diff --git a/docker/hpu.dockerfile b/docker/hpu.dockerfile
index a483af4b..0e5ce6d1 100644
--- a/docker/hpu.dockerfile
+++ b/docker/hpu.dockerfile
@@ -1,4 +1,4 @@
-FROM vault.habana.ai/gaudi-docker/1.13.0/ubuntu22.04/habanalabs/pytorch-installer-2.1.0:latest as hpu
+FROM vault.habana.ai/gaudi-docker/1.16.1/ubuntu22.04/habanalabs/pytorch-installer-2.2.2:latest as hpu
 
 ENV LANG=en_US.UTF-8
 ENV PYTHONPATH=/root:/usr/lib/habanalabs/
@@ -24,4 +24,4 @@ RUN cd /GenAIEval && \
     pip install --upgrade-strategy eager optimum[habana] && \
     pip list
 
-WORKDIR /GenAIEval/
\ No newline at end of file
+WORKDIR /GenAIEval/
diff --git a/evals/evaluation/rag_eval/examples/eval_multihop.py b/evals/evaluation/rag_eval/examples/eval_multihop.py
index a077d9f1..6bc49fbb 100644
--- a/evals/evaluation/rag_eval/examples/eval_multihop.py
+++ b/evals/evaluation/rag_eval/examples/eval_multihop.py
@@ -39,6 +39,22 @@ def get_document(self, data: dict):
             )
         return document
 
+    def get_reranked_documents(self, query, docs, arguments):
+        data = {
+            "initial_query": query,
+            "retrieved_docs": [{"text": doc} for doc in docs],
+            "top_n": 10,
+        }
+        headers = {"Content-Type": "application/json"}
+
+        response = requests.post(arguments.reranking_endpoint, data=json.dumps(data), headers=headers)
+        if response.ok:
+            reranked_documents = response.json()["documents"]
+            return reranked_documents
+        else:
+            print(f"Request for retrieval failed due to {response.text}.")
+            return []
+
     def get_retrieved_documents(self, query, arguments):
         data = {"text": query}
         headers = {"Content-Type": "application/json"}
@@ -77,6 +93,8 @@ def get_retrieval_metrics(self, all_queries, arguments):
                 continue
             query = data["query"]
             retrieved_documents = self.get_retrieved_documents(query, arguments)
+            if arguments.rerank:
+                retrieved_documents = self.get_reranked_documents(query, retrieved_documents, arguments)
             golden_context = [each["fact"] for each in data["evidence_list"]]
             test_case = {
                 "input": query,
@@ -212,6 +230,10 @@ def args_parser():
     parser.add_argument(
         "--retrieval_endpoint", type=str, default="http://localhost:7000/v1/retrieval", help="Service URL address."
     )
+    parser.add_argument("--rerank", action="store_true", help="Whether to use rerank microservice.")
+    parser.add_argument(
+        "--reranking_endpoint", type=str, default="http://localhost:8000/v1/reranking", help="Service URL address."
+    )
     parser.add_argument("--llm_endpoint", type=str, default=None, help="Service URL address.")
     parser.add_argument(
         "--show_progress_bar", action="store", default=True, type=bool, help="Whether to show a progress bar"
diff --git a/evals/metrics/ragas/ragas.py b/evals/metrics/ragas/ragas.py
index 15c11f84..4069a62c 100644
--- a/evals/metrics/ragas/ragas.py
+++ b/evals/metrics/ragas/ragas.py
@@ -31,7 +31,7 @@ def __init__(
         self.model = model
         self.embeddings = embeddings
         self.metrics = metrics
-        self.validated_list = ["answer_relevancy", "faithfulness"]
+        self.validated_list = ["answer_relevancy", "faithfulness", "answer_correctness"]
 
     async def a_measure(self, test_case: Dict):
         return self.measure(test_case)