AWS Bedrock Text Embedding Functions

AWS Bedrock supports multiple base models for generating text embeddings. You need to setup the AWS credentials to use this embedding function. You can do so by using awscli and also add your session_token:

aws configure
aws configure set aws_session_token "<your_session_token>"

to ensure that the credentials are set up correctly, you can run the following command:

aws sts get-caller-identity

Supported Embedding modelIDs are: * amazon.titan-embed-text-v1 * cohere.embed-english-v3 * cohere.embed-multilingual-v3

Supported parameters (to be passed in create method) are:

Parameter	Type	Default Value	Description
name	str	"amazon.titan-embed-text-v1"	The model ID of the bedrock model to use. Supported base models for Text Embeddings: amazon.titan-embed-text-v1, cohere.embed-english-v3, cohere.embed-multilingual-v3
region	str	"us-east-1"	Optional name of the AWS Region in which the service should be called (e.g., "us-east-1").
profile_name	str	None	Optional name of the AWS profile to use for calling the Bedrock service. If not specified, the default profile will be used.
assumed_role	str	None	Optional ARN of an AWS IAM role to assume for calling the Bedrock service. If not specified, the current active credentials will be used.
role_session_name	str	"lancedb-embeddings"	Optional name of the AWS IAM role session to use for calling the Bedrock service. If not specified, a "lancedb-embeddings" name will be used.
runtime	bool	True	Optional choice of getting different client to perform operations with the Amazon Bedrock service.
max_retries	int	7	Optional number of retries to perform when a request fails.

Usage Example:

import lancedb
from lancedb.pydantic import LanceModel, Vector
from lancedb.embeddings import get_registry
import pandas as pd

model = get_registry().get("bedrock-text").create()

class TextModel(LanceModel):
    text: str = model.SourceField()
    vector: Vector(model.ndims()) = model.VectorField()

df = pd.DataFrame({"text": ["hello world", "goodbye world"]})
db = lancedb.connect("tmp_path")
tbl = db.create_table("test", schema=TextModel, mode="overwrite")

tbl.add(df)
rs = tbl.search("hello").limit(1).to_pandas()