chore: update deprecated API usage in hello-python-ai examples

examples/bedrock_example.py

@@ -2,7 +2,7 @@
 import ldclient
 from ldclient import Context
 from ldclient.config import Config
-from ldai.client import LDAIClient
+from ldai import LDAIClient
 import boto3
 
 client = boto3.client("bedrock-runtime", region_name="us-east-1")
@@ -85,5 +85,6 @@ def main():
     # Continue the conversation by adding user input to the messages list and invoking the LLM again.
     print("Success.")
 
-    # Close the client to flush events and close the connection.
+    # Flush pending events and close the client.
+    ldclient.get().flush()
     ldclient.get().close()

examples/chat_judge_example.py

@@ -47,8 +47,8 @@ async def async_main():
         #       provider={'name': 'openai'},
         #       messages=[{'role': 'system', 'content': 'You are a helpful assistant.'}],
         #   )
-        #   chat = await aiclient.create_chat(ai_config_key, context, default, {'companyName': 'LaunchDarkly'})
-        chat = await aiclient.create_chat(ai_config_key, context, variables={
+        #   chat = await aiclient.create_model(ai_config_key, context, default, {'companyName': 'LaunchDarkly'})
+        chat = await aiclient.create_model(ai_config_key, context, variables={
             'companyName': 'LaunchDarkly',
         })
 
@@ -98,7 +98,8 @@ async def async_main():
     except Exception as err:
         print("Error:", err)
     finally:
-        # Close the client to flush events and close the connection.
+        # Flush pending events and close the client.
+        ldclient.get().flush()
         ldclient.get().close()
 
 

examples/chat_observability_example.py

@@ -67,8 +67,8 @@ async def async_main():
         #       provider={'name': 'openai'},
         #       messages=[{'role': 'system', 'content': 'You are a helpful assistant.'}],
         #   )
-        #   chat = await aiclient.create_chat(ai_config_key, context, default, {'example_type': 'observability_demo'})
-        chat = await aiclient.create_chat(
+        #   chat = await aiclient.create_model(ai_config_key, context, default, {'example_type': 'observability_demo'})
+        chat = await aiclient.create_model(
             ai_config_key,
             context,
             variables={
@@ -107,6 +107,8 @@ async def async_main():
     except Exception as err:
         print("Error:", err)
     finally:
+        # Flush pending events and close the client.
+        ldclient.get().flush()
         ldclient.get().close()
 
 

examples/direct_judge_example.py

@@ -86,7 +86,8 @@ async def async_main():
     except Exception as err:
         print("Error:", err)
     finally:
-        # Close the client to flush events and close the connection.
+        # Flush pending events and close the client.
+        ldclient.get().flush()
         ldclient.get().close()
 
 

examples/gemini_example.py

@@ -2,7 +2,7 @@
 import ldclient
 from ldclient import Context
 from ldclient.config import Config
-from ldai.client import LDAIClient, LDMessage
+from ldai import LDAIClient, LDMessage
 from ldai.tracker import TokenUsage
 from google import genai
 from google.genai import types
@@ -162,7 +162,8 @@ def main():
     # Continue the conversation by adding user input to the messages list and invoking the LLM again.
     print("Success.")
 
-    # Close the client to flush events and close the connection.
+    # Flush pending events and close the client.
+    ldclient.get().flush()
     ldclient.get().close()
 
 if __name__ == "__main__":

examples/langchain_example.py

@@ -3,7 +3,7 @@
 import ldclient
 from ldclient import Context
 from ldclient.config import Config
-from ldai.client import LDAIClient
+from ldai import LDAIClient
 from ldai_langchain import get_ai_metrics_from_response
 from langchain.chat_models import init_chat_model
 
@@ -104,7 +104,8 @@ async def async_main():
         print(f"Error during completion: {e}")
         print("Please ensure you have the correct API keys and credentials set up for the detected provider.")
 
-    # Close the client to flush events and close the connection.
+    # Flush pending events and close the client.
+    ldclient.get().flush()
     ldclient.get().close()
 
 

examples/langgraph_agent_example.py

@@ -3,7 +3,7 @@
 from pprint import pprint
 from ldclient import Context
 from ldclient.config import Config
-from ldai.client import LDAIClient
+from ldai import LDAIClient
 from ldai.tracker import TokenUsage
 from ldai_langchain import get_ai_metrics_from_response
 from langchain.chat_models import init_chat_model
@@ -123,7 +123,8 @@ def main():
         print(f"Error: {e}")
         print("Please ensure you have the correct API keys and credentials set up for the detected providers.")
 
-    # Close the client to flush events and close the connection.
+    # Flush pending events and close the client.
+    ldclient.get().flush()
     ldclient.get().close()
 
 if __name__ == "__main__":

examples/langgraph_multi_agent_example.py

@@ -2,7 +2,7 @@
 import ldclient
 from ldclient import Context
 from ldclient.config import Config
-from ldai.client import LDAIClient
+from ldai import LDAIClient
 from ldai.tracker import TokenUsage
 from ldai_langchain import get_ai_metrics_from_response
 from langchain.chat_models import init_chat_model
@@ -271,7 +271,8 @@ def calculate_average(numbers):
         print(f"❌ Error during workflow execution: {e}")
         print("Please ensure you have the correct API keys and credentials set up for the detected providers.")
 
-    # Close the client to flush events and close the connection.
+    # Flush pending events and close the client.
+    ldclient.get().flush()
     ldclient.get().close()
 
 if __name__ == "__main__":

examples/openai_example.py

@@ -2,7 +2,8 @@
 import ldclient
 from ldclient import Context
 from ldclient.config import Config
-from ldai.client import LDAIClient
+from ldai import LDAIClient
+from ldai_openai import get_ai_metrics_from_response
 from openai import OpenAI
 
 openai_client = OpenAI()
@@ -69,13 +70,14 @@ def main():
     print("User Input:\n", USER_INPUT)
     messages.append({'role': 'user', 'content': USER_INPUT})
 
-    # Track the OpenAI completion with LaunchDarkly metrics
-    completion = tracker.track_openai_metrics(
+    # Track the OpenAI completion with LaunchDarkly metrics using the LD OpenAI provider's extractor
+    completion = tracker.track_metrics_of(
         lambda:
             openai_client.chat.completions.create(
                 model=config_value.model.name,
                 messages=messages,
-            )
+            ),
+        get_ai_metrics_from_response,
     )
     ai_response = completion.choices[0].message.content
 
@@ -86,5 +88,6 @@ def main():
     # Continue the conversation by adding user input to the messages list and invoking the LLM again.
     print("Success.")
 
-    # Close the client to flush events and close the connection.
+    # Flush pending events and close the client.
+    ldclient.get().flush()
     ldclient.get().close()