Mask API key for Minimax LLM (langchain-ai#14309)

- **Description:** Added masking for the API key for Minimax LLM + tests
inspired by langchain-ai#12418.
- **Issue:** the issue # fixes
langchain-ai#12165
- **Dependencies:** this fix is dependent on Minimax instantiation fix
which is introduced in
langchain-ai#13439, so merge this one
after.
  - **Tag maintainer:** @eyurtsev

---------

Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>

libs/langchain/langchain/llms/minimax.py

@@ -10,14 +10,14 @@
 )
 
 import requests
-from langchain_core.pydantic_v1 import BaseModel, Field, root_validator
+from langchain_core.pydantic_v1 import BaseModel, 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__)
 
@@ -27,7 +27,7 @@ class _MinimaxEndpointClient(BaseModel):
 
     host: str
     group_id: str
-    api_key: str
+    api_key: SecretStr
     api_url: str
 
     @root_validator(pre=True, allow_reuse=True)
@@ -40,7 +40,7 @@ def set_api_url(cls, values: Dict[str, Any]) -> Dict[str, Any]:
         return values
 
     def post(self, request: Any) -> Any:
-        headers = {"Authorization": f"Bearer {self.api_key}"}
+        headers = {"Authorization": f"Bearer {self.api_key.get_secret_value()}"}
         response = requests.post(self.api_url, headers=headers, json=request)
         # TODO: error handling and automatic retries
         if not response.ok:
@@ -56,7 +56,7 @@ def post(self, request: Any) -> Any:
 class MinimaxCommon(BaseModel):
     """Common parameters for Minimax large language models."""
 
-    _client: Any = None
+    _client: _MinimaxEndpointClient
     model: str = "abab5.5-chat"
     """Model name to use."""
     max_tokens: int = 256
@@ -69,13 +69,13 @@ class MinimaxCommon(BaseModel):
     """Holds any model parameters valid for `create` call not explicitly specified."""
     minimax_api_host: Optional[str] = None
     minimax_group_id: Optional[str] = None
-    minimax_api_key: Optional[str] = None
+    minimax_api_key: Optional[SecretStr] = None
 
     @root_validator()
     def validate_environment(cls, values: Dict) -> Dict:
         """Validate that api key and python package exists in environment."""
-        values["minimax_api_key"] = get_from_dict_or_env(
-            values, "minimax_api_key", "MINIMAX_API_KEY"
+        values["minimax_api_key"] = convert_to_secret_str(
+            get_from_dict_or_env(values, "minimax_api_key", "MINIMAX_API_KEY")
         )
         values["minimax_group_id"] = get_from_dict_or_env(
             values, "minimax_group_id", "MINIMAX_GROUP_ID"
@@ -87,6 +87,11 @@ def validate_environment(cls, values: Dict) -> Dict:
             "MINIMAX_API_HOST",
             default="https://api.minimax.chat",
         )
+        values["_client"] = _MinimaxEndpointClient(
+            host=values["minimax_api_host"],
+            api_key=values["minimax_api_key"],
+            group_id=values["minimax_group_id"],
+        )
         return values
 
     @property
@@ -110,14 +115,6 @@ def _llm_type(self) -> str:
         """Return type of llm."""
         return "minimax"
 
-    def __init__(self, **data: Any):
-        super().__init__(**data)
-        self._client = _MinimaxEndpointClient(
-            host=self.minimax_api_host,
-            api_key=self.minimax_api_key,
-            group_id=self.minimax_group_id,
-        )
-
 
 class Minimax(MinimaxCommon, LLM):
     """Wrapper around Minimax large language models.

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

@@ -0,0 +1,42 @@
+"""Test Minimax llm"""
+from typing import cast
+
+from langchain_core.pydantic_v1 import SecretStr
+from pytest import CaptureFixture, MonkeyPatch
+
+from langchain.llms.minimax import Minimax
+
+
+def test_api_key_is_secret_string() -> None:
+    llm = Minimax(minimax_api_key="secret-api-key", minimax_group_id="group_id")
+    assert isinstance(llm.minimax_api_key, SecretStr)
+
+
+def test_api_key_masked_when_passed_from_env(
+    monkeypatch: MonkeyPatch, capsys: CaptureFixture
+) -> None:
+    """Test initialization with an API key provided via an env variable"""
+    monkeypatch.setenv("MINIMAX_API_KEY", "secret-api-key")
+    monkeypatch.setenv("MINIMAX_GROUP_ID", "group_id")
+    llm = Minimax()
+    print(llm.minimax_api_key, end="")
+    captured = capsys.readouterr()
+
+    assert captured.out == "**********"
+
+
+def test_api_key_masked_when_passed_via_constructor(
+    capsys: CaptureFixture,
+) -> None:
+    """Test initialization with an API key provided via the initializer"""
+    llm = Minimax(minimax_api_key="secret-api-key", minimax_group_id="group_id")
+    print(llm.minimax_api_key, end="")
+    captured = capsys.readouterr()
+
+    assert captured.out == "**********"
+
+
+def test_uses_actual_secret_value_from_secretstr() -> None:
+    """Test that actual secret is retrieved using `.get_secret_value()`."""
+    llm = Minimax(minimax_api_key="secret-api-key", minimax_group_id="group_id")
+    assert cast(SecretStr, llm.minimax_api_key).get_secret_value() == "secret-api-key"