diff --git a/redisvl/utils/vectorize/text/bedrock_vectorizer.py b/redisvl/utils/vectorize/text/bedrock_vectorizer.py
new file mode 100644
index 00000000..cc71f248
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+++ b/redisvl/utils/vectorize/text/bedrock_vectorizer.py
@@ -0,0 +1,143 @@
+import os, boto3
+from typing import Callable, Dict, List, Optional
+
+from tenacity import retry, stop_after_attempt, wait_random_exponential
+from tenacity.retry import retry_if_not_exception_type
+
+from langchain_community.embeddings.bedrock import BedrockEmbeddings
+from redisvl.utils.vectorize.base import BaseVectorizer
+
+class AmazonBedrockTextVectorizer(BaseVectorizer):
+    
+    """The AmazonTextVectorizer class utilizes Amazon Bedrock's API to generate
+    embeddings for text data.
+
+    This vectorizer is designed to interact with Amazon Bedrock API,
+    requiring an API key for authentication. The key can be provided
+    directly in the `api_config` dictionary or through the `AWS_ACCESS_KEY_ID`, `AWS_SECRET_ACCESS_KEY` & `AWS_REGION`
+    environment variable. User must obtain an API key from AWS's website
+    (https://aws.amazon.com/bedrock/).
+
+    The vectorizer supports only synchronous operations, allows for batch
+    processing of texts and flexibility in handling preprocessing tasks.
+
+    .. code-block:: python
+
+        from redisvl.utils.vectorize import AmazonBedrockTextVectorizer
+
+        vectorizer = AmazonBedrockTextVectorizer(
+            model_id="amazon.titan-embed-text-v1", 
+            api_config = { "aws_access_key_id": "your_aws_access_id", "aws_secret_access_key": "your_aws_secret_key","region_name": "us-east-1"} # OR set in your env
+        )
+
+    """
+
+    def __init__(
+        self, model_id: str = "amazon.titan-embed-text-v1", api_config: Optional[Dict] = None
+    ):
+        """Initialize the AWS Bedrock Vectorizer
+
+        Args:
+            model(str): Model to use for embedding. Defaults to amazon.titan-embed-text-v1.
+            api_config (Optional[Dict], optional): Dictionary containing the API key.
+                Defaults to None.
+        
+        """
+        # Set up AWS credentials
+        aws_access_key_id = api_config.get("aws_access_key_id") if api_config else os.getenv("AWS_ACCESS_KEY_ID")
+        aws_secret_access_key = api_config.get("aws_secret_access_key") if api_config else os.getenv("AWS_SECRET_ACCESS_KEY")
+        region_name = api_config.get("region_name", "us-east-1") if api_config else os.getenv("AWS_REGION", "us-east-1")
+        
+        if not aws_access_key_id or not aws_secret_access_key:
+            raise ValueError(
+                "AWS access key and secret key are required."
+                "Provide it in the api_config or set the environment varibales."
+                )
+        
+        # Initialize Bedrock client
+        client = boto3.client(
+            'bedrock-runtime',
+            aws_access_key_id=aws_access_key_id,
+            aws_secret_access_key=aws_secret_access_key,
+            region_name=region_name
+        )
+
+        dims = self._set_model_dims(model=model_id, client=client)
+        super().__init__(model=model_id, dims=dims, client=client)
+
+    @staticmethod
+    def _set_model_dims(client, model) -> int:
+        try:
+            bedrock_em_model = BedrockEmbeddings(model_id=model,client=client)
+            embedding = bedrock_em_model.embed_query("dimension test")
+        except (KeyError, IndexError) as ke:
+            raise ValueError(f"Unexpected response from the AWS Bedrock API: {str(ke)}")
+        except Exception as e:  # pylint: disable=broad-except
+            # fall back (TODO get more specific)
+            raise ValueError(f"Error setting embedding model dimensions: {str(e)}")
+        return len(embedding)
+    
+    @retry(
+        wait=wait_random_exponential(min=1, max=60),
+        stop=stop_after_attempt(6),
+        retry=retry_if_not_exception_type(TypeError),
+    )
+    def embed(
+        self,
+        text: str,
+        preprocess: Optional[Callable] = None,
+        as_buffer: bool = False,
+        **kwargs,
+    ) -> List[float]:
+        if not isinstance(text, str):
+            raise TypeError("Must pass in a str value to embed.")
+
+        if preprocess:
+            text = preprocess(text)
+        
+        bedrock_em_model = BedrockEmbeddings(model_id=self.model,client=self.client)
+
+        embedding = bedrock_em_model.embed_query(text)
+
+        return self._process_embedding(embedding, as_buffer)
+    
+    @retry(
+        wait=wait_random_exponential(min=1, max=60),
+        stop=stop_after_attempt(6),
+        retry=retry_if_not_exception_type(TypeError),
+    )
+    def embed_many(
+        self,
+        texts: List[str],
+        preprocess: Optional[Callable] = None,
+        batch_size: int = 10,
+        as_buffer: bool = False,
+        **kwargs,
+    ) -> List[List[float]]:
+        input_type = kwargs.get("input_type")
+
+        if not isinstance(texts, list):
+            raise TypeError("Must pass in a list of str values to embed.")
+        if len(texts) > 0 and not isinstance(texts[0], str):
+            raise TypeError("Must pass in a list of str values to embed.")
+        if not isinstance(input_type, str):
+            raise TypeError(
+                "Must pass in a str value for AWS Bedrock embedding input_type.\
+                    See AWS" 
+            )
+
+        embeddings: List = []
+
+        bedrock_em_model = BedrockEmbeddings(model_id=self.model,client=self.client)
+
+        for batch in self.batchify(texts, batch_size, preprocess):
+
+
+            response = bedrock_em_model.embed_query(texts=batch)
+
+            embeddings += [
+                self._process_embedding(embedding, as_buffer)
+                for embedding in response.embeddings
+            ]
+
+        return embeddings