mirror of
https://github.com/BerriAI/litellm.git
synced 2026-09-19 00:01:29 +00:00
Merge pull request #41660 from BerriAI/litellm_remove_commented_out_proxy_tests
chore(tests): remove fully commented-out proxy test files and their CI entries
This commit is contained in:
Mateo Wang
GitHub
commit
98b3564a5b
8 changed files with 0 additions and 490 deletions
4
.github/workflows/test-unit-proxy-db.yml
vendored
4
.github/workflows/test-unit-proxy-db.yml
vendored
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@ -109,8 +109,6 @@ jobs:
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- test-group: proxy-server-core
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test-path: >-
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tests/proxy_unit_tests/test_proxy_server.py
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tests/proxy_unit_tests/test_proxy_server_keys.py
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tests/proxy_unit_tests/test_proxy_server_spend.py
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tests/proxy_unit_tests/test_aproxy_startup.py
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workers: 4
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dist: loadscope
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@ -119,7 +117,6 @@ jobs:
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test-path: >-
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tests/proxy_unit_tests/test_proxy_config_unit_test.py
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tests/proxy_unit_tests/test_proxy_routes.py
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tests/proxy_unit_tests/test_proxy_gunicorn.py
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tests/proxy_unit_tests/test_server_root_path.py
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tests/proxy_unit_tests/test_proxy_pass_user_config.py
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tests/proxy_unit_tests/test_proxy_token_counter.py
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@ -197,7 +194,6 @@ jobs:
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tests/proxy_unit_tests/test_realtime_cache.py
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tests/proxy_unit_tests/test_proxy_exception_mapping.py
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tests/proxy_unit_tests/test_custom_tokenizer_bug.py
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tests/proxy_unit_tests/test_model_response_typing
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workers: 4
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dist: loadscope
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timeout: 15
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@ -1,23 +0,0 @@
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# #### What this tests ####
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# # This tests if the litellm model response type is returnable in a flask app
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# import sys, os
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# import traceback
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# from flask import Flask, request, jsonify, abort, Response
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# sys.path.insert(0, os.path.abspath('../../..')) # Adds the parent directory to the system path
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# import litellm
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# from litellm import completion
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# litellm.set_verbose = False
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# app = Flask(__name__)
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# @app.route('/')
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# def hello():
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# data = request.json
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# return completion(**data)
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# if __name__ == '__main__':
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# from waitress import serve
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# serve(app, host='localhost', port=8080, threads=10)
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@ -1,14 +0,0 @@
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# import requests, json
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# BASE_URL = 'http://localhost:8080'
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# def test_hello_route():
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# data = {"model": "claude-3-5-haiku-20241022", "messages": [{"role": "user", "content": "hey, how's it going?"}]}
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# headers = {'Content-Type': 'application/json'}
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# response = requests.get(BASE_URL, headers=headers, data=json.dumps(data))
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# print(response.text)
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# assert response.status_code == 200
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# print("Hello route test passed!")
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# if __name__ == '__main__':
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# test_hello_route()
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@ -1,23 +0,0 @@
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# #### What this tests ####
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# # This tests if the litellm model response type is returnable in a flask app
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# import sys, os
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# import traceback
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# from flask import Flask, request, jsonify, abort, Response
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# sys.path.insert(0, os.path.abspath('../../..')) # Adds the parent directory to the system path
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# import litellm
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# from litellm import completion
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# litellm.set_verbose = False
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# app = Flask(__name__)
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|
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# @app.route('/')
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# def hello():
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# data = request.json
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# return completion(**data)
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# if __name__ == '__main__':
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# from waitress import serve
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# serve(app, host='localhost', port=8080, threads=10)
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@ -1,14 +0,0 @@
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# import requests, json
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# BASE_URL = 'http://localhost:8080'
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# def test_hello_route():
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# data = {"model": "claude-3-5-haiku-20241022", "messages": [{"role": "user", "content": "hey, how's it going?"}]}
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# headers = {'Content-Type': 'application/json'}
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# response = requests.get(BASE_URL, headers=headers, data=json.dumps(data))
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# print(response.text)
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# assert response.status_code == 200
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# print("Hello route test passed!")
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# if __name__ == '__main__':
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# test_hello_route()
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@ -1,61 +0,0 @@
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# #### What this tests ####
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# # Allow the user to easily run the local proxy server with Gunicorn
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# # LOCAL TESTING ONLY
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# import sys, os, subprocess
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# import traceback
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# from dotenv import load_dotenv
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# load_dotenv()
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# import os, io
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# # this file is to test litellm/proxy
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# sys.path.insert(
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# 0, os.path.abspath("../..")
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# ) # Adds the parent directory to the system path
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# import pytest
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# import litellm
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# ### LOCAL Proxy Server INIT ###
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# from litellm.proxy.proxy_server import save_worker_config # Replace with the actual module where your FastAPI router is defined
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# filepath = os.path.dirname(os.path.abspath(__file__))
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# config_fp = f"{filepath}/test_configs/test_config_custom_auth.yaml"
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# def get_openai_info():
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# return {
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# "api_key": os.getenv("AZURE_API_KEY"),
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# "api_base": os.getenv("AZURE_API_BASE"),
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# }
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# def run_server(host="0.0.0.0",port=8008,num_workers=None):
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# if num_workers is None:
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# # Set it to min(8,cpu_count())
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# import multiprocessing
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# num_workers = min(4,multiprocessing.cpu_count())
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# ### LOAD KEYS ###
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# # Load the Azure keys. For now get them from openai-usage
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# azure_info = get_openai_info()
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# print(f"Azure info:{azure_info}")
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# os.environ["AZURE_API_KEY"] = azure_info['api_key']
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# os.environ["AZURE_API_BASE"] = azure_info['api_base']
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# os.environ["AZURE_API_VERSION"] = "2023-09-01-preview"
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# ### SAVE CONFIG ###
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# os.environ["WORKER_CONFIG"] = config_fp
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# # In order for the app to behave well with signals, run it with gunicorn
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# # The first argument must be the "name of the command run"
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# cmd = f"gunicorn litellm.proxy.proxy_server:app --workers {num_workers} --worker-class uvicorn.workers.UvicornWorker --bind {host}:{port}"
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# cmd = cmd.split()
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# print(f"Running command: {cmd}")
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# import sys
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# sys.stdout.flush()
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# sys.stderr.flush()
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# # Make sure to propage env variables
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# subprocess.run(cmd) # This line actually starts Gunicorn
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# if __name__ == "__main__":
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# run_server()
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@ -1,269 +0,0 @@
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# import sys, os, time
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# import traceback
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# from dotenv import load_dotenv
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# load_dotenv()
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# import os, io
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# # this file is to test litellm/proxy
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# sys.path.insert(
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# 0, os.path.abspath("../..")
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# ) # Adds the parent directory to the system path
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# import pytest, logging
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# import litellm
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# from litellm import embedding, completion, completion_cost, Timeout
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# from litellm import RateLimitError
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|
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# import sys, os, time
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# import traceback
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# from dotenv import load_dotenv
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|
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# load_dotenv()
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# import os, io
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# # this file is to test litellm/proxy
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# from concurrent.futures import ThreadPoolExecutor
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# sys.path.insert(
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# 0, os.path.abspath("../..")
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# ) # Adds the parent directory to the system path
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# import pytest, logging, requests
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# import litellm
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# from litellm import embedding, completion, completion_cost, Timeout
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# from litellm import RateLimitError
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# from github import Github
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# import subprocess
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# # Function to execute a command and return the output
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# def run_command(command):
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# process = subprocess.Popen(command, stdout=subprocess.PIPE, shell=True)
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# output, _ = process.communicate()
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# return output.decode().strip()
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# # Retrieve the current branch name
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# branch_name = run_command("git rev-parse --abbrev-ref HEAD")
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# # GitHub personal access token (with repo scope) or use username and password
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# access_token = os.getenv("GITHUB_ACCESS_TOKEN")
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# # Instantiate the PyGithub library's Github object
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# g = Github(access_token)
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# # Provide the owner and name of the repository where the pull request is located
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# repository_owner = "BerriAI"
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# repository_name = "litellm"
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# # Get the repository object
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# repo = g.get_repo(f"{repository_owner}/{repository_name}")
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# # Iterate through the pull requests to find the one related to your branch
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# for pr in repo.get_pulls():
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# print(f"in here! {pr.head.ref}")
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# if pr.head.ref == branch_name:
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# pr_number = pr.number
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# break
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# print(f"The pull request number for branch {branch_name} is: {pr_number}")
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# def test_add_new_key():
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# max_retries = 3
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# retry_delay = 10 # seconds
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# for retry in range(max_retries + 1):
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# try:
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# # Your test data
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# test_data = {
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# "models": ["gpt-3.5-turbo", "gpt-4", "claude-2", "azure-model"],
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# "aliases": {"mistral-7b": "gpt-3.5-turbo"},
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# "duration": "20m",
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# }
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# print("testing proxy server")
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# # Your bearer token
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# token = os.getenv("PROXY_MASTER_KEY")
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# headers = {"Authorization": f"Bearer {token}"}
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# endpoint = f"https://litellm-litellm-pr-{pr_number}.up.railway.app"
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# # Make a request to the staging endpoint
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# response = requests.post(
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# endpoint + "/key/generate", json=test_data, headers=headers
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# )
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# print(f"response: {response.text}")
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# if response.status_code == 200:
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# result = response.json()
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# break # Successful response, exit the loop
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# elif response.status_code == 503 and retry < max_retries:
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# print(
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# f"Retrying in {retry_delay} seconds... (Retry {retry + 1}/{max_retries})"
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# )
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# time.sleep(retry_delay)
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# else:
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# assert False, f"Unexpected response status code: {response.status_code}"
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# except Exception as e:
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# print(traceback.format_exc())
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# pytest.fail(f"An error occurred {e}")
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# def test_update_new_key():
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# try:
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# # Your test data
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# test_data = {
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# "models": ["gpt-3.5-turbo", "gpt-4", "claude-2", "azure-model"],
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# "aliases": {"mistral-7b": "gpt-3.5-turbo"},
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# "duration": "20m",
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# }
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# print("testing proxy server")
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# # Your bearer token
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# token = os.getenv("PROXY_MASTER_KEY")
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# headers = {"Authorization": f"Bearer {token}"}
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# endpoint = f"https://litellm-litellm-pr-{pr_number}.up.railway.app"
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# # Make a request to the staging endpoint
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# response = requests.post(
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# endpoint + "/key/generate", json=test_data, headers=headers
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# )
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# assert response.status_code == 200
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# result = response.json()
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# assert result["key"].startswith("sk-")
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# def _post_data():
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# json_data = {"models": ["bedrock-models"], "key": result["key"]}
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# response = requests.post(
|
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# endpoint + "/key/generate", json=json_data, headers=headers
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# )
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# print(f"response text: {response.text}")
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# assert response.status_code == 200
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# return response
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|
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# _post_data()
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# print(f"Received response: {result}")
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# except Exception as e:
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# pytest.fail(f"LiteLLM Proxy test failed. Exception: {str(e)}")
|
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|
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# def test_add_new_key_max_parallel_limit():
|
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# try:
|
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# # Your test data
|
||||
# test_data = {"duration": "20m", "max_parallel_requests": 1}
|
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# # Your bearer token
|
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# token = os.getenv("PROXY_MASTER_KEY")
|
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# headers = {"Authorization": f"Bearer {token}"}
|
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|
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# endpoint = f"https://litellm-litellm-pr-{pr_number}.up.railway.app"
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# print(f"endpoint: {endpoint}")
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# # Make a request to the staging endpoint
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# response = requests.post(
|
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# endpoint + "/key/generate", json=test_data, headers=headers
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# )
|
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# assert response.status_code == 200
|
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# result = response.json()
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|
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# # load endpoint with model
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# model_data = {
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# "model_name": "azure-model",
|
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# "litellm_params": {
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# "model": "azure/gpt-4.1-mini",
|
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# "api_key": os.getenv("AZURE_API_KEY"),
|
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# "api_base": os.getenv("AZURE_API_BASE"),
|
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# "api_version": os.getenv("AZURE_API_VERSION")
|
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# }
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||||
# }
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# response = requests.post(endpoint + "/model/new", json=model_data, headers=headers)
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# assert response.status_code == 200
|
||||
# print(f"response text: {response.text}")
|
||||
|
||||
|
||||
# def _post_data():
|
||||
# json_data = {
|
||||
# "model": "azure-model",
|
||||
# "messages": [
|
||||
# {
|
||||
# "role": "user",
|
||||
# "content": f"this is a test request, write a short poem {time.time()}",
|
||||
# }
|
||||
# ],
|
||||
# }
|
||||
# # Your bearer token
|
||||
# response = requests.post(
|
||||
# endpoint + "/chat/completions", json=json_data, headers={"Authorization": f"Bearer {result['key']}"}
|
||||
# )
|
||||
# return response
|
||||
|
||||
# def _run_in_parallel():
|
||||
# with ThreadPoolExecutor(max_workers=2) as executor:
|
||||
# future1 = executor.submit(_post_data)
|
||||
# future2 = executor.submit(_post_data)
|
||||
|
||||
# # Obtain the results from the futures
|
||||
# response1 = future1.result()
|
||||
# print(f"response1 text: {response1.text}")
|
||||
# response2 = future2.result()
|
||||
# print(f"response2 text: {response2.text}")
|
||||
# if response1.status_code == 429 or response2.status_code == 429:
|
||||
# pass
|
||||
# else:
|
||||
# raise Exception()
|
||||
|
||||
# _run_in_parallel()
|
||||
# except Exception as e:
|
||||
# pytest.fail(f"LiteLLM Proxy test failed. Exception: {str(e)}")
|
||||
|
||||
# def test_add_new_key_max_parallel_limit_streaming():
|
||||
# try:
|
||||
# # Your test data
|
||||
# test_data = {"duration": "20m", "max_parallel_requests": 1}
|
||||
# # Your bearer token
|
||||
# token = os.getenv("PROXY_MASTER_KEY")
|
||||
# headers = {"Authorization": f"Bearer {token}"}
|
||||
|
||||
# endpoint = f"https://litellm-litellm-pr-{pr_number}.up.railway.app"
|
||||
|
||||
# # Make a request to the staging endpoint
|
||||
# response = requests.post(
|
||||
# endpoint + "/key/generate", json=test_data, headers=headers
|
||||
# )
|
||||
# print(f"response: {response.text}")
|
||||
# assert response.status_code == 200
|
||||
# result = response.json()
|
||||
|
||||
# def _post_data():
|
||||
# json_data = {
|
||||
# "model": "azure-model",
|
||||
# "messages": [
|
||||
# {
|
||||
# "role": "user",
|
||||
# "content": f"this is a test request, write a short poem {time.time()}",
|
||||
# }
|
||||
# ],
|
||||
# "stream": True,
|
||||
# }
|
||||
# response = requests.post(
|
||||
# endpoint + "/chat/completions", json=json_data, headers={"Authorization": f"Bearer {result['key']}"}
|
||||
# )
|
||||
# return response
|
||||
|
||||
# def _run_in_parallel():
|
||||
# with ThreadPoolExecutor(max_workers=2) as executor:
|
||||
# future1 = executor.submit(_post_data)
|
||||
# future2 = executor.submit(_post_data)
|
||||
|
||||
# # Obtain the results from the futures
|
||||
# response1 = future1.result()
|
||||
# response2 = future2.result()
|
||||
# if response1.status_code == 429 or response2.status_code == 429:
|
||||
# pass
|
||||
# else:
|
||||
# raise Exception()
|
||||
|
||||
# _run_in_parallel()
|
||||
# except Exception as e:
|
||||
# pytest.fail(f"LiteLLM Proxy test failed. Exception: {str(e)}")
|
||||
|
|
@ -1,82 +0,0 @@
|
|||
# import openai, json, time, asyncio
|
||||
# client = openai.AsyncOpenAI(
|
||||
# api_key="sk-1234",
|
||||
# base_url="http://0.0.0.0:8000"
|
||||
# )
|
||||
|
||||
# super_fake_messages = [
|
||||
# {
|
||||
# "role": "user",
|
||||
# "content": f"What's the weather like in San Francisco, Tokyo, and Paris? {time.time()}"
|
||||
# },
|
||||
# {
|
||||
# "content": None,
|
||||
# "role": "assistant",
|
||||
# "tool_calls": [
|
||||
# {
|
||||
# "id": "1",
|
||||
# "function": {
|
||||
# "arguments": "{\"location\": \"San Francisco\", \"unit\": \"celsius\"}",
|
||||
# "name": "get_current_weather"
|
||||
# },
|
||||
# "type": "function"
|
||||
# },
|
||||
# {
|
||||
# "id": "2",
|
||||
# "function": {
|
||||
# "arguments": "{\"location\": \"Tokyo\", \"unit\": \"celsius\"}",
|
||||
# "name": "get_current_weather"
|
||||
# },
|
||||
# "type": "function"
|
||||
# },
|
||||
# {
|
||||
# "id": "3",
|
||||
# "function": {
|
||||
# "arguments": "{\"location\": \"Paris\", \"unit\": \"celsius\"}",
|
||||
# "name": "get_current_weather"
|
||||
# },
|
||||
# "type": "function"
|
||||
# }
|
||||
# ]
|
||||
# },
|
||||
# {
|
||||
# "tool_call_id": "1",
|
||||
# "role": "tool",
|
||||
# "name": "get_current_weather",
|
||||
# "content": "{\"location\": \"San Francisco\", \"temperature\": \"90\", \"unit\": \"celsius\"}"
|
||||
# },
|
||||
# {
|
||||
# "tool_call_id": "2",
|
||||
# "role": "tool",
|
||||
# "name": "get_current_weather",
|
||||
# "content": "{\"location\": \"Tokyo\", \"temperature\": \"30\", \"unit\": \"celsius\"}"
|
||||
# },
|
||||
# {
|
||||
# "tool_call_id": "3",
|
||||
# "role": "tool",
|
||||
# "name": "get_current_weather",
|
||||
# "content": "{\"location\": \"Paris\", \"temperature\": \"50\", \"unit\": \"celsius\"}"
|
||||
# }
|
||||
# ]
|
||||
|
||||
# async def chat_completions():
|
||||
# super_fake_response = await client.chat.completions.create(
|
||||
# model="gpt-3.5-turbo",
|
||||
# messages=super_fake_messages,
|
||||
# seed=1337,
|
||||
# stream=False
|
||||
# ) # get a new response from the model where it can see the function response
|
||||
# await asyncio.sleep(1)
|
||||
# return super_fake_response
|
||||
|
||||
# async def loadtest_fn(n = 1):
|
||||
# global num_task_cancelled_errors, exception_counts, chat_completions
|
||||
# start = time.time()
|
||||
# tasks = [chat_completions() for _ in range(n)]
|
||||
# chat_completions = await asyncio.gather(*tasks)
|
||||
# successful_completions = [c for c in chat_completions if c is not None]
|
||||
# print(n, time.time() - start, len(successful_completions))
|
||||
|
||||
# # print(json.dumps(super_fake_response.model_dump(), indent=4))
|
||||
|
||||
# asyncio.run(loadtest_fn())
|
||||
Loading…
Add table
Reference in a new issue