fix!: Use client provided logger and remove optional parameter

Author: jsonbailey

packages/ai-providers/server-ai-langchain/src/ldai_langchain/langchain_provider.py

@@ -4,7 +4,7 @@
 
 from langchain_core.language_models.chat_models import BaseChatModel
 from langchain_core.messages import AIMessage, BaseMessage, HumanMessage, SystemMessage
-from ldai import LDMessage
+from ldai import LDMessage, log
 from ldai.models import AIConfigKind
 from ldai.providers import AIProvider
 from ldai.providers.types import ChatResponse, LDAIMetrics, StructuredResponse
@@ -18,27 +18,24 @@ class LangChainProvider(AIProvider):
     This provider integrates LangChain models with LaunchDarkly's tracking capabilities.
     """
 
-    def __init__(self, llm: BaseChatModel, logger: Optional[Any] = None):
+    def __init__(self, llm: BaseChatModel):
         """
         Initialize the LangChain provider.
 
         :param llm: A LangChain BaseChatModel instance
-        :param logger: Optional logger for logging provider operations
         """
-        super().__init__(logger)
         self._llm = llm
 
     @staticmethod
-    async def create(ai_config: AIConfigKind, logger: Optional[Any] = None) -> 'LangChainProvider':
+    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
-        :param logger: Optional logger for the provider
         :return: Configured LangChainProvider instance
         """
         llm = LangChainProvider.create_langchain_model(ai_config)
-        return LangChainProvider(llm, logger)
+        return LangChainProvider(llm)
 
     async def invoke_model(self, messages: List[LDMessage]) -> ChatResponse:
         """
@@ -56,20 +53,18 @@ async def invoke_model(self, messages: List[LDMessage]) -> ChatResponse:
             if isinstance(response.content, str):
                 content = response.content
             else:
-                if self.logger:
-                    self.logger.warn(
-                        f'Multimodal response not supported, expecting a string. '
-                        f'Content type: {type(response.content)}, Content: {response.content}'
-                    )
+                log.warn(
+                    f'Multimodal response not supported, expecting a string. '
+                    f'Content type: {type(response.content)}, Content: {response.content}'
+                )
                 metrics = LDAIMetrics(success=False, usage=metrics.usage)
 
             return ChatResponse(
                 message=LDMessage(role='assistant', content=content),
                 metrics=metrics,
             )
         except Exception as error:
-            if self.logger:
-                self.logger.warn(f'LangChain model invocation failed: {error}')
+            log.warn(f'LangChain model invocation failed: {error}')
 
             return ChatResponse(
                 message=LDMessage(role='assistant', content=''),
@@ -94,11 +89,10 @@ async def invoke_structured_model(
             response = await structured_llm.ainvoke(langchain_messages)
 
             if not isinstance(response, dict):
-                if self.logger:
-                    self.logger.warn(
-                        f'Structured output did not return a dict. '
-                        f'Got: {type(response)}'
-                    )
+                log.warn(
+                    f'Structured output did not return a dict. '
+                    f'Got: {type(response)}'
+                )
                 return StructuredResponse(
                     data={},
                     raw_response='',
@@ -117,8 +111,7 @@ async def invoke_structured_model(
                 ),
             )
         except Exception as error:
-            if self.logger:
-                self.logger.warn(f'LangChain structured model invocation failed: {error}')
+            log.warn(f'LangChain structured model invocation failed: {error}')
 
             return StructuredResponse(
                 data={},