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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.
44 lines
1.2 KiB
Python
44 lines
1.2 KiB
Python
#### What this tests ####
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# This tests the model alias mapping - if user passes in an alias, and has set an alias, set it to the actual value
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import os
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import sys
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import traceback
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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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import litellm
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from litellm import completion, embedding
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litellm.set_verbose = True
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model_alias_map = {"good-model": "groq/llama-3.1-8b-instant"}
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def test_model_alias_map(caplog):
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try:
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litellm.model_alias_map = model_alias_map
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response = completion(
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"good-model",
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messages=[{"role": "user", "content": "Hey, how's it going?"}],
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top_p=0.1,
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temperature=0.01,
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max_tokens=10,
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)
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print(response.model)
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for rec in caplog.records:
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if rec.levelname == "ERROR" and rec.name.startswith("LiteLLM"):
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pytest.fail(f"Unexpected litellm ERROR log: {rec.getMessage()}")
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assert "llama-3.1-8b-instant" in response.model
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except litellm.ServiceUnavailableError:
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pass
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except Exception as e:
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pytest.fail(f"Error occurred: {e}")
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# test_model_alias_map()
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