Updates to Python tracking

ldai/client.py

@@ -1,19 +1,18 @@
-import json
 from typing import Any, Dict, Optional
 from ldclient import Context
-#from config import LDAIConfig, LDAIConfigTracker
 from ldclient.client import LDClient
 import chevron
 
 from ldai.tracker import LDAIConfigTracker
+from ldai.types import AIConfig
 
 class LDAIClient:
     """The LaunchDarkly AI SDK client object."""
 
     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) -> Any:
+    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:
@@ -31,7 +30,6 @@ def model_config(self, key: str, context: Context, default_value: str, variables
         if variables:
             all_variables.update(variables)
 
-        print(variation)
         variation['prompt'] = [
             {
                 **entry,
@@ -40,12 +38,7 @@ def model_config(self, key: str, context: Context, default_value: str, variables
             for entry in variation['prompt']
         ]
 
-        #return detail.value,
-
-        return {
-            'config': variation,
-            'tracker': LDAIConfigTracker(self.client, variation['_ldMeta']['variationId'], key, context)
-        }
+        return AIConfig(config=variation, tracker=LDAIConfigTracker(self.client, variation['_ldMeta']['variationId'], key, context))
 
     def interpolate_template(self, template: str, variables: Dict[str, Any]) -> str:
         """Interpolate the template with the given variables.

ldai/tracker.py

@@ -1,7 +1,7 @@
+import time
 from typing import Dict, Union
 from ldclient import Context, LDClient
-from ldai.tracking_utils import usage_to_token_metrics
-from ldai.types import BedrockTokenUsage, FeedbackKind, TokenUsage, UnderscoreTokenUsage
+from ldai.types import BedrockTokenUsage, FeedbackKind, OpenAITokenUsage, TokenUsage, UnderscoreTokenUsage
 
 class LDAIConfigTracker:
     def __init__(self, ld_client: LDClient, variation_id: str, config_key: str, context: Context):
@@ -19,23 +19,37 @@ 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_tokens(self, tokens: Union[TokenUsage, UnderscoreTokenUsage, BedrockTokenUsage]) -> None:
-        token_metrics = usage_to_token_metrics(tokens)
-        if token_metrics['total'] > 0:
-            self.ld_client.track('$ld:ai:tokens:total', self.context, self.get_track_data(), token_metrics['total'])
-        if token_metrics['input'] > 0:
-            self.ld_client.track('$ld:ai:tokens:input', self.context, self.get_track_data(), token_metrics['input'])
-        if token_metrics['output'] > 0:
-            self.ld_client.track('$ld:ai:tokens:output', self.context, self.get_track_data(), token_metrics['output'])
+    def track_duration_of(self, func, *args, **kwargs):
+        start_time = time.time()
+        result = func(*args, **kwargs)
+        end_time = time.time()
+        duration = int((end_time - start_time) * 1000)  # duration in milliseconds
+        self.track_duration(duration)
+        return result
 
     def track_error(self, error: int) -> None:
         self.ld_client.track('$ld:ai:error', self.context, self.get_track_data(), error)
 
-    def track_generation(self, generation: int) -> None:
-        self.ld_client.track('$ld:ai:generation', self.context, self.get_track_data(), generation)
-
     def track_feedback(self, feedback: Dict[str, FeedbackKind]) -> None:
         if feedback['kind'] == FeedbackKind.Positive:
             self.ld_client.track('$ld:ai:feedback:user:positive', self.context, self.get_track_data(), 1)
         elif feedback['kind'] == FeedbackKind.Negative:
-            self.ld_client.track('$ld:ai:feedback:user:negative', self.context, self.get_track_data(), 1)
+            self.ld_client.track('$ld:ai:feedback:user:negative', self.context, self.get_track_data(), 1)
+
+    def track_generation(self, generation: int) -> None:
+        self.ld_client.track('$ld:ai:generation', self.context, self.get_track_data(), generation)
+
+    def track_openai(self, func, *args, **kwargs):
+        result = self.track_duration_of(func, *args, **kwargs)
+        if result.usage:
+            self.track_tokens(OpenAITokenUsage(result.usage))
+        return result
+
+    def track_tokens(self, tokens: Union[TokenUsage, UnderscoreTokenUsage, BedrockTokenUsage]) -> None:
+        token_metrics = tokens.to_metrics()
+        if token_metrics['total'] > 0:
+            self.ld_client.track('$ld:ai:tokens:total', self.context, self.get_track_data(), token_metrics['total'])
+        if token_metrics['input'] > 0:
+            self.ld_client.track('$ld:ai:tokens:input', self.context, self.get_track_data(), token_metrics['input'])
+        if token_metrics['output'] > 0:
+            self.ld_client.track('$ld:ai:tokens:output', self.context, self.get_track_data(), token_metrics['output'])

ldai/types.py

@@ -1,26 +1,85 @@
 from enum import Enum
-from typing import TypedDict
+from typing import Callable
+from dataclasses import dataclass
+
+@dataclass
+class TokenMetrics():
+    total: int
+    input: int
+    output: int # type: ignore
+
+@dataclass
+
+class AIConfigData():
+    config: dict
+    prompt: any
+    _ldMeta: dict
+
+class AITracker():
+    track_duration: Callable[..., None]
+    track_tokens: Callable[..., None]
+    track_error: Callable[..., None]
+    track_generation: Callable[..., None]
+    track_feedback: Callable[..., None]
+
+class AIConfig():
+    def __init__(self, config: AIConfigData, tracker: AITracker):
+        self.config = config
+        self.tracker = tracker
 
 class FeedbackKind(Enum):
     Positive = "positive"
     Negative = "negative"
 
-class TokenUsage(TypedDict):
-    total_tokens: int
-    prompt_tokens: int
-    completion_tokens: int
+@dataclass
 
-class UnderscoreTokenUsage(TypedDict):
+class TokenUsage():
     total_tokens: int
     prompt_tokens: int
     completion_tokens: int
 
-class BedrockTokenUsage(TypedDict):
-    totalTokens: int
-    inputTokens: int
-    outputTokens: int
+    def to_metrics(self):
+        return {
+            'total': self['total_tokens'],
+            'input': self['prompt_tokens'],
+            'output': self['completion_tokens'],
+        }
 
-class TokenMetrics(TypedDict):
-    total: int
-    input: int
-    output: int # type: ignore
+class OpenAITokenUsage:
+    def __init__(self, data: any):
+        self.total_tokens = data.total_tokens
+        self.prompt_tokens = data.prompt_tokens
+        self.completion_tokens = data.completion_tokens
+
+    def to_metrics(self) -> TokenMetrics:
+        return {
+            'total': self.total_tokens,
+            'input': self.prompt_tokens,
+            'output': self.completion_tokens,
+        }
+
+class UnderscoreTokenUsage:
+    def __init__(self, data: dict):
+        self.total_tokens = data.get('total_tokens', 0)
+        self.prompt_tokens = data.get('prompt_tokens', 0)
+        self.completion_tokens = data.get('completion_tokens', 0)
+
+    def to_metrics(self) -> TokenMetrics:
+        return {
+            'total': self.total_tokens,
+            'input': self.prompt_tokens,
+            'output': self.completion_tokens,
+        }
+
+class BedrockTokenUsage:
+    def __init__(self, data: dict):
+        self.totalTokens = data.get('totalTokens', 0)
+        self.inputTokens = data.get('inputTokens', 0)
+        self.outputTokens = data.get('outputTokens', 0)
+
+    def to_metrics(self) -> TokenMetrics:
+        return {
+            'total': self.totalTokens,
+            'input': self.inputTokens,
+            'output': self.outputTokens,
+        }

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "launchdarkly-server-sdk-ai"
-version = "0.0.14"
+version = "0.0.1"
 description = "LaunchDarkly SDK for AI"
 authors = ["LaunchDarkly <[email protected]>"]
 license = "Apache-2.0"