description: AWSBedrock embedding models to generate embeddings for a given text.

AWS Bedrock Embeddings

The AWS Bedrock Embeddings node is a component that integrates AWS Bedrock’s embedding models into a larger system, allowing for the generation of embeddings for given text inputs. This node is particularly useful for tasks such as semantic search, text classification, and clustering.

Parameters

Credential (Optional)

• Type: AWS API Credential

Inputs

1. Region

   • Type: Async Options (listRegions)
   • Default: “us-east-1”
   • Description: AWS region for the Bedrock service

2. Model Name

   • Type: Async Options (listModels)
   • Default: “amazon.titan-embed-text-v1”
   • Description: The embedding model to use

3. Custom Model Name

   • Type: String
   • Optional: Yes
   • Description: If provided, overrides the selected Model Name

4. Cohere Input Type

   • Type: Options
   • Optional: Yes
   • Description: Specifies the type of input for Cohere models (v3+)
   • Options:
     • search_document
     • search_query
     • classification
     • clustering

5. Batch Size

   • Type: Number
   • Optional: Yes
   • Default: 50
   • Description: Document batch size for Titan model API calls

6. Max AWS API retries

   • Type: Number
     • Optional: Yes
     • Default: 5
     • Description: Maximum number of API call retries for Titan model ​

Functionality

1. Model Initialization:

   • Sets up the BedrockEmbeddings model with provided parameters
   • Configures AWS credentials if provided

2. Embedding Generation:

   • For single queries: Uses embedQuery method
   • For multiple documents: Uses embedDocuments method
   • Handles different processing for Titan and Cohere models

3. Batch Processing:

   • Implements a batch processing system for Titan models to manage API rate limits
   • Includes retry logic with exponential backoff for handling throttling exceptions

4. Error Handling:

   • Provides specific error messages for invalid responses or exceeded retry limits ​

Use Cases

1. Semantic search in vector databases
2. Text classification tasks
3. Document clustering
4. Enhancing natural language processing pipelines

Notes

• The node dynamically loads available models and regions
• It supports both Amazon Titan and Cohere embedding models
• Special handling is implemented for Cohere models, requiring input type specification
• Batch processing and retry logic are implemented to handle API rate limits efficiently

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