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import sys
import os
from dotenv import load_dotenv
load_dotenv()
sys.path.insert(
0, os.path.abspath("../..")
) # Adds the parent directory to the system path
import asyncio
from litellm import Timeout
import time
import openai
### Test just calling AsyncAzureOpenAI
openai_client = openai.AsyncAzureOpenAI(
azure_endpoint=os.getenv("AZURE_API_BASE"),
api_key=os.getenv("AZURE_API_KEY"),
)
async def call_acompletion(semaphore, input_data):
async with semaphore:
try:
# Use asyncio.wait_for to set a timeout for the task
response = await openai_client.chat.completions.create(**input_data)
# Handle the response as needed
print(response)
return response
except Timeout:
print(f"Task timed out: {input_data}")
return None # You may choose to return something else or raise an exception
async def main():
# Initialize the Router
# Create a semaphore with a capacity of 100
semaphore = asyncio.Semaphore(100)
# List to hold all task references
tasks = []
start_time_all_tasks = time.time()
# Launch 1000 tasks
for _ in range(500):
task = asyncio.create_task(
call_acompletion(
semaphore,
{
"model": "chatgpt-v-2",
"messages": [{"role": "user", "content": "Hey, how's it going?"}],
},
)
)
tasks.append(task)
# Wait for all tasks to complete
responses = await asyncio.gather(*tasks)
# Process responses as needed
# Record the end time for all tasks
end_time_all_tasks = time.time()
# Calculate the total time for all tasks
total_time_all_tasks = end_time_all_tasks - start_time_all_tasks
print(f"Total time for all tasks: {total_time_all_tasks} seconds")
# Calculate the average time per response
average_time_per_response = total_time_all_tasks / len(responses)
print(f"Average time per response: {average_time_per_response} seconds")
print(f"NUMBER OF COMPLETED TASKS: {len(responses)}")
# Run the main function
asyncio.run(main())
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