community[patch]: Resolve more linting issues (#26115)

Resolve a bunch of errors caught with mypy

libs/community/langchain_community/agent_toolkits/ainetwork/toolkit.py

@@ -56,7 +56,7 @@ class AINetworkToolkit(BaseToolkit):
 
     model_config = ConfigDict(
         arbitrary_types_allowed=True,
-        validate_all=True,
+        validate_default=True,
     )
 
     def get_tools(self) -> List[BaseTool]:

libs/community/langchain_community/chat_models/gpt_router.py

@@ -31,7 +31,8 @@ from langchain_core.language_models.llms import create_base_retry_decorator
 from langchain_core.messages import AIMessageChunk, BaseMessage, BaseMessageChunk
 from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
 from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env
-from pydantic import BaseModel, Field, SecretStr, model_validator, root_validator
+from pydantic import BaseModel, Field, SecretStr, model_validator
+from typing_extensions import Self
 
 from langchain_community.adapters.openai import (
     convert_dict_to_message,
@@ -150,7 +151,7 @@ class GPTRouter(BaseChatModel):
     """
 
     client: Any = Field(default=None, exclude=True)  #: :meta private:
-    models_priority_list: List[GPTRouterModel] = Field(min_items=1)
+    models_priority_list: List[GPTRouterModel] = Field(min_length=1)
     gpt_router_api_base: str = Field(default=None)
     """WriteSonic GPTRouter custom endpoint"""
     gpt_router_api_key: Optional[SecretStr] = None
@@ -186,8 +187,8 @@ class GPTRouter(BaseChatModel):
         )
         return values
 
-    @root_validator(pre=True, skip_on_failure=True)
-    def post_init(cls, values: Dict) -> Dict:
+    @model_validator(mode="after")
+    def post_init(self) -> Self:
         try:
             from gpt_router.client import GPTRouterClient
 
@@ -198,12 +199,14 @@ class GPTRouter(BaseChatModel):
             )
 
         gpt_router_client = GPTRouterClient(
-            values["gpt_router_api_base"],
-            values["gpt_router_api_key"].get_secret_value(),
+            self.gpt_router_api_base,
+            self.gpt_router_api_key.get_secret_value()
+            if self.gpt_router_api_key
+            else None,
         )
-        values["client"] = gpt_router_client
+        self.client = gpt_router_client
 
-        return values
+        return self
 
     @property
     def lc_secrets(self) -> Dict[str, str]:

libs/community/langchain_community/document_loaders/base_o365.py

@@ -34,11 +34,11 @@ CHUNK_SIZE = 1024 * 1024 * 5
 
 
 class _O365Settings(BaseSettings):
-    client_id: str = Field(..., env="O365_CLIENT_ID")
-    client_secret: SecretStr = Field(..., env="O365_CLIENT_SECRET")
+    client_id: str = Field(..., alias="O365_CLIENT_ID")
+    client_secret: SecretStr = Field(..., alias="O365_CLIENT_SECRET")
 
     model_config = SettingsConfigDict(
-        case_sentive=False, env_file=".env", env_prefix=""
+        case_sensitive=False, env_file=".env", env_prefix=""
     )
 
 

libs/community/langchain_community/document_loaders/onenote.py

@@ -11,19 +11,20 @@ from pydantic import (
     FilePath,
     SecretStr,
 )
-from pydantic_settings import BaseSettings
+from pydantic_settings import BaseSettings, SettingsConfigDict
 
 from langchain_community.document_loaders.base import BaseLoader
 
 
 class _OneNoteGraphSettings(BaseSettings):
-    client_id: str = Field(..., env="MS_GRAPH_CLIENT_ID")
-    client_secret: SecretStr = Field(..., env="MS_GRAPH_CLIENT_SECRET")
+    client_id: str = Field(..., alias="MS_GRAPH_CLIENT_ID")
+    client_secret: SecretStr = Field(..., alias="MS_GRAPH_CLIENT_SECRET")
 
-    class Config:
-        case_sensitive = False
-        env_file = ".env"
-        env_prefix = ""
+    model_config = SettingsConfigDict(
+        case_sensitive=False,
+        env_file=".env",
+        env_prefix="",
+    )
 
 
 class OneNoteLoader(BaseLoader, BaseModel):

libs/community/langchain_community/embeddings/baichuan.py

@@ -62,7 +62,7 @@ class BaichuanTextEmbeddings(BaseModel, Embeddings):
     session: Any  #: :meta private:
     model_name: str = Field(default="Baichuan-Text-Embedding", alias="model")
     """The model used to embed the documents."""
-    baichuan_api_key: Optional[SecretStr] = Field(
+    baichuan_api_key: SecretStr = Field(
         alias="api_key",
         default_factory=secret_from_env(["BAICHUAN_API_KEY", "BAICHUAN_AUTH_TOKEN"]),
     )

libs/community/langchain_community/llms/oci_generative_ai.py

@@ -194,6 +194,12 @@ class OCIGenAIBase(BaseModel, ABC):
         if self.provider is not None:
             provider = self.provider
         else:
+            if self.model_id is None:
+                raise ValueError(
+                    "model_id is required to derive the provider, "
+                    "please provide the provider explicitly or specify "
+                    "the model_id to derive the provider."
+                )
             provider = self.model_id.split(".")[0].lower()
 
         if provider not in provider_map:
@@ -267,6 +273,12 @@ class OCIGenAI(LLM, OCIGenAIBase):
         if stop is not None:
             _model_kwargs[self._provider.stop_sequence_key] = stop
 
+        if self.model_id is None:
+            raise ValueError(
+                "model_id is required to call the model, "
+                "please provide the model_id."
+            )
+
         if self.model_id.startswith(CUSTOM_ENDPOINT_PREFIX):
             serving_mode = models.DedicatedServingMode(endpoint_id=self.model_id)
         else:

libs/community/langchain_community/tools/cassandra_database/tool.py

@@ -20,9 +20,6 @@ class BaseCassandraDatabaseTool(BaseModel):
 
     db: CassandraDatabase = Field(exclude=True)
 
-    class Config(BaseTool.Config):
-        pass
-
 
 class _QueryCassandraDatabaseToolInput(BaseModel):
     query: str = Field(..., description="A detailed and correct CQL query.")

libs/community/langchain_community/tools/zenguard/tool.py

@@ -30,13 +30,12 @@ class DetectorAPI(str, Enum):
 class ZenGuardInput(BaseModel):
     prompts: List[str] = Field(
         ...,
-        min_items=1,
         min_length=1,
         description="Prompt to check",
     )
     detectors: List[Detector] = Field(
         ...,
-        min_items=1,
+        min_length=1,
         description="List of detectors by which you want to check the prompt",
     )
     in_parallel: bool = Field(

libs/community/langchain_community/utilities/searx_search.py

@@ -139,7 +139,6 @@ from pydantic import (
     Field,
     PrivateAttr,
     model_validator,
-    validator,
 )
 
 
@@ -215,20 +214,6 @@ class SearxSearchWrapper(BaseModel):
     k: int = 10
     aiosession: Optional[Any] = None
 
-    @validator("unsecure")
-    def disable_ssl_warnings(cls, v: bool) -> bool:
-        """Disable SSL warnings."""
-        if v:
-            # requests.urllib3.disable_warnings()
-            try:
-                import urllib3
-
-                urllib3.disable_warnings()
-            except ImportError as e:
-                print(e)  # noqa: T201
-
-        return v
-
     @model_validator(mode="before")
     @classmethod
     def validate_params(cls, values: Dict) -> Any:
@@ -254,7 +239,6 @@ class SearxSearchWrapper(BaseModel):
             searx_host = "https://" + searx_host
         elif searx_host.startswith("http://"):
             values["unsecure"] = True
-            cls.disable_ssl_warnings(True)
         values["searx_host"] = searx_host
 
         return values

libs/community/langchain_community/vectorstores/docarray/base.py

@@ -51,9 +51,9 @@ class DocArrayIndex(VectorStore, ABC):
         from docarray.typing import NdArray
 
         class DocArrayDoc(BaseDoc):
-            text: Optional[str] = Field(default=None, required=False)
+            text: Optional[str] = Field(default=None)
             embedding: Optional[NdArray] = Field(**embeddings_params)
-            metadata: Optional[dict] = Field(default=None, required=False)
+            metadata: Optional[dict] = Field(default=None)
 
         return DocArrayDoc
 

libs/community/tests/unit_tests/retrievers/test_bedrock.py

@@ -28,11 +28,11 @@ def amazon_retriever(
     )
 
 
-def test_create_client(amazon_retriever: AmazonKnowledgeBasesRetriever) -> None:
+def test_create_client() -> None:
     # Import error if boto3 is not installed
     # Value error if credentials are not supplied.
     with pytest.raises((ImportError, ValueError)):
-        amazon_retriever.create_client({})
+        AmazonKnowledgeBasesRetriever()  # type: ignore
 
 
 def test_standard_params(amazon_retriever: AmazonKnowledgeBasesRetriever) -> None:

libs/community/tests/unit_tests/utilities/test_nvidia_riva_asr.py

@@ -128,7 +128,7 @@ def test_init(asr: RivaASR) -> None:
     """Test that ASR accepts valid arguments."""
     for key, expected_val in CONFIG.items():
         if key == "url":
-            assert asr.url == AnyHttpUrl(expected_val)
+            assert asr.url == AnyHttpUrl(expected_val)  # type: ignore
         else:
             assert getattr(asr, key, None) == expected_val
 

libs/community/tests/unit_tests/utilities/test_nvidia_riva_tts.py

@@ -58,7 +58,7 @@ def test_init(tts: RivaTTS) -> None:
     """Test that ASR accepts valid arguments."""
     for key, expected_val in CONFIG.items():
         if key == "url":
-            assert str(tts.url) == expected_val + "/"
+            assert str(tts.url) == expected_val + "/"  # type: ignore
         else:
             assert getattr(tts, key, None) == expected_val