import os import sys import traceback from litellm._uuid import uuid from dotenv import load_dotenv from fastapi import Request from fastapi.routing import APIRoute load_dotenv() import io import os import time # this file is to test litellm/proxy sys.path.insert( 0, os.path.abspath("../..") ) # Adds the parent directory to the system path import asyncio import datetime import json import logging from typing import Optional import pytest import litellm from litellm.proxy.spend_tracking.spend_tracking_utils import get_logging_payload, _sanitize_request_body_for_spend_logs_payload from litellm.proxy._types import SpendLogsMetadata, SpendLogsPayload @pytest.mark.parametrize( "model_id", ["chatcmpl-9XZmkzS1uPhRCoVdGQvBqqIbSgECt", "", None], ) def test_spend_logs_payload(model_id: Optional[str]): """ Ensure only expected values are logged in spend logs payload. """ input_args: dict = { "kwargs": { "model": "chatgpt-v-3", "messages": [ {"role": "system", "content": "you are a helpful assistant.\n"}, {"role": "user", "content": "bom dia"}, ], "custom_llm_provider": "azure", "optional_params": { "stream": False, "max_tokens": 10, "user": "116544810872468347480", "extra_body": {}, }, "litellm_params": { "acompletion": True, "api_key": "23c217a5b59f41b6b7a198017f4792f2", "force_timeout": 600, "logger_fn": None, "verbose": False, "custom_llm_provider": "azure", "api_base": "https://openai-gpt-4-test-v-1.openai.azure.com//openai/", "litellm_call_id": "b9929bf6-7b80-4c8c-b486-034e6ac0c8b7", "model_alias_map": {}, "completion_call_id": None, "metadata": { "tags": ["model-anthropic-claude-v2.1", "app-ishaan-prod"], "user_api_key": "88dc28d0f030c55ed4ab77ed8faf098196cb1c05df778539800c9f1243fe6b4b", "user_api_key_alias": "custom-key-alias", "user_api_end_user_max_budget": None, "litellm_api_version": "0.0.0", "global_max_parallel_requests": None, "user_api_key_user_id": "116544810872468347480", "user_api_key_org_id": "custom-org-id", "user_api_key_team_id": "custom-team-id", "user_api_key_team_alias": "custom-team-alias", "user_api_key_metadata": {}, "requester_ip_address": "127.0.0.1", "spend_logs_metadata": {"hello": "world"}, "headers": { "content-type": "application/json", "user-agent": "PostmanRuntime/7.32.3", "accept": "*/*", "postman-token": "92300061-eeaa-423b-a420-0b44896ecdc4", "host": "localhost:4000", "accept-encoding": "gzip, deflate, br", "connection": "keep-alive", "content-length": "163", }, "endpoint": "http://localhost:4000/chat/completions", "model_group": "gpt-3.5-turbo", "deployment": "azure/gpt-4.1-mini", "model_info": { "id": "4bad40a1eb6bebd1682800f16f44b9f06c52a6703444c99c7f9f32e9de3693b4", "db_model": False, }, "api_base": "https://openai-gpt-4-test-v-1.openai.azure.com/", "caching_groups": None, "error_information": None, "status": "success", "proxy_server_request": "{}", "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", }, "model_info": { "id": "4bad40a1eb6bebd1682800f16f44b9f06c52a6703444c99c7f9f32e9de3693b4", "db_model": False, }, "proxy_server_request": { "url": "http://localhost:4000/chat/completions", "method": "POST", "headers": { "content-type": "application/json", "user-agent": "PostmanRuntime/7.32.3", "accept": "*/*", "postman-token": "92300061-eeaa-423b-a420-0b44896ecdc4", "host": "localhost:4000", "accept-encoding": "gzip, deflate, br", "connection": "keep-alive", "content-length": "163", }, "body": { "messages": [ { "role": "system", "content": "you are a helpful assistant.\n", }, {"role": "user", "content": "bom dia"}, ], "model": "gpt-3.5-turbo", "max_tokens": 10, }, }, "preset_cache_key": None, "no-log": False, "stream_response": {}, "input_cost_per_token": None, "input_cost_per_second": None, "output_cost_per_token": None, "output_cost_per_second": None, }, "start_time": datetime.datetime(2024, 6, 7, 12, 43, 30, 307665), "stream": False, "user": "116544810872468347480", "call_type": "acompletion", "litellm_call_id": "b9929bf6-7b80-4c8c-b486-034e6ac0c8b7", "completion_start_time": datetime.datetime(2024, 6, 7, 12, 43, 30, 954146), "max_tokens": 10, "extra_body": {}, "custom_llm_provider": "azure", "input": [ {"role": "system", "content": "you are a helpful assistant.\n"}, {"role": "user", "content": "bom dia"}, ], "api_key": "1234", "original_response": "", "additional_args": { "headers": {"Authorization": "Bearer 1234"}, "api_base": "openai-gpt-4-test-v-1.openai.azure.com", "acompletion": True, "complete_input_dict": { "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": {}, }, }, "log_event_type": "post_api_call", "end_time": datetime.datetime(2024, 6, 7, 12, 43, 30, 954146), "cache_hit": None, "response_cost": 2.4999999999999998e-05, "standard_logging_object": { "request_tags": ["model-anthropic-claude-v2.1", "app-ishaan-prod"], "metadata": { "user_api_key_end_user_id": "test-user", }, "model_map_information": { "tpm": 1000, "rpm": 1000, }, }, }, "response_obj": litellm.ModelResponse( id=model_id, choices=[ litellm.Choices( finish_reason="length", index=0, message=litellm.Message( content="Bom dia! Como posso ajudar você", role="assistant" ), ) ], created=1717789410, model="gpt-35-turbo", object="chat.completion", system_fingerprint=None, usage=litellm.Usage( completion_tokens=10, prompt_tokens=20, total_tokens=30 ), ), "start_time": datetime.datetime(2024, 6, 7, 12, 43, 30, 308604), "end_time": datetime.datetime(2024, 6, 7, 12, 43, 30, 954146), } payload: SpendLogsPayload = get_logging_payload(**input_args) assert len(payload["request_id"]) > 0 # Define the expected metadata keys expected_metadata_keys = SpendLogsMetadata.__annotations__.keys() # Validate only specified metadata keys are logged assert "metadata" in payload assert isinstance(payload["metadata"], str) payload["metadata"] = json.loads(payload["metadata"]) assert set(payload["metadata"].keys()) == set(expected_metadata_keys) # This is crucial - used in PROD, it should pass, related issue: https://github.com/BerriAI/litellm/issues/4334 assert ( payload["request_tags"] == '["model-anthropic-claude-v2.1", "app-ishaan-prod"]' ) assert payload["metadata"]["user_api_key_org_id"] == "custom-org-id" assert payload["metadata"]["user_api_key_team_id"] == "custom-team-id" assert payload["metadata"]["user_api_key_team_alias"] == "custom-team-alias" assert payload["metadata"]["user_api_key_alias"] == "custom-key-alias" assert payload["custom_llm_provider"] == "azure" def test_spend_logs_payload_whisper(): """ Ensure we can write /transcription request/responses to spend logs """ kwargs: dict = { "model": "whisper-1", "messages": [{"role": "user", "content": "audio_file"}], "optional_params": {}, "litellm_params": { "api_base": "", "metadata": { "user_api_key": "88dc28d0f030c55ed4ab77ed8faf098196cb1c05df778539800c9f1243fe6b4b", "user_api_key_alias": None, "user_api_key_end_user_id": "test-user", "user_api_end_user_max_budget": None, "litellm_api_version": "1.40.19", "global_max_parallel_requests": None, "user_api_key_user_id": "default_user_id", "user_api_key_org_id": None, "user_api_key_team_id": None, "user_api_key_team_alias": None, "user_api_key_team_max_budget": None, "user_api_key_team_spend": None, "user_api_key_spend": 0.0, "user_api_key_max_budget": None, "user_api_key_metadata": {}, "headers": { "host": "localhost:4000", "user-agent": "curl/7.88.1", "accept": "*/*", "content-length": "775501", "content-type": "multipart/form-data; boundary=------------------------21d518e191326d20", }, "endpoint": "http://localhost:4000/v1/audio/transcriptions", "litellm_parent_otel_span": None, "model_group": "whisper-1", "deployment": "whisper-1", "model_info": { "id": "d7761582311451c34d83d65bc8520ce5c1537ea9ef2bec13383cf77596d49eeb", "db_model": False, }, "caching_groups": None, }, }, "start_time": datetime.datetime(2024, 6, 26, 14, 20, 11, 313291), "stream": False, "user": "", "call_type": "atranscription", "litellm_call_id": "05921cf7-33f9-421c-aad9-33310c1e2702", "completion_start_time": datetime.datetime(2024, 6, 26, 14, 20, 13, 653149), "stream_options": None, "input": "tmp-requestc8640aee-7d85-49c3-b3ef-bdc9255d8e37.wav", "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."}', "additional_args": { "complete_input_dict": { "model": "whisper-1", "file": "<_io.BufferedReader name='tmp-requestc8640aee-7d85-49c3-b3ef-bdc9255d8e37.wav'>", "language": None, "prompt": None, "response_format": None, "temperature": None, } }, "log_event_type": "post_api_call", "end_time": datetime.datetime(2024, 6, 26, 14, 20, 13, 653149), "cache_hit": None, "response_cost": 0.00023398580000000003, } response = litellm.utils.TranscriptionResponse( 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." ) payload: SpendLogsPayload = get_logging_payload( kwargs=kwargs, response_obj=response, start_time=datetime.datetime.now(), end_time=datetime.datetime.now(), ) print("payload: ", payload) assert payload["call_type"] == "atranscription" assert payload["spend"] == 0.00023398580000000003 def test_spend_logs_payload_with_prompts_enabled(monkeypatch): """ Test that messages and responses are logged in spend logs when store_prompts_in_spend_logs is enabled """ # Mock general_settings from litellm.proxy.proxy_server import general_settings general_settings["store_prompts_in_spend_logs"] = True input_args: dict = { "kwargs": { "model": "gpt-3.5-turbo", "messages": [{"role": "user", "content": "Hello!"}], "litellm_params": { "metadata": { "user_api_key": "fake_key", } }, }, "response_obj": litellm.ModelResponse( id="chatcmpl-123", choices=[ litellm.Choices( finish_reason="stop", index=0, message=litellm.Message(content="Hi there!", role="assistant"), ) ], model="gpt-3.5-turbo", usage=litellm.Usage(completion_tokens=2, prompt_tokens=1, total_tokens=3), ), "start_time": datetime.datetime.now(), "end_time": datetime.datetime.now(), } # Create a standard logging payload standard_logging_payload = { "messages": [{"role": "user", "content": "Hello!"}], "response": {"role": "assistant", "content": "Hi there!"}, "metadata": { "user_api_key_end_user_id": "test-user", }, "request_tags": ["model-anthropic-claude-v2.1", "app-ishaan-prod"], "model_map_information": { "tpm": 1000, "rpm": 1000, }, } litellm_params = { "proxy_server_request": { "body": { "model": "gpt-4", "messages": [{"role": "user", "content": "Hello!"}], } } } input_args["kwargs"]["standard_logging_object"] = standard_logging_payload input_args["kwargs"]["litellm_params"] = litellm_params payload: SpendLogsPayload = get_logging_payload(**input_args) print("json payload: ", json.dumps(payload, indent=4, default=str)) # Verify messages and response are included in payload assert payload["response"] == json.dumps( {"role": "assistant", "content": "Hi there!"} ) proxy_server_request = json.loads(payload["proxy_server_request"] or "{}") assert proxy_server_request["model"] == "gpt-4" assert proxy_server_request["messages"] == [{"role": "user", "content": "Hello!"}] # Clean up - reset general_settings general_settings["store_prompts_in_spend_logs"] = False # Verify messages and response are not included when disabled payload_disabled: SpendLogsPayload = get_logging_payload(**input_args) assert payload_disabled["messages"] == "{}" assert payload_disabled["response"] == "{}" def test_large_request_no_truncation_threshold(): """ Test that MAX_STRING_LENGTH_PROMPT_IN_DB constant is used for request body sanitization and that the new truncation logic keeps beginning (35%) and end (65%) of the string """ from litellm.constants import MAX_STRING_LENGTH_PROMPT_IN_DB, LITELLM_TRUNCATED_PAYLOAD_FIELD # Create a large string that exceeds the threshold # Use a pattern that allows us to verify beginning and end are preserved start_pattern = "START" * 250 # 1250 chars middle_pattern = "MIDDLE" * 200 # 1200 chars end_pattern = "END" * 250 # 750 chars large_content = start_pattern + middle_pattern + end_pattern request_body = { "messages": [ {"role": "user", "content": large_content} ], "model": "gpt-4" } sanitized = _sanitize_request_body_for_spend_logs_payload(request_body) # Verify the content was truncated truncated_content = sanitized["messages"][0]["content"] # Calculate expected character counts (35% start, 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) # 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-4" } 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-4" } 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-4" } 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