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4f93e2c3da
* test: point CircleCI-only suites at models still in the cost map #42435 removed cost map entries past their deprecation date and #42437 added litellm_uisettings to the config-synced tables, but both only updated tests/test_litellm. The CircleCI-only suites (local_testing, llm_translation, logging_callback_tests, litellm_utils_tests, unit) kept using the removed models or the old table list and went red on main. Each test keeps its assertions and swaps the removed model for a current one with the same provider and capabilities. The fireworks tests pick a vision model from the cost map because #34941 set supports_vision false on minimax-m3, and the vertex image provider test injects the image model set because #42435 removed every vertex_ai-image-models entry. * test(vertex_ai): register the image model through add_known_models in the provider test
136 lines
3.6 KiB
Python
136 lines
3.6 KiB
Python
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import pytest
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from dotenv import load_dotenv
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from fastapi import Request
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from fastapi.routing import APIRoute
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import litellm
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from litellm.proxy._types import SpendCalculateRequest
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from litellm.proxy.spend_tracking.spend_management_endpoints import calculate_spend
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from litellm.router import Router
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# this file is to test litellm/proxy
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@pytest.mark.asyncio
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async def test_spend_calc_model_messages():
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cost_obj = await calculate_spend(
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request=SpendCalculateRequest(
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model="gpt-3.5-turbo",
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messages=[
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{"role": "user", "content": "What is the capital of France?"},
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],
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)
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)
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print("calculated cost", cost_obj)
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cost = cost_obj["cost"]
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assert cost > 0.0
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@pytest.mark.asyncio
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async def test_spend_calc_model_on_router_messages():
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from litellm.proxy.proxy_server import llm_router as init_llm_router
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temp_llm_router = Router(
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model_list=[
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{
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"model_name": "special-llama-model",
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"litellm_params": {
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"model": "groq/openai/gpt-oss-20b",
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},
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}
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]
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)
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setattr(litellm.proxy.proxy_server, "llm_router", temp_llm_router)
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cost_obj = await calculate_spend(
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request=SpendCalculateRequest(
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model="special-llama-model",
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messages=[
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{"role": "user", "content": "What is the capital of France?"},
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],
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)
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)
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print("calculated cost", cost_obj)
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_cost = cost_obj["cost"]
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assert _cost > 0.0
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# set router to init value
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setattr(litellm.proxy.proxy_server, "llm_router", init_llm_router)
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@pytest.mark.asyncio
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async def test_spend_calc_using_response():
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cost_obj = await calculate_spend(
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request=SpendCalculateRequest(
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completion_response={
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"id": "chatcmpl-3bc7abcd-f70b-48ab-a16c-dfba0b286c86",
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"choices": [
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{
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"finish_reason": "stop",
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"index": 0,
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"message": {
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"content": "Yooo! What's good?",
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"role": "assistant",
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},
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}
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],
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"created": "1677652288",
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"model": "groq/openai/gpt-oss-20b",
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"object": "chat.completion",
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"system_fingerprint": "fp_873a560973",
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"usage": {
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"completion_tokens": 8,
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"prompt_tokens": 12,
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"total_tokens": 20,
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},
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}
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)
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)
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print("calculated cost", cost_obj)
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cost = cost_obj["cost"]
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assert cost > 0.0
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@pytest.mark.asyncio
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async def test_spend_calc_model_alias_on_router_messages():
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from litellm.proxy.proxy_server import llm_router as init_llm_router
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temp_llm_router = Router(
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model_list=[
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{
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"model_name": "gpt-4o",
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"litellm_params": {
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"model": "gpt-4o",
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},
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}
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],
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model_group_alias={
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"gpt4o": "gpt-4o",
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},
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)
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setattr(litellm.proxy.proxy_server, "llm_router", temp_llm_router)
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cost_obj = await calculate_spend(
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request=SpendCalculateRequest(
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model="gpt4o",
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messages=[
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{"role": "user", "content": "What is the capital of France?"},
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],
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)
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)
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print("calculated cost", cost_obj)
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_cost = cost_obj["cost"]
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assert _cost > 0.0
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# set router to init value
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setattr(litellm.proxy.proxy_server, "llm_router", init_llm_router)
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