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d5173e3d70
* test: move 76 live top-level tests to offline unit and integration coverage * test: keep the mock-param opt-in guard valid once the top-level senders are gone * test: address review on offline replacements * docs(test): drop the deleted harness test file from the harness check command * test: make the key rebind, team member delete and routes integration tests exercise the legacy paths * test: make fallback, rpm and spend integration contracts deterministic and clean up their rows, assert the rpm limit in usage-based routing * test: expect the no-deployments error at the rpm limit and cover the strategy check without pre-call checks * test: hand member cleanups to the scenario instead of growing a budget list, flatten callback kinds * test: scope the admin health check to the test's own deployment --------- Co-authored-by: yuneng <yuneng@berri.ai>
117 lines
3.5 KiB
Python
117 lines
3.5 KiB
Python
import os
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from typing import Final
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# What this tests ?
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## Tests /chat/completions by generating a key and then making a chat completions-request
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import pytest
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from openai import AsyncOpenAI
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LITELLM_MASTER_KEY = os.environ["LITELLM_MASTER_KEY"]
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def response_header_check(response):
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"""
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- assert if response headers < 4kb (nginx limit).
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"""
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headers_size = sum(len(k) + len(v) for k, v in response.raw_headers)
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assert headers_size < 4096, "Response headers exceed the 4kb limit"
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async def moderation(session, key):
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url = "http://0.0.0.0:4000/moderations"
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headers = {
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"Authorization": f"Bearer {key}",
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"Content-Type": "application/json",
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}
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data = {"model": "text-moderation-stable", "input": "I want to kill the cat."}
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async with session.post(url, headers=headers, json=data) as response:
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status = response.status
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response_text = await response.text()
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print(response_text)
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print()
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if status != 200:
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raise Exception(f"Request did not return a 200 status code: {status}")
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return await response.json()
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async def embeddings(session, key, model="text-embedding-ada-002"):
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url = "http://0.0.0.0:4000/embeddings"
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headers = {
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"Authorization": f"Bearer {key}",
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"Content-Type": "application/json",
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}
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data = {
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"model": model,
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"input": ["hello world"],
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}
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async with session.post(url, headers=headers, json=data) as response:
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status = response.status
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response_text = await response.text()
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print(response_text)
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if status != 200:
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raise Exception(f"Request did not return a 200 status code: {status}")
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response_header_check(
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response
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) # calling the function to check response headers
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@pytest.mark.asyncio
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async def test_chat_completion_streaming():
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"""
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[PROD Test] Ensures logprobs are returned correctly
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"""
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client = AsyncOpenAI(api_key=os.environ["LITELLM_MASTER_KEY"], base_url="http://0.0.0.0:4000")
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response = await client.chat.completions.create(
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model="gpt-3.5-turbo-large",
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messages=[{"role": "user", "content": "Hello!"}],
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logprobs=True,
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top_logprobs=2,
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stream=True,
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)
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response_str = ""
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async for chunk in response:
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response_str += chunk.choices[0].delta.content or ""
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print(f"response_str: {response_str}")
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@pytest.mark.asyncio
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async def test_completion_streaming_usage_metrics():
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"""
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[PROD Test] Ensures usage metrics are returned correctly when `include_usage` is set to `True`
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"""
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client: Final = AsyncOpenAI(
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api_key=os.environ["LITELLM_MASTER_KEY"], base_url=os.environ.get("LITELLM_PROXY_BASE_URL", "http://0.0.0.0:4000")
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)
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response = await client.completions.create(
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model="gpt-6-luna",
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prompt="hey",
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stream=True,
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stream_options={"include_usage": True},
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max_tokens=4,
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temperature=0.00000001,
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)
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last_chunk = None
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async for chunk in response:
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print("chunk", chunk)
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last_chunk = chunk
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assert last_chunk is not None, "No chunks were received"
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assert last_chunk.usage is not None, "Usage information was not received"
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assert last_chunk.usage.prompt_tokens > 0, "Prompt tokens should be greater than 0"
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assert last_chunk.usage.completion_tokens > 0, "Completion tokens should be greater than 0"
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assert last_chunk.usage.total_tokens > 0, "Total tokens should be greater than 0"
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