feat: mask api key for cerebriumai llm

libs/langchain/langchain/llms/cerebriumai.py

@@ -2,21 +2,22 @@
 from typing import Any, Dict, List, Mapping, Optional
 
 import requests
-from langchain_core.pydantic_v1 import Extra, Field, root_validator
+from langchain_core.pydantic_v1 import Extra, Field, SecretStr, root_validator
 
 from langchain.callbacks.manager import CallbackManagerForLLMRun
 from langchain.llms.base import LLM
 from langchain.llms.utils import enforce_stop_tokens
-from langchain.utils import get_from_dict_or_env
+from langchain.utils import convert_to_secret_str, get_from_dict_or_env
 
 logger = logging.getLogger(__name__)
 
 
 class CerebriumAI(LLM):
     """CerebriumAI large language models.
 
-    To use, you should have the ``cerebrium`` python package installed, and the
-    environment variable ``CEREBRIUMAI_API_KEY`` set with your API key.
+    To use, you should have the ``cerebrium`` python package installed.
+    You should also have the environment variable ``CEREBRIUMAI_API_KEY``
+    set with your API key or pass it as a named argument in the constructor.
 
     Any parameters that are valid to be passed to the call can be passed
     in, even if not explicitly saved on this class.
@@ -25,7 +26,7 @@ class CerebriumAI(LLM):
         .. code-block:: python
 
             from langchain.llms import CerebriumAI
-            cerebrium = CerebriumAI(endpoint_url="")
+            cerebrium = CerebriumAI(endpoint_url="", cerebriumai_api_key="my-api-key")
 
     """
 
@@ -36,7 +37,7 @@ class CerebriumAI(LLM):
     """Holds any model parameters valid for `create` call not
     explicitly specified."""
 
-    cerebriumai_api_key: Optional[str] = None
+    cerebriumai_api_key: Optional[SecretStr] = None
 
     class Config:
         """Configuration for this pydantic config."""
@@ -64,8 +65,8 @@ def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:
     @root_validator()
     def validate_environment(cls, values: Dict) -> Dict:
         """Validate that api key and python package exists in environment."""
-        cerebriumai_api_key = get_from_dict_or_env(
-            values, "cerebriumai_api_key", "CEREBRIUMAI_API_KEY"
+        cerebriumai_api_key = convert_to_secret_str(
+            get_from_dict_or_env(values, "cerebriumai_api_key", "CEREBRIUMAI_API_KEY")
         )
         values["cerebriumai_api_key"] = cerebriumai_api_key
         return values
@@ -91,7 +92,8 @@ def _call(
         **kwargs: Any,
     ) -> str:
         headers: Dict = {
-            "Authorization": self.cerebriumai_api_key,
+            "Authorization": self.cerebriumai_api_key
+            and self.cerebriumai_api_key.get_secret_value(),
             "Content-Type": "application/json",
         }
         params = self.model_kwargs or {}

libs/langchain/tests/unit_tests/llms/test_cerebriumai.py

@@ -0,0 +1,33 @@
+"""Test CerebriumAI llm"""
+
+
+from langchain_core.pydantic_v1 import SecretStr
+from pytest import CaptureFixture, MonkeyPatch
+
+from langchain.llms.cerebriumai import CerebriumAI
+
+
+def test_api_key_is_secret_string() -> None:
+    llm = CerebriumAI(cerebriumai_api_key="test-cerebriumai-api-key")
+    assert isinstance(llm.cerebriumai_api_key, SecretStr)
+
+
+def test_api_key_masked_when_passed_via_constructor(capsys: CaptureFixture) -> None:
+    llm = CerebriumAI(cerebriumai_api_key="secret-api-key")
+    print(llm.cerebriumai_api_key, end="")
+    captured = capsys.readouterr()
+
+    assert captured.out == "**********"
+    assert repr(llm.cerebriumai_api_key) == "SecretStr('**********')"
+
+
+def test_api_key_masked_when_passed_from_env(
+    monkeypatch: MonkeyPatch, capsys: CaptureFixture
+) -> None:
+    monkeypatch.setenv("CEREBRIUMAI_API_KEY", "secret-api-key")
+    llm = CerebriumAI()
+    print(llm.cerebriumai_api_key, end="")
+    captured = capsys.readouterr()
+
+    assert captured.out == "**********"
+    assert repr(llm.cerebriumai_api_key) == "SecretStr('**********')"