Error while using RetrievalQA chain type of Langchain for vector retrieval using FastChat LLM model which is hosted on Endpoint(GPU machine).

Our LLM model is on endpoint :

from langchain.indexes import VectorstoreIndexCreator

os.environ["OPENAI_API_BASE"] = "my_endpoint_ip"
os.environ["OPENAI_API_KEY"] = "empty"
embedding = OpenAIEmbeddings(model="text-embedding-ada-002")
local_llm = OpenAI(model="gpt-3.5-turbo")

So while performing below retrieval task we are not able to fetch llm model from endpoint.
qa_chain = RetrievalQA.from_chain_type(llm=local_llm,
chain_type="stuff",
retriever=retriever, return_source_documents=True)

ERROR : ConnectionResetError

ConnectionResetError Traceback (most recent call last)
File ~/SageMaker/AmazonSageMaker-IAG/virtual_environment/lib/python3.10/site-packages/urllib3/connectionpool.py:714, in HTTPConnectionPool.urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)
713 # Make the request on the httplib connection object.
--> 714 httplib_response = self._make_request(
715 conn,
716 method,
717 url,
718 timeout=timeout_obj,
719 body=body,
720 headers=headers,
721 chunked=chunked,
722 )
ConnectionError: ('Connection aborted.', ConnectionResetError(104, 'Connection reset by peer'))

Start by reproducing the RetrievalQA.from_chain_type call using the provided OpenAIEmbeddings and OpenAI configuration, then inspect the FastChat endpoint and its request or server logs for the connection reset. No repository file or test is named in the issue; done means identifying the endpoint or integration cause and confirming a working retrieval request.