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https://github.com/BerriAI/litellm.git
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ab78459097
Response transformation crashed with TypeError when counting tokens for multimodal (dict-shaped) embedding inputs, since encoding.encode() was called directly on the dict instead of its text parts. Also removed a dead, equally-buggy duplicate of the same loop in _transform_response. Separately, the embeddings guardrail handler silently skipped multimodal dict inputs instead of scanning them, letting prohibited text/image content bypass the configured guardrail. It now extracts and scans the text parts while leaving image parts untouched.
100 lines
3.4 KiB
Python
100 lines
3.4 KiB
Python
import asyncio
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import httpx
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import json
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import pytest
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import sys
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from typing import Any, Dict, List
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from unittest.mock import MagicMock, Mock, patch
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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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import litellm
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from litellm.exceptions import BadRequestError
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from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler
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from litellm.utils import CustomStreamWrapper
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from litellm.types.utils import ModelResponse
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# test_example.py
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from abc import ABC, abstractmethod
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class BaseLoggingCallbackTest(ABC):
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"""
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Abstract base test class that enforces a common test across all test classes.
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"""
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@pytest.fixture
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def mock_response_obj(self):
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from litellm.types.utils import (
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ModelResponse,
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Choices,
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Message,
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ChatCompletionMessageToolCall,
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Function,
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Usage,
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CompletionTokensDetailsWrapper,
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PromptTokensDetailsWrapper,
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)
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# Create a mock response object with the structure you need
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return ModelResponse(
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id="chatcmpl-ASId3YJWagBpBskWfoNEMPFSkmrEw",
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created=1731308157,
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model="gpt-4o-mini-2024-07-18",
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object="chat.completion",
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system_fingerprint="fp_0ba0d124f1",
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choices=[
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Choices(
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finish_reason="tool_calls",
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index=0,
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message=Message(
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content=None,
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role="assistant",
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tool_calls=[
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ChatCompletionMessageToolCall(
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function=Function(
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arguments='{"city": "New York"}', name="get_weather"
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),
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id="call_PngsQS5YGmIZKnswhnUOnOVb",
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type="function",
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),
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ChatCompletionMessageToolCall(
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function=Function(
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arguments='{"city": "New York"}', name="get_news"
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),
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id="call_1zsDThBu0VSK7KuY7eCcJBnq",
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type="function",
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),
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],
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function_call=None,
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),
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)
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],
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usage=Usage(
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completion_tokens=46,
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prompt_tokens=86,
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total_tokens=132,
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completion_tokens_details=CompletionTokensDetailsWrapper(
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accepted_prediction_tokens=0,
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audio_tokens=0,
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reasoning_tokens=0,
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rejected_prediction_tokens=0,
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text_tokens=None,
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),
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prompt_tokens_details=PromptTokensDetailsWrapper(
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audio_tokens=0, cached_tokens=0, text_tokens=None, image_tokens=None
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),
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),
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service_tier=None,
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)
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@abstractmethod
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def test_parallel_tool_calls(self, mock_response_obj: ModelResponse):
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"""
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Check if parallel tool calls are correctly logged by Logging callback
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Relevant issue - https://github.com/BerriAI/litellm/issues/6677
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"""
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pass
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