use the region-based constructor, which relies on the default AWS credential provider chain (environment variables, shared profile, or an IAM role).
Constructors
- BedrockEmbedder(IAmazonBedrockRuntime, string, ModelFamily?, int?, bool)
Initializes a new instance of the BedrockEmbedder class with an already-configured Bedrock runtime client.
- BedrockEmbedder(string, RegionEndpoint, ModelFamily?, int?, bool)
Initializes a new instance of the BedrockEmbedder class, creating a Bedrock runtime client for the given region using the default AWS credential provider chain.
Properties
- EmbeddingSize
Gets the dimension of the vectors this embedder produces.
- ModelId
Gets a stable identifier for the underlying model or deployment.
Methods
- GetEmbeddingsAsync(IEnumerable<string>, CancellationToken)
Asynchronously generates embedding vectors for a batch of texts.
- GetQueryEmbeddingsAsync(string, CancellationToken)
Asynchronously generates the embedding vector for a search query, applying any query-specific instruction the model supports.