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import json
import os
import sys
from datetime import datetime
from unittest.mock import AsyncMock, patch, MagicMock
sys.path.insert(
0, os.path.abspath("../..")
) # Adds the parent directory to the system path
import httpx
import pytest
from respx import MockRouter
import litellm
from litellm import Choices, Message, ModelResponse
from base_llm_unit_tests import BaseLLMChatTest, BaseOSeriesModelsTest
@pytest.mark.parametrize("model", ["o1-preview", "o1-mini", "o1"])
@pytest.mark.asyncio
async def test_o1_handle_system_role(model):
"""
Tests that:
- max_tokens is translated to 'max_completion_tokens'
- role 'system' is translated to 'user'
"""
from openai import AsyncOpenAI
from litellm.utils import supports_system_messages
os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
litellm.model_cost = litellm.get_model_cost_map(url="")
litellm.set_verbose = True
client = AsyncOpenAI(api_key="fake-api-key")
with patch.object(
client.chat.completions.with_raw_response, "create"
) as mock_client:
try:
await litellm.acompletion(
model=model,
max_tokens=10,
messages=[{"role": "system", "content": "Be a good bot!"}],
client=client,
)
except Exception as e:
print(f"Error: {e}")
mock_client.assert_called_once()
request_body = mock_client.call_args.kwargs
print("request_body: ", request_body)
assert request_body["model"] == model
assert request_body["max_completion_tokens"] == 10
if supports_system_messages(model, "openai"):
assert request_body["messages"] == [
{"role": "system", "content": "Be a good bot!"}
]
else:
assert request_body["messages"] == [
{"role": "user", "content": "Be a good bot!"}
]
@pytest.mark.parametrize(
"model, expected_tool_calling_support",
[("o1-preview", False), ("o1-mini", False), ("o1", True)],
)
@pytest.mark.asyncio
async def test_o1_handle_tool_calling_optional_params(
model, expected_tool_calling_support
):
"""
Tests that:
- max_tokens is translated to 'max_completion_tokens'
- role 'system' is translated to 'user'
"""
from openai import AsyncOpenAI
from litellm.utils import ProviderConfigManager
from litellm.types.utils import LlmProviders
os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
litellm.model_cost = litellm.get_model_cost_map(url="")
config = ProviderConfigManager.get_provider_chat_config(
model=model, provider=LlmProviders.OPENAI
)
supported_params = config.get_supported_openai_params(model=model)
assert expected_tool_calling_support == ("tools" in supported_params)
@pytest.mark.asyncio
@pytest.mark.parametrize("model", ["gpt-4", "gpt-4-0314", "gpt-4-32k", "o1-preview"])
async def test_o1_max_completion_tokens(model: str):
"""
Tests that:
- max_completion_tokens is passed directly to OpenAI chat completion models
"""
from openai import AsyncOpenAI
litellm.set_verbose = True
client = AsyncOpenAI(api_key="fake-api-key")
with patch.object(
client.chat.completions.with_raw_response, "create"
) as mock_client:
try:
await litellm.acompletion(
model=model,
max_completion_tokens=10,
messages=[{"role": "user", "content": "Hello!"}],
client=client,
)
except Exception as e:
print(f"Error: {e}")
mock_client.assert_called_once()
request_body = mock_client.call_args.kwargs
print("request_body: ", request_body)
assert request_body["model"] == model
assert request_body["max_completion_tokens"] == 10
assert request_body["messages"] == [{"role": "user", "content": "Hello!"}]
def test_litellm_responses():
"""
ensures that type of completion_tokens_details is correctly handled / returned
"""
from litellm import ModelResponse
from litellm.types.utils import CompletionTokensDetails
response = ModelResponse(
usage={
"completion_tokens": 436,
"prompt_tokens": 14,
"total_tokens": 450,
"completion_tokens_details": {"reasoning_tokens": 0},
}
)
print("response: ", response)
assert isinstance(response.usage.completion_tokens_details, CompletionTokensDetails)
class TestOpenAIO1(BaseOSeriesModelsTest, BaseLLMChatTest):
def get_base_completion_call_args(self):
return {
"model": "o1",
}
def get_client(self):
from openai import OpenAI
return OpenAI(api_key="fake-api-key")
def test_tool_call_no_arguments(self, tool_call_no_arguments):
"""Test that tool calls with no arguments is translated correctly. Relevant issue: https://github.com/BerriAI/litellm/issues/6833"""
pass
def test_prompt_caching(self):
"""Temporary override. o1 prompt caching is not working."""
pass
class TestOpenAIO3(BaseOSeriesModelsTest, BaseLLMChatTest):
def get_base_completion_call_args(self):
return {
"model": "o3-mini",
}
def get_client(self):
from openai import OpenAI
return OpenAI(api_key="fake-api-key")
def test_tool_call_no_arguments(self, tool_call_no_arguments):
"""Test that tool calls with no arguments is translated correctly. Relevant issue: https://github.com/BerriAI/litellm/issues/6833"""
pass
def test_prompt_caching(self):
"""Override, as o3 prompt caching is flaky"""
pass
def test_o1_supports_vision():
"""Test that o1 supports vision"""
os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
litellm.model_cost = litellm.get_model_cost_map(url="")
for k, v in litellm.model_cost.items():
if k.startswith("o1") and v.get("litellm_provider") == "openai":
assert v.get("supports_vision") is True, f"{k} does not support vision"
def test_o3_reasoning_effort():
resp = litellm.completion(
model="o3-mini",
messages=[{"role": "user", "content": "Hello!"}],
reasoning_effort="high",
)
assert resp.choices[0].message.content is not None
@pytest.mark.parametrize("model", ["o1-preview", "o1-mini", "o1", "o3-mini"])
def test_streaming_response(model):
"""Test that streaming response is returned correctly"""
from litellm import completion
response = completion(
model=model,
messages=[
{"role": "system", "content": "Be a good bot!"},
{"role": "user", "content": "Hello!"},
],
stream=True,
)
assert response is not None
chunks = []
for chunk in response:
chunks.append(chunk)
resp = litellm.stream_chunk_builder(chunks=chunks)
print(resp)
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