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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.
35 lines
1 KiB
Python
35 lines
1 KiB
Python
import os
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import sys
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import threading
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from datetime import datetime
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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 pytest
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from litellm.integrations.humanloop import HumanLoopPromptManager
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from litellm.types.utils import StandardCallbackDynamicParams
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from litellm.litellm_core_utils.litellm_logging import DynamicLoggingCache
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from unittest.mock import Mock, patch
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def test_compile_prompt():
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prompt_manager = HumanLoopPromptManager()
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prompt_template = [
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{
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"content": "You are {{person}}. Answer questions as this person. Do not break character.",
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"name": None,
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"tool_call_id": None,
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"role": "system",
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"tool_calls": None,
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}
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]
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prompt_variables = {"person": "John"}
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compiled_prompt = prompt_manager._compile_prompt_helper(
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prompt_template, prompt_variables
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)
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assert (
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compiled_prompt[0]["content"]
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== "You are John. Answer questions as this person. Do not break character."
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)
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