opensearchpy.exceptions.ConnectionError: ConnectionError(HTTPSConnectionPool(host='qa43g2l2a1mw4vt481a3.aoss.amazonaws.com', port=443): Max retries exceeded with url: /oscars-index/_search.

opensearchpy.exceptions.ConnectionError: ConnectionError(HTTPSConnectionPool(host='qa43g2l2a1mw4vt481a3.aoss.amazonaws.com', port=443): Max retries exceeded with url: /oscars-index/_search (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f1b20506770>: Failed to establish a new connection: [Errno -2] Name or service not known'))) caused by: ConnectionError(HTTPSConnectionPool(host='qa43g2l2a1mw4vt481a3.aoss.amazonaws.com', port=443): Max retries exceeded with url: /oscars-index/_search (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f1b20506770>: Failed to establish a new connection: [Errno -2] Name or service not known'))).

Getting this Error while running the gradio application on sagemaker jupyterlab instance. Local url of gradio doesn't open at all but the gradio live url is opening but is I'm asking question to the chatbot It will reflect this error always.

Whole Code:
import gradio as gr
from opensearchpy import OpenSearch, RequestsHttpConnection, AWSV4SignerAuth, helpers
import boto3
import json

host = "qa43g2l2a1mw4vt481a3.aoss.amazonaws.com"
region = "us-east-1"
service = "aoss"
credentials = boto3.Session().get_credentials()
auth = AWSV4SignerAuth(credentials, region, service)

os_client = OpenSearch(
hosts = [{"host": host, "port": 443}],
http_auth = auth,
use_ssl = True,
verify_certs = True,
connection_class = RequestsHttpConnection,
pool_maxsize = 20
)

bedrock = boto3.client(
service_name='bedrock-runtime',
region_name='us-east-1',
endpoint_url='https://bedrock-runtime.us-east-1.amazonaws.com'
)

def generate_context(text):
body=json.dumps({"inputText": text})
response = bedrock.invoke_model(body=body, modelId='amazon.titan-embed-text-v1',
accept='application/json', contentType='application/json')
response_body = json.loads(response.get('body').read())
embedding = response_body.get('embedding')

document = { "size": 15, "_source": {"excludes": ["nominee_vector"]}, "query": { "knn": { "nominee_vector": { "vector": embedding, "k":15 } } } } response = os_client.search( body = document, index = "oscars-index" ) data=response['hits']['hits']
context = ''
for item in data:
context += item['_source']['nominee_text'] + '\n'
return context\

def invoke_titan(prompt):
config={
"maxTokenCount": 1000,
"stopSequences": [],
"temperature":0.1,
"topP":1
}
body = json.dumps({'inputText': prompt,'textGenerationConfig':config})

response = bedrock.invoke_model(
modelId='amazon.titan-text-lite-v1',
body=body
)
response_body = json.loads(response.get('body').read())
return response_body.get('results')[0].get('outputText')

def build_prompt(message, history):
context=generate_context(message)
messages=[]
prompt=f'Context - {context}\nBased on the above context, answer this question - {message}'
print(prompt)
return invoke_titan(prompt)

gr.ChatInterface(
build_prompt,
chatbot=gr.Chatbot(height=300),
textbox=gr.Textbox(placeholder="Ask me a question", container=False, scale=7),
title="Oscar 2023 Bot - Amazon Bedrock and Titan",
theme="soft",
examples=[
"Who won the best music award?",
"Which award did Avatar win?",
"Who won the Best Actor award in a supporting role?",
"Who is the lyricist for the song Natu Natu from RRR?",
"How many awards did the film RRR win?",
"Which was the Best International Feature Film?",
],
cache_examples=False,
retry_btn=None,
undo_btn="Delete Previous",
clear_btn="Clear",
).launch(share=True)\