feat(sagemaker.py): support huggingface embedding models

litellm/llms/sagemaker.py

@@ -196,6 +196,133 @@ def completion(
     model_response.usage = usage
     return model_response
 
-def embedding():
-    # logic for parsing in - calling - parsing out model embedding calls
-    pass
+def embedding(model: str,
+        input: list,
+        model_response: ModelResponse,
+        print_verbose: Callable,
+        encoding,
+        logging_obj,
+        custom_prompt_dict={},
+        optional_params=None,
+        litellm_params=None,
+        logger_fn=None):
+    """
+    Supports Huggingface Jumpstart embeddings like GPT-6B 
+    """
+    ### BOTO3 INIT
+    import boto3 
+    # pop aws_secret_access_key, aws_access_key_id, aws_region_name from kwargs, since completion calls fail with them
+    aws_secret_access_key = optional_params.pop("aws_secret_access_key", None)
+    aws_access_key_id = optional_params.pop("aws_access_key_id", None)
+    aws_region_name = optional_params.pop("aws_region_name", None)
+
+    if aws_access_key_id != None:
+        # uses auth params passed to completion
+        # aws_access_key_id is not None, assume user is trying to auth using litellm.completion
+        client = boto3.client(
+            service_name="sagemaker-runtime",
+            aws_access_key_id=aws_access_key_id,
+            aws_secret_access_key=aws_secret_access_key,
+            region_name=aws_region_name,
+        )
+    else:
+        # aws_access_key_id is None, assume user is trying to auth using env variables 
+        # boto3 automaticaly reads env variables
+
+        # we need to read region name from env
+        # I assume majority of users use .env for auth 
+        region_name = (
+            get_secret("AWS_REGION_NAME") or
+            "us-west-2"  # default to us-west-2 if user not specified
+        )
+        client = boto3.client(
+            service_name="sagemaker-runtime",
+            region_name=region_name,
+        )
+    
+    # pop streaming if it's in the optional params as 'stream' raises an error with sagemaker
+    inference_params = deepcopy(optional_params)
+    inference_params.pop("stream", None)
+
+    ## Load Config
+    config = litellm.SagemakerConfig.get_config() 
+    for k, v in config.items(): 
+        if k not in inference_params: # completion(top_k=3) > sagemaker_config(top_k=3) <- allows for dynamic variables to be passed in
+            inference_params[k] = v
+
+    #### HF EMBEDDING LOGIC 
+    data = json.dumps({
+        "text_inputs": input
+    }).encode('utf-8')
+
+    ## LOGGING
+    request_str = f"""
+    response = client.invoke_endpoint(
+        EndpointName={model},
+        ContentType="application/json",
+        Body={data},
+        CustomAttributes="accept_eula=true",
+    )"""
+    logging_obj.pre_call(
+            input=input,
+            api_key="",
+            additional_args={"complete_input_dict": data, "request_str": request_str},
+        )
+    ## EMBEDDING CALL
+    try: 
+        response = client.invoke_endpoint(
+            EndpointName=model,
+            ContentType="application/json",
+            Body=data,
+            CustomAttributes="accept_eula=true",
+        )
+    except Exception as e: 
+        raise SagemakerError(status_code=500, message=f"{str(e)}")
+
+    ## LOGGING
+    logging_obj.post_call(
+            input=input,
+            api_key="",
+            additional_args={"complete_input_dict": data},
+            original_response=response,
+        )
+
+
+    response = json.loads(response["Body"].read().decode("utf8"))
+    ## LOGGING
+    logging_obj.post_call(
+            input=input,
+            api_key="",
+            original_response=response,
+            additional_args={"complete_input_dict": data},
+        )
+    print_verbose(f"raw model_response: {response}")
+    if "embedding" not in response: 
+        raise SagemakerError(status_code=500, message="embedding not found in response")
+    embeddings = response['embedding'] 
+
+    if not isinstance(embeddings, list):
+        raise SagemakerError(status_code=422, message=f"Response not in expected format - {embeddings}")
+
+
+    output_data = []
+    for idx, embedding in enumerate(embeddings):
+        output_data.append(
+            {
+                "object": "embedding",
+                "index": idx,
+                "embedding": embedding
+            }
+        )
+
+    model_response["object"] = "list"
+    model_response["data"] = output_data
+    model_response["model"] = model
+    
+    input_tokens = 0
+    for text in input:
+        input_tokens+=len(encoding.encode(text)) 
+
+    model_response["usage"] = Usage(prompt_tokens=input_tokens, completion_tokens=0, total_tokens=input_tokens)
+    
+    return model_response