fix: Surface relevant exception when initializing langchain model

Author: JashG

nemoguardrails/llm/models/langchain_initializer.py

@@ -225,7 +225,7 @@ def _init_chat_completion_model(model_name: str, provider_name: str, kwargs: Dic
         raise
 
 
-def _init_text_completion_model(model_name: str, provider_name: str, kwargs: Dict[str, Any]) -> BaseLLM:
+def _init_text_completion_model(model_name: str, provider_name: str, kwargs: Dict[str, Any]) -> BaseLLM | None:
     """Initialize a text completion model.
 
     Args:
@@ -234,22 +234,24 @@ def _init_text_completion_model(model_name: str, provider_name: str, kwargs: Dic
         kwargs: Additional arguments to pass to the model initialization
 
     Returns:
-        An initialized text completion model
-
-    Raises:
-        RuntimeError: If the provider is not found
+        An initialized text completion model, or None if the provider is not found
     """
-    provider_cls = _get_text_completion_provider(provider_name)
+    try:
+        provider_cls = _get_text_completion_provider(provider_name)
+    except RuntimeError:
+        return None
+
     if provider_cls is None:
-        raise ValueError()
+        return None
+
     kwargs = _update_model_kwargs(provider_cls, model_name, kwargs)
     # remove stream_usage parameter as it's not supported by text completion APIs
     # (e.g., OpenAI's AsyncCompletions.create() doesn't accept this parameter)
     kwargs.pop("stream_usage", None)
     return provider_cls(**kwargs)
 
 
-def _init_community_chat_models(model_name: str, provider_name: str, kwargs: Dict[str, Any]) -> BaseChatModel:
+def _init_community_chat_models(model_name: str, provider_name: str, kwargs: Dict[str, Any]) -> BaseChatModel | None:
     """Initialize community chat models.
 
     Args:
@@ -264,14 +266,19 @@ def _init_community_chat_models(model_name: str, provider_name: str, kwargs: Dic
         ImportError: If langchain_community is not installed
         ModelInitializationError: If model initialization fails
     """
-    provider_cls = _get_chat_completion_provider(provider_name)
+    try:
+        provider_cls = _get_chat_completion_provider(provider_name)
+    except RuntimeError:
+        return None
+
     if provider_cls is None:
-        raise ValueError()
+        return None
+
     kwargs = _update_model_kwargs(provider_cls, model_name, kwargs)
     return provider_cls(**kwargs)
 
 
-def _init_gpt35_turbo_instruct(model_name: str, provider_name: str, kwargs: Dict[str, Any]) -> BaseLLM:
+def _init_gpt35_turbo_instruct(model_name: str, provider_name: str, kwargs: Dict[str, Any]) -> BaseLLM | None:
     """Initialize GPT-3.5 Turbo Instruct model.
 
     Currently init_chat_model from langchain infers this as a chat model.

tests/llm_providers/test_langchain_initialization_methods.py

@@ -116,8 +116,7 @@ def test_init_community_chat_models_no_provider(self):
             "nemoguardrails.llm.models.langchain_initializer._get_chat_completion_provider"
         ) as mock_get_provider:
             mock_get_provider.return_value = None
-            with pytest.raises(ValueError):
-                _init_community_chat_models("community-model", "provider", {})
+            assert _init_community_chat_models("community-model", "provider", {}) is None
 
 
 class TestTextCompletionInitializer:
@@ -156,8 +155,7 @@ def test_init_text_completion_model_no_provider(self):
             "nemoguardrails.llm.models.langchain_initializer._get_text_completion_provider"
         ) as mock_get_provider:
             mock_get_provider.return_value = None
-            with pytest.raises(ValueError):
-                _init_text_completion_model("text-model", "provider", {})
+            assert _init_text_completion_model("text-model", "provider", {}) is None
 
 
 class TestUpdateModelKwargs:

tests/llm_providers/test_langchain_initializer.py

@@ -194,3 +194,32 @@ def test_text_completion_supports_chat_mode(mock_initializers):
     mock_initializers["chat"].assert_called_once()
     mock_initializers["community"].assert_called_once()
     mock_initializers["text"].assert_called_once()
+
+
+# Tests for error masking prevention (issue where later None returns mask earlier exceptions)
+
+
+def test_exception_not_masked_by_none_return(mock_initializers):
+    """Test that an exception from an initializer is preserved when later ones return None.
+
+    For example: if community chat throws an error (e.g., invalid API key), but text completion
+    returns None because that provider type doesn't exist, the community error should be raised.
+    """
+    mock_initializers["special"].return_value = None
+    mock_initializers["chat"].return_value = None
+    mock_initializers["community"].side_effect = ValueError("Invalid API key for provider")
+    mock_initializers["text"].return_value = None  # Provider not found, returns None
+
+    with pytest.raises(ModelInitializationError, match="Invalid API key for provider"):
+        init_langchain_model("community-model", "provider", "chat", {})
+
+
+def test_import_error_prioritized_over_other_exceptions(mock_initializers):
+    """Test that ImportError is surfaced to help users know when packages are missing."""
+    mock_initializers["special"].return_value = None
+    mock_initializers["chat"].side_effect = ValueError("Some config error")
+    mock_initializers["community"].side_effect = ImportError("Missing langchain_community package")
+    mock_initializers["text"].return_value = None
+
+    with pytest.raises(ModelInitializationError, match="Missing langchain_community package"):
+        init_langchain_model("model", "provider", "chat", {})