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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
554 lines
22 KiB
Python
554 lines
22 KiB
Python
import traceback
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from litellm._uuid import uuid
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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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load_dotenv()
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import io
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import time
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# this file is to test litellm/proxy
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import asyncio
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import datetime
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import json
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import logging
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from typing import Optional
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import pytest
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import litellm
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from litellm.proxy.spend_tracking.spend_tracking_utils import (
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get_logging_payload,
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_sanitize_request_body_for_spend_logs_payload,
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)
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from litellm.proxy._types import SpendLogsMetadata, SpendLogsPayload
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@pytest.mark.parametrize(
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"model_id",
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["chatcmpl-9XZmkzS1uPhRCoVdGQvBqqIbSgECt", "", None],
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)
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def test_spend_logs_payload(model_id: Optional[str]):
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"""
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Ensure only expected values are logged in spend logs payload.
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"""
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input_args: dict = {
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"kwargs": {
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"model": "chatgpt-v-3",
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"messages": [
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{"role": "system", "content": "you are a helpful assistant.\n"},
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{"role": "user", "content": "bom dia"},
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],
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"custom_llm_provider": "azure",
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"optional_params": {
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"stream": False,
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"max_tokens": 10,
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"user": "116544810872468347480",
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"extra_body": {},
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},
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"litellm_params": {
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"acompletion": True,
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"api_key": "sk-test-mock-key-707",
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"force_timeout": 600,
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"logger_fn": None,
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"verbose": False,
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"custom_llm_provider": "azure",
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"api_base": "https://openai-gpt-4-test-v-1.openai.azure.com//openai/",
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"litellm_call_id": "b9929bf6-7b80-4c8c-b486-034e6ac0c8b7",
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"model_alias_map": {},
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"completion_call_id": None,
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"metadata": {
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"tags": ["model-anthropic-claude-v2.1", "app-ishaan-prod"],
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"user_api_key": "sk-test-mock-api-key-123",
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"user_api_key_alias": "custom-key-alias",
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"user_api_end_user_max_budget": None,
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"litellm_api_version": "0.0.0",
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"global_max_parallel_requests": None,
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"user_api_key_user_id": "116544810872468347480",
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"user_api_key_org_id": "custom-org-id",
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"user_api_key_team_id": "custom-team-id",
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"user_api_key_team_alias": "custom-team-alias",
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"user_api_key_metadata": {},
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"requester_ip_address": "127.0.0.1",
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"spend_logs_metadata": {"hello": "world"},
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"headers": {
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"content-type": "application/json",
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"user-agent": "PostmanRuntime/7.32.3",
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"accept": "*/*",
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"postman-token": "92300061-eeaa-423b-a420-0b44896ecdc4",
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"host": "localhost:4000",
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"accept-encoding": "gzip, deflate, br",
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"connection": "keep-alive",
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"content-length": "163",
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},
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"endpoint": "http://localhost:4000/chat/completions",
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"model_group": "gpt-5-mini",
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"deployment": "azure/gpt-4.1-mini",
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"model_info": {
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"id": "4bad40a1eb6bebd1682800f16f44b9f06c52a6703444c99c7f9f32e9de3693b4",
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"db_model": False,
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},
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"api_base": "https://openai-gpt-4-test-v-1.openai.azure.com/",
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"caching_groups": None,
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"error_information": None,
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"status": "success",
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"proxy_server_request": "{}",
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"raw_request": "\n\nPOST Request Sent from LiteLLM:\ncurl -X POST \\\nhttps://openai-gpt-4-test-v-1.openai.azure.com//openai/ \\\n-H 'Authorization: *****' \\\n-d '{'model': 'chatgpt-v-3', 'messages': [{'role': 'system', 'content': 'you are a helpful assistant.\\n'}, {'role': 'user', 'content': 'bom dia'}], 'stream': False, 'max_tokens': 10, 'user': '116544810872468347480', 'extra_body': {}}'\n",
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},
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"model_info": {
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"id": "4bad40a1eb6bebd1682800f16f44b9f06c52a6703444c99c7f9f32e9de3693b4",
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"db_model": False,
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},
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"proxy_server_request": {
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"url": "http://localhost:4000/chat/completions",
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"method": "POST",
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"headers": {
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"content-type": "application/json",
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"user-agent": "PostmanRuntime/7.32.3",
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"accept": "*/*",
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"postman-token": "92300061-eeaa-423b-a420-0b44896ecdc4",
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"host": "localhost:4000",
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"accept-encoding": "gzip, deflate, br",
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"connection": "keep-alive",
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"content-length": "163",
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},
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"body": {
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"messages": [
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{
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"role": "system",
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"content": "you are a helpful assistant.\n",
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},
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{"role": "user", "content": "bom dia"},
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],
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"model": "gpt-5-mini",
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"max_tokens": 10,
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},
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},
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"preset_cache_key": None,
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"no-log": False,
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"stream_response": {},
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"input_cost_per_token": None,
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"input_cost_per_second": None,
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"output_cost_per_token": None,
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"output_cost_per_second": None,
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},
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"start_time": datetime.datetime(2024, 6, 7, 12, 43, 30, 307665),
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"stream": False,
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"user": "116544810872468347480",
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"call_type": "acompletion",
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"litellm_call_id": "b9929bf6-7b80-4c8c-b486-034e6ac0c8b7",
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"completion_start_time": datetime.datetime(2024, 6, 7, 12, 43, 30, 954146),
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"max_tokens": 10,
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"extra_body": {},
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"custom_llm_provider": "azure",
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"input": [
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{"role": "system", "content": "you are a helpful assistant.\n"},
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{"role": "user", "content": "bom dia"},
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],
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"api_key": "1234",
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"original_response": "",
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"additional_args": {
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"headers": {"Authorization": "Bearer 1234"},
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"api_base": "openai-gpt-4-test-v-1.openai.azure.com",
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"acompletion": True,
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"complete_input_dict": {
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"model": "chatgpt-v-3",
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"messages": [
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{"role": "system", "content": "you are a helpful assistant.\n"},
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{"role": "user", "content": "bom dia"},
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],
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"stream": False,
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"max_tokens": 10,
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"user": "116544810872468347480",
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"extra_body": {},
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},
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},
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"log_event_type": "post_api_call",
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"end_time": datetime.datetime(2024, 6, 7, 12, 43, 30, 954146),
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"cache_hit": None,
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"response_cost": 2.4999999999999998e-05,
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"standard_logging_object": {
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"request_tags": ["model-anthropic-claude-v2.1", "app-ishaan-prod"],
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"metadata": {
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"user_api_key_end_user_id": "test-user",
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},
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"model_map_information": {
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"tpm": 1000,
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"rpm": 1000,
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},
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},
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},
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"response_obj": litellm.ModelResponse(
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id=model_id,
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choices=[
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litellm.Choices(
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finish_reason="length",
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index=0,
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message=litellm.Message(
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content="Bom dia! Como posso ajudar você", role="assistant"
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),
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)
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],
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created=1717789410,
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model="gpt-35-turbo",
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object="chat.completion",
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system_fingerprint=None,
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usage=litellm.Usage(
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completion_tokens=10, prompt_tokens=20, total_tokens=30
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),
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),
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"start_time": datetime.datetime(2024, 6, 7, 12, 43, 30, 308604),
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"end_time": datetime.datetime(2024, 6, 7, 12, 43, 30, 954146),
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}
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payload: SpendLogsPayload = get_logging_payload(**input_args)
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assert len(payload["request_id"]) > 0
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# Define the expected metadata keys
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expected_metadata_keys = SpendLogsMetadata.__annotations__.keys()
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# Validate only specified metadata keys are logged
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assert "metadata" in payload
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assert isinstance(payload["metadata"], str)
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payload["metadata"] = json.loads(payload["metadata"])
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assert set(payload["metadata"].keys()) == set(expected_metadata_keys)
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# This is crucial - used in PROD, it should pass, related issue: https://github.com/BerriAI/litellm/issues/4334
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assert (
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payload["request_tags"] == '["model-anthropic-claude-v2.1", "app-ishaan-prod"]'
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)
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assert payload["metadata"]["user_api_key_org_id"] == "custom-org-id"
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assert payload["metadata"]["user_api_key_team_id"] == "custom-team-id"
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assert payload["metadata"]["user_api_key_team_alias"] == "custom-team-alias"
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assert payload["metadata"]["user_api_key_alias"] == "custom-key-alias"
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assert payload["custom_llm_provider"] == "azure"
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def test_spend_logs_payload_whisper():
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"""
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Ensure we can write /transcription request/responses to spend logs
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"""
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kwargs: dict = {
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"model": "whisper-1",
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"messages": [{"role": "user", "content": "audio_file"}],
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"optional_params": {},
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"litellm_params": {
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"api_base": "",
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"metadata": {
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"user_api_key": "sk-test-mock-api-key-123",
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"user_api_key_alias": None,
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"user_api_key_end_user_id": "test-user",
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"user_api_end_user_max_budget": None,
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"litellm_api_version": "1.40.19",
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"global_max_parallel_requests": None,
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"user_api_key_user_id": "default_user_id",
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"user_api_key_org_id": None,
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"user_api_key_team_id": None,
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"user_api_key_team_alias": None,
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"user_api_key_team_max_budget": None,
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"user_api_key_team_spend": None,
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"user_api_key_spend": 0.0,
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"user_api_key_max_budget": None,
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"user_api_key_metadata": {},
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"headers": {
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"host": "localhost:4000",
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"user-agent": "curl/7.88.1",
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"accept": "*/*",
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"content-length": "775501",
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"content-type": "multipart/form-data; boundary=------------------------21d518e191326d20",
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},
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"endpoint": "http://localhost:4000/v1/audio/transcriptions",
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"litellm_parent_otel_span": None,
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"model_group": "whisper-1",
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"deployment": "whisper-1",
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"model_info": {
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"id": "d7761582311451c34d83d65bc8520ce5c1537ea9ef2bec13383cf77596d49eeb",
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"db_model": False,
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},
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"caching_groups": None,
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},
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},
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"start_time": datetime.datetime(2024, 6, 26, 14, 20, 11, 313291),
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"stream": False,
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"user": "",
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"call_type": "atranscription",
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"litellm_call_id": "05921cf7-33f9-421c-aad9-33310c1e2702",
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"completion_start_time": datetime.datetime(2024, 6, 26, 14, 20, 13, 653149),
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"stream_options": None,
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"input": "tmp-requestc8640aee-7d85-49c3-b3ef-bdc9255d8e37.wav",
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"original_response": '{"text": "Four score and seven years ago, our fathers brought forth on this continent a new nation, conceived in liberty and dedicated to the proposition that all men are created equal. Now we are engaged in a great civil war, testing whether that nation, or any nation so conceived and so dedicated, can long endure."}',
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"additional_args": {
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"complete_input_dict": {
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"model": "whisper-1",
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"file": "<_io.BufferedReader name='tmp-requestc8640aee-7d85-49c3-b3ef-bdc9255d8e37.wav'>",
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"language": None,
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"prompt": None,
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"response_format": None,
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"temperature": None,
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}
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},
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"log_event_type": "post_api_call",
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"end_time": datetime.datetime(2024, 6, 26, 14, 20, 13, 653149),
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"cache_hit": None,
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"response_cost": 0.00023398580000000003,
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}
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response = litellm.utils.TranscriptionResponse(
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text="Four score and seven years ago, our fathers brought forth on this continent a new nation, conceived in liberty and dedicated to the proposition that all men are created equal. Now we are engaged in a great civil war, testing whether that nation, or any nation so conceived and so dedicated, can long endure."
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)
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payload: SpendLogsPayload = get_logging_payload(
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kwargs=kwargs,
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response_obj=response,
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start_time=datetime.datetime.now(),
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end_time=datetime.datetime.now(),
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)
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print("payload: ", payload)
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assert payload["call_type"] == "atranscription"
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assert payload["spend"] == 0.00023398580000000003
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def test_spend_logs_payload_with_prompts_enabled(monkeypatch):
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"""
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Test that messages and responses are logged in spend logs when store_prompts_in_spend_logs is enabled
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"""
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# Mock general_settings
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from litellm.proxy.proxy_server import general_settings
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general_settings["store_prompts_in_spend_logs"] = True
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input_args: dict = {
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"kwargs": {
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"model": "gpt-5-mini",
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"messages": [{"role": "user", "content": "Hello!"}],
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"litellm_params": {
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"metadata": {
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"user_api_key": "fake_key",
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}
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},
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},
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"response_obj": litellm.ModelResponse(
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id="chatcmpl-123",
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choices=[
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litellm.Choices(
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finish_reason="stop",
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index=0,
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message=litellm.Message(content="Hi there!", role="assistant"),
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)
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],
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model="gpt-5-mini",
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usage=litellm.Usage(completion_tokens=2, prompt_tokens=1, total_tokens=3),
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),
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"start_time": datetime.datetime.now(),
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"end_time": datetime.datetime.now(),
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}
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# Create a standard logging payload
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standard_logging_payload = {
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"messages": [{"role": "user", "content": "Hello!"}],
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"response": {"role": "assistant", "content": "Hi there!"},
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"metadata": {
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"user_api_key_end_user_id": "test-user",
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},
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"request_tags": ["model-anthropic-claude-v2.1", "app-ishaan-prod"],
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"model_map_information": {
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"tpm": 1000,
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"rpm": 1000,
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},
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}
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litellm_params = {
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"proxy_server_request": {
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"body": {
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"model": "gpt-5.5",
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"messages": [{"role": "user", "content": "Hello!"}],
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}
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}
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}
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input_args["kwargs"]["standard_logging_object"] = standard_logging_payload
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input_args["kwargs"]["litellm_params"] = litellm_params
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payload: SpendLogsPayload = get_logging_payload(**input_args)
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print("json payload: ", json.dumps(payload, indent=4, default=str))
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# Verify messages and response are included in payload
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assert payload["response"] == json.dumps(
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{"role": "assistant", "content": "Hi there!"}
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)
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proxy_server_request = json.loads(payload["proxy_server_request"] or "{}")
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assert proxy_server_request["model"] == "gpt-5.5"
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assert proxy_server_request["messages"] == [{"role": "user", "content": "Hello!"}]
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# Clean up - reset general_settings
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general_settings["store_prompts_in_spend_logs"] = False
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# Verify messages and response are not included when disabled
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payload_disabled: SpendLogsPayload = get_logging_payload(**input_args)
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assert payload_disabled["messages"] == "{}"
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assert payload_disabled["response"] == "{}"
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def test_large_request_no_truncation_threshold():
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"""
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Test that MAX_STRING_LENGTH_PROMPT_IN_DB constant is used for request body sanitization
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and that the new truncation logic keeps beginning (35%) and end (65%) of the string
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"""
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from litellm.constants import (
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MAX_STRING_LENGTH_PROMPT_IN_DB,
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LITELLM_TRUNCATED_PAYLOAD_FIELD,
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)
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# Create a large string that exceeds the threshold
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# Use a pattern that allows us to verify beginning and end are preserved
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start_pattern = "START" * 250 # 1250 chars
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middle_pattern = "MIDDLE" * 200 # 1200 chars
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end_pattern = "END" * 250 # 750 chars
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large_content = start_pattern + middle_pattern + end_pattern
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request_body = {
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"messages": [{"role": "user", "content": large_content}],
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"model": "gpt-5.5",
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}
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sanitized = _sanitize_request_body_for_spend_logs_payload(request_body)
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# Verify the content was truncated
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truncated_content = sanitized["messages"][0]["content"]
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# Calculate expected character counts (35% start, 65% end)
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expected_start_chars = int(MAX_STRING_LENGTH_PROMPT_IN_DB * 0.35)
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expected_end_chars = int(MAX_STRING_LENGTH_PROMPT_IN_DB * 0.65)
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|
|
|
# Should keep first 35% of MAX_STRING_LENGTH_PROMPT_IN_DB chars
|
|
assert truncated_content.startswith(large_content[:expected_start_chars])
|
|
|
|
# Should keep last 65% of MAX_STRING_LENGTH_PROMPT_IN_DB chars
|
|
assert truncated_content.endswith(large_content[-expected_end_chars:])
|
|
|
|
# Should have truncation marker
|
|
assert LITELLM_TRUNCATED_PAYLOAD_FIELD in truncated_content
|
|
assert "skipped" in truncated_content
|
|
|
|
|
|
def test_small_request_no_truncation():
|
|
"""
|
|
Test that small strings are not truncated by MAX_STRING_LENGTH_PROMPT_IN_DB
|
|
"""
|
|
from litellm.constants import MAX_STRING_LENGTH_PROMPT_IN_DB
|
|
|
|
# Create a small string that's under the threshold
|
|
small_content = "x" * (MAX_STRING_LENGTH_PROMPT_IN_DB - 100)
|
|
|
|
request_body = {
|
|
"messages": [{"role": "user", "content": small_content}],
|
|
"model": "gpt-5.5",
|
|
}
|
|
|
|
sanitized = _sanitize_request_body_for_spend_logs_payload(request_body)
|
|
|
|
# Verify the content was NOT truncated
|
|
assert sanitized["messages"][0]["content"] == small_content
|
|
assert (
|
|
len(sanitized["messages"][0]["content"]) == MAX_STRING_LENGTH_PROMPT_IN_DB - 100
|
|
)
|
|
|
|
|
|
def test_configurable_string_length_env_var(monkeypatch):
|
|
"""
|
|
Test that MAX_STRING_LENGTH_PROMPT_IN_DB can be configured via environment variable
|
|
"""
|
|
# Set environment variable to a custom value
|
|
monkeypatch.setenv("MAX_STRING_LENGTH_PROMPT_IN_DB", "1000")
|
|
|
|
# Import after setting env var to ensure it picks up the new value
|
|
import importlib
|
|
import litellm.constants
|
|
import litellm.proxy.spend_tracking.spend_tracking_utils
|
|
|
|
importlib.reload(litellm.constants)
|
|
importlib.reload(litellm.proxy.spend_tracking.spend_tracking_utils)
|
|
|
|
from litellm.constants import (
|
|
MAX_STRING_LENGTH_PROMPT_IN_DB,
|
|
LITELLM_TRUNCATED_PAYLOAD_FIELD,
|
|
)
|
|
from litellm.proxy.spend_tracking.spend_tracking_utils import (
|
|
_sanitize_request_body_for_spend_logs_payload,
|
|
)
|
|
|
|
# Verify the constant was set to the env var value
|
|
assert MAX_STRING_LENGTH_PROMPT_IN_DB == 1000
|
|
|
|
# Test truncation with the custom value
|
|
large_content = "A" * 500 + "B" * 800 + "C" * 500 # 1800 chars total
|
|
|
|
request_body = {
|
|
"messages": [{"role": "user", "content": large_content}],
|
|
"model": "gpt-5.5",
|
|
}
|
|
|
|
sanitized = _sanitize_request_body_for_spend_logs_payload(request_body)
|
|
|
|
# Verify truncation occurred with 35% beginning and 65% end preserved
|
|
truncated_content = sanitized["messages"][0]["content"]
|
|
expected_start = int(1000 * 0.35) # 350 chars from beginning
|
|
expected_end = int(1000 * 0.65) # 650 chars from end
|
|
|
|
assert truncated_content.startswith(large_content[:expected_start])
|
|
assert truncated_content.endswith(large_content[-expected_end:])
|
|
assert LITELLM_TRUNCATED_PAYLOAD_FIELD in truncated_content
|
|
assert "skipped" in truncated_content
|
|
assert "800" in truncated_content # Should mention skipped 800 chars
|
|
|
|
|
|
def test_truncation_preserves_beginning_and_end():
|
|
"""
|
|
Test that truncation preserves the beginning (35%) and end (65%) of content for better debugging
|
|
"""
|
|
from litellm.constants import (
|
|
MAX_STRING_LENGTH_PROMPT_IN_DB,
|
|
LITELLM_TRUNCATED_PAYLOAD_FIELD,
|
|
)
|
|
|
|
# Create content with distinct beginning, middle, and end
|
|
beginning = "BEGIN_" * 200 # 1200 chars
|
|
middle = "MIDDLE_" * 300 # 2100 chars
|
|
end = "_END" * 300 # 1200 chars
|
|
large_content = beginning + middle + end
|
|
|
|
request_body = {
|
|
"messages": [{"role": "user", "content": large_content}],
|
|
"model": "gpt-5.5",
|
|
}
|
|
|
|
sanitized = _sanitize_request_body_for_spend_logs_payload(request_body)
|
|
truncated_content = sanitized["messages"][0]["content"]
|
|
|
|
# Calculate expected splits (35% beginning, 65% end)
|
|
expected_start_chars = int(MAX_STRING_LENGTH_PROMPT_IN_DB * 0.35)
|
|
expected_end_chars = int(MAX_STRING_LENGTH_PROMPT_IN_DB * 0.65)
|
|
|
|
# Check that beginning is preserved
|
|
expected_beginning = large_content[:expected_start_chars]
|
|
assert truncated_content.startswith(expected_beginning)
|
|
|
|
# Check that end is preserved
|
|
expected_end = large_content[-expected_end_chars:]
|
|
assert truncated_content.endswith(expected_end)
|
|
|
|
# Check truncation marker is present
|
|
assert LITELLM_TRUNCATED_PAYLOAD_FIELD in truncated_content
|
|
assert "skipped" in truncated_content
|
|
|
|
# Calculate expected skipped chars
|
|
total_chars = len(large_content)
|
|
kept_chars = expected_start_chars + expected_end_chars
|
|
expected_skipped = total_chars - kept_chars
|
|
assert str(expected_skipped) in truncated_content
|