community: minor changes sambanova integration (#21231)

- **Description:** fix: variable names in root validator not allowing
pass credentials as named parameters in llm instancing, also added
sambanova's sambaverse and sambastudio llms to __init__.py for module
import

libs/community/langchain_community/llms/__init__.py

@@ -510,6 +510,18 @@ def _import_sagemaker_endpoint() -> Type[BaseLLM]:
  return SagemakerEndpoint
 
 
+def _import_sambaverse() -> Type[BaseLLM]:
+ from langchain_community.llms.sambanova import Sambaverse
+
+ return Sambaverse
+
+
+def _import_sambastudio() -> Type[BaseLLM]:
+ from langchain_community.llms.sambanova import SambaStudio
+
+ return SambaStudio
+
+
 def _import_self_hosted() -> Type[BaseLLM]:
  from langchain_community.llms.self_hosted import SelfHostedPipeline
 
@@ -793,6 +805,10 @@ def __getattr__(name: str) -> Any:
  return _import_rwkv()
  elif name == "SagemakerEndpoint":
  return _import_sagemaker_endpoint()
+ elif name == "Sambaverse":
+ return _import_sambaverse()
+ elif name == "SambaStudio":
+ return _import_sambastudio()
  elif name == "SelfHostedPipeline":
  return _import_self_hosted()
  elif name == "SelfHostedHuggingFaceLLM":
@@ -922,6 +938,8 @@ def __getattr__(name: str) -> Any:
  "RWKV",
  "Replicate",
  "SagemakerEndpoint",
+ "Sambaverse",
+ "SambaStudio",
  "SelfHostedHuggingFaceLLM",
  "SelfHostedPipeline",
  "SparkLLM",
@@ -1015,6 +1033,8 @@ def get_type_to_cls_dict() -> Dict[str, Callable[[], Type[BaseLLM]]]:
  "replicate": _import_replicate,
  "rwkv": _import_rwkv,
  "sagemaker_endpoint": _import_sagemaker_endpoint,
+ "sambaverse": _import_sambaverse,
+ "sambastudio": _import_sambastudio,
  "self_hosted": _import_self_hosted,
  "self_hosted_hugging_face": _import_self_hosted_hugging_face,
  "stochasticai": _import_stochasticai,

libs/community/langchain_community/llms/sambanova.py

@@ -618,10 +618,10 @@ class SambaStudio(LLM):
 
  from langchain_community.llms.sambanova import Sambaverse
  SambaStudio(
- base_url="your SambaStudio environment URL",
- project_id=set with your SambaStudio project ID.,
- endpoint_id=set with your SambaStudio endpoint ID.,
- api_token= set with your SambaStudio endpoint API key.,
+ sambastudio_base_url="your SambaStudio environment URL",
+ sambastudio_project_id=set with your SambaStudio project ID.,
+ sambastudio_endpoint_id=set with your SambaStudio endpoint ID.,
+ sambastudio_api_key= set with your SambaStudio endpoint API key.,
  streaming=false
  model_kwargs={
  "do_sample": False,
@@ -634,16 +634,16 @@ class SambaStudio(LLM):
  )
  """
 
- base_url: str = ""
+ sambastudio_base_url: str = ""
  """Base url to use"""
 
- project_id: str = ""
+ sambastudio_project_id: str = ""
  """Project id on sambastudio for model"""
 
- endpoint_id: str = ""
+ sambastudio_endpoint_id: str = ""
  """endpoint id on sambastudio for model"""
 
- api_key: str = ""
+ sambastudio_api_key: str = ""
  """sambastudio api key"""
 
  model_kwargs: Optional[dict] = None
@@ -674,16 +674,16 @@ def _llm_type(self) -> str:
  @root_validator()
  def validate_environment(cls, values: Dict) -> Dict:
  """Validate that api key and python package exists in environment."""
- values["base_url"] = get_from_dict_or_env(
+ values["sambastudio_base_url"] = get_from_dict_or_env(
  values, "sambastudio_base_url", "SAMBASTUDIO_BASE_URL"
  )
- values["project_id"] = get_from_dict_or_env(
+ values["sambastudio_project_id"] = get_from_dict_or_env(
  values, "sambastudio_project_id", "SAMBASTUDIO_PROJECT_ID"
  )
- values["endpoint_id"] = get_from_dict_or_env(
+ values["sambastudio_endpoint_id"] = get_from_dict_or_env(
  values, "sambastudio_endpoint_id", "SAMBASTUDIO_ENDPOINT_ID"
  )
- values["api_key"] = get_from_dict_or_env(
+ values["sambastudio_api_key"] = get_from_dict_or_env(
  values, "sambastudio_api_key", "SAMBASTUDIO_API_KEY"
  )
  return values
@@ -729,7 +729,11 @@ def _handle_nlp_predict(
  ValueError: If the prediction fails.
  """
  response = sdk.nlp_predict(
- self.project_id, self.endpoint_id, self.api_key, prompt, tuning_params
+ self.sambastudio_project_id,
+ self.sambastudio_endpoint_id,
+ self.sambastudio_api_key,
+ prompt,
+ tuning_params,
  )
  if response["status_code"] != 200:
  optional_detail = response["detail"]
@@ -755,7 +759,7 @@ def _handle_completion_requests(
  Raises:
  ValueError: If the prediction fails.
  """
- ss_endpoint = SSEndpointHandler(self.base_url)
+ ss_endpoint = SSEndpointHandler(self.sambastudio_base_url)
  tuning_params = self._get_tuning_params(stop)
  return self._handle_nlp_predict(ss_endpoint, prompt, tuning_params)
 
@@ -774,7 +778,11 @@ def _handle_nlp_predict_stream(
  An iterator of GenerationChunks.
  """
  for chunk in sdk.nlp_predict_stream(
- self.project_id, self.endpoint_id, self.api_key, prompt, tuning_params
+ self.sambastudio_project_id,
+ self.sambastudio_endpoint_id,
+ self.sambastudio_api_key,
+ prompt,
+ tuning_params,
  ):
  yield chunk
 
@@ -794,7 +802,7 @@ def _stream(
  Returns:
  The string generated by the model.
  """
- ss_endpoint = SSEndpointHandler(self.base_url)
+ ss_endpoint = SSEndpointHandler(self.sambastudio_base_url)
  tuning_params = self._get_tuning_params(stop)
  try:
  if self.streaming:

libs/community/tests/unit_tests/llms/test_imports.py

@@ -77,6 +77,8 @@
  "RWKV",
  "Replicate",
  "SagemakerEndpoint",
+ "Sambaverse",
+ "SambaStudio",
  "SelfHostedHuggingFaceLLM",
  "SelfHostedPipeline",
  "StochasticAI",