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* 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
204 lines
7 KiB
Python
204 lines
7 KiB
Python
"""
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Test search API logging and cost tracking in proxy.
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Tests that search API requests are properly logged to LiteLLM_SpendLogs
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with correct fields populated (call_type, model, custom_llm_provider,
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model_group, spend, etc.)
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"""
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import asyncio
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import os
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import time
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from datetime import datetime
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from unittest.mock import AsyncMock, patch
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import pytest
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import litellm
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from litellm import Router
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from litellm.caching import DualCache
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from litellm.proxy._types import UserAPIKeyAuth
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from litellm.proxy.hooks.proxy_track_cost_callback import _ProxyDBLogger
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from litellm.proxy.spend_tracking.spend_management_endpoints import view_spend_logs
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from litellm.proxy.utils import ProxyLogging, hash_token, update_spend
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from litellm.llms.base_llm.search.transformation import SearchResponse, SearchResult
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@pytest.fixture
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def prisma_client():
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from litellm.proxy import proxy_server
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from litellm.proxy.proxy_cli import append_query_params
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from litellm.proxy.utils import PrismaClient
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params = {"connection_limit": 100, "pool_timeout": 60}
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database_url = os.getenv("DATABASE_URL")
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if database_url is None:
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pytest.skip("DATABASE_URL not set")
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modified_url = append_query_params(database_url, params)
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os.environ["DATABASE_URL"] = modified_url
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user_api_key_cache = DualCache()
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proxy_logging_obj = ProxyLogging(user_api_key_cache=user_api_key_cache)
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prisma_client = PrismaClient(
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database_url=os.environ["DATABASE_URL"], proxy_logging_obj=proxy_logging_obj
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)
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proxy_server.litellm_proxy_budget_name = f"litellm-proxy-budget-{time.time()}"
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proxy_server.user_custom_key_generate = None
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return prisma_client
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@pytest.mark.skip(reason="Requires reliable external DB connection (prisma).")
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@pytest.mark.asyncio
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async def test_search_api_logging_and_cost_tracking(prisma_client):
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"""
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Test that search API requests are logged with correct fields and cost tracking.
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Verifies:
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1. Search request creates a spend log entry
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2. call_type is set to "asearch"
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3. model is set to search_tool_name
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4. custom_llm_provider is set correctly
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5. model_group is set to search_tool_name
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6. spend is calculated and logged
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"""
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setattr(litellm.proxy.proxy_server, "prisma_client", prisma_client)
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setattr(litellm.proxy.proxy_server, "master_key", "sk-1234")
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await litellm.proxy.proxy_server.prisma_client.connect()
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# Setup router with search tool
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search_tool_name = "tavily-search"
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search_provider = "tavily"
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router = Router(model_list=[])
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router.search_tools = [
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{
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"search_tool_name": search_tool_name,
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"litellm_params": {
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"search_provider": search_provider,
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},
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}
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]
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setattr(litellm.proxy.proxy_server, "llm_router", router)
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# Generate a test API key
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from litellm.proxy.management_endpoints.key_management_endpoints import (
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generate_key_fn,
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)
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from litellm.proxy._types import GenerateKeyRequest
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from litellm.proxy._types import LitellmUserRoles
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user_api_key_dict = UserAPIKeyAuth(
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user_role=LitellmUserRoles.PROXY_ADMIN,
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api_key="sk-1234",
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user_id="test_user",
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)
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key_request = GenerateKeyRequest(models=[], duration=None)
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key_response = await generate_key_fn(
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data=key_request, user_api_key_dict=user_api_key_dict
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)
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generated_key = key_response.key
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user_id = key_response.user_id
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# Create mock search response
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mock_search_result = SearchResult(
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title="Test Result",
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url="https://example.com",
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snippet="Test snippet",
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)
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mock_search_response = SearchResponse(
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object="search",
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results=[mock_search_result],
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)
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# Mock the search function to return our mock response
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with patch("litellm.search.main.asearch", new_callable=AsyncMock) as mock_asearch:
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mock_asearch.return_value = mock_search_response
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# Setup proxy logging
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user_api_key_cache = DualCache()
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proxy_logging_obj = ProxyLogging(user_api_key_cache=user_api_key_cache)
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setattr(litellm.proxy.proxy_server, "proxy_logging_obj", proxy_logging_obj)
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# Call the track_cost_callback directly to simulate what happens after a search
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proxy_db_logger = _ProxyDBLogger()
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# Simulate the kwargs that would be passed from the search endpoint
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request_id = "search_test_123"
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kwargs = {
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"call_type": "asearch",
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"model": search_tool_name,
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"custom_llm_provider": search_provider,
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"litellm_call_id": request_id, # Set request_id in kwargs
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"litellm_params": {
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"metadata": {
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"user_api_key": hash_token(generated_key),
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"user_api_key_user_id": user_id,
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"model_group": search_tool_name,
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}
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},
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"metadata": {
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"user_api_key": hash_token(generated_key),
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"user_api_key_user_id": user_id,
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"model_group": search_tool_name,
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},
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"response_cost": 0.008, # Mock cost for tavily search
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}
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# Set id on the response object
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mock_search_response.id = request_id
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await proxy_db_logger._PROXY_track_cost_callback(
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kwargs=kwargs,
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completion_response=mock_search_response,
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start_time=datetime.now(),
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end_time=datetime.now(),
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)
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# Wait for async operations
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await asyncio.sleep(2)
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await update_spend(
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prisma_client=prisma_client,
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db_writer_client=None,
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proxy_logging_obj=proxy_logging_obj,
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)
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# Query spend logs
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spend_logs = await view_spend_logs(
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request_id=request_id,
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user_api_key_dict=UserAPIKeyAuth(api_key=generated_key),
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)
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# Verify spend log was created
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assert len(spend_logs) == 1, f"Expected 1 spend log, got {len(spend_logs)}"
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spend_log = spend_logs[0]
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# Verify all fields are populated correctly
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assert spend_log.request_id == request_id
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assert spend_log.call_type == "asearch"
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assert spend_log.model == search_tool_name
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assert spend_log.custom_llm_provider == search_provider
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assert spend_log.model_group == search_tool_name
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assert spend_log.spend == 0.008
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# API key should be hashed (either the generated key or the one from metadata)
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assert spend_log.api_key != "" # Should be populated
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# Note: user field may be empty if not set in the request, but user_id should be in metadata
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assert (
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spend_log.metadata.get("user_api_key_user_id") == user_id
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or spend_log.user == user_id
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)
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print(f"✅ Search API logging test passed!")
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print(f" - call_type: {spend_log.call_type}")
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print(f" - model: {spend_log.model}")
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print(f" - custom_llm_provider: {spend_log.custom_llm_provider}")
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print(f" - model_group: {spend_log.model_group}")
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print(f" - spend: {spend_log.spend}")
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