Update README.md

README.md

@@ -6794,9 +6794,9 @@ model-index:
 ---
 
 ## Bedrock Titan Text Embeddings v2
-This repository contains the MTEB scores and usage examples of Bedrock Titan Text Embeddings v2. You can use the embedding model either via the Bedrock streaming API or via Bedrock's batch jobs. For RAG use cases we recommend the former to embed queries during search (latency optimized) and the latter to index corpus (throughput optimized).
+This repository contains the MTEB scores and usage examples of Bedrock Titan Text Embeddings v2. You can use the embedding model either via the Bedrock InvokeModel API or via Bedrock's batch jobs. For RAG use cases we recommend the former to embed queries during search (latency optimized) and the latter to index corpus (throughput optimized).
 
-## Using Bedrock's streaming API
+## Using Bedrock's InvokeModel API
 
 ```python
 import json
@@ -6805,7 +6805,7 @@ class TitanEmbeddings(object):
     accept = "application/json"
     content_type = "application/json"
     
-    def __init__(self, model_id="amazon.titan-embed-text-v2"):
+    def __init__(self, model_id="amazon.titan-embed-text-v2:0"):
         self.bedrock = boto3.client(service_name='bedrock-runtime')
         self.model_id = model_id
     def __call__(self, text, dimensions, normalize=True):
@@ -6837,7 +6837,7 @@ if __name__ == '__main__':
     dimensions = 1024
     normalize = True
     
-    titan_embeddings_v2 = TitanEmbeddings(model_id="amazon.titan-embed-text-v2")
+    titan_embeddings_v2 = TitanEmbeddings(model_id="amazon.titan-embed-text-v2:0")
 
     input_text = "What are the different services that you offer?"
     embedding = titan_embeddings_v2(input_text, dimensions, normalize)
@@ -6870,7 +6870,7 @@ payload = {
         }
    },
    "jobName": "embeddings-v2-batch-job",
-   "modelId": "amazon.titan-embed-text-v2",
+   "modelId": "amazon.titan-embed-text-v2:0",
    "outputDataConfig": {
         "s3OutputDataConfig": {
             "s3Uri": "s3://my-output-bucket/batch-output/"