diff --git a/libs/community/langchain_community/llms/sambanova.py b/libs/community/langchain_community/llms/sambanova.py
index b728eb56781..187c1708fe4 100644
--- a/libs/community/langchain_community/llms/sambanova.py
+++ b/libs/community/langchain_community/llms/sambanova.py
@@ -514,7 +514,7 @@ class SSEndpointHandler:
         response: requests.Response,
     ) -> Generator[Dict, None, None]:
         """Process the streaming response"""
-        if "nlp" in self.api_base_uri:
+        if "api/predict/nlp" in self.api_base_uri:
             try:
                 import sseclient
             except ImportError:
@@ -535,14 +535,15 @@ class SSEndpointHandler:
                 yield chunk
             if close_conn:
                 client.close()
-        elif "generic" in self.api_base_uri:
+        elif (
+            "api/v2/predict/generic" in self.api_base_uri
+            or "api/predict/generic" in self.api_base_uri
+        ):
             try:
                 for line in response.iter_lines():
                     chunk = json.loads(line)
                     if "status_code" not in chunk:
                         chunk["status_code"] = response.status_code
-                    if chunk["status_code"] == 200 and chunk.get("error"):
-                        chunk["result"] = {"responses": [{"stream_token": ""}]}
                     yield chunk
             except Exception as e:
                 raise RuntimeError(f"Error processing streaming response: {e}")
@@ -583,12 +584,18 @@ class SSEndpointHandler:
         """
         if isinstance(input, str):
             input = [input]
-        if "nlp" in self.api_base_uri:
+        if "api/predict/nlp" in self.api_base_uri:
             if params:
                 data = {"inputs": input, "params": json.loads(params)}
             else:
                 data = {"inputs": input}
-        elif "generic" in self.api_base_uri:
+        elif "api/v2/predict/generic" in self.api_base_uri:
+            items = [{"id": f"item{i}", "value": item} for i, item in enumerate(input)]
+            if params:
+                data = {"items": items, "params": json.loads(params)}
+            else:
+                data = {"items": items}
+        elif "api/predict/generic" in self.api_base_uri:
             if params:
                 data = {"instances": input, "params": json.loads(params)}
             else:
@@ -623,14 +630,22 @@ class SSEndpointHandler:
         :returns: Prediction results
         :type: dict
         """
-        if "nlp" in self.api_base_uri:
+        if "api/predict/nlp" in self.api_base_uri:
             if isinstance(input, str):
                 input = [input]
             if params:
                 data = {"inputs": input, "params": json.loads(params)}
             else:
                 data = {"inputs": input}
-        elif "generic" in self.api_base_uri:
+        elif "api/v2/predict/generic" in self.api_base_uri:
+            if isinstance(input, str):
+                input = [input]
+            items = [{"id": f"item{i}", "value": item} for i, item in enumerate(input)]
+            if params:
+                data = {"items": items, "params": json.loads(params)}
+            else:
+                data = {"items": items}
+        elif "api/predict/generic" in self.api_base_uri:
             if isinstance(input, list):
                 input = input[0]
             if params:
@@ -770,10 +785,13 @@ class SambaStudio(LLM):
         # _model_kwargs["stop_sequences"] = ",".join(
         #    f'"{x}"' for x in _stop_sequences
         # )
-        tuning_params_dict = {
-            k: {"type": type(v).__name__, "value": str(v)}
-            for k, v in (_model_kwargs.items())
-        }
+        if "api/v2/predict/generic" in self.sambastudio_base_uri:
+            tuning_params_dict = _model_kwargs
+        else:
+            tuning_params_dict = {
+                k: {"type": type(v).__name__, "value": str(v)}
+                for k, v in (_model_kwargs.items())
+            }
         # _model_kwargs["stop_sequences"] = _kwarg_stop_sequences
         tuning_params = json.dumps(tuning_params_dict)
         return tuning_params
@@ -814,9 +832,11 @@ class SambaStudio(LLM):
                     f"Sambanova /complete call failed with status code "
                     f"{response['status_code']}.\n response {response}"
                 )
-        if "nlp" in self.sambastudio_base_uri:
+        if "api/predict/nlp" in self.sambastudio_base_uri:
             return response["data"][0]["completion"]
-        elif "generic" in self.sambastudio_base_uri:
+        elif "api/v2/predict/generic" in self.sambastudio_base_uri:
+            return response["items"][0]["value"]["completion"]
+        elif "api/predict/generic" in self.sambastudio_base_uri:
             return response["predictions"][0]["completion"]
         else:
             raise ValueError(
@@ -885,10 +905,15 @@ class SambaStudio(LLM):
                         f"{chunk['status_code']}."
                         f"{chunk}."
                     )
-            if "nlp" in self.sambastudio_base_uri:
+            if "api/predict/nlp" in self.sambastudio_base_uri:
                 text = json.loads(chunk["data"])["stream_token"]
-            elif "generic" in self.sambastudio_base_uri:
-                text = chunk["result"]["responses"][0]["stream_token"]
+            elif "api/v2/predict/generic" in self.sambastudio_base_uri:
+                text = chunk["result"]["items"][0]["value"]["stream_token"]
+            elif "api/predict/generic" in self.sambastudio_base_uri:
+                if len(chunk["result"]["responses"]) > 0:
+                    text = chunk["result"]["responses"][0]["stream_token"]
+                else:
+                    text = ""
             else:
                 raise ValueError(
                     f"handling of endpoint uri: {self.sambastudio_base_uri}"