linting

Author: edwinokonkwo

packages/core/ldai/client.py

@@ -160,7 +160,7 @@ async def create_judge(
         try:
             # Overwrite reserved variables to ensure they remain as placeholders for judge evaluation
             extended_variables = dict(variables) if variables else {}
-            
+
             # Warn if reserved variables are provided
             if variables:
                 if 'message_history' in variables:
@@ -171,7 +171,7 @@ async def create_judge(
                     self._logger.warning(
                         'Variable "response_to_evaluate" is reserved for judge evaluation and will be overwritten'
                     )
-            
+
             extended_variables['message_history'] = '{{message_history}}'
             extended_variables['response_to_evaluate'] = '{{response_to_evaluate}}'
 

packages/core/ldai/providers/ai_provider.py

@@ -22,7 +22,7 @@ class AIProvider(ABC):
     def __init__(self):
         """
         Initialize the AI provider.
-        
+
         Creates a logger for this provider instance.
         """
         self._logger = logging.getLogger(f'{__name__}.{self.__class__.__name__}')

packages/langchain/ldai/providers/langchain/__init__.py

@@ -3,4 +3,3 @@
 from ldai.providers.langchain.langchain_provider import LangChainProvider
 
 __all__ = ['LangChainProvider']
-

packages/langchain/ldai/providers/langchain/langchain_provider.py

@@ -16,14 +16,14 @@
 class LangChainProvider(AIProvider):
     """
     LangChain implementation of AIProvider.
-    
+
     This provider integrates LangChain models with LaunchDarkly's tracking capabilities.
     """
 
     def __init__(self, llm: BaseChatModel):
         """
         Initialize the LangChain provider.
-        
+
         :param llm: LangChain BaseChatModel instance
         """
         super().__init__()
@@ -33,7 +33,7 @@ def __init__(self, llm: BaseChatModel):
     async def create(ai_config: AIConfigKind) -> 'LangChainProvider':
         """
         Static factory method to create a LangChain AIProvider from an AI configuration.
-        
+
         :param ai_config: The LaunchDarkly AI configuration
         :return: Configured LangChainProvider instance
         """
@@ -43,7 +43,7 @@ async def create(ai_config: AIConfigKind) -> 'LangChainProvider':
     async def invoke_model(self, messages: List[LDMessage]) -> ChatResponse:
         """
         Invoke the LangChain model with an array of messages.
-        
+
         :param messages: Array of LDMessage objects representing the conversation
         :return: ChatResponse containing the model's response
         """
@@ -92,7 +92,7 @@ async def invoke_structured_model(
     ) -> StructuredResponse:
         """
         Invoke the LangChain model with structured output support.
-        
+
         :param messages: Array of LDMessage objects representing the conversation
         :param response_structure: Dictionary of output configurations keyed by output name
         :return: StructuredResponse containing the structured data
@@ -143,7 +143,7 @@ async def invoke_structured_model(
     def get_chat_model(self) -> BaseChatModel:
         """
         Get the underlying LangChain model instance.
-        
+
         :return: The LangChain BaseChatModel instance
         """
         return self._llm
@@ -152,10 +152,10 @@ def get_chat_model(self) -> BaseChatModel:
     def map_provider(ld_provider_name: str) -> str:
         """
         Map LaunchDarkly provider names to LangChain provider names.
-        
+
         This method enables seamless integration between LaunchDarkly's standardized
         provider naming and LangChain's naming conventions.
-        
+
         :param ld_provider_name: LaunchDarkly provider name
         :return: LangChain provider name
         """
@@ -171,10 +171,10 @@ def map_provider(ld_provider_name: str) -> str:
     def get_ai_metrics_from_response(response: AIMessage) -> LDAIMetrics:
         """
         Get AI metrics from a LangChain provider response.
-        
+
         This method extracts token usage information and success status from LangChain responses
         and returns a LaunchDarkly LDAIMetrics object.
-        
+
         :param response: The response from the LangChain model
         :return: LDAIMetrics with success status and token usage
         """
@@ -196,10 +196,10 @@ def get_ai_metrics_from_response(response: AIMessage) -> LDAIMetrics:
     def convert_messages_to_langchain(messages: List[LDMessage]) -> List[BaseMessage]:
         """
         Convert LaunchDarkly messages to LangChain messages.
-        
+
         This helper method enables developers to work directly with LangChain message types
         while maintaining compatibility with LaunchDarkly's standardized message format.
-        
+
         :param messages: List of LDMessage objects
         :return: List of LangChain message objects
         """
@@ -219,10 +219,10 @@ def convert_messages_to_langchain(messages: List[LDMessage]) -> List[BaseMessage
     async def create_langchain_model(ai_config: AIConfigKind) -> BaseChatModel:
         """
         Create a LangChain model from an AI configuration.
-        
+
         This public helper method enables developers to initialize their own LangChain models
         using LaunchDarkly AI configurations.
-        
+
         :param ai_config: The LaunchDarkly AI configuration
         :return: A configured LangChain BaseChatModel
         """
@@ -242,15 +242,15 @@ async def create_langchain_model(ai_config: AIConfigKind) -> BaseChatModel:
             except ImportError:
                 # Fallback for older versions or different import path
                 from langchain.chat_models.universal import init_chat_model
-            
+
             # Map provider name
             langchain_provider = LangChainProvider.map_provider(provider)
-            
+
             # Create model configuration
             model_kwargs = {**parameters}
             if langchain_provider:
                 model_kwargs['model_provider'] = langchain_provider
-            
+
             # Initialize the chat model (init_chat_model may be async or sync)
             result = init_chat_model(model_name, **model_kwargs)
             # Handle both sync and async initialization
@@ -262,4 +262,3 @@ async def create_langchain_model(ai_config: AIConfigKind) -> BaseChatModel:
                 'langchain package is required for LangChainProvider. '
                 'Install it with: pip install langchain langchain-core'
             ) from e
-