diff --git a/ChatQnA/chatqna.py b/ChatQnA/chatqna.py
index e9ace60885..81dedb063c 100644
--- a/ChatQnA/chatqna.py
+++ b/ChatQnA/chatqna.py
@@ -58,11 +58,17 @@ def generate_rag_prompt(question, documents):
 LLM_SERVER_HOST_IP = os.getenv("LLM_SERVER_HOST_IP", "0.0.0.0")
 LLM_SERVER_PORT = int(os.getenv("LLM_SERVER_PORT", 80))
 LLM_MODEL = os.getenv("LLM_MODEL", "Intel/neural-chat-7b-v3-3")
+EMBEDDINGS_MODEL_ID = os.getenv("EMBEDDINGS_MODEL_ID", "BAAI/bge-base-en-v1.5")
+EMBEDDINGS_USE_VLLM = os.getenv("EMBEDDINGS_USE_VLLM", "false")
 
 
 def align_inputs(self, inputs, cur_node, runtime_graph, llm_parameters_dict, **kwargs):
     if self.services[cur_node].service_type == ServiceType.EMBEDDING:
-        inputs["inputs"] = inputs["text"]
+        if EMBEDDINGS_USE_VLLM == "true":
+            inputs["input"] = inputs["text"]
+            inputs["model"] = EMBEDDINGS_MODEL_ID
+        else:
+            inputs["inputs"] = inputs["text"]
         del inputs["text"]
     elif self.services[cur_node].service_type == ServiceType.RETRIEVER:
         # prepare the retriever params
@@ -88,8 +94,12 @@ def align_inputs(self, inputs, cur_node, runtime_graph, llm_parameters_dict, **k
 def align_outputs(self, data, cur_node, inputs, runtime_graph, llm_parameters_dict, **kwargs):
     next_data = {}
     if self.services[cur_node].service_type == ServiceType.EMBEDDING:
-        assert isinstance(data, list)
-        next_data = {"text": inputs["inputs"], "embedding": data[0]}
+        if EMBEDDINGS_USE_VLLM == "true":
+            assert isinstance(data["data"][0]["embedding"], list)
+            next_data = {"text": inputs["input"], "embedding": data["data"][0]["embedding"]}
+        else:
+            assert isinstance(data, list)
+            next_data = {"text": inputs["inputs"], "embedding": data[0]}
     elif self.services[cur_node].service_type == ServiceType.RETRIEVER:
 
         docs = [doc["text"] for doc in data["retrieved_docs"]]
@@ -329,6 +339,37 @@ def add_remote_service_with_guardrails(self):
         self.megaservice.flow_to(rerank, llm)
         # self.megaservice.flow_to(llm, guardrail_out)
 
+    def add_remote_service_with_vllm_embeddings(self):
+        embedding = MicroService(
+            name="embedding",
+            host=EMBEDDING_SERVER_HOST_IP,
+            port=EMBEDDING_SERVER_PORT,
+            endpoint="/v1/embeddings",
+            use_remote_service=True,
+            service_type=ServiceType.EMBEDDING,
+        )
+
+        retriever = MicroService(
+            name="retriever",
+            host=RETRIEVER_SERVICE_HOST_IP,
+            port=RETRIEVER_SERVICE_PORT,
+            endpoint="/v1/retrieval",
+            use_remote_service=True,
+            service_type=ServiceType.RETRIEVER,
+        )
+
+        llm = MicroService(
+            name="llm",
+            host=LLM_SERVER_HOST_IP,
+            port=LLM_SERVER_PORT,
+            endpoint="/v1/chat/completions",
+            use_remote_service=True,
+            service_type=ServiceType.LLM,
+        )
+        self.megaservice.add(embedding).add(retriever).add(llm)
+        self.megaservice.flow_to(embedding, retriever)
+        self.megaservice.flow_to(retriever, llm)
+
     async def handle_request(self, request: Request):
         data = await request.json()
         stream_opt = data.get("stream", True)
@@ -403,7 +444,10 @@ def start(self):
     args = parser.parse_args()
 
     chatqna = ChatQnAService(port=MEGA_SERVICE_PORT)
-    if args.without_rerank:
+    if EMBEDDINGS_USE_VLLM == "true":
+        # this also doesn't use rerank
+        chatqna.add_remote_service_with_vllm_embeddings()
+    elif args.without_rerank:
         chatqna.add_remote_service_without_rerank()
     elif args.with_guardrails:
         chatqna.add_remote_service_with_guardrails()