forked from phoenix/litellm-mirror
test: update tests
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Krrish Dholakia
parent
d72407515c
commit
7bdc940588
8 changed files with 199 additions and 248 deletions
277
tests/llm_translation/test_openai.py
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277
tests/llm_translation/test_openai.py
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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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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 httpx
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import pytest
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from respx import MockRouter
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import litellm
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from litellm import Choices, Message, ModelResponse
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def test_openai_prediction_param():
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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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completion = litellm.completion(
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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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)
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print(completion)
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assert (
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completion.usage.completion_tokens_details.accepted_prediction_tokens > 0
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or completion.usage.completion_tokens_details.rejected_prediction_tokens > 0
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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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Tests using `prediction` parameter with caching
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"""
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from litellm.caching.caching import LiteLLMCacheType
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import logging
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from litellm._logging import verbose_logger
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verbose_logger.setLevel(logging.DEBUG)
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import time
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litellm.set_verbose = True
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litellm.cache = litellm.Cache(type=LiteLLMCacheType.LOCAL)
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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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completion_response_1 = litellm.completion(
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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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)
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time.sleep(0.5)
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# cache hit
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completion_response_2 = litellm.completion(
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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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)
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assert completion_response_1.id == completion_response_2.id
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completion_response_3 = litellm.completion(
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model="gpt-4o-mini",
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messages=[
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{"role": "user", "content": "What is the first name of the user?"},
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],
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prediction={"type": "content", "content": code + "FirstName"},
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)
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assert completion_response_3.id != completion_response_1.id
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@pytest.mark.asyncio()
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@pytest.mark.respx
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async def test_vision_with_custom_model(respx_mock: MockRouter):
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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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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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mock_response = ModelResponse(
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id="cmpl-mock",
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choices=[Choices(message=Message(content="Mocked response", role="assistant"))],
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created=int(datetime.now().timestamp()),
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model="my-custom-model",
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)
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mock_request = respx_mock.post(f"{api_base}/chat/completions").mock(
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return_value=httpx.Response(200, json=mock_response.dict())
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)
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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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)
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assert mock_request.called
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request_body = json.loads(mock_request.calls[0].request.content)
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print("request_body: ", request_body)
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assert request_body == {
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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": {
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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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"model": "my-custom-model",
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"max_tokens": 10,
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}
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print(f"response: {response}")
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assert isinstance(response, ModelResponse)
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