Remove module docstrings and use log.warning instead of log.warn

Author: jsonbailey

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

@@ -1,5 +1,3 @@
-"""LaunchDarkly AI SDK - LangChain Connector."""
-
 from ldai_langchain.langchain_helper import LangChainHelper
 from ldai_langchain.langchain_model_runner import LangChainModelRunner
 from ldai_langchain.langchain_runner_factory import LangChainRunnerFactory

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

@@ -1,5 +1,3 @@
-"""Shared LangChain utilities for the LaunchDarkly AI SDK."""
-
 from typing import Any, Dict, List, Optional, Union
 
 from langchain_core.language_models.chat_models import BaseChatModel

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

@@ -1,5 +1,3 @@
-"""LangChain model runner for LaunchDarkly AI SDK."""
-
 from typing import Any, Dict, List
 
 from langchain_core.language_models.chat_models import BaseChatModel

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

@@ -1,5 +1,3 @@
-"""LangChain connector for LaunchDarkly AI SDK."""
-
 from ldai.models import AIConfigKind
 from ldai.providers import AIProvider
 from ldai_langchain.langchain_helper import LangChainHelper

packages/ai-providers/server-ai-openai/src/ldai_openai/__init__.py

@@ -1,5 +1,3 @@
-"""LaunchDarkly AI SDK OpenAI Connector."""
-
 from ldai_openai.openai_helper import OpenAIHelper
 from ldai_openai.openai_model_runner import OpenAIModelRunner
 from ldai_openai.openai_runner_factory import OpenAIRunnerFactory

packages/ai-providers/server-ai-openai/src/ldai_openai/openai_helper.py

@@ -1,5 +1,3 @@
-"""Shared OpenAI utilities for the LaunchDarkly AI SDK."""
-
 from typing import Any, Iterable, List, Optional, cast
 
 from ldai import LDMessage

packages/ai-providers/server-ai-openai/src/ldai_openai/openai_model_runner.py

@@ -1,5 +1,3 @@
-"""OpenAI model runner for LaunchDarkly AI SDK."""
-
 import json
 from typing import Any, Dict, List
 

packages/ai-providers/server-ai-openai/src/ldai_openai/openai_runner_factory.py

@@ -1,5 +1,3 @@
-"""OpenAI connector for LaunchDarkly AI SDK."""
-
 import os
 from typing import Optional
 

packages/sdk/server-ai/src/ldai/client.py

@@ -371,7 +371,7 @@ async def create_chat(
         Creates and returns a ManagedModel for AI conversations.
         This method is a deprecated alias for :meth:`create_model`.
         """
-        log.warn('create_chat() is deprecated, use create_model() instead')
+        log.warning('create_chat() is deprecated, use create_model() instead')
         return await self.create_model(key, context, default, variables, default_ai_provider)
 
     def agent_config(

packages/sdk/server-ai/src/ldai/judge/__init__.py

@@ -55,13 +55,13 @@ async def evaluate(
         """
         try:
             if not self._ai_config.evaluation_metric_key:
-                log.warn(
+                log.warning(
                     'Judge configuration is missing required evaluationMetricKey'
                 )
                 return None
 
             if not self._ai_config.messages:
-                log.warn('Judge configuration must include messages')
+                log.warning('Judge configuration must include messages')
                 return None
 
             if random.random() > sampling_rate:
@@ -80,7 +80,7 @@ async def evaluate(
             evals = self._parse_evaluation_response(response.data)
 
             if not evals:
-                log.warn('Judge evaluation did not return the expected evaluation')
+                log.warning('Judge evaluation did not return the expected evaluation')
                 success = False
 
             return JudgeResponse(
@@ -179,20 +179,20 @@ def _parse_evaluation_response(self, data: Dict[str, Any]) -> Dict[str, EvalScor
         results: Dict[str, EvalScore] = {}
         metric_key = self._ai_config.evaluation_metric_key
         if not metric_key:
-            log.warn('Evaluation metric key is missing')
+            log.warning('Evaluation metric key is missing')
             return results
 
         if not isinstance(data, dict):
-            log.warn('Invalid response: missing or invalid evaluation')
+            log.warning('Invalid response: missing or invalid evaluation')
             return results
 
         score = data.get('score')
         reasoning = data.get('reasoning')
         if not isinstance(score, (int, float)) or score < 0 or score > 1:
-            log.warn(f'Invalid score: {score}. Score must be a number between 0 and 1 inclusive')
+            log.warning(f'Invalid score: {score}. Score must be a number between 0 and 1 inclusive')
             return results
         if not isinstance(reasoning, str):
-            log.warn('Invalid reasoning: must be a string')
+            log.warning('Invalid reasoning: must be a string')
             return results
 
         results[metric_key] = EvalScore(score=float(score), reasoning=reasoning)