Added LiteLLM to the stack
This commit is contained in:
666
Development/litellm/tests/llm_translation/test_openai.py
Normal file
666
Development/litellm/tests/llm_translation/test_openai.py
Normal file
@@ -0,0 +1,666 @@
|
||||
import json
|
||||
import os
|
||||
import sys
|
||||
from datetime import datetime
|
||||
from unittest.mock import AsyncMock, patch
|
||||
from typing import Optional
|
||||
|
||||
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
|
||||
import asyncio
|
||||
from litellm.types.llms.openai import (
|
||||
ChatCompletionAnnotation,
|
||||
ChatCompletionAnnotationURLCitation,
|
||||
)
|
||||
from base_audio_transcription_unit_tests import BaseLLMAudioTranscriptionTest
|
||||
|
||||
|
||||
def test_openai_prediction_param():
|
||||
litellm.set_verbose = True
|
||||
code = """
|
||||
/// <summary>
|
||||
/// Represents a user with a first name, last name, and username.
|
||||
/// </summary>
|
||||
public class User
|
||||
{
|
||||
/// <summary>
|
||||
/// Gets or sets the user's first name.
|
||||
/// </summary>
|
||||
public string FirstName { get; set; }
|
||||
|
||||
/// <summary>
|
||||
/// Gets or sets the user's last name.
|
||||
/// </summary>
|
||||
public string LastName { get; set; }
|
||||
|
||||
/// <summary>
|
||||
/// Gets or sets the user's username.
|
||||
/// </summary>
|
||||
public string Username { get; set; }
|
||||
}
|
||||
"""
|
||||
|
||||
completion = litellm.completion(
|
||||
model="gpt-4o-mini",
|
||||
messages=[
|
||||
{
|
||||
"role": "user",
|
||||
"content": "Replace the Username property with an Email property. Respond only with code, and with no markdown formatting.",
|
||||
},
|
||||
{"role": "user", "content": code},
|
||||
],
|
||||
prediction={"type": "content", "content": code},
|
||||
)
|
||||
|
||||
print(completion)
|
||||
|
||||
assert (
|
||||
completion.usage.completion_tokens_details.accepted_prediction_tokens > 0
|
||||
or completion.usage.completion_tokens_details.rejected_prediction_tokens > 0
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_openai_prediction_param_mock():
|
||||
"""
|
||||
Tests that prediction parameter is correctly passed to the API
|
||||
"""
|
||||
litellm.set_verbose = True
|
||||
|
||||
code = """
|
||||
/// <summary>
|
||||
/// Represents a user with a first name, last name, and username.
|
||||
/// </summary>
|
||||
public class User
|
||||
{
|
||||
/// <summary>
|
||||
/// Gets or sets the user's first name.
|
||||
/// </summary>
|
||||
public string FirstName { get; set; }
|
||||
|
||||
/// <summary>
|
||||
/// Gets or sets the user's last name.
|
||||
/// </summary>
|
||||
public string LastName { get; set; }
|
||||
|
||||
/// <summary>
|
||||
/// Gets or sets the user's username.
|
||||
/// </summary>
|
||||
public string Username { get; set; }
|
||||
}
|
||||
"""
|
||||
from openai import AsyncOpenAI
|
||||
|
||||
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="gpt-4o-mini",
|
||||
messages=[
|
||||
{
|
||||
"role": "user",
|
||||
"content": "Replace the Username property with an Email property. Respond only with code, and with no markdown formatting.",
|
||||
},
|
||||
{"role": "user", "content": code},
|
||||
],
|
||||
prediction={"type": "content", "content": code},
|
||||
client=client,
|
||||
)
|
||||
except Exception as e:
|
||||
print(f"Error: {e}")
|
||||
|
||||
mock_client.assert_called_once()
|
||||
request_body = mock_client.call_args.kwargs
|
||||
|
||||
# Verify the request contains the prediction parameter
|
||||
assert "prediction" in request_body
|
||||
# verify prediction is correctly sent to the API
|
||||
assert request_body["prediction"] == {"type": "content", "content": code}
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_openai_prediction_param_with_caching():
|
||||
"""
|
||||
Tests using `prediction` parameter with caching
|
||||
"""
|
||||
from litellm.caching.caching import LiteLLMCacheType
|
||||
import logging
|
||||
from litellm._logging import verbose_logger
|
||||
|
||||
verbose_logger.setLevel(logging.DEBUG)
|
||||
import time
|
||||
|
||||
litellm.set_verbose = True
|
||||
litellm.cache = litellm.Cache(type=LiteLLMCacheType.LOCAL)
|
||||
code = """
|
||||
/// <summary>
|
||||
/// Represents a user with a first name, last name, and username.
|
||||
/// </summary>
|
||||
public class User
|
||||
{
|
||||
/// <summary>
|
||||
/// Gets or sets the user's first name.
|
||||
/// </summary>
|
||||
public string FirstName { get; set; }
|
||||
|
||||
/// <summary>
|
||||
/// Gets or sets the user's last name.
|
||||
/// </summary>
|
||||
public string LastName { get; set; }
|
||||
|
||||
/// <summary>
|
||||
/// Gets or sets the user's username.
|
||||
/// </summary>
|
||||
public string Username { get; set; }
|
||||
}
|
||||
"""
|
||||
|
||||
completion_response_1 = litellm.completion(
|
||||
model="gpt-4o-mini",
|
||||
messages=[
|
||||
{
|
||||
"role": "user",
|
||||
"content": "Replace the Username property with an Email property. Respond only with code, and with no markdown formatting.",
|
||||
},
|
||||
{"role": "user", "content": code},
|
||||
],
|
||||
prediction={"type": "content", "content": code},
|
||||
)
|
||||
|
||||
time.sleep(0.5)
|
||||
|
||||
# cache hit
|
||||
completion_response_2 = litellm.completion(
|
||||
model="gpt-4o-mini",
|
||||
messages=[
|
||||
{
|
||||
"role": "user",
|
||||
"content": "Replace the Username property with an Email property. Respond only with code, and with no markdown formatting.",
|
||||
},
|
||||
{"role": "user", "content": code},
|
||||
],
|
||||
prediction={"type": "content", "content": code},
|
||||
)
|
||||
|
||||
assert completion_response_1.id == completion_response_2.id
|
||||
|
||||
completion_response_3 = litellm.completion(
|
||||
model="gpt-4o-mini",
|
||||
messages=[
|
||||
{"role": "user", "content": "What is the first name of the user?"},
|
||||
],
|
||||
prediction={"type": "content", "content": code + "FirstName"},
|
||||
)
|
||||
|
||||
assert completion_response_3.id != completion_response_1.id
|
||||
|
||||
|
||||
@pytest.mark.asyncio()
|
||||
async def test_vision_with_custom_model():
|
||||
"""
|
||||
Tests that an OpenAI compatible endpoint when sent an image will receive the image in the request
|
||||
|
||||
"""
|
||||
import base64
|
||||
import requests
|
||||
from openai import AsyncOpenAI
|
||||
|
||||
client = AsyncOpenAI(api_key="fake-api-key")
|
||||
|
||||
litellm.set_verbose = True
|
||||
api_base = "https://my-custom.api.openai.com"
|
||||
|
||||
# Fetch and encode a test image
|
||||
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}"
|
||||
|
||||
with patch.object(
|
||||
client.chat.completions.with_raw_response, "create"
|
||||
) as mock_client:
|
||||
try:
|
||||
response = await litellm.acompletion(
|
||||
model="openai/my-custom-model",
|
||||
max_tokens=10,
|
||||
api_base=api_base, # use the mock api
|
||||
messages=[
|
||||
{
|
||||
"role": "user",
|
||||
"content": [
|
||||
{"type": "text", "text": "What's in this image?"},
|
||||
{
|
||||
"type": "image_url",
|
||||
"image_url": {"url": base64_image},
|
||||
},
|
||||
],
|
||||
}
|
||||
],
|
||||
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["messages"] == [
|
||||
{
|
||||
"role": "user",
|
||||
"content": [
|
||||
{"type": "text", "text": "What's in this image?"},
|
||||
{
|
||||
"type": "image_url",
|
||||
"image_url": {
|
||||
"url": "data:image/png;base64,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"
|
||||
},
|
||||
},
|
||||
],
|
||||
},
|
||||
]
|
||||
assert request_body["model"] == "my-custom-model"
|
||||
assert request_body["max_tokens"] == 10
|
||||
|
||||
|
||||
class TestOpenAIChatCompletion(BaseLLMChatTest):
|
||||
def get_base_completion_call_args(self) -> dict:
|
||||
return {"model": "gpt-4o-mini"}
|
||||
|
||||
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):
|
||||
"""
|
||||
Test that prompt caching works correctly.
|
||||
Skip for now, as it's working locally but not in CI
|
||||
"""
|
||||
pass
|
||||
|
||||
def test_prompt_caching(self):
|
||||
"""
|
||||
Works locally but CI/CD is failing this test. Temporary skip to push out a new release.
|
||||
"""
|
||||
pass
|
||||
|
||||
|
||||
def test_completion_bad_org():
|
||||
import litellm
|
||||
|
||||
litellm.set_verbose = True
|
||||
_old_org = os.environ.get("OPENAI_ORGANIZATION", None)
|
||||
os.environ["OPENAI_ORGANIZATION"] = "bad-org"
|
||||
messages = [{"role": "user", "content": "hi"}]
|
||||
|
||||
with pytest.raises(Exception) as exc_info:
|
||||
comp = litellm.completion(
|
||||
model="gpt-4o-mini", messages=messages, organization="bad-org"
|
||||
)
|
||||
|
||||
print(exc_info.value)
|
||||
assert "header should match organization for API key" in str(exc_info.value)
|
||||
|
||||
if _old_org is not None:
|
||||
os.environ["OPENAI_ORGANIZATION"] = _old_org
|
||||
else:
|
||||
del os.environ["OPENAI_ORGANIZATION"]
|
||||
|
||||
|
||||
@patch("litellm.main.openai_chat_completions._get_openai_client")
|
||||
def test_openai_max_retries_0(mock_get_openai_client):
|
||||
import litellm
|
||||
|
||||
litellm.set_verbose = True
|
||||
response = litellm.completion(
|
||||
model="gpt-4o-mini",
|
||||
messages=[{"role": "user", "content": "hi"}],
|
||||
max_retries=0,
|
||||
)
|
||||
|
||||
mock_get_openai_client.assert_called_once()
|
||||
assert mock_get_openai_client.call_args.kwargs["max_retries"] == 0
|
||||
|
||||
|
||||
@pytest.mark.parametrize("model", ["o1", "o1-mini", "o3-mini"])
|
||||
def test_o1_parallel_tool_calls(model):
|
||||
litellm.completion(
|
||||
model=model,
|
||||
messages=[
|
||||
{
|
||||
"role": "user",
|
||||
"content": "foo",
|
||||
}
|
||||
],
|
||||
parallel_tool_calls=True,
|
||||
drop_params=True,
|
||||
)
|
||||
|
||||
|
||||
def test_openai_chat_completion_streaming_handler_reasoning_content():
|
||||
from litellm.llms.openai.chat.gpt_transformation import (
|
||||
OpenAIChatCompletionStreamingHandler,
|
||||
)
|
||||
from unittest.mock import MagicMock
|
||||
|
||||
streaming_handler = OpenAIChatCompletionStreamingHandler(
|
||||
streaming_response=MagicMock(),
|
||||
sync_stream=True,
|
||||
)
|
||||
response = streaming_handler.chunk_parser(
|
||||
chunk={
|
||||
"id": "e89b6501-8ac2-464c-9550-7cd3daf94350",
|
||||
"object": "chat.completion.chunk",
|
||||
"created": 1741037890,
|
||||
"model": "deepseek-reasoner",
|
||||
"system_fingerprint": "fp_5417b77867_prod0225",
|
||||
"choices": [
|
||||
{
|
||||
"index": 0,
|
||||
"delta": {"content": None, "reasoning_content": "."},
|
||||
"logprobs": None,
|
||||
"finish_reason": None,
|
||||
}
|
||||
],
|
||||
}
|
||||
)
|
||||
|
||||
assert response.choices[0].delta.reasoning_content == "."
|
||||
|
||||
|
||||
def validate_response_url_citation(url_citation: ChatCompletionAnnotationURLCitation):
|
||||
assert "end_index" in url_citation
|
||||
assert "start_index" in url_citation
|
||||
assert "url" in url_citation
|
||||
|
||||
|
||||
def validate_web_search_annotations(annotations: ChatCompletionAnnotation):
|
||||
"""validates litellm response contains web search annotations"""
|
||||
print("annotations: ", annotations)
|
||||
assert annotations is not None
|
||||
assert isinstance(annotations, list)
|
||||
for annotation in annotations:
|
||||
assert annotation["type"] == "url_citation"
|
||||
url_citation: ChatCompletionAnnotationURLCitation = annotation["url_citation"]
|
||||
validate_response_url_citation(url_citation)
|
||||
|
||||
|
||||
@pytest.mark.flaky(reruns=3)
|
||||
def test_openai_web_search():
|
||||
"""Makes a simple web search request and validates the response contains web search annotations and all expected fields are present"""
|
||||
litellm._turn_on_debug()
|
||||
response = litellm.completion(
|
||||
model="openai/gpt-4o-search-preview",
|
||||
messages=[
|
||||
{
|
||||
"role": "user",
|
||||
"content": "What was a positive news story from today?",
|
||||
}
|
||||
],
|
||||
)
|
||||
print("litellm response: ", response.model_dump_json(indent=4))
|
||||
message = response.choices[0].message
|
||||
if hasattr(message, "annotations"):
|
||||
annotations: ChatCompletionAnnotation = message.annotations
|
||||
validate_web_search_annotations(annotations)
|
||||
|
||||
|
||||
def test_openai_web_search_streaming():
|
||||
"""Makes a simple web search request and validates the response contains web search annotations and all expected fields are present"""
|
||||
# litellm._turn_on_debug()
|
||||
test_openai_web_search: Optional[ChatCompletionAnnotation] = None
|
||||
response = litellm.completion(
|
||||
model="openai/gpt-4o-search-preview",
|
||||
messages=[
|
||||
{
|
||||
"role": "user",
|
||||
"content": "What was a positive news story from today?",
|
||||
}
|
||||
],
|
||||
stream=True,
|
||||
)
|
||||
for chunk in response:
|
||||
print("litellm response chunk: ", chunk)
|
||||
if (
|
||||
hasattr(chunk.choices[0].delta, "annotations")
|
||||
and chunk.choices[0].delta.annotations is not None
|
||||
):
|
||||
test_openai_web_search = chunk.choices[0].delta.annotations
|
||||
|
||||
# Assert this request has at-least one web search annotation
|
||||
if test_openai_web_search is not None:
|
||||
validate_web_search_annotations(test_openai_web_search)
|
||||
|
||||
|
||||
class TestOpenAIGPT4OAudioTranscription(BaseLLMAudioTranscriptionTest):
|
||||
def get_base_audio_transcription_call_args(self) -> dict:
|
||||
return {
|
||||
"model": "openai/gpt-4o-transcribe",
|
||||
}
|
||||
|
||||
def get_custom_llm_provider(self) -> litellm.LlmProviders:
|
||||
return litellm.LlmProviders.OPENAI
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
@pytest.mark.parametrize("model", ["gpt-4o"])
|
||||
async def test_openai_pdf_url(model):
|
||||
from litellm.utils import return_raw_request, CallTypes
|
||||
|
||||
request = return_raw_request(
|
||||
CallTypes.completion,
|
||||
{
|
||||
"model": model,
|
||||
"messages": [
|
||||
{
|
||||
"role": "user",
|
||||
"content": [
|
||||
{"type": "text", "text": "What is the first page of the PDF?"},
|
||||
{
|
||||
"type": "file",
|
||||
"file": {"file_id": "https://arxiv.org/pdf/2303.08774"},
|
||||
},
|
||||
],
|
||||
}
|
||||
],
|
||||
},
|
||||
)
|
||||
print("request: ", request)
|
||||
|
||||
assert (
|
||||
"file_data" in request["raw_request_body"]["messages"][0]["content"][1]["file"]
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.parametrize("sync_mode", [True, False])
|
||||
@pytest.mark.asyncio
|
||||
async def test_openai_codex_stream(sync_mode):
|
||||
from litellm.main import stream_chunk_builder
|
||||
|
||||
kwargs = {
|
||||
"model": "openai/codex-mini-latest",
|
||||
"messages": [{"role": "user", "content": "Hey!"}],
|
||||
"stream": True,
|
||||
}
|
||||
|
||||
chunks = []
|
||||
if sync_mode:
|
||||
response = litellm.completion(**kwargs)
|
||||
for chunk in response:
|
||||
chunks.append(chunk)
|
||||
else:
|
||||
response = await litellm.acompletion(**kwargs)
|
||||
async for chunk in response:
|
||||
chunks.append(chunk)
|
||||
|
||||
complete_response = stream_chunk_builder(chunks=chunks)
|
||||
print("complete_response: ", complete_response)
|
||||
|
||||
assert complete_response.choices[0].message.content is not None
|
||||
|
||||
|
||||
@pytest.mark.parametrize("sync_mode", [True, False])
|
||||
@pytest.mark.asyncio
|
||||
async def test_openai_codex(sync_mode):
|
||||
|
||||
from litellm import Router
|
||||
|
||||
router = Router(
|
||||
model_list=[
|
||||
{
|
||||
"model_name": "openai-codex-mini-latest",
|
||||
"litellm_params": {
|
||||
"model": "openai/codex-mini-latest",
|
||||
},
|
||||
}
|
||||
]
|
||||
)
|
||||
|
||||
kwargs = {
|
||||
"model": "openai-codex-mini-latest",
|
||||
"messages": [{"role": "user", "content": "Hey!"}],
|
||||
}
|
||||
|
||||
if sync_mode:
|
||||
response = router.completion(**kwargs)
|
||||
else:
|
||||
response = await router.acompletion(**kwargs)
|
||||
print("response: ", response)
|
||||
|
||||
assert response.choices[0].message.content is not None
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_openai_via_gemini_streaming_bridge():
|
||||
"""
|
||||
Test that the openai via gemini streaming bridge works correctly
|
||||
"""
|
||||
from litellm import Router
|
||||
from litellm.types.utils import ModelResponseStream
|
||||
|
||||
router = Router(
|
||||
model_list=[
|
||||
{
|
||||
"model_name": "openai/gpt-3.5-turbo",
|
||||
"litellm_params": {
|
||||
"model": "openai/gpt-3.5-turbo",
|
||||
},
|
||||
"model_info": {
|
||||
"version": 2,
|
||||
},
|
||||
}
|
||||
],
|
||||
model_group_alias={"gemini-2.5-pro": "openai/gpt-3.5-turbo"},
|
||||
)
|
||||
|
||||
response = await router.agenerate_content_stream(
|
||||
model="openai/gpt-3.5-turbo",
|
||||
contents=[
|
||||
{
|
||||
"parts": [{"text": "Write a long story about space exploration"}],
|
||||
"role": "user",
|
||||
}
|
||||
],
|
||||
generationConfig={"maxOutputTokens": 500},
|
||||
)
|
||||
|
||||
printed_chunks = []
|
||||
async for chunk in response:
|
||||
print("chunk: ", chunk)
|
||||
printed_chunks.append(chunk)
|
||||
assert not isinstance(chunk, ModelResponseStream)
|
||||
|
||||
assert len(printed_chunks) > 0
|
||||
|
||||
|
||||
def test_openai_deepresearch_model_bridge():
|
||||
"""
|
||||
Test that the deepresearch model bridge works correctly
|
||||
"""
|
||||
litellm._turn_on_debug()
|
||||
response = litellm.completion(
|
||||
model="o3-deep-research-2025-06-26",
|
||||
messages=[{"role": "user", "content": "Hey, how's it going?"}],
|
||||
tools=[
|
||||
{"type": "web_search_preview"},
|
||||
{"type": "code_interpreter", "container": {"type": "auto"}},
|
||||
],
|
||||
)
|
||||
|
||||
print("response: ", response)
|
||||
|
||||
|
||||
def test_openai_tool_calling():
|
||||
from pydantic import BaseModel
|
||||
from typing import Any, Literal
|
||||
|
||||
class OpenAIFunction(BaseModel):
|
||||
description: Optional[str] = None
|
||||
name: str
|
||||
parameters: Optional[dict[str, Any]] = None
|
||||
|
||||
class OpenAITool(BaseModel):
|
||||
type: Literal["function"]
|
||||
function: OpenAIFunction
|
||||
|
||||
completion_params = {
|
||||
"model": "openai/gpt-4.1",
|
||||
"messages": [
|
||||
{
|
||||
"role": "user",
|
||||
"content": [
|
||||
{"type": "text", "text": "What is TSLA stock price at today?"}
|
||||
],
|
||||
}
|
||||
],
|
||||
"stream": False,
|
||||
"temperature": 0.5,
|
||||
"stop": None,
|
||||
"max_tokens": 1600,
|
||||
"tools": [
|
||||
OpenAITool(
|
||||
type="function",
|
||||
function=OpenAIFunction(
|
||||
description="Get the current stock price for a given ticker symbol.",
|
||||
name="get_stock_price",
|
||||
parameters={
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"ticker": {
|
||||
"type": "string",
|
||||
"description": "The stock ticker symbol, e.g. AAPL for Apple Inc.",
|
||||
}
|
||||
},
|
||||
"required": ["ticker"],
|
||||
},
|
||||
),
|
||||
)
|
||||
],
|
||||
}
|
||||
|
||||
response = litellm.completion(**completion_params)
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_openai_gpt5_reasoning():
|
||||
response = await litellm.acompletion(
|
||||
model="openai/gpt-5-mini",
|
||||
messages=[{"role": "user", "content": "What is the capital of France?"}],
|
||||
reasoning_effort="minimal",
|
||||
)
|
||||
print("response: ", response)
|
||||
assert response.choices[0].message.content is not None
|
||||
Reference in New Issue
Block a user