llama-index-embeddings-bedrock 0.8.2

llama-index embeddings bedrock integration

LlamaIndex Embeddings Integration: Bedrock

This integration provides support for Amazon Bedrock embedding models through LlamaIndex.

Installation

pip install llama-index-embeddings-bedrock

Usage

from llama_index.embeddings.bedrock import BedrockEmbedding

# Initialize the embedding model
embed_model = BedrockEmbedding(
    model_name="cohere.embed-english-v3",
    region_name="us-east-1",
)

# Get a single embedding
embedding = embed_model.get_text_embedding("Hello world")

# Get batch embeddings
embeddings = embed_model.get_text_embedding_batch(["Hello", "World"])

Supported Models

Amazon Titan

• amazon.titan-embed-text-v1
• amazon.titan-embed-text-v2:0
• amazon.titan-embed-g1-text-02

Cohere

• cohere.embed-english-v3
• cohere.embed-multilingual-v3
• cohere.embed-v4:0 (multimodal, supports text and images)

To list all supported models:

from llama_index.embeddings.bedrock import BedrockEmbedding

supported_models = BedrockEmbedding.list_supported_models()
print(supported_models)

Configuration

You can configure AWS credentials in several ways:

# Option 1: Pass credentials directly
embed_model = BedrockEmbedding(
    model_name="cohere.embed-english-v3",
    aws_access_key_id="YOUR_ACCESS_KEY",
    aws_secret_access_key="YOUR_SECRET_KEY",
    region_name="us-east-1",
)

# Option 2: Use AWS profile
embed_model = BedrockEmbedding(
    model_name="cohere.embed-english-v3",
    profile_name="your-aws-profile",
    region_name="us-east-1",
)

# Option 3: Use environment variables (AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, AWS_REGION)
embed_model = BedrockEmbedding(
    model_name="cohere.embed-english-v3",
)

Cohere v4 Support

This integration supports both Cohere v3 and v4 embedding models, including the new multimodal cohere.embed-v4:0 model. The integration automatically detects and handles different response formats (v3 and v4), maintaining full backward compatibility.

# Using Cohere v4 model
embed_model = BedrockEmbedding(
    model_name="cohere.embed-v4:0",
    region_name="us-east-1",
)

# Text embeddings work seamlessly
embeddings = embed_model.get_text_embedding_batch(
    ["Hello world", "Another document"]
)

Note: Cohere v4 introduces a new response format that wraps embeddings in a float key when multiple embedding types are requested. This integration handles both the v3 format ({"embeddings": [[...]]}) and v4 formats ({"embeddings": {"float": [[...]]}} or {"float": [[...]]}) automatically.

Use an Application Inference Profile

Amazon Bedrock supports user-created Application Inference Profiles, which are a sort of provisioned proxy to LLMs on Bedrock that allow for cost and model usage tracking.

Since these profile ARNs are account-specific, they must be handled specially in BedrockEmbedding.

When an application inference profile is created as an AWS resource, it references an existing Bedrock foundation model or a cross-region inference profile. The referenced model must be provided to the BedrockEmbedding initializer via the model_name argument, and the ARN of the application inference profile must be provided via the application_inference_profile_arn argument.

Important: BedrockEmbedding does not validate that the model_name argument matches the underlying model referenced by the provided application inference profile. The caller is responsible for making sure that they match. As such, the behavior for when they do not match is considered undefined.

# Assumes the existence of a provisioned application inference profile
# that references a foundation model or cross-region inference profile.

from llama_index.embeddings.bedrock import BedrockEmbedding


# Instantiate the BedrockEmbedding model
# with the model_name and application_inference_profile
# Make sure the model is the one that the
# application inference profile refers to in AWS
embed_model = BedrockEmbedding(
    model_name="amazon.titan-embed-text-v2:0",  # this is the model referenced by the application inference profile
    application_inference_profile_arn="arn:aws:bedrock:us-east-1:012345678901:application-inference-profile/someProfileId",
)

Examples

For more examples, see the Bedrock Embeddings notebook.