diff --git a/lmdeploy/messages.py b/lmdeploy/messages.py
index 52a71ae569..a8d22bad10 100644
--- a/lmdeploy/messages.py
+++ b/lmdeploy/messages.py
@@ -249,8 +249,7 @@ class Response:
         generate_token_len (int): the response token length.
         input_token_len (int): the input prompt token length. Note that it may
             contains chat template part.
-        session_id (int): the id for running the session. Basically, it refers
-            to the position index of the input request batch.
+        session_id (int): the id for running the session.
         finish_reason ('stop' | 'length' | None): the reason the model stopped
             generating tokens. This will be 'stop' if the model hit a natural
             stop point or a provided stop sequence, 'length' if the maximum
@@ -258,6 +257,8 @@ class Response:
         token_ids: (List[int]): the output token ids.
         logprobs: (List[Dict[int, float]]): the top logprobs for each output
             position.
+        index (int): it refers to the position index of the input request
+            batch
     """
     text: str
     generate_token_len: int
@@ -266,6 +267,7 @@ class Response:
     finish_reason: Optional[Literal['stop', 'length']] = None
     token_ids: List[int] = field(default_factory=list)
     logprobs: List[Dict[int, float]] = None
+    index: int = 0
 
 
 @dataclass
diff --git a/lmdeploy/serve/async_engine.py b/lmdeploy/serve/async_engine.py
index 039e136e43..f9c7d969dc 100644
--- a/lmdeploy/serve/async_engine.py
+++ b/lmdeploy/serve/async_engine.py
@@ -180,6 +180,7 @@ def __init__(self,
         self.gens_set = set()
         for i in range(self.instance_num):
             self.gens_set.add(self.engine.create_instance())
+        self._session_id = count(0)
 
     def _build_turbomind(
             self,
@@ -328,7 +329,11 @@ def batch_infer(
         assert len(prompts) == len(gen_config),\
                 'input gen_confg length differs from the length of prompts' # noqa
         prompt_num = len(prompts)
-        outputs = [Response('', 0, 0, i) for i in range(prompt_num)]
+        session_ids = [next(self._session_id) for _ in range(prompt_num)]
+        outputs = [
+            Response('', 0, 0, session_ids[i], index=i)
+            for i in range(prompt_num)
+        ]
         generators = []
         if use_tqdm:
             import tqdm
@@ -336,7 +341,7 @@ def batch_infer(
         for i, prompt in enumerate(prompts):
             generators.append(
                 self.generate(prompt,
-                              i,
+                              session_ids[i],
                               gen_config=gen_config[i],
                               stream_response=True,
                               sequence_start=True,
@@ -404,12 +409,13 @@ def stream_infer(
             gen_config = [gen_config] * len(prompts)
         assert len(prompts) == len(gen_config),\
                 'input gen_confg length differs from the length of prompts' # noqa
+        session_ids = [next(self._session_id) for _ in range(len(prompts))]
         outputs = Queue()
         generators = []
         for i, prompt in enumerate(prompts):
             generators.append(
                 self.generate(prompt,
-                              i,
+                              session_ids[i],
                               gen_config=gen_config[i],
                               stream_response=True,
                               sequence_start=True,
@@ -421,9 +427,14 @@ def stream_infer(
         async def _inner_call(i, generator):
             async for out in generator:
                 outputs.put(
-                    Response(out.response, out.generate_token_len,
-                             out.input_token_len, i, out.finish_reason,
-                             out.token_ids, out.logprobs))
+                    Response(out.response,
+                             out.generate_token_len,
+                             out.input_token_len,
+                             session_ids[i],
+                             out.finish_reason,
+                             out.token_ids,
+                             out.logprobs,
+                             index=i))
 
         async def gather():
             await asyncio.gather(