chore(logging): Log model name and base URL before invoking LLMs

nemoguardrails/actions/llm/utils.py

@@ -36,6 +36,8 @@
 from nemoguardrails.logging.callbacks import logging_callbacks
 from nemoguardrails.logging.explain import LLMCallInfo
 
+log = logging.getLogger(__name__)
+
 
 class LLMCallException(Exception):
     """A wrapper around the LLM call invocation exception.
@@ -113,7 +115,7 @@ def get_llm_provider(llm: BaseLanguageModel) -> Optional[str]:
     return _infer_provider_from_module(llm)
 
 
-def _infer_model_name(llm: BaseLanguageModel):
+def _infer_model_name(llm: Union[BaseLanguageModel, Runnable]) -> str:
     """Helper to infer the model name based from an LLM instance.
 
     Because not all models implement correctly _identifying_params from LangChain, we have to

nemoguardrails/logging/callbacks.py

@@ -32,6 +32,7 @@
 from nemoguardrails.logging.explain import LLMCallInfo
 from nemoguardrails.logging.processing_log import processing_log_var
 from nemoguardrails.logging.stats import LLMStats
+from nemoguardrails.logging.utils import extract_model_name_and_base_url
 from nemoguardrails.utils import new_uuid
 
 log = logging.getLogger(__name__)
@@ -64,6 +65,15 @@ async def on_llm_start(
         if explain_info:
             explain_info.llm_calls.append(llm_call_info)
 
+        # Log model name and base URL
+        model_name, base_url = extract_model_name_and_base_url(serialized)
+        if base_url:
+            log.info(f"Invoking LLM: model={model_name}, url={base_url}")
+        elif model_name:
+            log.info(f"Invoking LLM: model={model_name}")
+        else:
+            log.info("Invoking LLM")
+
         log.info("Invocation Params :: %s", kwargs.get("invocation_params", {}))
         log.info(
             "Prompt :: %s",
@@ -105,6 +115,15 @@ async def on_chat_model_start(
         if explain_info:
             explain_info.llm_calls.append(llm_call_info)
 
+        # Log model name and base URL
+        model_name, base_url = extract_model_name_and_base_url(serialized)
+        if base_url:
+            log.info(f"Invoking LLM: model={model_name}, url={base_url}")
+        elif model_name:
+            log.info(f"Invoking LLM: model={model_name}")
+        else:
+            log.info("Invoking LLM")
+
         type_map = {
             "human": "User",
             "ai": "Bot",

nemoguardrails/logging/utils.py

@@ -0,0 +1,77 @@
+# 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.
+
+import logging
+import re
+from typing import Any, Dict, Optional
+
+log = logging.getLogger(__name__)
+
+
+def extract_model_name_and_base_url(
+    serialized: Dict[str, Any]
+) -> tuple[Optional[str], Optional[str]]:
+    """Extract model name and base URL from serialized LLM parameters.
+
+    Args:
+        serialized: The serialized LLM configuration
+
+    Returns:
+        A tuple of (model_name, base_url). Either value can be None if not found
+    """
+    model_name = None
+    base_url = None
+
+    # Case 1: Try to extract from kwargs (we expect kwargs to be populated for the `ChatOpenAI` class).
+    if "kwargs" in serialized:
+        kwargs = serialized["kwargs"]
+
+        # Check for model_name in kwargs (ChatOpenAI attribute)
+        if "model_name" in kwargs and kwargs["model_name"]:
+            model_name = str(kwargs["model_name"])
+
+        # Check for openai_api_base in kwargs (ChatOpenAI attribute)
+        if "openai_api_base" in kwargs and kwargs["openai_api_base"]:
+            base_url = str(kwargs["openai_api_base"])
+
+    # Case 2: For other providers, parse `repr`, a string representation of the provider class. We don't have
+    # a reference to the actual class, so we need to parse the string representation.
+    if "repr" in serialized and isinstance(serialized["repr"], str):
+        repr_str = serialized["repr"]
+
+        # Extract model name. We expect the property to be formatted like model='...' or model_name='...'
+        if not model_name:
+            match = re.search(r"model(?:_name)?=['\"]([^'\"]+)['\"]", repr_str)
+            if match:
+                model_name = match.group(1)
+
+        # Extract base URL. The propety name may vary between providers, so try common attribute patterns.
+        if not base_url:
+            url_attrs = [
+                "api_base",
+                "api_host",
+                "azure_endpoint",
+                "base_url",
+                "endpoint",
+                "endpoint_url",
+                "openai_api_base",
+            ]
+            for attr in url_attrs:
+                match = re.search(rf"{attr}=['\"]([^'\"]+)['\"]", repr_str)
+                if match:
+                    base_url = match.group(1)
+                    break
+
+    return model_name, base_url

tests/test_callbacks.py

@@ -31,6 +31,7 @@
 from nemoguardrails.logging.callbacks import LoggingCallbackHandler
 from nemoguardrails.logging.explain import ExplainInfo, LLMCallInfo
 from nemoguardrails.logging.stats import LLMStats
+from nemoguardrails.logging.utils import extract_model_name_and_base_url
 
 
 @pytest.mark.asyncio
@@ -261,3 +262,122 @@ def __init__(self, content, msg_type):
         assert logged_prompt is not None
         assert "[cyan]Custom[/]" in logged_prompt
         assert "[cyan]Function[/]" in logged_prompt
+
+
+def test_extract_model_and_url_from_kwargs():
+    """Test extracting model_name and openai_api_base from kwargs (ChatOpenAI case)."""
+    serialized = {
+        "kwargs": {
+            "model_name": "gpt-4",
+            "openai_api_base": "https://api.openai.com/v1",
+            "temperature": 0.7,
+        }
+    }
+
+    model_name, base_url = extract_model_name_and_base_url(serialized)
+
+    assert model_name == "gpt-4"
+    assert base_url == "https://api.openai.com/v1"
+
+
+def test_extract_model_and_url_from_repr():
+    """Test extracting from repr string (ChatNIM case)."""
+    # Property values in single-quotes
+    serialized = {
+        "kwargs": {"temperature": 0.1},
+        "repr": "ChatNIM(model='meta/llama-3.3-70b-instruct', client=<openai.OpenAI object at 0x10d8e4e90>, endpoint_url='https://nim.int.aire.nvidia.com/v1')",
+    }
+
+    model_name, base_url = extract_model_name_and_base_url(serialized)
+
+    assert model_name == "meta/llama-3.3-70b-instruct"
+    assert base_url == "https://nim.int.aire.nvidia.com/v1"
+
+    # Property values in double-quotes
+    serialized = {
+        "repr": 'ChatOpenAI(model="gpt-3.5-turbo", base_url="https://custom.api.com/v1")'
+    }
+
+    model_name, base_url = extract_model_name_and_base_url(serialized)
+
+    assert model_name == "gpt-3.5-turbo"
+    assert base_url == "https://custom.api.com/v1"
+
+    # Model is stored in the `model_name` property
+    serialized = {
+        "repr": "SomeProvider(model_name='custom-model-v2', api_base='https://example.com')"
+    }
+
+    model_name, base_url = extract_model_name_and_base_url(serialized)
+
+    assert model_name == "custom-model-v2"
+    assert base_url == "https://example.com"
+
+
+def test_extract_model_and_url_from_various_url_properties():
+    """Test extracting various URL property names."""
+    test_cases = [
+        ("api_base='https://api1.com'", "https://api1.com"),
+        ("api_host='https://api2.com'", "https://api2.com"),
+        ("azure_endpoint='https://azure.com'", "https://azure.com"),
+        ("endpoint='https://endpoint.com'", "https://endpoint.com"),
+        ("openai_api_base='https://openai.com'", "https://openai.com"),
+    ]
+
+    for url_pattern, expected_url in test_cases:
+        serialized = {"repr": f"Provider(model='test-model', {url_pattern})"}
+        model_name, base_url = extract_model_name_and_base_url(serialized)
+        assert base_url == expected_url, f"Failed for pattern: {url_pattern}"
+
+
+def test_extract_model_and_url_kwargs_priority_over_repr():
+    """Test that kwargs values, if present, take priority over repr values."""
+    serialized = {
+        "kwargs": {
+            "model_name": "gpt-4-from-kwargs",
+            "openai_api_base": "https://kwargs.api.com",
+        },
+        "repr": "ChatOpenAI(model='gpt-3.5-from-repr', base_url='https://repr.api.com')",
+    }
+
+    model_name, base_url = extract_model_name_and_base_url(serialized)
+
+    assert model_name == "gpt-4-from-kwargs"
+    assert base_url == "https://kwargs.api.com"
+
+
+def test_extract_model_and_url_with_missing_values():
+    """Test extraction when values are missing."""
+    # No model or URL
+    serialized = {"kwargs": {"temperature": 0.7}}
+    model_name, base_url = extract_model_name_and_base_url(serialized)
+    assert model_name is None
+    assert base_url is None
+
+    # Only model, no URL
+    serialized = {"kwargs": {"model_name": "gpt-4"}}
+    model_name, base_url = extract_model_name_and_base_url(serialized)
+    assert model_name == "gpt-4"
+    assert base_url is None
+
+    # Only URL, no model
+    serialized = {"repr": "Provider(endpoint_url='https://example.com')"}
+    model_name, base_url = extract_model_name_and_base_url(serialized)
+    assert model_name is None
+    assert base_url == "https://example.com"
+
+
+def test_extract_model_and_url_with_empty_values():
+    """Test extraction when values are empty strings."""
+    serialized = {"kwargs": {"model_name": "", "openai_api_base": ""}}
+    model_name, base_url = extract_model_name_and_base_url(serialized)
+    assert model_name is None
+    assert base_url is None
+
+
+def test_extract_model_and_url_with_empty_serialized_data():
+    """Test extraction with empty or minimal serialized dict."""
+    serialized = {}
+    model_name, base_url = extract_model_name_and_base_url(serialized)
+    assert model_name is None
+    assert base_url is None