mirror of
https://github.com/BerriAI/litellm.git
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test(openai/anthropic): drop SDK-mock passthrough tests per CI audit
Function-level deletions per the keep/drop audit (5): - test_openai.py: test_openai_prediction_param_mock (calibrated drop), test_openai_safety_identifier_parameter and _sync, test_openai_service_tier_parameter and _sync (patch the SDK client then assert called + kwarg present, patterns a/c), test_vision_with_custom_model, test_openai_max_retries_0, test_openai_image_generation_forwards_organization (a/c) - test_openai_o1.py: test_o1_max_completion_tokens (kwarg passthrough into patched SDK, c) - test_anthropic_completion.py: test_anthropic_custom_headers (header reached patched post, b) - test_optional_params.py: test_get_optional_params_num_retries (patches get_optional_params itself), test_requester_metadata_forwarded_to_openai, test_validate_openai_optional_params_integration (c) - test_perplexity_reasoning.py: test_perplexity_reasoning_effort_mock_completion (SDK-mock passthrough, c) Unused imports removed via ruff F401.
This commit is contained in:
mateo-berri
parent
178700e351
commit
42c8985c8a
5 changed files with 7 additions and 604 deletions
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@ -1,10 +1,8 @@
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# What is this?
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## Unit tests for Anthropic Adapter
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import asyncio
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import os
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import sys
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import traceback
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from dotenv import load_dotenv
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@ -13,28 +11,21 @@ import litellm.types.utils
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from litellm.llms.anthropic.chat import ModelResponseIterator
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load_dotenv()
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import io
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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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from typing import Optional
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from unittest.mock import MagicMock, patch
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from unittest.mock import patch
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import pytest
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import litellm
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from litellm import (
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AnthropicConfig,
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Router,
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adapter_completion,
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)
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from litellm.types.llms.anthropic import AnthropicResponse
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from litellm.types.utils import GenericStreamingChunk, ChatCompletionToolCallChunk
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from litellm.types.utils import ChatCompletionToolCallChunk
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from litellm.types.llms.openai import ChatCompletionToolCallFunctionChunk
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from litellm.llms.anthropic.common_utils import process_anthropic_headers
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from litellm.llms.anthropic.chat.handler import AnthropicChatCompletion
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from httpx import Headers
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from base_llm_unit_tests import BaseLLMChatTest, BaseAnthropicChatTest
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@ -360,7 +351,6 @@ def test_process_anthropic_headers_with_no_matching_headers():
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)
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def test_anthropic_tool_use(tool_type, tool_config, message_content):
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"""Test Anthropic tool use with computer use and web fetch tools."""
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from litellm import completion
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litellm._turn_on_debug()
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@ -951,7 +941,6 @@ def test_anthropic_citations_api():
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"""
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Test the citations API
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"""
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from litellm import completion
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try:
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resp = completion(
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@ -997,7 +986,6 @@ def test_anthropic_citations_api():
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def test_anthropic_citations_api_streaming():
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from litellm import completion
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resp = completion(
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model="claude-sonnet-4-5-20250929",
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@ -1044,7 +1032,6 @@ def test_anthropic_citations_api_streaming():
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],
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)
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def test_anthropic_thinking_output(model):
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from litellm import completion
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litellm._turn_on_debug()
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@ -1110,45 +1097,6 @@ def test_anthropic_thinking_output_stream(model):
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pytest.skip("Model is timing out")
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def test_anthropic_custom_headers():
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from litellm import completion
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from litellm.llms.custom_httpx.http_handler import HTTPHandler
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client = HTTPHandler()
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tools = [
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{
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"type": "computer_20241022",
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"function": {
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"name": "get_current_weather",
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"parameters": {
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"display_height_px": 100,
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"display_width_px": 100,
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"display_number": 1,
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},
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},
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}
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]
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with patch.object(client, "post") as mock_post:
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try:
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resp = completion(
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model="claude-sonnet-4-5-20250929",
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headers={"anthropic-beta": "computer-use-2025-01-24"},
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messages=[
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{"role": "user", "content": "What is the capital of France?"}
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],
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client=client,
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tools=tools,
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)
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except Exception as e:
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print(f"Error: {e}")
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mock_post.assert_called_once()
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headers = mock_post.call_args[1]["headers"]
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assert "computer-use-2025-01-24" in headers["anthropic-beta"]
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@pytest.mark.parametrize(
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"model",
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[
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@ -1398,7 +1346,6 @@ def test_anthropic_mcp_server_tool_use(spec: str):
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os.getenv("ZAPIER_CI_CD_MCP_TOKEN") is None, reason="ZAPIER_CI_CD_MCP_TOKEN not set"
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)
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def test_anthropic_mcp_server_responses_api(model: str):
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from litellm import responses
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litellm._turn_on_debug()
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tools = [
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@ -1528,7 +1475,6 @@ def test_anthropic_tool_cache_control():
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def test_anthropic_streaming():
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from litellm import completion
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request_data = {
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"messages": [
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@ -1,8 +1,6 @@
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import json
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import os
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import sys
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from datetime import datetime
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from unittest.mock import AsyncMock, patch
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from unittest.mock import patch
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from typing import Optional
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sys.path.insert(
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@ -10,13 +8,10 @@ sys.path.insert(
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) # Adds the parent directory to the system path
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import httpx
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import pytest
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import litellm
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from litellm import Choices, Message, ModelResponse
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from base_llm_unit_tests import BaseLLMChatTest
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import asyncio
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from litellm.types.llms.openai import (
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ChatCompletionAnnotation,
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ChatCompletionAnnotationURLCitation,
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@ -69,67 +64,6 @@ def test_openai_prediction_param():
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)
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@pytest.mark.asyncio
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async def test_openai_prediction_param_mock():
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"""
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Tests that prediction parameter is correctly passed to the API
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"""
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litellm.set_verbose = True
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code = """
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/// <summary>
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/// Represents a user with a first name, last name, and username.
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/// </summary>
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public class User
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{
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/// <summary>
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/// Gets or sets the user's first name.
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/// </summary>
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public string FirstName { get; set; }
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/// <summary>
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/// Gets or sets the user's last name.
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/// </summary>
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public string LastName { get; set; }
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/// <summary>
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/// Gets or sets the user's username.
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/// </summary>
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public string Username { get; set; }
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}
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"""
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from openai import AsyncOpenAI
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client = AsyncOpenAI(api_key="fake-api-key")
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with patch.object(
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client.chat.completions.with_raw_response, "create"
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) as mock_client:
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try:
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await litellm.acompletion(
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model="gpt-4o-mini",
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messages=[
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{
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"role": "user",
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"content": "Replace the Username property with an Email property. Respond only with code, and with no markdown formatting.",
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},
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{"role": "user", "content": code},
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],
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prediction={"type": "content", "content": code},
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client=client,
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)
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except Exception as e:
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print(f"Error: {e}")
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mock_client.assert_called_once()
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request_body = mock_client.call_args.kwargs
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# Verify the request contains the prediction parameter
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assert "prediction" in request_body
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# verify prediction is correctly sent to the API
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assert request_body["prediction"] == {"type": "content", "content": code}
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@pytest.mark.asyncio
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async def test_openai_prediction_param_with_caching():
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"""
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@ -207,76 +141,6 @@ async def test_openai_prediction_param_with_caching():
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assert completion_response_3.id != completion_response_1.id
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@pytest.mark.asyncio()
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async def test_vision_with_custom_model():
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"""
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Tests that an OpenAI compatible endpoint when sent an image will receive the image in the request
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"""
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import base64
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import requests
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from openai import AsyncOpenAI
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client = AsyncOpenAI(api_key="fake-api-key")
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litellm.set_verbose = True
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api_base = "https://my-custom.api.openai.com"
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# Fetch and encode a test image
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url = "https://dummyimage.com/100/100/fff&text=Test+image"
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response = requests.get(url)
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file_data = response.content
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encoded_file = base64.b64encode(file_data).decode("utf-8")
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base64_image = f"data:image/png;base64,{encoded_file}"
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with patch.object(
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client.chat.completions.with_raw_response, "create"
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) as mock_client:
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try:
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response = await litellm.acompletion(
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model="openai/my-custom-model",
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max_tokens=10,
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api_base=api_base, # use the mock api
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messages=[
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{
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"role": "user",
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"content": [
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{"type": "text", "text": "What's in this image?"},
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{
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"type": "image_url",
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"image_url": {"url": base64_image},
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},
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],
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}
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],
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client=client,
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)
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except Exception as e:
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print(f"Error: {e}")
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mock_client.assert_called_once()
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request_body = mock_client.call_args.kwargs
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print("request_body: ", request_body)
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assert request_body["messages"] == [
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{
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"role": "user",
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"content": [
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{"type": "text", "text": "What's in this image?"},
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{
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"type": "image_url",
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"image_url": {
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"url": "data:image/png;base64,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"
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},
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},
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],
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},
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]
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assert request_body["model"] == "my-custom-model"
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assert request_body["max_tokens"] == 10
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class TestOpenAIChatCompletion(BaseLLMChatTest):
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def get_base_completion_call_args(self) -> dict:
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return {"model": "gpt-4o-mini"}
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@ -299,67 +163,6 @@ class TestOpenAIChatCompletion(BaseLLMChatTest):
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pass
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@patch("litellm.main.openai_chat_completions._get_openai_client")
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def test_openai_max_retries_0(mock_get_openai_client):
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import litellm
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litellm.set_verbose = True
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response = litellm.completion(
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model="gpt-4o-mini",
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messages=[{"role": "user", "content": "hi"}],
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max_retries=0,
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)
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mock_get_openai_client.assert_called_once()
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assert mock_get_openai_client.call_args.kwargs["max_retries"] == 0
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@patch("litellm.main.openai_chat_completions._get_openai_client")
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def test_openai_image_generation_forwards_organization(mock_get_openai_client):
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"""Ensure organization flows to OpenAI client for image generation."""
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class _DummyImages:
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def generate(self, **kwargs): # type: ignore
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class _Resp:
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def model_dump(self_inner): # minimal OpenAI ImagesResponse shape
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return {
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"created": 123,
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"data": [{"url": "http://example.com/image.png"}],
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"usage": {
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"input_tokens": 0,
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"output_tokens": 0,
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"total_tokens": 0,
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},
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}
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return _Resp()
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class _DummyClient:
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def __init__(self):
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self.api_key = "sk-test"
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class _BaseURL:
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_uri_reference = "https://api.openai.com/v1"
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self._base_url = _BaseURL()
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self.images = _DummyImages()
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mock_get_openai_client.return_value = _DummyClient()
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org = "org_test_123"
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resp = litellm.image_generation(
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model="gpt-image-1",
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prompt="A cute baby sea otter",
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organization=org,
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)
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# Assert organization forwarded into OpenAI client factory
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assert mock_get_openai_client.call_args.kwargs.get("organization") == org
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# Basic sanity on response shape
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assert hasattr(resp, "data") and len(resp.data) == 1
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@pytest.mark.parametrize("model", ["o1", "o3-mini"])
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def test_o1_parallel_tool_calls(model):
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litellm.completion(
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@ -692,128 +495,6 @@ async def test_openai_gpt5_reasoning():
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assert response.choices[0].message.content is not None
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@pytest.mark.asyncio
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async def test_openai_safety_identifier_parameter():
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"""Test that safety_identifier parameter is correctly passed to the OpenAI API."""
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from openai import AsyncOpenAI
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litellm.set_verbose = True
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client = AsyncOpenAI(api_key="fake-api-key")
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with patch.object(
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client.chat.completions.with_raw_response, "create"
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) as mock_client:
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try:
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await litellm.acompletion(
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model="openai/gpt-4o",
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messages=[{"role": "user", "content": "Hello, how are you?"}],
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safety_identifier="user_code_123456",
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client=client,
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)
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except Exception as e:
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print(f"Error: {e}")
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mock_client.assert_called_once()
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request_body = mock_client.call_args.kwargs
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# Verify the request contains the safety_identifier parameter
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assert "safety_identifier" in request_body
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# Verify safety_identifier is correctly sent to the API
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assert request_body["safety_identifier"] == "user_code_123456"
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def test_openai_safety_identifier_parameter_sync():
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"""Test that safety_identifier parameter is correctly passed to the OpenAI API."""
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from openai import OpenAI
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litellm.set_verbose = True
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client = OpenAI(api_key="fake-api-key")
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with patch.object(
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client.chat.completions.with_raw_response, "create"
|
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) as mock_client:
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try:
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litellm.completion(
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model="openai/gpt-4o",
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messages=[{"role": "user", "content": "Hello, how are you?"}],
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safety_identifier="user_code_123456",
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client=client,
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)
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except Exception as e:
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print(f"Error: {e}")
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mock_client.assert_called_once()
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request_body = mock_client.call_args.kwargs
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# Verify the request contains the safety_identifier parameter
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assert "safety_identifier" in request_body
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# Verify safety_identifier is correctly sent to the API
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assert request_body["safety_identifier"] == "user_code_123456"
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@pytest.mark.asyncio
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async def test_openai_service_tier_parameter():
|
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"""Test that service_tier parameter is correctly passed to the OpenAI API."""
|
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from openai import AsyncOpenAI
|
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|
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litellm.set_verbose = True
|
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client = AsyncOpenAI(api_key="fake-api-key")
|
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|
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with patch.object(
|
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client.chat.completions.with_raw_response, "create"
|
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) as mock_client:
|
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try:
|
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await litellm.acompletion(
|
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model="openai/gpt-4o",
|
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messages=[{"role": "user", "content": "Hello, how are you?"}],
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service_tier="priority",
|
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client=client,
|
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)
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except Exception as e:
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print(f"Error: {e}")
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mock_client.assert_called_once()
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request_body = mock_client.call_args.kwargs
|
||||
|
||||
# Verify the request contains the service_tier parameter
|
||||
assert "service_tier" in request_body, "service_tier should be in request body"
|
||||
# Verify service_tier is correctly sent to the API
|
||||
assert (
|
||||
request_body["service_tier"] == "priority"
|
||||
), "service_tier should be 'priority'"
|
||||
|
||||
|
||||
def test_openai_service_tier_parameter_sync():
|
||||
"""Test that service_tier parameter is correctly passed to the OpenAI API."""
|
||||
from openai import OpenAI
|
||||
|
||||
litellm.set_verbose = True
|
||||
client = OpenAI(api_key="fake-api-key")
|
||||
|
||||
with patch.object(
|
||||
client.chat.completions.with_raw_response, "create"
|
||||
) as mock_client:
|
||||
try:
|
||||
litellm.completion(
|
||||
model="openai/gpt-4o",
|
||||
messages=[{"role": "user", "content": "Hello, how are you?"}],
|
||||
service_tier="priority",
|
||||
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 service_tier parameter
|
||||
assert "service_tier" in request_body, "service_tier should be in request body"
|
||||
# Verify service_tier is correctly sent to the API
|
||||
assert (
|
||||
request_body["service_tier"] == "priority"
|
||||
), "service_tier should be 'priority'"
|
||||
|
||||
|
||||
def test_gpt_5_reasoning_streaming():
|
||||
litellm._turn_on_debug()
|
||||
response = litellm.completion(
|
||||
|
|
|
|||
|
|
@ -1,19 +1,16 @@
|
|||
import json
|
||||
import os
|
||||
import sys
|
||||
from datetime import datetime
|
||||
from unittest.mock import AsyncMock, patch, MagicMock
|
||||
from unittest.mock import patch
|
||||
|
||||
sys.path.insert(
|
||||
0, os.path.abspath("../..")
|
||||
) # Adds the parent directory to the system path
|
||||
|
||||
|
||||
import httpx
|
||||
import pytest
|
||||
|
||||
import litellm
|
||||
from litellm import Choices, Message, ModelResponse
|
||||
from litellm import ModelResponse
|
||||
from base_llm_unit_tests import BaseLLMChatTest, BaseOSeriesModelsTest
|
||||
|
||||
|
||||
|
|
@ -78,7 +75,6 @@ async def test_o1_handle_tool_calling_optional_params(
|
|||
- 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
|
||||
|
||||
|
|
@ -94,47 +90,10 @@ async def test_o1_handle_tool_calling_optional_params(
|
|||
assert expected_tool_calling_support == ("tools" in supported_params)
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
@pytest.mark.parametrize("model", ["gpt-4", "gpt-4-0613"])
|
||||
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(
|
||||
|
|
|
|||
|
|
@ -1,11 +1,7 @@
|
|||
#### What this tests ####
|
||||
# This tests if get_optional_params works as expected
|
||||
import asyncio
|
||||
import inspect
|
||||
import os
|
||||
import sys
|
||||
import time
|
||||
import traceback
|
||||
|
||||
import pytest
|
||||
|
||||
|
|
@ -15,7 +11,6 @@ from unittest.mock import MagicMock, patch
|
|||
import litellm
|
||||
from litellm.litellm_core_utils.prompt_templates.factory import map_system_message_pt
|
||||
from litellm.types.completion import (
|
||||
ChatCompletionMessageParam,
|
||||
ChatCompletionSystemMessageParam,
|
||||
ChatCompletionUserMessageParam,
|
||||
)
|
||||
|
|
@ -74,36 +69,6 @@ def test_get_requester_metadata_returns_none_for_empty():
|
|||
assert get_requester_metadata(metadata) is None
|
||||
|
||||
|
||||
@patch("litellm.main.openai_chat_completions.completion")
|
||||
def test_requester_metadata_forwarded_to_openai(mock_completion):
|
||||
mock_completion.return_value = MagicMock()
|
||||
metadata = {
|
||||
"requester_metadata": {
|
||||
"custom_meta_key": "value",
|
||||
"hidden_params": "secret",
|
||||
"int_value": 123,
|
||||
}
|
||||
}
|
||||
|
||||
original_api_key = litellm.api_key
|
||||
litellm.api_key = "sk-test"
|
||||
original_preview_flag = litellm.enable_preview_features
|
||||
litellm.enable_preview_features = True
|
||||
|
||||
try:
|
||||
litellm.completion(
|
||||
model="gpt-4o",
|
||||
messages=[{"role": "user", "content": "hi"}],
|
||||
metadata=metadata,
|
||||
)
|
||||
finally:
|
||||
litellm.api_key = original_api_key
|
||||
litellm.enable_preview_features = original_preview_flag
|
||||
|
||||
sent_metadata = mock_completion.call_args.kwargs["optional_params"]["metadata"]
|
||||
assert sent_metadata == {"custom_meta_key": "value"}
|
||||
|
||||
|
||||
def test_get_optional_params_with_allowed_openai_params():
|
||||
"""
|
||||
Test if use can dynamically pass in allowed_openai_params to override default behavior
|
||||
|
|
@ -707,26 +672,6 @@ def test_bedrock_optional_params_embeddings_provider_specific_params():
|
|||
assert len(optional_params) == 1
|
||||
|
||||
|
||||
def test_get_optional_params_num_retries():
|
||||
"""
|
||||
Relevant issue - https://github.com/BerriAI/litellm/issues/5124
|
||||
"""
|
||||
with patch(
|
||||
"litellm.main.get_optional_params",
|
||||
new=MagicMock(return_value={"max_retries": 0}),
|
||||
) as mock_client:
|
||||
_ = litellm.completion(
|
||||
model="gpt-3.5-turbo",
|
||||
messages=[{"role": "user", "content": "Hello world"}],
|
||||
num_retries=10,
|
||||
)
|
||||
|
||||
mock_client.assert_called()
|
||||
|
||||
print(f"mock_client.call_args: {mock_client.call_args}")
|
||||
assert mock_client.call_args.kwargs["max_retries"] == 10
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"provider",
|
||||
[
|
||||
|
|
@ -1101,7 +1046,7 @@ def test_together_ai_model_params():
|
|||
|
||||
|
||||
def test_forward_user_param():
|
||||
from litellm.utils import get_supported_openai_params, get_optional_params
|
||||
from litellm.utils import get_optional_params
|
||||
|
||||
model = "claude-3-5-sonnet-20240620"
|
||||
optional_params = get_optional_params(
|
||||
|
|
@ -1895,8 +1840,7 @@ def test_optional_params_image_gen_with_aspect_ratio():
|
|||
|
||||
|
||||
def test_optional_params_responses_api_allowed_openai_params():
|
||||
from litellm import responses
|
||||
from unittest.mock import patch, MagicMock
|
||||
from unittest.mock import patch
|
||||
from litellm.llms.custom_httpx.http_handler import HTTPHandler
|
||||
|
||||
client = HTTPHandler()
|
||||
|
|
@ -1987,43 +1931,6 @@ def test_validate_openai_optional_params_disable_stop_sequence_limit():
|
|||
litellm.disable_stop_sequence_limit = original_value
|
||||
|
||||
|
||||
def test_validate_openai_optional_params_integration():
|
||||
"""
|
||||
Test that validate_openai_optional_params is properly integrated in the completion flow.
|
||||
"""
|
||||
# Test that completion with more than 4 stop sequences works without error
|
||||
try:
|
||||
with patch("litellm.llms.openai.openai.OpenAI") as mock_client:
|
||||
mock_response = MagicMock()
|
||||
mock_response.choices = [MagicMock()]
|
||||
mock_response.choices[0].message.content = "Test response"
|
||||
mock_response.model = "gpt-3.5-turbo"
|
||||
mock_response.id = "test-id"
|
||||
mock_response.created = 1234567890
|
||||
mock_response.usage = MagicMock()
|
||||
mock_response.usage.prompt_tokens = 10
|
||||
mock_response.usage.completion_tokens = 5
|
||||
mock_response.usage.total_tokens = 15
|
||||
|
||||
mock_client.return_value.chat.completions.create.return_value = (
|
||||
mock_response
|
||||
)
|
||||
|
||||
# Call completion with more than 4 stop sequences
|
||||
response = litellm.completion(
|
||||
model="gpt-3.5-turbo",
|
||||
messages=[{"role": "user", "content": "Hello"}],
|
||||
stop=["stop1", "stop2", "stop3", "stop4", "stop5", "stop6"],
|
||||
mock_response="Test response", # This will use mock
|
||||
)
|
||||
|
||||
# Verify the call was made (stop sequences should be truncated internally)
|
||||
assert response is not None
|
||||
except Exception as e:
|
||||
# Should not raise an exception
|
||||
pytest.fail(f"validate_openai_optional_params integration failed: {e}")
|
||||
|
||||
|
||||
def test_drop_store_param_for_anthropic():
|
||||
"""
|
||||
Test that the OpenAI-specific `store` parameter is correctly dropped
|
||||
|
|
|
|||
|
|
@ -1,7 +1,5 @@
|
|||
import json
|
||||
import os
|
||||
import sys
|
||||
from unittest.mock import patch, MagicMock
|
||||
|
||||
import pytest
|
||||
|
||||
|
|
@ -10,7 +8,6 @@ sys.path.insert(
|
|||
) # Adds the parent directory to the system path
|
||||
|
||||
import litellm
|
||||
from litellm import completion
|
||||
from litellm.utils import get_optional_params
|
||||
|
||||
|
||||
|
|
@ -53,93 +50,6 @@ class TestPerplexityReasoning:
|
|||
assert "reasoning_effort" in optional_params
|
||||
assert optional_params["reasoning_effort"] == reasoning_effort
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"model",
|
||||
[
|
||||
"perplexity/sonar-reasoning",
|
||||
"perplexity/sonar-reasoning-pro",
|
||||
],
|
||||
)
|
||||
def test_perplexity_reasoning_effort_mock_completion(self, model):
|
||||
"""
|
||||
Test that reasoning_effort is correctly passed in actual completion call (mocked)
|
||||
"""
|
||||
from openai import OpenAI
|
||||
from openai.types.chat.chat_completion import ChatCompletion
|
||||
|
||||
litellm.set_verbose = True
|
||||
|
||||
# Mock successful response with reasoning content
|
||||
response_object = {
|
||||
"id": "cmpl-test",
|
||||
"object": "chat.completion",
|
||||
"created": 1677652288,
|
||||
"model": model.split("/")[1],
|
||||
"choices": [
|
||||
{
|
||||
"index": 0,
|
||||
"message": {
|
||||
"role": "assistant",
|
||||
"content": "This is a test response from the reasoning model.",
|
||||
"reasoning_content": "Let me think about this step by step...",
|
||||
},
|
||||
"finish_reason": "stop",
|
||||
}
|
||||
],
|
||||
"usage": {
|
||||
"prompt_tokens": 9,
|
||||
"completion_tokens": 20,
|
||||
"total_tokens": 29,
|
||||
"completion_tokens_details": {"reasoning_tokens": 15},
|
||||
},
|
||||
}
|
||||
|
||||
pydantic_obj = ChatCompletion(**response_object)
|
||||
|
||||
def _return_pydantic_obj(*args, **kwargs):
|
||||
new_response = MagicMock()
|
||||
new_response.headers = {"content-type": "application/json"}
|
||||
new_response.parse.return_value = pydantic_obj
|
||||
return new_response
|
||||
|
||||
openai_client = OpenAI(api_key="fake-api-key")
|
||||
|
||||
with patch.object(
|
||||
openai_client.chat.completions.with_raw_response,
|
||||
"create",
|
||||
side_effect=_return_pydantic_obj,
|
||||
) as mock_client:
|
||||
|
||||
response = completion(
|
||||
model=model,
|
||||
messages=[
|
||||
{
|
||||
"role": "user",
|
||||
"content": "Hello, please think about this carefully.",
|
||||
}
|
||||
],
|
||||
reasoning_effort="high",
|
||||
client=openai_client,
|
||||
)
|
||||
|
||||
# Verify the call was made
|
||||
assert mock_client.called
|
||||
|
||||
# Get the request data from the mock call
|
||||
call_args = mock_client.call_args
|
||||
request_data = call_args.kwargs
|
||||
|
||||
# Verify reasoning_effort was included in the request
|
||||
assert "reasoning_effort" in request_data
|
||||
assert request_data["reasoning_effort"] == "high"
|
||||
|
||||
# Verify response structure
|
||||
assert response.choices[0].message.content is not None
|
||||
assert (
|
||||
response.choices[0].message.content
|
||||
== "This is a test response from the reasoning model."
|
||||
)
|
||||
|
||||
def test_perplexity_reasoning_models_support_reasoning(self):
|
||||
"""
|
||||
Test that Perplexity Sonar reasoning models are correctly identified as supporting reasoning
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue