forked from phoenix/litellm-mirror
966 lines
29 KiB
Python
966 lines
29 KiB
Python
import copy
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import sys
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import time
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from datetime import datetime
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from unittest import mock
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from dotenv import load_dotenv
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load_dotenv()
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import os
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sys.path.insert(
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0, os.path.abspath("../..")
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) # Adds the parent directory to the system path
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import pytest
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import litellm
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from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, headers
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from litellm.proxy.utils import (
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_duration_in_seconds,
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_extract_from_regex,
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get_last_day_of_month,
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)
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from litellm.utils import (
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check_valid_key,
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create_pretrained_tokenizer,
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create_tokenizer,
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function_to_dict,
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get_llm_provider,
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get_max_tokens,
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get_supported_openai_params,
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get_token_count,
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get_valid_models,
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token_counter,
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trim_messages,
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validate_environment,
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)
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# Assuming your trim_messages, shorten_message_to_fit_limit, and get_token_count functions are all in a module named 'message_utils'
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# Test 1: Check trimming of normal message
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def test_basic_trimming():
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messages = [
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{
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"role": "user",
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"content": "This is a long message that definitely exceeds the token limit.",
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}
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]
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trimmed_messages = trim_messages(messages, model="claude-2", max_tokens=8)
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print("trimmed messages")
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print(trimmed_messages)
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# print(get_token_count(messages=trimmed_messages, model="claude-2"))
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assert (get_token_count(messages=trimmed_messages, model="claude-2")) <= 8
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# test_basic_trimming()
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def test_basic_trimming_no_max_tokens_specified():
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messages = [
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{
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"role": "user",
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"content": "This is a long message that is definitely under the token limit.",
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}
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]
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trimmed_messages = trim_messages(messages, model="gpt-4")
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print("trimmed messages for gpt-4")
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print(trimmed_messages)
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# print(get_token_count(messages=trimmed_messages, model="claude-2"))
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assert (
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get_token_count(messages=trimmed_messages, model="gpt-4")
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) <= litellm.model_cost["gpt-4"]["max_tokens"]
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# test_basic_trimming_no_max_tokens_specified()
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def test_multiple_messages_trimming():
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messages = [
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{
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"role": "user",
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"content": "This is a long message that will exceed the token limit.",
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},
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{
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"role": "user",
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"content": "This is another long message that will also exceed the limit.",
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},
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]
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trimmed_messages = trim_messages(
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messages=messages, model="gpt-3.5-turbo", max_tokens=20
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)
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# print(get_token_count(messages=trimmed_messages, model="gpt-3.5-turbo"))
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assert (get_token_count(messages=trimmed_messages, model="gpt-3.5-turbo")) <= 20
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# test_multiple_messages_trimming()
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def test_multiple_messages_no_trimming():
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messages = [
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{
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"role": "user",
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"content": "This is a long message that will exceed the token limit.",
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},
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{
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"role": "user",
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"content": "This is another long message that will also exceed the limit.",
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},
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]
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trimmed_messages = trim_messages(
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messages=messages, model="gpt-3.5-turbo", max_tokens=100
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)
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print("Trimmed messages")
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print(trimmed_messages)
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assert messages == trimmed_messages
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# test_multiple_messages_no_trimming()
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def test_large_trimming_multiple_messages():
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messages = [
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{"role": "user", "content": "This is a singlelongwordthatexceedsthelimit."},
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{"role": "user", "content": "This is a singlelongwordthatexceedsthelimit."},
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{"role": "user", "content": "This is a singlelongwordthatexceedsthelimit."},
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{"role": "user", "content": "This is a singlelongwordthatexceedsthelimit."},
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{"role": "user", "content": "This is a singlelongwordthatexceedsthelimit."},
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]
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trimmed_messages = trim_messages(messages, max_tokens=20, model="gpt-4-0613")
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print("trimmed messages")
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print(trimmed_messages)
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assert (get_token_count(messages=trimmed_messages, model="gpt-4-0613")) <= 20
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# test_large_trimming()
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def test_large_trimming_single_message():
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messages = [
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{"role": "user", "content": "This is a singlelongwordthatexceedsthelimit."}
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]
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trimmed_messages = trim_messages(messages, max_tokens=5, model="gpt-4-0613")
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assert (get_token_count(messages=trimmed_messages, model="gpt-4-0613")) <= 5
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assert (get_token_count(messages=trimmed_messages, model="gpt-4-0613")) > 0
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def test_trimming_with_system_message_within_max_tokens():
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# This message is 33 tokens long
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messages = [
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{"role": "system", "content": "This is a short system message"},
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{
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"role": "user",
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"content": "This is a medium normal message, let's say litellm is awesome.",
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},
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]
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trimmed_messages = trim_messages(
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messages, max_tokens=30, model="gpt-4-0613"
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) # The system message should fit within the token limit
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assert len(trimmed_messages) == 2
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assert trimmed_messages[0]["content"] == "This is a short system message"
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def test_trimming_with_system_message_exceeding_max_tokens():
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# This message is 33 tokens long. The system message is 13 tokens long.
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messages = [
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{"role": "system", "content": "This is a short system message"},
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{
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"role": "user",
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"content": "This is a medium normal message, let's say litellm is awesome.",
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},
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]
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trimmed_messages = trim_messages(messages, max_tokens=12, model="gpt-4-0613")
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assert len(trimmed_messages) == 1
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def test_trimming_with_tool_calls():
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from litellm.types.utils import ChatCompletionMessageToolCall, Function, Message
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messages = [
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{
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"role": "user",
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"content": "What's the weather like in San Francisco, Tokyo, and Paris?",
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},
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Message(
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content=None,
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role="assistant",
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tool_calls=[
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ChatCompletionMessageToolCall(
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function=Function(
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arguments='{"location": "San Francisco, CA", "unit": "celsius"}',
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name="get_current_weather",
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),
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id="call_G11shFcS024xEKjiAOSt6Tc9",
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type="function",
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),
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ChatCompletionMessageToolCall(
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function=Function(
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arguments='{"location": "Tokyo, Japan", "unit": "celsius"}',
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name="get_current_weather",
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),
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id="call_e0ss43Bg7H8Z9KGdMGWyZ9Mj",
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type="function",
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),
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ChatCompletionMessageToolCall(
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function=Function(
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arguments='{"location": "Paris, France", "unit": "celsius"}',
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name="get_current_weather",
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),
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id="call_nRjLXkWTJU2a4l9PZAf5as6g",
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type="function",
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),
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],
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function_call=None,
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),
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{
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"tool_call_id": "call_G11shFcS024xEKjiAOSt6Tc9",
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"role": "tool",
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"name": "get_current_weather",
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"content": '{"location": "San Francisco", "temperature": "72", "unit": "fahrenheit"}',
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},
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{
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"tool_call_id": "call_e0ss43Bg7H8Z9KGdMGWyZ9Mj",
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"role": "tool",
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"name": "get_current_weather",
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"content": '{"location": "Tokyo", "temperature": "10", "unit": "celsius"}',
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},
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{
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"tool_call_id": "call_nRjLXkWTJU2a4l9PZAf5as6g",
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"role": "tool",
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"name": "get_current_weather",
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"content": '{"location": "Paris", "temperature": "22", "unit": "celsius"}',
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},
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]
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result = trim_messages(messages=messages, max_tokens=1, return_response_tokens=True)
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print(result)
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assert len(result[0]) == 3 # final 3 messages are tool calls
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def test_trimming_should_not_change_original_messages():
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messages = [
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{"role": "system", "content": "This is a short system message"},
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{
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"role": "user",
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"content": "This is a medium normal message, let's say litellm is awesome.",
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},
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]
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messages_copy = copy.deepcopy(messages)
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trimmed_messages = trim_messages(messages, max_tokens=12, model="gpt-4-0613")
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assert messages == messages_copy
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@pytest.mark.parametrize("model", ["gpt-4-0125-preview", "claude-3-opus-20240229"])
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def test_trimming_with_model_cost_max_input_tokens(model):
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messages = [
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{"role": "system", "content": "This is a normal system message"},
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{
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"role": "user",
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"content": "This is a sentence" * 100000,
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},
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]
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trimmed_messages = trim_messages(messages, model=model)
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assert (
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get_token_count(trimmed_messages, model=model)
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< litellm.model_cost[model]["max_input_tokens"]
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)
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def test_aget_valid_models():
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old_environ = os.environ
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os.environ = {"OPENAI_API_KEY": "temp"} # mock set only openai key in environ
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valid_models = get_valid_models()
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print(valid_models)
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# list of openai supported llms on litellm
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expected_models = (
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litellm.open_ai_chat_completion_models + litellm.open_ai_text_completion_models
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)
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assert valid_models == expected_models
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# reset replicate env key
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os.environ = old_environ
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# GEMINI
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expected_models = litellm.gemini_models
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old_environ = os.environ
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os.environ = {"GEMINI_API_KEY": "temp"} # mock set only openai key in environ
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valid_models = get_valid_models()
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print(valid_models)
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assert valid_models == expected_models
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# reset replicate env key
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os.environ = old_environ
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# test_get_valid_models()
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def test_bad_key():
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key = "bad-key"
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response = check_valid_key(model="gpt-3.5-turbo", api_key=key)
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print(response, key)
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assert response == False
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def test_good_key():
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key = os.environ["OPENAI_API_KEY"]
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response = check_valid_key(model="gpt-3.5-turbo", api_key=key)
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assert response == True
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# test validate environment
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def test_validate_environment_empty_model():
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api_key = validate_environment()
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if api_key is None:
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raise Exception()
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def test_validate_environment_api_key():
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response_obj = validate_environment(model="gpt-3.5-turbo", api_key="sk-my-test-key")
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assert (
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response_obj["keys_in_environment"] is True
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), f"Missing keys={response_obj['missing_keys']}"
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def test_validate_environment_api_base_dynamic():
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for provider in ["ollama", "ollama_chat"]:
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kv = validate_environment(provider + "/mistral", api_base="https://example.com")
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assert kv["keys_in_environment"]
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assert kv["missing_keys"] == []
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@mock.patch.dict(os.environ, {"OLLAMA_API_BASE": "foo"}, clear=True)
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def test_validate_environment_ollama():
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for provider in ["ollama", "ollama_chat"]:
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kv = validate_environment(provider + "/mistral")
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assert kv["keys_in_environment"]
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assert kv["missing_keys"] == []
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@mock.patch.dict(os.environ, {}, clear=True)
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def test_validate_environment_ollama_failed():
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for provider in ["ollama", "ollama_chat"]:
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kv = validate_environment(provider + "/mistral")
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assert not kv["keys_in_environment"]
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assert kv["missing_keys"] == ["OLLAMA_API_BASE"]
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def test_function_to_dict():
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print("testing function to dict for get current weather")
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def get_current_weather(location: str, unit: str):
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"""Get the current weather in a given location
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Parameters
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----------
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location : str
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The city and state, e.g. San Francisco, CA
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unit : {'celsius', 'fahrenheit'}
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Temperature unit
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Returns
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-------
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str
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a sentence indicating the weather
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"""
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if location == "Boston, MA":
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return "The weather is 12F"
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function_json = litellm.utils.function_to_dict(get_current_weather)
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print(function_json)
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expected_output = {
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"name": "get_current_weather",
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"description": "Get the current weather in a given location",
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"parameters": {
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"type": "object",
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"properties": {
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"location": {
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"type": "string",
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"description": "The city and state, e.g. San Francisco, CA",
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},
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"unit": {
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"type": "string",
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"description": "Temperature unit",
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"enum": "['fahrenheit', 'celsius']",
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},
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},
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"required": ["location", "unit"],
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},
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}
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print(expected_output)
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assert function_json["name"] == expected_output["name"]
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assert function_json["description"] == expected_output["description"]
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assert function_json["parameters"]["type"] == expected_output["parameters"]["type"]
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assert (
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function_json["parameters"]["properties"]["location"]
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== expected_output["parameters"]["properties"]["location"]
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)
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# the enum can change it can be - which is why we don't assert on unit
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# {'type': 'string', 'description': 'Temperature unit', 'enum': "['fahrenheit', 'celsius']"}
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# {'type': 'string', 'description': 'Temperature unit', 'enum': "['celsius', 'fahrenheit']"}
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assert (
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function_json["parameters"]["required"]
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== expected_output["parameters"]["required"]
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)
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print("passed")
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# test_function_to_dict()
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def test_token_counter():
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try:
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messages = [{"role": "user", "content": "hi how are you what time is it"}]
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tokens = token_counter(model="gpt-3.5-turbo", messages=messages)
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print("gpt-35-turbo")
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print(tokens)
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assert tokens > 0
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tokens = token_counter(model="claude-2", messages=messages)
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print("claude-2")
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print(tokens)
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assert tokens > 0
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tokens = token_counter(model="palm/chat-bison", messages=messages)
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print("palm/chat-bison")
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print(tokens)
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assert tokens > 0
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tokens = token_counter(model="ollama/llama2", messages=messages)
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print("ollama/llama2")
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print(tokens)
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assert tokens > 0
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tokens = token_counter(model="anthropic.claude-instant-v1", messages=messages)
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print("anthropic.claude-instant-v1")
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print(tokens)
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assert tokens > 0
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except Exception as e:
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pytest.fail(f"Error occurred: {e}")
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|
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# test_token_counter()
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|
|
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@pytest.mark.parametrize(
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"model, expected_bool",
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[
|
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("gpt-3.5-turbo", True),
|
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("azure/gpt-4-1106-preview", True),
|
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("groq/gemma-7b-it", True),
|
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("anthropic.claude-instant-v1", False),
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("palm/chat-bison", False),
|
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],
|
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)
|
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def test_supports_function_calling(model, expected_bool):
|
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try:
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assert litellm.supports_function_calling(model=model) == expected_bool
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except Exception as e:
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pytest.fail(f"Error occurred: {e}")
|
|
|
|
|
|
def test_get_max_token_unit_test():
|
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"""
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More complete testing in `test_completion_cost.py`
|
|
"""
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model = "bedrock/anthropic.claude-3-haiku-20240307-v1:0"
|
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max_tokens = get_max_tokens(
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model
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) # Returns a number instead of throwing an Exception
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assert isinstance(max_tokens, int)
|
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|
|
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|
def test_get_supported_openai_params() -> None:
|
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# Mapped provider
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assert isinstance(get_supported_openai_params("gpt-4"), list)
|
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|
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# Unmapped provider
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assert get_supported_openai_params("nonexistent") is None
|
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|
|
|
|
def test_redact_msgs_from_logs():
|
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"""
|
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Tests that turn_off_message_logging does not modify the response_obj
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On the proxy some users were seeing the redaction impact client side responses
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"""
|
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from litellm.litellm_core_utils.litellm_logging import Logging
|
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from litellm.litellm_core_utils.redact_messages import (
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redact_message_input_output_from_logging,
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)
|
|
|
|
litellm.turn_off_message_logging = True
|
|
|
|
response_obj = litellm.ModelResponse(
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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": {
|
|
"content": "I'm LLaMA, an AI assistant developed by Meta AI that can understand and respond to human input in a conversational manner.",
|
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"role": "assistant",
|
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},
|
|
}
|
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]
|
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)
|
|
|
|
litellm_logging_obj = Logging(
|
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model="gpt-3.5-turbo",
|
|
messages=[{"role": "user", "content": "hi"}],
|
|
stream=False,
|
|
call_type="acompletion",
|
|
litellm_call_id="1234",
|
|
start_time=datetime.now(),
|
|
function_id="1234",
|
|
)
|
|
|
|
_redacted_response_obj = redact_message_input_output_from_logging(
|
|
result=response_obj,
|
|
model_call_details=litellm_logging_obj.model_call_details,
|
|
)
|
|
|
|
# Assert the response_obj content is NOT modified
|
|
assert (
|
|
response_obj.choices[0].message.content
|
|
== "I'm LLaMA, an AI assistant developed by Meta AI that can understand and respond to human input in a conversational manner."
|
|
)
|
|
|
|
litellm.turn_off_message_logging = False
|
|
print("Test passed")
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"duration, unit",
|
|
[("7s", "s"), ("7m", "m"), ("7h", "h"), ("7d", "d"), ("7mo", "mo")],
|
|
)
|
|
def test_extract_from_regex(duration, unit):
|
|
value, _unit = _extract_from_regex(duration=duration)
|
|
|
|
assert value == 7
|
|
assert _unit == unit
|
|
|
|
|
|
def test_duration_in_seconds():
|
|
"""
|
|
Test if duration int is correctly calculated for different str
|
|
"""
|
|
import time
|
|
|
|
now = time.time()
|
|
current_time = datetime.fromtimestamp(now)
|
|
|
|
if current_time.month == 12:
|
|
target_year = current_time.year + 1
|
|
target_month = 1
|
|
else:
|
|
target_year = current_time.year
|
|
target_month = current_time.month + 1
|
|
|
|
# Determine the day to set for next month
|
|
target_day = current_time.day
|
|
last_day_of_target_month = get_last_day_of_month(target_year, target_month)
|
|
|
|
if target_day > last_day_of_target_month:
|
|
target_day = last_day_of_target_month
|
|
|
|
next_month = datetime(
|
|
year=target_year,
|
|
month=target_month,
|
|
day=target_day,
|
|
hour=current_time.hour,
|
|
minute=current_time.minute,
|
|
second=current_time.second,
|
|
microsecond=current_time.microsecond,
|
|
)
|
|
|
|
# Calculate the duration until the first day of the next month
|
|
duration_until_next_month = next_month - current_time
|
|
expected_duration = int(duration_until_next_month.total_seconds())
|
|
|
|
value = _duration_in_seconds(duration="1mo")
|
|
|
|
assert value - expected_duration < 2
|
|
|
|
|
|
def test_get_llm_provider_ft_models():
|
|
"""
|
|
All ft prefixed models should map to OpenAI
|
|
gpt-3.5-turbo-0125 (recommended),
|
|
gpt-3.5-turbo-1106,
|
|
gpt-3.5-turbo,
|
|
gpt-4-0613 (experimental)
|
|
gpt-4o-2024-05-13.
|
|
babbage-002, davinci-002,
|
|
|
|
"""
|
|
model, custom_llm_provider, _, _ = get_llm_provider(model="ft:gpt-3.5-turbo-0125")
|
|
assert custom_llm_provider == "openai"
|
|
|
|
model, custom_llm_provider, _, _ = get_llm_provider(model="ft:gpt-3.5-turbo-1106")
|
|
assert custom_llm_provider == "openai"
|
|
|
|
model, custom_llm_provider, _, _ = get_llm_provider(model="ft:gpt-3.5-turbo")
|
|
assert custom_llm_provider == "openai"
|
|
|
|
model, custom_llm_provider, _, _ = get_llm_provider(model="ft:gpt-4-0613")
|
|
assert custom_llm_provider == "openai"
|
|
|
|
model, custom_llm_provider, _, _ = get_llm_provider(model="ft:gpt-3.5-turbo")
|
|
assert custom_llm_provider == "openai"
|
|
|
|
model, custom_llm_provider, _, _ = get_llm_provider(model="ft:gpt-4o-2024-05-13")
|
|
assert custom_llm_provider == "openai"
|
|
|
|
|
|
@pytest.mark.parametrize("langfuse_trace_id", [None, "my-unique-trace-id"])
|
|
@pytest.mark.parametrize(
|
|
"langfuse_existing_trace_id", [None, "my-unique-existing-trace-id"]
|
|
)
|
|
def test_logging_trace_id(langfuse_trace_id, langfuse_existing_trace_id):
|
|
"""
|
|
- Unit test for `_get_trace_id` function in Logging obj
|
|
"""
|
|
from litellm.litellm_core_utils.litellm_logging import Logging
|
|
|
|
litellm.success_callback = ["langfuse"]
|
|
litellm_call_id = "my-unique-call-id"
|
|
litellm_logging_obj = Logging(
|
|
model="gpt-3.5-turbo",
|
|
messages=[{"role": "user", "content": "hi"}],
|
|
stream=False,
|
|
call_type="acompletion",
|
|
litellm_call_id=litellm_call_id,
|
|
start_time=datetime.now(),
|
|
function_id="1234",
|
|
)
|
|
|
|
metadata = {}
|
|
|
|
if langfuse_trace_id is not None:
|
|
metadata["trace_id"] = langfuse_trace_id
|
|
if langfuse_existing_trace_id is not None:
|
|
metadata["existing_trace_id"] = langfuse_existing_trace_id
|
|
|
|
litellm.completion(
|
|
model="gpt-3.5-turbo",
|
|
messages=[{"role": "user", "content": "Hey how's it going?"}],
|
|
mock_response="Hey!",
|
|
litellm_logging_obj=litellm_logging_obj,
|
|
metadata=metadata,
|
|
)
|
|
|
|
time.sleep(3)
|
|
assert litellm_logging_obj._get_trace_id(service_name="langfuse") is not None
|
|
|
|
## if existing_trace_id exists
|
|
if langfuse_existing_trace_id is not None:
|
|
assert (
|
|
litellm_logging_obj._get_trace_id(service_name="langfuse")
|
|
== langfuse_existing_trace_id
|
|
)
|
|
## if trace_id exists
|
|
elif langfuse_trace_id is not None:
|
|
assert (
|
|
litellm_logging_obj._get_trace_id(service_name="langfuse")
|
|
== langfuse_trace_id
|
|
)
|
|
## if existing_trace_id exists
|
|
else:
|
|
assert (
|
|
litellm_logging_obj._get_trace_id(service_name="langfuse")
|
|
== litellm_call_id
|
|
)
|
|
|
|
|
|
def test_convert_model_response_object():
|
|
"""
|
|
Unit test to ensure model response object correctly handles openrouter errors.
|
|
"""
|
|
args = {
|
|
"response_object": {
|
|
"id": None,
|
|
"choices": None,
|
|
"created": None,
|
|
"model": None,
|
|
"object": None,
|
|
"service_tier": None,
|
|
"system_fingerprint": None,
|
|
"usage": None,
|
|
"error": {
|
|
"message": '{"type":"error","error":{"type":"invalid_request_error","message":"Output blocked by content filtering policy"}}',
|
|
"code": 400,
|
|
},
|
|
},
|
|
"model_response_object": litellm.ModelResponse(
|
|
id="chatcmpl-b88ce43a-7bfc-437c-b8cc-e90d59372cfb",
|
|
choices=[
|
|
litellm.Choices(
|
|
finish_reason="stop",
|
|
index=0,
|
|
message=litellm.Message(content="default", role="assistant"),
|
|
)
|
|
],
|
|
created=1719376241,
|
|
model="openrouter/anthropic/claude-3.5-sonnet",
|
|
object="chat.completion",
|
|
system_fingerprint=None,
|
|
usage=litellm.Usage(),
|
|
),
|
|
"response_type": "completion",
|
|
"stream": False,
|
|
"start_time": None,
|
|
"end_time": None,
|
|
"hidden_params": None,
|
|
}
|
|
|
|
try:
|
|
litellm.convert_to_model_response_object(**args)
|
|
pytest.fail("Expected this to fail")
|
|
except Exception as e:
|
|
assert hasattr(e, "status_code")
|
|
assert e.status_code == 400
|
|
assert hasattr(e, "message")
|
|
assert (
|
|
e.message
|
|
== '{"type":"error","error":{"type":"invalid_request_error","message":"Output blocked by content filtering policy"}}'
|
|
)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"model, expected_bool",
|
|
[
|
|
("vertex_ai/gemini-1.5-pro", True),
|
|
("gemini/gemini-1.5-pro", True),
|
|
("predibase/llama3-8b-instruct", True),
|
|
("gpt-3.5-turbo", False),
|
|
],
|
|
)
|
|
def test_supports_response_schema(model, expected_bool):
|
|
"""
|
|
Unit tests for 'supports_response_schema' helper function.
|
|
|
|
Should be true for gemini-1.5-pro on google ai studio / vertex ai AND predibase models
|
|
Should be false otherwise
|
|
"""
|
|
os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
|
|
litellm.model_cost = litellm.get_model_cost_map(url="")
|
|
|
|
from litellm.utils import supports_response_schema
|
|
|
|
response = supports_response_schema(model=model, custom_llm_provider=None)
|
|
|
|
assert expected_bool == response
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"model, expected_bool",
|
|
[
|
|
("gpt-3.5-turbo", True),
|
|
("gpt-4", True),
|
|
("command-nightly", False),
|
|
("gemini-pro", True),
|
|
],
|
|
)
|
|
def test_supports_function_calling_v2(model, expected_bool):
|
|
"""
|
|
Unit test for 'supports_function_calling' helper function.
|
|
"""
|
|
from litellm.utils import supports_function_calling
|
|
|
|
response = supports_function_calling(model=model, custom_llm_provider=None)
|
|
assert expected_bool == response
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"model, expected_bool",
|
|
[
|
|
("gpt-4-vision-preview", True),
|
|
("gpt-3.5-turbo", False),
|
|
("claude-3-opus-20240229", True),
|
|
("gemini-pro-vision", True),
|
|
("command-nightly", False),
|
|
],
|
|
)
|
|
def test_supports_vision(model, expected_bool):
|
|
"""
|
|
Unit test for 'supports_vision' helper function.
|
|
"""
|
|
from litellm.utils import supports_vision
|
|
|
|
response = supports_vision(model=model, custom_llm_provider=None)
|
|
assert expected_bool == response
|
|
|
|
|
|
def test_usage_object_null_tokens():
|
|
"""
|
|
Unit test.
|
|
|
|
Asserts Usage obj always returns int.
|
|
|
|
Fixes https://github.com/BerriAI/litellm/issues/5096
|
|
"""
|
|
usage_obj = litellm.Usage(prompt_tokens=2, completion_tokens=None, total_tokens=2)
|
|
|
|
assert usage_obj.completion_tokens == 0
|
|
|
|
|
|
def test_is_base64_encoded():
|
|
import base64
|
|
|
|
import requests
|
|
|
|
litellm.set_verbose = True
|
|
url = "https://dummyimage.com/100/100/fff&text=Test+image"
|
|
response = requests.get(url)
|
|
file_data = response.content
|
|
|
|
encoded_file = base64.b64encode(file_data).decode("utf-8")
|
|
base64_image = f"data:image/png;base64,{encoded_file}"
|
|
|
|
from litellm.utils import is_base64_encoded
|
|
|
|
assert is_base64_encoded(s=base64_image) is True
|
|
|
|
|
|
@mock.patch("httpx.AsyncClient")
|
|
@mock.patch.dict(
|
|
os.environ,
|
|
{"SSL_VERIFY": "/certificate.pem", "SSL_CERTIFICATE": "/client.pem"},
|
|
clear=True,
|
|
)
|
|
def test_async_http_handler(mock_async_client):
|
|
import httpx
|
|
|
|
timeout = 120
|
|
event_hooks = {"request": [lambda r: r]}
|
|
concurrent_limit = 2
|
|
|
|
AsyncHTTPHandler(timeout, event_hooks, concurrent_limit)
|
|
|
|
mock_async_client.assert_called_with(
|
|
cert="/client.pem",
|
|
event_hooks=event_hooks,
|
|
headers=headers,
|
|
limits=httpx.Limits(
|
|
max_connections=concurrent_limit,
|
|
max_keepalive_connections=concurrent_limit,
|
|
),
|
|
timeout=timeout,
|
|
verify="/certificate.pem",
|
|
)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"model, expected_bool", [("gpt-3.5-turbo", False), ("gpt-4o-audio-preview", True)]
|
|
)
|
|
def test_supports_audio_input(model, expected_bool):
|
|
os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
|
|
litellm.model_cost = litellm.get_model_cost_map(url="")
|
|
|
|
from litellm.utils import supports_audio_input, supports_audio_output
|
|
|
|
supports_pc = supports_audio_input(model=model)
|
|
|
|
assert supports_pc == expected_bool
|
|
|
|
|
|
def test_is_base64_encoded_2():
|
|
from litellm.utils import is_base64_encoded
|
|
|
|
assert (
|
|
is_base64_encoded(
|
|
s="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAADUlEQVR42mP8/x+AAwMCAO+ip1sAAAAASUVORK5CYII="
|
|
)
|
|
is True
|
|
)
|
|
|
|
assert is_base64_encoded(s="Dog") is False
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"messages, expected_bool",
|
|
[
|
|
([{"role": "user", "content": "hi"}], True),
|
|
([{"role": "user", "content": [{"type": "text", "text": "hi"}]}], True),
|
|
(
|
|
[
|
|
{
|
|
"role": "user",
|
|
"content": [
|
|
{"type": "image_url", "url": "https://example.com/image.png"}
|
|
],
|
|
}
|
|
],
|
|
True,
|
|
),
|
|
(
|
|
[
|
|
{
|
|
"role": "user",
|
|
"content": [
|
|
{"type": "text", "text": "hi"},
|
|
{
|
|
"type": "image",
|
|
"source": {
|
|
"type": "image",
|
|
"source": {
|
|
"type": "base64",
|
|
"media_type": "image/png",
|
|
"data": "1234",
|
|
},
|
|
},
|
|
},
|
|
],
|
|
}
|
|
],
|
|
False,
|
|
),
|
|
],
|
|
)
|
|
def test_validate_chat_completion_user_messages(messages, expected_bool):
|
|
from litellm.utils import validate_chat_completion_user_messages
|
|
|
|
if expected_bool:
|
|
## Valid message
|
|
validate_chat_completion_user_messages(messages=messages)
|
|
else:
|
|
## Invalid message
|
|
with pytest.raises(Exception):
|
|
validate_chat_completion_user_messages(messages=messages)
|
|
|
|
|
|
def test_models_by_provider():
|
|
"""
|
|
Make sure all providers from model map are in the valid providers list
|
|
"""
|
|
from litellm import models_by_provider
|
|
|
|
providers = set()
|
|
for k, v in litellm.model_cost.items():
|
|
if "_" in v["litellm_provider"] and "-" in v["litellm_provider"]:
|
|
continue
|
|
elif k == "sample_spec":
|
|
continue
|
|
elif v["litellm_provider"] == "sagemaker":
|
|
continue
|
|
else:
|
|
providers.add(v["litellm_provider"])
|
|
|
|
for provider in providers:
|
|
assert provider in models_by_provider.keys()
|