10634: Added the capability to inject boto3 client in SagemakerEndpointEmbeddings (#12146)

**Description: Allow to inject boto3 client for Cross account access
type of scenarios in using SagemakerEndpointEmbeddings and also updated
the documentation for same in the sample notebook**

**Issue:SagemakerEndpointEmbeddings cross account capability #10634
#10184**

Dependencies: None
Tag maintainer:
Twitter handle:lethargicoder

Co-authored-by: Vikram(VS) <vssht@amazon.com>

docs/docs/integrations/llms/sagemaker.ipynb

@@ -82,6 +82,15 @@
     "]"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example to initialize with external boto3 session\n",
+    "\n",
+    "### for cross account scenarios"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -92,7 +101,77 @@
    "source": [
     "from typing import Dict\n",
     "\n",
-    "from langchain.prompts import PromptTemplate\nfrom langchain.llms import SagemakerEndpoint\n",
+    "from langchain.prompts import PromptTemplate\n",
+    "from langchain.llms import SagemakerEndpoint\n",
+    "from langchain.llms.sagemaker_endpoint import LLMContentHandler\n",
+    "from langchain.chains.question_answering import load_qa_chain\n",
+    "import json\n",
+    "import boto3\n",
+    "\n",
+    "query = \"\"\"How long was Elizabeth hospitalized?\n",
+    "\"\"\"\n",
+    "\n",
+    "prompt_template = \"\"\"Use the following pieces of context to answer the question at the end.\n",
+    "\n",
+    "{context}\n",
+    "\n",
+    "Question: {question}\n",
+    "Answer:\"\"\"\n",
+    "PROMPT = PromptTemplate(\n",
+    "    template=prompt_template, input_variables=[\"context\", \"question\"]\n",
+    ")\n",
+    "\n",
+    "roleARN = 'arn:aws:iam::123456789:role/cross-account-role'\n",
+    "sts_client = boto3.client('sts')\n",
+    "response = sts_client.assume_role(RoleArn=roleARN, \n",
+    "                                    RoleSessionName='CrossAccountSession')\n",
+    "\n",
+    "client = boto3.client(\n",
+    "    \"sagemaker-runtime\",\n",
+    "    region_name=\"us-west-2\",    \n",
+    "    aws_access_key_id=response['Credentials']['AccessKeyId'],\n",
+    "    aws_secret_access_key=response['Credentials']['SecretAccessKey'],\n",
+    "    aws_session_token = response['Credentials']['SessionToken']\n",
+    ")\n",
+    "\n",
+    "class ContentHandler(LLMContentHandler):\n",
+    "    content_type = \"application/json\"\n",
+    "    accepts = \"application/json\"\n",
+    "\n",
+    "    def transform_input(self, prompt: str, model_kwargs: Dict) -> bytes:\n",
+    "        input_str = json.dumps({prompt: prompt, **model_kwargs})\n",
+    "        return input_str.encode(\"utf-8\")\n",
+    "\n",
+    "    def transform_output(self, output: bytes) -> str:\n",
+    "        response_json = json.loads(output.read().decode(\"utf-8\"))\n",
+    "        return response_json[0][\"generated_text\"]\n",
+    "\n",
+    "\n",
+    "content_handler = ContentHandler()\n",
+    "\n",
+    "chain = load_qa_chain(\n",
+    "    llm=SagemakerEndpoint(\n",
+    "        endpoint_name=\"endpoint-name\",\n",
+    "        client=client,\n",
+    "        model_kwargs={\"temperature\": 1e-10},\n",
+    "        content_handler=content_handler,\n",
+    "    ),\n",
+    "    prompt=PROMPT,\n",
+    ")\n",
+    "\n",
+    "chain({\"input_documents\": docs, \"question\": query}, return_only_outputs=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from typing import Dict\n",
+    "\n",
+    "from langchain.prompts import PromptTemplate\n",
+    "from langchain.llms import SagemakerEndpoint\n",
     "from langchain.llms.sagemaker_endpoint import LLMContentHandler\n",
     "from langchain.chains.question_answering import load_qa_chain\n",
     "import json\n",

docs/docs/integrations/text_embedding/sagemaker-endpoint.ipynb

@@ -43,7 +43,7 @@
     "from langchain.embeddings import SagemakerEndpointEmbeddings\n",
     "from langchain.embeddings.sagemaker_endpoint import EmbeddingsContentHandler\n",
     "import json\n",
-    "\n",
+    "import boto3\n",
     "\n",
     "class ContentHandler(EmbeddingsContentHandler):\n",
     "    content_type = \"application/json\"\n",
@@ -87,7 +87,18 @@
     "    endpoint_name=\"huggingface-pytorch-inference-2023-03-21-16-14-03-834\",\n",
     "    region_name=\"us-east-1\",\n",
     "    content_handler=content_handler,\n",
-    ")"
+    ")\n",
+    "\n",
+    "\n",
+    "# client = boto3.client(\n",
+    "#     \"sagemaker-runtime\",\n",
+    "#     region_name=\"us-west-2\"  \n",
+    "# )\n",
+    "# embeddings = SagemakerEndpointEmbeddings(\n",
+    "#     endpoint_name=\"huggingface-pytorch-inference-2023-03-21-16-14-03-834\",    \n",
+    "#     client=client\n",
+    "#     content_handler=content_handler,\n",
+    "# )"
    ]
   },
   {

libs/langchain/langchain/embeddings/sagemaker_endpoint.py

@@ -46,8 +46,18 @@ class SagemakerEndpointEmbeddings(BaseModel, Embeddings):
                 region_name=region_name,
                 credentials_profile_name=credentials_profile_name
             )
+
+            #Use with boto3 client
+            client = boto3.client(
+                        "sagemaker-runtime",
+                        region_name=region_name
+                    )
+            se = SagemakerEndpointEmbeddings(
+                endpoint_name=endpoint_name,
+                client=client
+            )
     """
-    client: Any  #: :meta private:
+    client: Any = None
 
     endpoint_name: str = ""
     """The name of the endpoint from the deployed Sagemaker model.
@@ -106,6 +116,10 @@ class SagemakerEndpointEmbeddings(BaseModel, Embeddings):
 
     @root_validator()
     def validate_environment(cls, values: Dict) -> Dict:
+        """Dont do anything if client provided externally"""
+        if values.get("client") is not None:
+            return values
+
         """Validate that AWS credentials to and python package exists in environment."""
         try:
             import boto3