fix: Propagate model and base URL in LLMCallException; improve error handling

nemoguardrails/actions/action_dispatcher.py

@@ -26,7 +26,7 @@
 from langchain_core.runnables import Runnable
 
 from nemoguardrails import utils
-from nemoguardrails.actions.llm.utils import LLMCallException
+from nemoguardrails.exceptions import LLMCallException
 
 log = logging.getLogger(__name__)
 

nemoguardrails/actions/llm/utils.py

@@ -15,7 +15,7 @@
 
 import logging
 import re
-from typing import Any, Dict, List, Optional, Sequence, Union
+from typing import Dict, List, Optional, Sequence, Union
 
 from langchain_core.callbacks.base import AsyncCallbackHandler, BaseCallbackManager
 from langchain_core.language_models import BaseLanguageModel
@@ -30,23 +30,25 @@
     reasoning_trace_var,
     tool_calls_var,
 )
+from nemoguardrails.exceptions import LLMCallException
 from nemoguardrails.integrations.langchain.message_utils import dicts_to_messages
 from nemoguardrails.logging.callbacks import logging_callbacks
 from nemoguardrails.logging.explain import LLMCallInfo
 
 logger = logging.getLogger(__name__)
 
-
-class LLMCallException(Exception):
-    """A wrapper around the LLM call invocation exception.
-
-    This is used to propagate the exception out of the `generate_async` call (the default behavior is to
-    catch it and return an "Internal server error." message.
-    """
-
-    def __init__(self, inner_exception: Any):
-        super().__init__(f"LLM Call Exception: {str(inner_exception)}")
-        self.inner_exception = inner_exception
+# Since different providers have different attributes for the base URL, we'll use this list
+# to attempt to extract the base URL from a `BaseLanguageModel` instance.
+BASE_URL_ATTRIBUTES = [
+    "api_base",
+    "api_host",
+    "azure_endpoint",
+    "base_url",
+    "endpoint",
+    "endpoint_url",
+    "openai_api_base",
+    "server_url",
+]
 
 
 def _infer_provider_from_module(llm: BaseLanguageModel) -> Optional[str]:
@@ -202,6 +204,58 @@ def _prepare_callbacks(
     return logging_callbacks
 
 
+def _raise_llm_call_exception(
+    exception: Exception,
+    llm: Union[BaseLanguageModel, Runnable],
+) -> None:
+    """Raise an LLMCallException with enriched context about the failed invocation.
+
+    Args:
+        exception: The original exception that occurred
+        llm: The LLM instance that was being invoked
+
+    Raises:
+        LLMCallException with context message including model name and endpoint
+    """
+    # Extract model name from context
+    llm_call_info = llm_call_info_var.get()
+    model_name = (
+        llm_call_info.llm_model_name
+        if llm_call_info
+        else _infer_model_name(llm)
+        if isinstance(llm, BaseLanguageModel)
+        else ""
+    )
+
+    # Extract endpoint URL from the LLM instance
+    endpoint_url = None
+    for attr in BASE_URL_ATTRIBUTES:
+        if hasattr(llm, attr):
+            value = getattr(llm, attr, None)
+            if value:
+                endpoint_url = str(value)
+                break
+
+    # If we didn't find endpoint URL, check the nested client object.
+    if not endpoint_url and hasattr(llm, "client"):
+        client = getattr(llm, "client", None)
+        if client and hasattr(client, "base_url"):
+            endpoint_url = str(client.base_url)
+
+    # Build context message with model and endpoint info
+    context_parts = []
+    if model_name:
+        context_parts.append(f"model={model_name}")
+    if endpoint_url:
+        context_parts.append(f"endpoint={endpoint_url}")
+
+    if context_parts:
+        context_message = f"Error invoking LLM ({', '.join(context_parts)})"
+        raise LLMCallException(exception, context_message=context_message)
+    else:
+        raise LLMCallException(exception)
+
+
 async def _invoke_with_string_prompt(
     llm: Union[BaseLanguageModel, Runnable],
     prompt: str,
@@ -211,7 +265,7 @@ async def _invoke_with_string_prompt(
     try:
         return await llm.ainvoke(prompt, config=RunnableConfig(callbacks=callbacks))
     except Exception as e:
-        raise LLMCallException(e)
+        _raise_llm_call_exception(e, llm)
 
 
 async def _invoke_with_message_list(
@@ -225,7 +279,7 @@ async def _invoke_with_message_list(
     try:
         return await llm.ainvoke(messages, config=RunnableConfig(callbacks=callbacks))
     except Exception as e:
-        raise LLMCallException(e)
+        _raise_llm_call_exception(e, llm)
 
 
 def _convert_messages_to_langchain_format(prompt: List[dict]) -> List:

nemoguardrails/exceptions.py

@@ -0,0 +1,70 @@
+# SPDX-FileCopyrightText: Copyright (c) 2023-2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import Any, Optional
+
+__all__ = [
+    "ConfigurationError",
+    "InvalidModelConfigurationError",
+    "InvalidRailsConfigurationError",
+    "LLMCallException",
+]
+
+
+class ConfigurationError(ValueError):
+    """
+    Base class for Guardrails Configuration validation errors.
+    """
+
+    pass
+
+
+class InvalidModelConfigurationError(ConfigurationError):
+    """Raised when a guardrail configuration's model is invalid."""
+
+    pass
+
+
+class InvalidRailsConfigurationError(ConfigurationError):
+    """Raised when rails configuration is invalid.
+
+    Examples:
+        - Input/output rail references a model that doesn't exist in config
+        - Rail references a flow that doesn't exist
+        - Missing required prompt template
+        - Invalid rail parameters
+    """
+
+    pass
+
+
+class LLMCallException(Exception):
+    """A wrapper around the LLM call invocation exception.
+
+    This is used to propagate the exception out of the `generate_async` call. The default behavior is to
+    catch it and return an "Internal server error." message.
+    """
+
+    def __init__(self, inner_exception: Any, context_message: Optional[str] = None):
+        """Initialize LLMCallException.
+
+        Args:
+            inner_exception: The original exception that occurred
+            context_message: Optional context to prepend (for example, the model name or endpoint)
+        """
+        message = f"{context_message or 'LLM Call Exception'}: {str(inner_exception)}"
+        super().__init__(message)
+
+        self.inner_exception = inner_exception
+        self.context_message = context_message

nemoguardrails/rails/llm/config.py

@@ -37,6 +37,10 @@
 from nemoguardrails.colang.v1_0.runtime.flows import _normalize_flow_id
 from nemoguardrails.colang.v2_x.lang.utils import format_colang_parsing_error_message
 from nemoguardrails.colang.v2_x.runtime.errors import ColangParsingError
+from nemoguardrails.exceptions import (
+    InvalidModelConfigurationError,
+    InvalidRailsConfigurationError,
+)
 
 log = logging.getLogger(__name__)
 
@@ -136,8 +140,8 @@ def set_and_validate_model(cls, data: Any) -> Any:
             model_from_params = parameters.get("model_name") or parameters.get("model")
 
             if model_field and model_from_params:
-                raise ValueError(
-                    "Model name must be specified in exactly one place: either in the 'model' field or in parameters, not both."
+                raise InvalidModelConfigurationError(
+                    "Model name must be specified in exactly one place: either the `model` field, or in `parameters` (`parameters.model` or `parameters.model_name`).",
                 )
             if not model_field and model_from_params:
                 data["model"] = model_from_params
@@ -151,8 +155,8 @@ def set_and_validate_model(cls, data: Any) -> Any:
     def model_must_be_none_empty(self) -> "Model":
         """Validate that a model name is present either directly or in parameters."""
         if not self.model or not self.model.strip():
-            raise ValueError(
-                "Model name must be specified either directly in the 'model' field or through 'model_name'/'model' in parameters"
+            raise InvalidModelConfigurationError(
+                "Model name must be specified in exactly one place: either the `model` field, or in `parameters` (`parameters.model` or `parameters.model_name`)."
             )
         return self
 
@@ -334,10 +338,10 @@ class TaskPrompt(BaseModel):
     @root_validator(pre=True, allow_reuse=True)
     def check_fields(cls, values):
         if not values.get("content") and not values.get("messages"):
-            raise ValueError("One of `content` or `messages` must be provided.")
+            raise InvalidRailsConfigurationError("One of `content` or `messages` must be provided.")
 
         if values.get("content") and values.get("messages"):
-            raise ValueError("Only one of `content` or `messages` must be provided.")
+            raise InvalidRailsConfigurationError("Only one of `content` or `messages` must be provided.")
 
         return values
 
@@ -1414,7 +1418,11 @@ def check_model_exists_for_input_rails(cls, values):
             if not flow_model:
                 continue
             if flow_model not in model_types:
-                raise ValueError(f"No `{flow_model}` model provided for input flow `{_normalize_flow_id(flow)}`")
+                flow_id = _normalize_flow_id(flow)
+                available_types = ", ".join(f"'{str(t)}'" for t in sorted(model_types)) if model_types else "none"
+                raise InvalidRailsConfigurationError(
+                    f"Input flow '{flow_id}' references model type '{flow_model}' that is not defined in the configuration. Detected model types: {available_types}."
+                )
         return values
 
     @root_validator(pre=True)
@@ -1436,7 +1444,11 @@ def check_model_exists_for_output_rails(cls, values):
             if not flow_model:
                 continue
             if flow_model not in model_types:
-                raise ValueError(f"No `{flow_model}` model provided for output flow `{_normalize_flow_id(flow)}`")
+                flow_id = _normalize_flow_id(flow)
+                available_types = ", ".join(f"'{str(t)}'" for t in sorted(model_types)) if model_types else "none"
+                raise InvalidRailsConfigurationError(
+                    f"Output flow '{flow_id}' references model type '{flow_model}' that is not defined in the configuration. Detected model types: {available_types}."
+                )
         return values
 
     @root_validator(pre=True)
@@ -1450,9 +1462,13 @@ def check_prompt_exist_for_self_check_rails(cls, values):
 
         # Input moderation prompt verification
         if "self check input" in enabled_input_rails and "self_check_input" not in provided_task_prompts:
-            raise ValueError("You must provide a `self_check_input` prompt template.")
+            raise InvalidRailsConfigurationError(
+                "Missing a `self_check_input` prompt template, which is required for the `self check input` rail."
+            )
         if "llama guard check input" in enabled_input_rails and "llama_guard_check_input" not in provided_task_prompts:
-            raise ValueError("You must provide a `llama_guard_check_input` prompt template.")
+            raise InvalidRailsConfigurationError(
+                "Missing a `llama_guard_check_input` prompt template, which is required for the `llama guard check input` rail."
+            )
 
         # Only content-safety and topic-safety include a $model reference in the rail flow text
         # Need to match rails with flow_id (excluding $model reference) and match prompts
@@ -1462,20 +1478,28 @@ def check_prompt_exist_for_self_check_rails(cls, values):
 
         # Output moderation prompt verification
         if "self check output" in enabled_output_rails and "self_check_output" not in provided_task_prompts:
-            raise ValueError("You must provide a `self_check_output` prompt template.")
+            raise InvalidRailsConfigurationError(
+                "Missing a `self_check_output` prompt template, which is required for the `self check output` rail."
+            )
         if (
             "llama guard check output" in enabled_output_rails
             and "llama_guard_check_output" not in provided_task_prompts
         ):
-            raise ValueError("You must provide a `llama_guard_check_output` prompt template.")
+            raise InvalidRailsConfigurationError(
+                "Missing a `llama_guard_check_output` prompt template, which is required for the `llama guard check output` rail."
+            )
         if (
             "patronus lynx check output hallucination" in enabled_output_rails
             and "patronus_lynx_check_output_hallucination" not in provided_task_prompts
         ):
-            raise ValueError("You must provide a `patronus_lynx_check_output_hallucination` prompt template.")
+            raise InvalidRailsConfigurationError(
+                "Missing a `patronus_lynx_check_output_hallucination` prompt template, which is required for the `patronus lynx check output hallucination` rail."
+            )
 
         if "self check facts" in enabled_output_rails and "self_check_facts" not in provided_task_prompts:
-            raise ValueError("You must provide a `self_check_facts` prompt template.")
+            raise InvalidRailsConfigurationError(
+                "Missing a `self_check_facts` prompt template, which is required for the `self check facts` rail."
+            )
 
         # Only content-safety and topic-safety include a $model reference in the rail flow text
         # Need to match rails with flow_id (excluding $model reference) and match prompts
@@ -1528,7 +1552,7 @@ def validate_models_api_key_env_var(cls, models):
         api_keys = [m.api_key_env_var for m in models]
         for api_key in api_keys:
             if api_key and not os.environ.get(api_key):
-                raise ValueError(f"Model API Key environment variable '{api_key}' not set.")
+                raise InvalidRailsConfigurationError(f"Model API Key environment variable '{api_key}' not set.")
         return models
 
     raw_llm_call_action: Optional[str] = Field(
@@ -1801,4 +1825,6 @@ def _validate_rail_prompts(rails: list[str], prompts: list[Any], validation_rail
             prompt_flow_id = flow_id.replace(" ", "_")
             expected_prompt = f"{prompt_flow_id} $model={flow_model}"
             if expected_prompt not in prompts:
-                raise ValueError(f"You must provide a `{expected_prompt}` prompt template.")
+                raise InvalidRailsConfigurationError(
+                    f"Missing a `{expected_prompt}` prompt template, which is required for the `{validation_rail}` rail."
+                )