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6a0d03914c
* test: drop the cwd-relative sys.path.insert calls from the test suite
TQ003 stands at 1,077 across 1,058 files, and 1,015 of them are the same shape:
sys.path.insert(0, os.path.abspath("../..")) and its deeper siblings. The
argument resolves against the working directory rather than the file, so from
the repo root, where every job runs pytest, it inserts the directory two levels
above the checkout. It has never pointed at litellm. The package is installed
into the environment anyway, which is what actually makes the import work, and
what the rule's message has said all along.
Removing them leaves 1,634 imports of sys and os with no remaining reference,
and those go too, except where another test module imports the name back out of
the file. The rest of TQ003 is 62 call sites that resolve against __file__ or a
variable, which are a different question and are left alone.
Collection is identical either way: 45,871 tests and the same 51 pre-existing
collection errors before and after, and ruff reports no new undefined name.
* test: drop the duplicate imports the sys.path sweep exposed to F811
* test(pre-call-utils): restore the os import the new bedrock tests need
120 lines
4 KiB
Python
120 lines
4 KiB
Python
"""
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Load test on vertex AI embeddings to ensure vertex median response time is less than 300ms
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"""
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import os
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import asyncio
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import litellm
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import pytest
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import time
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from statistics import mean, median
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import json
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import tempfile
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def load_vertex_ai_credentials():
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# Define the path to the vertex_key.json file
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print("loading vertex ai credentials")
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filepath = os.path.dirname(os.path.abspath(__file__))
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vertex_key_path = filepath + "/vertex_key.json"
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# Read the existing content of the file or create an empty dictionary
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try:
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with open(vertex_key_path, "r") as file:
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# Read the file content
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print("Read vertexai file path")
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content = file.read()
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# If the file is empty or not valid JSON, create an empty dictionary
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if not content or not content.strip():
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service_account_key_data = {}
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else:
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# Attempt to load the existing JSON content
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file.seek(0)
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service_account_key_data = json.load(file)
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except FileNotFoundError:
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# If the file doesn't exist, create an empty dictionary
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service_account_key_data = {}
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# Update the service_account_key_data with environment variables
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private_key_id = os.environ.get("VERTEX_AI_PRIVATE_KEY_ID", "")
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private_key = os.environ.get("VERTEX_AI_PRIVATE_KEY", "")
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private_key = private_key.replace("\\n", "\n")
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service_account_key_data["private_key_id"] = private_key_id
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service_account_key_data["private_key"] = private_key
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# Create a temporary file
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with tempfile.NamedTemporaryFile(mode="w+", delete=False) as temp_file:
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# Write the updated content to the temporary files
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json.dump(service_account_key_data, temp_file, indent=2)
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# Export the temporary file as GOOGLE_APPLICATION_CREDENTIALS
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os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = os.path.abspath(temp_file.name)
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async def create_async_vertex_embedding_task():
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load_vertex_ai_credentials()
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base_url = "https://exampleopenaiendpoint-production.up.railway.app/v1/projects/pathrise-convert-1606954137718/locations/us-central1/publishers/google/models/textembedding-gecko@001"
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embedding_args = {
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"model": "vertex_ai/textembedding-gecko",
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"input": "This is a test sentence for embedding.",
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"timeout": 10,
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"api_base": base_url,
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}
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start_time = time.time()
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response = await litellm.aembedding(**embedding_args)
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end_time = time.time()
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print(f"Vertex AI embedding time: {end_time - start_time:.2f} seconds")
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return response, end_time - start_time
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async def run_load_test(duration_seconds, requests_per_second):
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end_time = time.time() + duration_seconds
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vertex_times = []
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print(
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f"Running Load Test for {duration_seconds} seconds at {requests_per_second} RPS..."
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)
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while time.time() < end_time:
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vertex_tasks = [
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create_async_vertex_embedding_task() for _ in range(requests_per_second)
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]
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vertex_results = await asyncio.gather(*vertex_tasks)
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vertex_times.extend([duration for _, duration in vertex_results])
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# Sleep for 1 second to maintain the desired RPS
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await asyncio.sleep(1)
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return vertex_times
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def analyze_results(vertex_times):
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median_vertex = median(vertex_times)
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print(f"Vertex AI median response time: {median_vertex:.4f} seconds")
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if median_vertex > 3:
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pytest.fail(
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f"Vertex AI median response time is greater than 500ms: {median_vertex:.4f} seconds"
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)
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else:
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print("Performance is good")
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return True
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@pytest.mark.asyncio
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async def test_embedding_performance(monkeypatch):
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"""
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Run load test on vertex AI embeddings to ensure vertex median response time is less than 300ms
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20 RPS for 20 seconds
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"""
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monkeypatch.setattr(litellm, "api_base", None)
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duration_seconds = 20
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requests_per_second = 20
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vertex_times = await run_load_test(duration_seconds, requests_per_second)
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result = analyze_results(vertex_times)
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