diff --git a/libs/community/langchain_community/embeddings/sambanova.py b/libs/community/langchain_community/embeddings/sambanova.py
index 6f86b417454..57601b5c629 100644
--- a/libs/community/langchain_community/embeddings/sambanova.py
+++ b/libs/community/langchain_community/embeddings/sambanova.py
@@ -98,10 +98,13 @@ class SambaStudioEmbeddings(BaseModel, Embeddings):
         Returns:
             The tuning parameters as a JSON string.
         """
-        tuning_params_dict = {
-            k: {"type": type(v).__name__, "value": str(v)}
-            for k, v in (self.model_kwargs.items())
-        }
+        if "api/v2/predict/generic" in self.sambastudio_embeddings_base_uri:
+            tuning_params_dict = self.model_kwargs
+        else:
+            tuning_params_dict = {
+                k: {"type": type(v).__name__, "value": str(v)}
+                for k, v in (self.model_kwargs.items())
+            }
         tuning_params = json.dumps(tuning_params_dict)
         return tuning_params
 
@@ -148,7 +151,7 @@ class SambaStudioEmbeddings(BaseModel, Embeddings):
         params = json.loads(self._get_tuning_params())
         embeddings = []
 
-        if "nlp" in self.sambastudio_embeddings_base_uri:
+        if "api/predict/nlp" in self.sambastudio_embeddings_base_uri:
             for batch in self._iterate_over_batches(texts, batch_size):
                 data = {"inputs": batch, "params": params}
                 response = http_session.post(
@@ -156,6 +159,11 @@ class SambaStudioEmbeddings(BaseModel, Embeddings):
                     headers={"key": self.sambastudio_embeddings_api_key},
                     json=data,
                 )
+                if response.status_code != 200:
+                    raise RuntimeError(
+                        f"Sambanova /complete call failed with status code "
+                        f"{response.status_code}.\n Details: {response.text}"
+                    )
                 try:
                     embedding = response.json()["data"]
                     embeddings.extend(embedding)
@@ -165,7 +173,32 @@ class SambaStudioEmbeddings(BaseModel, Embeddings):
                         response.json(),
                     )
 
-        elif "generic" in self.sambastudio_embeddings_base_uri:
+        elif "api/v2/predict/generic" in self.sambastudio_embeddings_base_uri:
+            for batch in self._iterate_over_batches(texts, batch_size):
+                items = [
+                    {"id": f"item{i}", "value": item} for i, item in enumerate(batch)
+                ]
+                data = {"items": items, "params": params}
+                response = http_session.post(
+                    url,
+                    headers={"key": self.sambastudio_embeddings_api_key},
+                    json=data,
+                )
+                if response.status_code != 200:
+                    raise RuntimeError(
+                        f"Sambanova /complete call failed with status code "
+                        f"{response.status_code}.\n Details: {response.text}"
+                    )
+                try:
+                    embedding = [item["value"] for item in response.json()["items"]]
+                    embeddings.extend(embedding)
+                except KeyError:
+                    raise KeyError(
+                        "'items' not found in endpoint response",
+                        response.json(),
+                    )
+
+        elif "api/predict/generic" in self.sambastudio_embeddings_base_uri:
             for batch in self._iterate_over_batches(texts, batch_size):
                 data = {"instances": batch, "params": params}
                 response = http_session.post(
@@ -173,6 +206,11 @@ class SambaStudioEmbeddings(BaseModel, Embeddings):
                     headers={"key": self.sambastudio_embeddings_api_key},
                     json=data,
                 )
+                if response.status_code != 200:
+                    raise RuntimeError(
+                        f"Sambanova /complete call failed with status code "
+                        f"{response.status_code}.\n Details: {response.text}"
+                    )
                 try:
                     if params.get("select_expert"):
                         embedding = response.json()["predictions"][0]
@@ -207,13 +245,18 @@ class SambaStudioEmbeddings(BaseModel, Embeddings):
         )
         params = json.loads(self._get_tuning_params())
 
-        if "nlp" in self.sambastudio_embeddings_base_uri:
+        if "api/predict/nlp" in self.sambastudio_embeddings_base_uri:
             data = {"inputs": [text], "params": params}
             response = http_session.post(
                 url,
                 headers={"key": self.sambastudio_embeddings_api_key},
                 json=data,
             )
+            if response.status_code != 200:
+                raise RuntimeError(
+                    f"Sambanova /complete call failed with status code "
+                    f"{response.status_code}.\n Details: {response.text}"
+                )
             try:
                 embedding = response.json()["data"][0]
             except KeyError:
@@ -222,13 +265,38 @@ class SambaStudioEmbeddings(BaseModel, Embeddings):
                     response.json(),
                 )
 
-        elif "generic" in self.sambastudio_embeddings_base_uri:
+        elif "api/v2/predict/generic" in self.sambastudio_embeddings_base_uri:
+            data = {"items": [{"id": "item0", "value": text}], "params": params}
+            response = http_session.post(
+                url,
+                headers={"key": self.sambastudio_embeddings_api_key},
+                json=data,
+            )
+            if response.status_code != 200:
+                raise RuntimeError(
+                    f"Sambanova /complete call failed with status code "
+                    f"{response.status_code}.\n Details: {response.text}"
+                )
+            try:
+                embedding = response.json()["items"][0]["value"]
+            except KeyError:
+                raise KeyError(
+                    "'items' not found in endpoint response",
+                    response.json(),
+                )
+
+        elif "api/predict/generic" in self.sambastudio_embeddings_base_uri:
             data = {"instances": [text], "params": params}
             response = http_session.post(
                 url,
                 headers={"key": self.sambastudio_embeddings_api_key},
                 json=data,
             )
+            if response.status_code != 200:
+                raise RuntimeError(
+                    f"Sambanova /complete call failed with status code "
+                    f"{response.status_code}.\n Details: {response.text}"
+                )
             try:
                 if params.get("select_expert"):
                     embedding = response.json()["predictions"][0][0]