cr

langchain/llms/__init__.py

@@ -6,10 +6,10 @@ from langchain.llms.anthropic import Anthropic
 from langchain.llms.base import BaseLLM
 from langchain.llms.cohere import Cohere
 from langchain.llms.huggingface_hub import HuggingFaceHub
-from langchain.llms.sagemaker_endpoint import SagemakerEndpoint
 from langchain.llms.huggingface_pipeline import HuggingFacePipeline
 from langchain.llms.nlpcloud import NLPCloud
 from langchain.llms.openai import AzureOpenAI, OpenAI
+from langchain.llms.sagemaker_endpoint import SagemakerEndpoint
 
 __all__ = [
     "Anthropic",

langchain/llms/sagemaker_endpoint.py

@@ -1,13 +1,11 @@
 """Wrapper around Sagemaker InvokeEndpoint API."""
-from typing import Any, Dict, List, Mapping, Optional
-
-import boto3
 import json
-from pydantic import BaseModel, Extra, root_validator
+from typing import Any, List, Mapping, Optional
+
+from pydantic import BaseModel, Extra
 
 from langchain.llms.base import LLM
 from langchain.llms.utils import enforce_stop_tokens
-from langchain.utils import get_from_dict_or_env
 
 VALID_TASKS = ("text2text-generation", "text-generation")
 
@@ -15,7 +13,8 @@ VALID_TASKS = ("text2text-generation", "text-generation")
 class SagemakerEndpoint(LLM, BaseModel):
     """Wrapper around custom Sagemaker Inference Endpoints.
 
-    To use, you should pass the AWS IAM Role and Role Session Name as named parameters to the constructor.
+    To use, you should pass the AWS IAM Role and Role Session Name as
+    named parameters to the constructor.
 
     Only supports `text-generation` and `text2text-generation` for now.
     """
@@ -36,7 +35,7 @@ class SagemakerEndpoint(LLM, BaseModel):
     """
 
     endpoint_name: str = ""
-    """# The name of the endpoint. The name must be unique within an AWS Region in your AWS account."""
+    """# The name of the endpoint. Must be unique within an AWS Region."""
     task: Optional[str] = None
     """Task to call the model with. Should be a task that returns `generated_text`."""
     model_kwargs: Optional[dict] = None
@@ -79,6 +78,7 @@ class SagemakerEndpoint(LLM, BaseModel):
 
                 response = se("Tell me a joke.")
         """
+        import boto3
 
         session = boto3.Session(profile_name="test-profile-name")
         sagemaker_runtime = session.client("sagemaker-runtime", region_name="us-west-2")
@@ -101,30 +101,32 @@ class SagemakerEndpoint(LLM, BaseModel):
 
         # session = role_arn_to_session(RoleArn="$role-arn",
         # RoleSessionName="test-role-session-name")
-        # sagemaker_runtime = session.client("sagemaker-runtime", region_name="us-west-2")
+        # sagemaker_runtime = session.client(
+        # "sagemaker-runtime", region_name="us-west-2"
+        # )
 
         _model_kwargs = self.model_kwargs or {}
 
         # payload samples
         parameter_payload = {"inputs": prompt, "parameters": _model_kwargs}
 
-        input_en = json.dumps(parameter_payload).encode('utf-8')
+        input_en = json.dumps(parameter_payload).encode("utf-8")
 
         # send request
         try:
             response = sagemaker_runtime.invoke_endpoint(
                 EndpointName=self.endpoint_name,
                 Body=input_en,
-                            ContentType='application/json'
+                ContentType="application/json",
             )
         except Exception as e:
             raise ValueError(f"Error raised by inference endpoint: {e}")
 
+        response_json = json.loads(response["Body"].read().decode("utf-8"))
+        text = response_json[0]["generated_text"]
         if stop is not None:
             # This is a bit hacky, but I can't figure out a better way to enforce
             # stop tokens when making calls to huggingface_hub.
             text = enforce_stop_tokens(text, stop)
 
-        response_json = json.loads(response['Body'].read().decode('utf-8'))
+        return text
-
-        return response_json[0]["generated_text"]