litellm-mirror/litellm/proxy/tests/test_q.py
2023-11-21 16:59:33 -08:00

73 lines
1.8 KiB
Python

import requests
import time
# Step 1 Add a config to the proxy, generate a temp key
config = {
}
response = requests.post(
url = "http://0.0.0.0:8000/key/generate",
json={
"config": config,
"duration": "30d" # default to 30d, set it to 30m if you want a temp key
},
headers={
"Authorization": "Bearer sk-hosted-litellm"
}
)
print("\nresponse from generating key", response.json())
generated_key = response.json()["key"]
print("\ngenerated key for proxy", generated_key)
# Step 2: Queue a request to the proxy, using your generated_key
job_response = requests.post(
url = "http://0.0.0.0:8000/queue/request",
json={
'model': 'gpt-3.5-turbo',
'messages': [
{'role': 'system', 'content': f'You are a helpful assistant. What is your name'},
],
},
headers={
"Authorization": f"Bearer {generated_key}"
}
)
job_response = job_response.json()
job_id = job_response["id"]
polling_url = job_response["url"]
polling_url = f"http://0.0.0.0:8000{polling_url}"
print("\nCreated Job, Polling Url", polling_url)
# Step 3: Poll the request
while True:
try:
print("\nPolling URL", polling_url)
polling_response = requests.get(
url=polling_url,
headers={
"Authorization": f"Bearer {generated_key}"
}
)
polling_response = polling_response.json()
print("\nResponse from polling url", polling_response)
status = polling_response["status"]
if status == "finished":
llm_response = polling_response["result"]
print("LLM Response")
print(llm_response)
break
time.sleep(0.5)
except Exception as e:
print("got exception in polling", e)
break