forked from phoenix/litellm-mirror
1973ae8fb8
* add test for using images with custom openai endpoints * run all otel tests * update name of test * add custom openai model to test config * add test for setting supports_vision=True for model * fix test guardrails aporia * docs supports vison * fix yaml * fix yaml * docs supports vision * fix bedrock guardrail test * fix cohere rerank test * update model_group doc string * add better prints on test
94 lines
3.1 KiB
Python
94 lines
3.1 KiB
Python
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
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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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@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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