add tutorial python files for sync and async model_client

tutorials/adalflow_modelclient_sync_and_async.py

@@ -0,0 +1,111 @@
+import asyncio
+import time
+from adalflow.components.model_client import (
+    OpenAIClient,
+)  # Assuming OpenAIClient with .call() and .acall() is available
+from adalflow.core.types import ModelType
+
+from getpass import getpass
+import os
+
+from adalflow.utils import setup_env
+
+# Load environment variables - Make sure to have OPENAI_API_KEY in .env file and .env is present in current folder
+if os.path.isfile(".env"):
+    setup_env(".env")
+
+# Prompt user to enter their API keys securely
+if "OPENAI_API_KEY" not in os.environ:
+    openai_api_key = getpass("Please enter your OpenAI API key: ")
+    # Set environment variables
+    os.environ["OPENAI_API_KEY"] = openai_api_key
+    print("API keys have been set.")
+
+
+# Synchronous function for benchmarking .call()
+def benchmark_sync_call(api_kwargs, runs=10):
+    """
+    Benchmark the synchronous .call() method by running it multiple times.
+
+    Parameters:
+    - api_kwargs: The arguments to be passed to the API call
+    - runs: The number of times to run the call (default is 10)
+    """
+    # List to store responses
+    responses = []
+
+    # Record the start time of the benchmark
+    start_time = time.time()
+
+    # Perform synchronous API calls for the specified number of runs
+    responses = [
+        openai_client.call(
+            api_kwargs=api_kwargs,  # API arguments
+            model_type=ModelType.LLM,  # Model type (e.g., LLM for language models)
+        )
+        for _ in range(runs)  # Repeat 'runs' times
+    ]
+
+    # Record the end time after all calls are completed
+    end_time = time.time()
+
+    # Output the results of each synchronous call
+    for i, response in enumerate(responses):
+        print(f"sync call {i + 1} completed: {response}")
+
+    # Print the total time taken for all synchronous calls
+    print(f"\nSynchronous benchmark completed in {end_time - start_time:.2f} seconds")
+
+
+# Asynchronous function for benchmarking .acall()
+async def benchmark_async_acall(api_kwargs, runs=10):
+    """
+    Benchmark the asynchronous .acall() method by running it multiple times concurrently.
+
+    Parameters:
+    - api_kwargs: The arguments to be passed to the API call
+    - runs: The number of times to run the asynchronous call (default is 10)
+    """
+    # Record the start time of the benchmark
+    start_time = time.time()
+
+    # Create a list of asynchronous tasks for the specified number of runs
+    tasks = [
+        openai_client.acall(
+            api_kwargs=api_kwargs,  # API arguments
+            model_type=ModelType.LLM,  # Model type (e.g., LLM for language models)
+        )
+        for _ in range(runs)  # Repeat 'runs' times
+    ]
+
+    # Execute all tasks concurrently and wait for them to finish
+    responses = await asyncio.gather(*tasks)
+
+    # Record the end time after all tasks are completed
+    end_time = time.time()
+
+    # Output the results of each asynchronous call
+    for i, response in enumerate(responses):
+        print(f"Async call {i + 1} completed: {response}")
+
+    # Print the total time taken for all asynchronous calls
+    print(f"\nAsynchronous benchmark completed in {end_time - start_time:.2f} seconds")
+
+
+if __name__ == "__main__":
+    # Initialize the OpenAI client
+    openai_client = OpenAIClient()
+
+    # Sample prompt for testing
+    prompt = "Tell me a joke."
+
+    model_kwargs = {"model": "gpt-3.5-turbo", "temperature": 0.5, "max_tokens": 100}
+    api_kwargs = openai_client.convert_inputs_to_api_kwargs(
+        input=prompt, model_kwargs=model_kwargs, model_type=ModelType.LLM
+    )
+    # Run both benchmarks
+    print("Starting synchronous benchmark...\n")
+    benchmark_sync_call(api_kwargs)
+
+    print("\nStarting asynchronous benchmark...\n")
+    asyncio.run(benchmark_async_acall(api_kwargs))