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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
303 lines
8.9 KiB
Python
303 lines
8.9 KiB
Python
# What is this?
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## This tests the llm guard integration
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import asyncio
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import random
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# What is this?
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## Unit test for presidio pii masking
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import time
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import traceback
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from datetime import datetime
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from dotenv import load_dotenv
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load_dotenv()
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import pytest
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from fastapi import Request, Response
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from starlette.datastructures import URL
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import litellm
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from litellm import Router, mock_completion
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from litellm.caching.caching import DualCache
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from litellm.integrations.custom_logger import CustomLogger
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from litellm.proxy._types import UserAPIKeyAuth
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from litellm_enterprise.enterprise_callbacks.secret_detection import (
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_ENTERPRISE_SecretDetection,
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)
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from litellm.proxy.proxy_server import chat_completion
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from litellm.proxy.utils import ProxyLogging, hash_token
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from tests.fake_openai_endpoint import FAKE_OPENAI_API_BASE
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### UNIT TESTS FOR OpenAI Moderation ###
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@pytest.mark.asyncio
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async def test_basic_secret_detection_chat():
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"""
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Tests to see if secret detection hook will mask api keys
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It should mask the following API_KEY = 'sk_1234567890abcdef' and OPENAI_API_KEY = 'sk_1234567890abcdef'
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"""
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secret_instance = _ENTERPRISE_SecretDetection()
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_api_key = "sk-12345"
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_api_key = hash_token("sk-12345")
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user_api_key_dict = UserAPIKeyAuth(api_key=_api_key)
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local_cache = DualCache()
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from litellm.proxy.proxy_server import llm_router
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test_data = {
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"messages": [
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{
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"role": "user",
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"content": "Hey, how's it going, API_KEY = 'sk_1234567890abcdef'",
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},
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{
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"role": "assistant",
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"content": "Hello! I'm doing well. How can I assist you today?",
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},
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{
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"role": "user",
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"content": "this is my OPENAI_API_KEY = 'sk_1234567890abcdef'",
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},
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{
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"role": "user",
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"content": "My hi API Key is sk-Pc4nlxVoMz41290028TbMCxx, does it seem to be in the correct format?",
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},
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{"role": "user", "content": "i think it is +1 412-555-5555"},
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],
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"model": "gpt-3.5-turbo",
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}
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await secret_instance.async_pre_call_hook(
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cache=local_cache,
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data=test_data,
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user_api_key_dict=user_api_key_dict,
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call_type="completion",
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)
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print(
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"test data after running pre_call_hook: Expect all API Keys to be masked",
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test_data,
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)
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assert test_data == {
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"messages": [
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{"role": "user", "content": "Hey, how's it going, API_KEY = '[REDACTED]'"},
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{
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"role": "assistant",
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"content": "Hello! I'm doing well. How can I assist you today?",
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},
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{"role": "user", "content": "this is my OPENAI_API_KEY = '[REDACTED]'"},
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{
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"role": "user",
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"content": "My hi API Key is [REDACTED], does it seem to be in the correct format?",
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},
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{"role": "user", "content": "i think it is +1 412-555-5555"},
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],
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"model": "gpt-3.5-turbo",
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}, "Expect all API Keys to be masked"
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@pytest.mark.asyncio
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async def test_basic_secret_detection_text_completion():
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"""
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Tests to see if secret detection hook will mask api keys
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It should mask the following API_KEY = 'sk_1234567890abcdef' and OPENAI_API_KEY = 'sk_1234567890abcdef'
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"""
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secret_instance = _ENTERPRISE_SecretDetection()
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_api_key = "sk-12345"
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_api_key = hash_token("sk-12345")
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user_api_key_dict = UserAPIKeyAuth(api_key=_api_key)
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local_cache = DualCache()
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from litellm.proxy.proxy_server import llm_router
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test_data = {
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"prompt": "Hey, how's it going, API_KEY = 'sk_1234567890abcdef', my OPENAI_API_KEY = 'sk_1234567890abcdef' and i want to know what is the weather",
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"model": "gpt-3.5-turbo",
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}
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await secret_instance.async_pre_call_hook(
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cache=local_cache,
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data=test_data,
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user_api_key_dict=user_api_key_dict,
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call_type="completion",
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)
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assert test_data == {
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"prompt": "Hey, how's it going, API_KEY = '[REDACTED]', my OPENAI_API_KEY = '[REDACTED]' and i want to know what is the weather",
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"model": "gpt-3.5-turbo",
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}
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print(
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"test data after running pre_call_hook: Expect all API Keys to be masked",
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test_data,
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)
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@pytest.mark.asyncio
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async def test_basic_secret_detection_embeddings():
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"""
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Tests to see if secret detection hook will mask api keys
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It should mask the following API_KEY = 'sk_1234567890abcdef' and OPENAI_API_KEY = 'sk_1234567890abcdef'
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"""
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secret_instance = _ENTERPRISE_SecretDetection()
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_api_key = "sk-12345"
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_api_key = hash_token("sk-12345")
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user_api_key_dict = UserAPIKeyAuth(api_key=_api_key)
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local_cache = DualCache()
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from litellm.proxy.proxy_server import llm_router
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test_data = {
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"input": "Hey, how's it going, API_KEY = 'sk_1234567890abcdef', my OPENAI_API_KEY = 'sk_1234567890abcdef' and i want to know what is the weather",
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"model": "gpt-3.5-turbo",
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}
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await secret_instance.async_pre_call_hook(
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cache=local_cache,
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data=test_data,
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user_api_key_dict=user_api_key_dict,
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call_type="embedding",
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)
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assert test_data == {
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"input": "Hey, how's it going, API_KEY = '[REDACTED]', my OPENAI_API_KEY = '[REDACTED]' and i want to know what is the weather",
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"model": "gpt-3.5-turbo",
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}
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print(
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"test data after running pre_call_hook: Expect all API Keys to be masked",
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test_data,
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)
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@pytest.mark.asyncio
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async def test_basic_secret_detection_embeddings_list():
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"""
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Tests to see if secret detection hook will mask api keys
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It should mask the following API_KEY = 'sk_1234567890abcdef' and OPENAI_API_KEY = 'sk_1234567890abcdef'
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"""
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secret_instance = _ENTERPRISE_SecretDetection()
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_api_key = "sk-12345"
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_api_key = hash_token("sk-12345")
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user_api_key_dict = UserAPIKeyAuth(api_key=_api_key)
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local_cache = DualCache()
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from litellm.proxy.proxy_server import llm_router
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test_data = {
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"input": [
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"hey",
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"how's it going, API_KEY = 'sk_1234567890abcdef'",
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"my OPENAI_API_KEY = 'sk_1234567890abcdef' and i want to know what is the weather",
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],
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"model": "gpt-3.5-turbo",
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}
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await secret_instance.async_pre_call_hook(
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cache=local_cache,
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data=test_data,
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user_api_key_dict=user_api_key_dict,
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call_type="embedding",
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)
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print(
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"test data after running pre_call_hook: Expect all API Keys to be masked",
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test_data,
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)
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assert test_data == {
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"input": [
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"hey",
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"how's it going, API_KEY = '[REDACTED]'",
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"my OPENAI_API_KEY = '[REDACTED]' and i want to know what is the weather",
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],
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"model": "gpt-3.5-turbo",
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}
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class testLogger(CustomLogger):
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def __init__(self):
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self.logged_message = None
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async def async_log_success_event(self, kwargs, response_obj, start_time, end_time):
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print(f"On Async Success")
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self.logged_message = kwargs.get("messages")
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router = Router(
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model_list=[
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{
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"model_name": "fake-model",
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"litellm_params": {
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"model": "openai/fake",
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"api_base": FAKE_OPENAI_API_BASE,
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"api_key": "sk-12345",
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},
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}
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]
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)
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@pytest.mark.asyncio
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async def test_chat_completion_request_with_redaction():
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"""
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IMPORTANT Enterprise Test - Do not delete it:
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Makes a /chat/completions request on LiteLLM Proxy
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Ensures that the secret is redacted EVEN on the callback
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"""
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from litellm.proxy import proxy_server
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setattr(proxy_server, "llm_router", router)
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_test_logger = testLogger()
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litellm.callbacks = [_ENTERPRISE_SecretDetection(), _test_logger]
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litellm._turn_on_debug()
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# Prepare the query string
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query_params = "param1=value1¶m2=value2"
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# Create the Request object with query parameters
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request = Request(
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scope={
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"type": "http",
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"method": "POST",
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"headers": [(b"content-type", b"application/json")],
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"query_string": query_params.encode(),
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}
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)
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request._url = URL(url="/chat/completions")
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async def return_body():
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return b'{"model": "fake-model", "messages": [{"role": "user", "content": "Hello here is my OPENAI_API_KEY = sk-12345"}]}'
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request.body = return_body
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response = await chat_completion(
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request=request,
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user_api_key_dict=UserAPIKeyAuth(
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api_key="sk-12345",
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token="hashed_sk-12345",
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),
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fastapi_response=Response(),
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)
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await asyncio.sleep(3)
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print("Info in callback after running request=", _test_logger.logged_message)
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assert _test_logger.logged_message == [
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{"role": "user", "content": "Hello here is my OPENAI_API_KEY = [REDACTED]"}
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]
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pass
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