AmazonBedrockTextEmbedder

Calculate text embeddings using models through Amazon Bedrock API. Use this component to embed strings, like user queries, for semantic search.

Amazon Bedrock is a fully managed service that makes state-of-the-art language models available for use through a unified API. To learn more, see Amazon Bedrock documentation.

You can use this component with the following models:

• amazon.titan-embed-text-v1
• cohere.embed-english-v3
• cohere.embed-multilingual-v3
• amazon.titan-embed-text-v2:0

Embedding Models in Query Pipelines and Indexes

The embedding model you use to embed documents in your indexing pipeline must be the same as the embedding model you use to embed the query in your query pipeline.

This means the embedders for your indexing and query pipelines must match. For example, if you use CohereDocumentEmbedder to embed your documents, you should use CohereTextEmbedder with the same model to embed your queries.

Key Features

• Embeds a single string using Amazon Bedrock embedding models.
• Supports Amazon Titan and Cohere embedding models.
• Use AmazonBedrockDocumentEmbedder to embed documents in indexes.
• Outputs a list of floats representing the embedding of the input text.

Configuration

Authentication

To use this component, connect Haystack Platform with Amazon Bedrock first. You'll need the region name, access key ID, and secret access key.

For detailed explanation, see Use Amazon Bedrock and SageMaker Models.

1. Drag the AmazonBedrockTextEmbedder component onto the canvas from the Component Library.
2. Click the component to open the configuration panel.
3. On the General tab:
   1. Select the embedding model from the list. Make sure to use the same model that was used to embed documents in the index.
4. Go to the Advanced tab to configure the AWS credentials and boto3 client settings.

Connections

AmazonBedrockTextEmbedder accepts a text string as input and outputs a list of floats representing the text embedding.

Connect the pipeline's query input to the text input. Connect the embedding output to an embedding retriever's query_embedding input to find matching documents.

Usage Example

Using the Component in a Pipeline

This is an example of a basic document search pipeline where AmazonBedrockTextEmbedder uses a Cohere model to embed the query and send the resulting embedding to an Embedding Retriever:

components:
  OpenSearchEmbeddingRetriever:
    type: haystack_integrations.components.retrievers.opensearch.embedding_retriever.OpenSearchEmbeddingRetriever
    init_parameters:
      document_store:
        type: haystack_integrations.document_stores.opensearch.document_store.OpenSearchDocumentStore
        init_parameters:
          use_ssl: true
          verify_certs: false
          hosts:
          - ${OPENSEARCH_HOST}
          http_auth:
          - ${OPENSEARCH_USER}
          - ${OPENSEARCH_PASSWORD}
          embedding_dim: 1024
          similarity: cosine
          index: Standard-Index-English
          max_chunk_bytes: 104857600
          return_embedding: false
          method:
          mappings:
          settings:
          create_index: true
          timeout:
      filters:
      top_k: 10
      filter_policy: replace
      custom_query:
      raise_on_failure: true
      efficient_filtering: false

  AmazonBedrockTextEmbedder:
    type: haystack_integrations.components.embedders.amazon_bedrock.text_embedder.AmazonBedrockTextEmbedder
    init_parameters:
      model: cohere.embed-english-v3
      aws_access_key_id:
        type: env_var
        env_vars:
        - AWS_ACCESS_KEY_ID
        strict: false
      aws_secret_access_key:
        type: env_var
        env_vars:
        - AWS_SECRET_ACCESS_KEY
        strict: false
      aws_session_token:
        type: env_var
        env_vars:
        - AWS_SESSION_TOKEN
        strict: false
      aws_region_name:
        type: env_var
        env_vars:
        - AWS_DEFAULT_REGION
        strict: false
      aws_profile_name:
        type: env_var
        env_vars:
        - AWS_PROFILE
        strict: false
      boto3_config:

connections:
- sender: AmazonBedrockTextEmbedder.embedding
  receiver: OpenSearchEmbeddingRetriever.query_embedding

max_runs_per_component: 100

metadata: {}

inputs:
  query:
  - AmazonBedrockTextEmbedder.text

outputs:
  documents: OpenSearchEmbeddingRetriever.documents

Parameters

Inputs

Parameter	Type	Default	Description
text	str		The text to embed.

Outputs

Parameter	Type	Default	Description
embedding	List[float]		The embedding of the input text.

Init Parameters

These are the parameters you can configure in Pipeline Builder:

Parameter	Type	Default	Description
model	Literal['amazon.titan-embed-text-v1', 'cohere.embed-english-v3', 'cohere.embed-multilingual-v3', 'amazon.titan-embed-text-v2:0']		The embedding model to use. The model has to be specified in the format outlined in the Amazon Bedrock documentation. Make sure the embedding model used for embedding the query is the same as the embedding model used to embed documents in the index.
aws_access_key_id	Optional[Secret]	Secret.from_env_var('AWS_ACCESS_KEY_ID', strict=False)	AWS access key ID.
aws_secret_access_key	Optional[Secret]	Secret.from_env_var('AWS_SECRET_ACCESS_KEY', strict=False)	AWS secret access key.
aws_session_token	Optional[Secret]	Secret.from_env_var('AWS_SESSION_TOKEN', strict=False)	AWS session token.
aws_region_name	Optional[Secret]	Secret.from_env_var('AWS_DEFAULT_REGION', strict=False)	AWS region name.
aws_profile_name	Optional[Secret]	Secret.from_env_var('AWS_PROFILE', strict=False)	AWS profile name.
boto3_config	Optional[Dict[str, Any]]	None	The configuration for the boto3 client.
kwargs	Any		Additional parameters to pass for model inference. For example, input_type and truncate for Cohere models.

Run Method Parameters

These are the parameters you can configure for the component's run() method. This means you can pass these parameters at query time through the API, in Playground, or when running a job. For details, see Modify Pipeline Parameters at Query Time.

Parameter	Type	Default	Description
text	str		The input text to embed.