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import json
import os
import sys
from datetime import datetime
from unittest.mock import AsyncMock
sys.path.insert(
0, os.path.abspath("../..")
) # Adds the parent directory to the system-path
import litellm
from litellm import completion, embedding
import pytest
from unittest.mock import MagicMock, patch
from litellm.llms.custom_httpx.http_handler import HTTPHandler, AsyncHTTPHandler
import pytest_asyncio
from openai import AsyncOpenAI
@pytest.mark.asyncio
async def test_litellm_gateway_from_sdk():
litellm.set_verbose = True
messages = [
{
"role": "user",
"content": "Hello world",
}
]
from openai import OpenAI
openai_client = OpenAI(api_key="fake-key")
with patch.object(
openai_client.chat.completions.with_raw_response, "create", new=MagicMock()
) as mock_call:
try:
completion(
model="litellm_proxy/my-vllm-model",
messages=messages,
response_format={"type": "json_object"},
client=openai_client,
api_base="my-custom-api-base",
hello="world",
)
except Exception as e:
print(e)
mock_call.assert_called_once()
print("Call KWARGS - {}".format(mock_call.call_args.kwargs))
assert "hello" in mock_call.call_args.kwargs["extra_body"]
@pytest.mark.asyncio
async def test_litellm_gateway_from_sdk_structured_output():
from pydantic import BaseModel
class Result(BaseModel):
answer: str
litellm.set_verbose = True
from openai import OpenAI
openai_client = OpenAI(api_key="fake-key")
with patch.object(
openai_client.chat.completions, "create", new=MagicMock()
) as mock_call:
try:
litellm.completion(
model="litellm_proxy/openai/gpt-4o",
messages=[
{"role": "user", "content": "What is the capital of France?"}
],
api_key="my-test-api-key",
user="test",
response_format=Result,
base_url="https://litellm.ml-serving-internal.scale.com",
client=openai_client,
)
except Exception as e:
print(e)
mock_call.assert_called_once()
print("Call KWARGS - {}".format(mock_call.call_args.kwargs))
json_schema = mock_call.call_args.kwargs["response_format"]
assert "json_schema" in json_schema
@pytest.mark.parametrize("is_async", [False, True])
@pytest.mark.asyncio
async def test_litellm_gateway_from_sdk_embedding(is_async):
litellm.set_verbose = True
litellm._turn_on_debug()
if is_async:
from openai import AsyncOpenAI
openai_client = AsyncOpenAI(api_key="fake-key")
mock_method = AsyncMock()
patch_target = openai_client.embeddings.create
else:
from openai import OpenAI
openai_client = OpenAI(api_key="fake-key")
mock_method = MagicMock()
patch_target = openai_client.embeddings.create
with patch.object(patch_target.__self__, patch_target.__name__, new=mock_method):
try:
if is_async:
await litellm.aembedding(
model="litellm_proxy/my-vllm-model",
input="Hello world",
client=openai_client,
api_base="my-custom-api-base",
)
else:
litellm.embedding(
model="litellm_proxy/my-vllm-model",
input="Hello world",
client=openai_client,
api_base="my-custom-api-base",
)
except Exception as e:
print(e)
mock_method.assert_called_once()
print("Call KWARGS - {}".format(mock_method.call_args.kwargs))
assert "Hello world" == mock_method.call_args.kwargs["input"]
assert "my-vllm-model" == mock_method.call_args.kwargs["model"]
@pytest.mark.parametrize("is_async", [False, True])
@pytest.mark.asyncio
async def test_litellm_gateway_from_sdk_image_generation(is_async):
litellm._turn_on_debug()
if is_async:
from openai import AsyncOpenAI
openai_client = AsyncOpenAI(api_key="fake-key")
mock_method = AsyncMock()
patch_target = openai_client.images.generate
else:
from openai import OpenAI
openai_client = OpenAI(api_key="fake-key")
mock_method = MagicMock()
patch_target = openai_client.images.generate
with patch.object(patch_target.__self__, patch_target.__name__, new=mock_method):
try:
if is_async:
response = await litellm.aimage_generation(
model="litellm_proxy/dall-e-3",
prompt="A beautiful sunset over mountains",
client=openai_client,
api_base="my-custom-api-base",
)
else:
response = litellm.image_generation(
model="litellm_proxy/dall-e-3",
prompt="A beautiful sunset over mountains",
client=openai_client,
api_base="my-custom-api-base",
)
print("response=", response)
except Exception as e:
print("got error", e)
mock_method.assert_called_once()
print("Call KWARGS - {}".format(mock_method.call_args.kwargs))
assert (
"A beautiful sunset over mountains"
== mock_method.call_args.kwargs["prompt"]
)
assert "dall-e-3" == mock_method.call_args.kwargs["model"]
@pytest.mark.parametrize("is_async", [False, True])
@pytest.mark.asyncio
async def test_litellm_gateway_from_sdk_transcription(is_async):
litellm.set_verbose = True
litellm._turn_on_debug()
if is_async:
from openai import AsyncOpenAI
openai_client = AsyncOpenAI(api_key="fake-key")
mock_method = AsyncMock()
patch_target = openai_client.audio.transcriptions.create
else:
from openai import OpenAI
openai_client = OpenAI(api_key="fake-key")
mock_method = MagicMock()
patch_target = openai_client.audio.transcriptions.create
with patch.object(patch_target.__self__, patch_target.__name__, new=mock_method):
try:
if is_async:
await litellm.atranscription(
model="litellm_proxy/whisper-1",
file=b"sample_audio",
client=openai_client,
api_base="my-custom-api-base",
)
else:
litellm.transcription(
model="litellm_proxy/whisper-1",
file=b"sample_audio",
client=openai_client,
api_base="my-custom-api-base",
)
except Exception as e:
print(e)
mock_method.assert_called_once()
print("Call KWARGS - {}".format(mock_method.call_args.kwargs))
assert "whisper-1" == mock_method.call_args.kwargs["model"]
@pytest.mark.parametrize("is_async", [False, True])
@pytest.mark.asyncio
async def test_litellm_gateway_from_sdk_speech(is_async):
litellm.set_verbose = True
if is_async:
from openai import AsyncOpenAI
openai_client = AsyncOpenAI(api_key="fake-key")
mock_method = AsyncMock()
patch_target = openai_client.audio.speech.create
else:
from openai import OpenAI
openai_client = OpenAI(api_key="fake-key")
mock_method = MagicMock()
patch_target = openai_client.audio.speech.create
with patch.object(patch_target.__self__, patch_target.__name__, new=mock_method):
try:
if is_async:
await litellm.aspeech(
model="litellm_proxy/tts-1",
input="Hello, this is a test of text to speech",
voice="alloy",
client=openai_client,
api_base="my-custom-api-base",
)
else:
litellm.speech(
model="litellm_proxy/tts-1",
input="Hello, this is a test of text to speech",
voice="alloy",
client=openai_client,
api_base="my-custom-api-base",
)
except Exception as e:
print(e)
mock_method.assert_called_once()
print("Call KWARGS - {}".format(mock_method.call_args.kwargs))
assert (
"Hello, this is a test of text to speech"
== mock_method.call_args.kwargs["input"]
)
assert "tts-1" == mock_method.call_args.kwargs["model"]
assert "alloy" == mock_method.call_args.kwargs["voice"]
@pytest.mark.parametrize("is_async", [False, True])
@pytest.mark.asyncio
async def test_litellm_gateway_from_sdk_rerank(is_async):
litellm.set_verbose = True
litellm._turn_on_debug()
if is_async:
client = AsyncHTTPHandler()
mock_method = AsyncMock()
patch_target = client.post
else:
client = HTTPHandler()
mock_method = MagicMock()
patch_target = client.post
with patch.object(client, "post", new=mock_method):
mock_response = MagicMock()
# Create a mock response similar to OpenAI's rerank response
mock_response.text = json.dumps(
{
"id": "rerank-123456",
"object": "reranking",
"results": [
{
"index": 0,
"relevance_score": 0.9,
"document": {
"id": "0",
"text": "Machine learning is a field of study in artificial intelligence",
},
},
{
"index": 1,
"relevance_score": 0.2,
"document": {
"id": "1",
"text": "Biology is the study of living organisms",
},
},
],
"model": "rerank-english-v2.0",
"usage": {"prompt_tokens": 10, "total_tokens": 10},
}
)
mock_response.status_code = 200
mock_response.headers = {"Content-Type": "application/json"}
mock_response.json = lambda: json.loads(mock_response.text)
if is_async:
mock_method.return_value = mock_response
else:
mock_method.return_value = mock_response
try:
if is_async:
response = await litellm.arerank(
model="litellm_proxy/rerank-english-v2.0",
query="What is machine learning?",
documents=[
"Machine learning is a field of study in artificial intelligence",
"Biology is the study of living organisms",
],
client=client,
api_base="my-custom-api-base",
)
else:
response = litellm.rerank(
model="litellm_proxy/rerank-english-v2.0",
query="What is machine learning?",
documents=[
"Machine learning is a field of study in artificial intelligence",
"Biology is the study of living organisms",
],
client=client,
api_base="my-custom-api-base",
)
except Exception as e:
print(e)
# Verify the request
mock_method.assert_called_once()
call_args = mock_method.call_args
print("call_args=", call_args)
# Check that the URL is correct
assert "my-custom-api-base/v1/rerank" == call_args.kwargs["url"]
# Check that the request body contains the expected data
request_body = json.loads(call_args.kwargs["data"])
assert request_body["query"] == "What is machine learning?"
assert request_body["model"] == "rerank-english-v2.0"
assert len(request_body["documents"]) == 2
def test_litellm_gateway_from_sdk_with_response_cost_in_additional_headers():
litellm.set_verbose = True
litellm._turn_on_debug()
from openai import OpenAI
openai_client = OpenAI(api_key="fake-key")
# Create mock response object
mock_response = MagicMock()
mock_response.headers = {"x-litellm-response-cost": "120"}
mock_response.parse.return_value = litellm.ModelResponse(
**{
"id": "chatcmpl-BEkxQvRGp9VAushfAsOZCbhMFLsoy",
"choices": [
{
"finish_reason": "stop",
"index": 0,
"logprobs": None,
"message": {
"content": "Hello! How can I assist you today?",
"refusal": None,
"role": "assistant",
"annotations": [],
"audio": None,
"function_call": None,
"tool_calls": None,
},
}
],
"created": 1742856796,
"model": "gpt-4o-2024-08-06",
"object": "chat.completion",
"service_tier": "default",
"system_fingerprint": "fp_6ec83003ad",
"usage": {
"completion_tokens": 10,
"prompt_tokens": 9,
"total_tokens": 19,
"completion_tokens_details": {
"accepted_prediction_tokens": 0,
"audio_tokens": 0,
"reasoning_tokens": 0,
"rejected_prediction_tokens": 0,
},
"prompt_tokens_details": {"audio_tokens": 0, "cached_tokens": 0},
},
}
)
with patch.object(
openai_client.chat.completions.with_raw_response,
"create",
return_value=mock_response,
) as mock_call:
response = litellm.completion(
model="litellm_proxy/gpt-4o",
messages=[{"role": "user", "content": "Hello world"}],
api_base="http://0.0.0.0:4000",
api_key="sk-PIp1h0RekR",
client=openai_client,
)
# Assert the headers were properly passed through
print(f"additional_headers: {response._hidden_params['additional_headers']}")
assert (
response._hidden_params["additional_headers"][
"llm_provider-x-litellm-response-cost"
]
== "120"
)
assert response._hidden_params["response_cost"] == 120
def test_litellm_gateway_from_sdk_with_thinking_param():
try:
response = litellm.completion(
model="litellm_proxy/anthropic.claude-3-7-sonnet-20250219-v1:0",
messages=[{"role": "user", "content": "Hello world"}],
api_base="http://0.0.0.0:4000",
api_key="sk-PIp1h0RekR",
# client=openai_client,
thinking={"type": "enabled", "max_budget": 100},
)
pytest.fail("Expected an error to be raised")
except Exception as e:
assert "Connection error." in str(e)
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