review feedback

Author: Daniel OBrien

.release-please-manifest.json

@@ -1,3 +1,3 @@
 {
-  ".": "1.0.0"
+  ".": "0.1.0"
 }

CONTRIBUTING.md

@@ -4,7 +4,7 @@ LaunchDarkly has published an [SDK contributor's guide](https://docs.launchdarkl
 
 ## Submitting bug reports and feature requests
 
-The LaunchDarkly SDK team monitors the [issue tracker](https://github.com/launchdarkly/python-server-sdk-AI/issues) in the SDK repository. Bug reports and feature requests specific to this library should be filed in this issue tracker. The SDK team will respond to all newly filed issues within two business days.
+The LaunchDarkly SDK team monitors the [issue tracker](https://github.com/launchdarkly/python-server-sdk-ai/issues) in the SDK repository. Bug reports and feature requests specific to this library should be filed in this issue tracker. The SDK team will respond to all newly filed issues within two business days.
 
 ## Submitting pull requests
 
@@ -55,8 +55,6 @@ make lint
 
 The library's module structure is as follows:
 
-<!-- TODO: Add structure description -->
-
 ### Type hints
 
 Python does not require the use of type hints, but they can be extremely helpful for spotting mistakes and for improving the IDE experience, so we should always use them in the library. Every method in the public API is expected to have type hints for all non-`self` parameters, and for its return value if any.

ldai/client.py

@@ -12,43 +12,41 @@ class LDAIClient:
     def __init__(self, client: LDClient):
         self.client = client
 
-    def model_config(self, key: str, context: Context, default_value: str, variables: Optional[Dict[str, Any]] = None) -> AIConfig:
-        """Get the value of a model configuration asynchronously.
-
-        Args:
-            key: The key of the model configuration.
-            context: The context to evaluate the model configuration in.
-            default_value: The default value of the model configuration.
-            variables: Additional variables for the model configuration.
+    def model_config(self, key: str, context: Context, default_value: AIConfig, variables: Optional[Dict[str, Any]] = None) -> AIConfig:
+        """
+        Get the value of a model configuration asynchronously.
 
-        Returns:
-            The value of the model configuration.
+        :param key: The key of the model configuration.
+        :param context: The context to evaluate the model configuration in.
+        :param default_value: The default value of the model configuration.
+        :param variables: Additional variables for the model configuration.
+        :return: The value of the model configuration.
         """
         variation = self.client.variation(key, context, default_value)
 
-        all_variables = {'ldctx': context}
+        all_variables = {}
         if variables:
             all_variables.update(variables)
+        all_variables['ldctx'] = context
 
-        variation['prompt'] = [
-            {
-                **entry,
-                'content': self.interpolate_template(entry['content'], all_variables)
-            }
-            for entry in variation['prompt']
-        ]
+        if isinstance(variation['prompt'], list) and all(isinstance(entry, dict) for entry in variation['prompt']):
+            variation['prompt'] = [
+                {
+                    'role': entry['role'],
+                    'content': self.interpolate_template(entry['content'], all_variables)
+                }
+                for entry in variation['prompt']
+            ]
 
-        enabled = variation['_ldMeta'].get('enabled')
-        return AIConfig(config=variation, tracker=LDAIConfigTracker(self.client, variation['_ldMeta']['versionKey'], key, context, bool(enabled)))
+        enabled = variation.get('_ldMeta',{}).get('enabled', False)
+        return AIConfig(config=variation, tracker=LDAIConfigTracker(self.client, variation.get('_ldMeta', {}).get('versionKey', ''), key, context, bool(enabled)))
 
     def interpolate_template(self, template: str, variables: Dict[str, Any]) -> str:
-        """Interpolate the template with the given variables.
-
-        Args:
-            template: The template string.
-            variables: The variables to interpolate into the template.
+        """
+        Interpolate the template with the given variables.
 
-        Returns:
-            The interpolated string.
+        :template: The template string.
+        :variables: The variables to interpolate into the template.
+        :return: The interpolated string.
         """
         return chevron.render(template, variables)

ldai/tracker.py

@@ -19,9 +19,9 @@ def get_track_data(self):
     def track_duration(self, duration: int) -> None:
         self.ld_client.track('$ld:ai:duration:total', self.context, self.get_track_data(), duration)
 
-    def track_duration_of(self, func, *args, **kwargs):
+    def track_duration_of(self, func):
         start_time = time.time()
-        result = func(*args, **kwargs)
+        result = func()
         end_time = time.time()
         duration = int((end_time - start_time) * 1000)  # duration in milliseconds
         self.track_duration(duration)
@@ -36,8 +36,8 @@ def track_feedback(self, feedback: Dict[str, FeedbackKind]) -> None:
     def track_success(self) -> None:
         self.ld_client.track('$ld:ai:generation', self.context, self.get_track_data(), 1)
 
-    def track_openai(self, func, *args, **kwargs):
-        result = self.track_duration_of(func, *args, **kwargs)
+    def track_openai(self, func):
+        result = self.track_duration_of(func)
         if result.usage:
             self.track_tokens(OpenAITokenUsage(result.usage))
         return result

ldai/types.py

@@ -9,7 +9,6 @@ class TokenMetrics():
     output: int # type: ignore
 
 @dataclass
-
 class AIConfigData():
     config: dict
     prompt: any

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "launchdarkly-server-sdk-ai"
-version = "0.0.1"
+version = "0.1.0"
 description = "LaunchDarkly SDK for AI"
 authors = ["LaunchDarkly <[email protected]>"]
 license = "Apache-2.0"