feat: New top-level scaffold

nemoguardrails/__init__.py

@@ -26,13 +26,27 @@
 
 import warnings
 
-from . import patch_asyncio
-from .rails import LLMRails, RailsConfig
+import nemoguardrails.patch_asyncio
+from nemoguardrails.rails import RailsConfig
 
-patch_asyncio.apply()
+nemoguardrails.patch_asyncio.apply()
 
 # Ignore a warning message from torch.
 warnings.filterwarnings("ignore", category=UserWarning, message="TypedStorage is deprecated")
 
+# Use Guardrails top-level if this environment variable is set
+_use_guardrails_wrapper = os.environ.get("NEMO_USE_GUARDRAILS_WRAPPER", "").lower() in (
+    "true",
+    "1",
+    "yes",
+)
+
+if _use_guardrails_wrapper:
+    # Use the Guardrails wrapper class (aliased as LLMRails for compatibility)
+    from nemoguardrails.guardrails.guardrails import Guardrails as LLMRails
+else:
+    # Use the original LLMRails class
+    from nemoguardrails.rails import LLMRails
+
 __version__ = version("nemoguardrails")
 __all__ = ["LLMRails", "RailsConfig"]

nemoguardrails/guardrails/__init__.py

@@ -0,0 +1,14 @@
+# SPDX-FileCopyrightText: Copyright (c) 2026 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.

nemoguardrails/guardrails/guardrails.py

@@ -0,0 +1,134 @@
+# SPDX-FileCopyrightText: Copyright (c) 2023-2026 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.
+
+"""Top-level Guardrails interface module.
+
+This module provides a simplified, user-friendly interface for interacting with
+NeMo Guardrails. The Guardrails class wraps the LLMRails functionality and provides
+a streamlined API for generating LLM responses with programmable guardrails.
+"""
+
+from enum import Enum
+from typing import AsyncIterator, Optional, Tuple, TypeAlias, Union, overload
+
+from langchain_core.language_models import BaseChatModel, BaseLLM
+
+from nemoguardrails.logging.explain import ExplainInfo
+from nemoguardrails.rails.llm.config import RailsConfig
+from nemoguardrails.rails.llm.llmrails import LLMRails
+from nemoguardrails.rails.llm.options import GenerationResponse
+
+
+class MessageRole(str, Enum):
+    """Enumeration of message roles in a conversation."""
+
+    USER = "user"
+    ASSISTANT = "assistant"
+    SYSTEM = "system"
+    CONTEXT = "context"
+    EVENT = "event"
+    TOOL = "tool"
+
+
+LLMMessages: TypeAlias = list[dict[str, str]]
+
+
+class Guardrails:
+    """Top-level interface for NeMo Guardrails functionality."""
+
+    def __init__(
+        self,
+        config: RailsConfig,
+        llm: Optional[Union[BaseLLM, BaseChatModel]] = None,
+        verbose: bool = False,
+    ):
+        """Initialize a Guardrails instance."""
+
+        self.config = config
+        self.llm = llm
+        self.verbose = verbose
+
+        self.llmrails = LLMRails(config, llm, verbose)
+
+    @staticmethod
+    def _convert_to_messages(prompt: str | None = None, messages: LLMMessages | None = None) -> LLMMessages:
+        """Convert prompt or simplified messages to LLMRails standard format.
+
+        Converts from Guardrails simplified format to LLMRails standard format:
+        - Simplified: [{"user": "text"}]
+        - Standard: [{"role": "user", "content": "Hello"}]
+        """
+
+        # Priority: messages first, then prompt
+        if messages:
+            return messages
+
+        if prompt:
+            # Convert string prompt to standard format
+            return [{"role": "user", "content": prompt}]
+
+        raise ValueError("Neither prompt nor messages provided for generation")
+
+    def generate(
+        self, prompt: str | None = None, messages: LLMMessages | None = None, **kwargs
+    ) -> Union[str, dict, GenerationResponse, Tuple[dict, dict]]:
+        """Generate an LLM response synchronously with guardrails applied."""
+
+        messages = self._convert_to_messages(prompt, messages)
+        return self.llmrails.generate(messages=messages, **kwargs)
+
+    @overload
+    async def generate_async(self, prompt: str | None = None, messages: LLMMessages | None = None, **kwargs) -> str: ...
+
+    @overload
+    async def generate_async(
+        self, prompt: str | None = None, messages: LLMMessages | None = None, **kwargs
+    ) -> dict: ...
+
+    @overload
+    async def generate_async(
+        self, prompt: str | None = None, messages: LLMMessages | None = None, **kwargs
+    ) -> GenerationResponse: ...
+
+    @overload
+    async def generate_async(
+        self, prompt: str | None = None, messages: LLMMessages | None = None, **kwargs
+    ) -> tuple[dict, dict]: ...
+
+    async def generate_async(
+        self, prompt: str | None = None, messages: LLMMessages | None = None, **kwargs
+    ) -> str | dict | GenerationResponse | tuple[dict, dict]:
+        """Generate an LLM response asynchronously with guardrails applied."""
+
+        messages = self._convert_to_messages(prompt, messages)
+        response = await self.llmrails.generate_async(messages=messages, **kwargs)
+        return response
+
+    def stream_async(
+        self, prompt: str | None = None, messages: LLMMessages | None = None, **kwargs
+    ) -> AsyncIterator[str | dict]:
+        """Generate an LLM response asynchronously with streaming support."""
+
+        messages = self._convert_to_messages(prompt, messages)
+        return self.llmrails.stream_async(messages=messages, **kwargs)
+
+    def explain(self) -> ExplainInfo:
+        """Get the latest ExplainInfo object for debugging."""
+        return self.llmrails.explain()
+
+    def update_llm(self, llm: Union[BaseLLM, BaseChatModel]) -> None:
+        """Replace the main LLM with a new one."""
+        self.llm = llm
+        self.llmrails.update_llm(llm)

pyproject.toml

@@ -170,6 +170,7 @@ include = [
   "nemoguardrails/server/**",
   "tests/test_callbacks.py",
   "nemoguardrails/benchmark/**",
+  "nemoguardrails/guardrails/**"
 ]
 exclude = [
   "nemoguardrails/llm/providers/trtllm/**",