litellm-mirror/tests/llm_translation/test_gemini.py
Krish Dholakia 55a17730fb
fix(transformation.py): pass back in gemini thinking content to api (#10173)
Ensures thinking content always returned
2025-04-19 18:03:05 -07:00

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4.1 KiB
Python

import os
import sys
import pytest
sys.path.insert(
0, os.path.abspath("../..")
) # Adds the parent directory to the system paths
from base_llm_unit_tests import BaseLLMChatTest
from litellm.llms.vertex_ai.context_caching.transformation import (
separate_cached_messages,
)
import litellm
from litellm import completion
class TestGoogleAIStudioGemini(BaseLLMChatTest):
def get_base_completion_call_args(self) -> dict:
return {"model": "gemini/gemini-2.0-flash"}
def get_base_completion_call_args_with_reasoning_model(self) -> dict:
return {"model": "gemini/gemini-2.5-flash-preview-04-17"}
def test_tool_call_no_arguments(self, tool_call_no_arguments):
"""Test that tool calls with no arguments is translated correctly. Relevant issue: https://github.com/BerriAI/litellm/issues/6833"""
from litellm.litellm_core_utils.prompt_templates.factory import (
convert_to_gemini_tool_call_invoke,
)
result = convert_to_gemini_tool_call_invoke(tool_call_no_arguments)
print(result)
def test_gemini_context_caching_separate_messages():
messages = [
# System Message
{
"role": "system",
"content": [
{
"type": "text",
"text": "Here is the full text of a complex legal agreement" * 400,
"cache_control": {"type": "ephemeral"},
}
],
},
# marked for caching with the cache_control parameter, so that this checkpoint can read from the previous cache.
{
"role": "user",
"content": [
{
"type": "text",
"text": "What are the key terms and conditions in this agreement?",
"cache_control": {"type": "ephemeral"},
}
],
},
{
"role": "assistant",
"content": "Certainly! the key terms and conditions are the following: the contract is 1 year long for $10/mo",
},
# The final turn is marked with cache-control, for continuing in followups.
{
"role": "user",
"content": [
{
"type": "text",
"text": "What are the key terms and conditions in this agreement?",
"cache_control": {"type": "ephemeral"},
}
],
},
]
cached_messages, non_cached_messages = separate_cached_messages(messages)
print(cached_messages)
print(non_cached_messages)
assert len(cached_messages) > 0, "Cached messages should be present"
assert len(non_cached_messages) > 0, "Non-cached messages should be present"
def test_gemini_image_generation():
# litellm._turn_on_debug()
response = completion(
model="gemini/gemini-2.0-flash-exp-image-generation",
messages=[{"role": "user", "content": "Generate an image of a cat"}],
modalities=["image", "text"],
)
assert response.choices[0].message.content is not None
def test_gemini_thinking():
litellm._turn_on_debug()
from litellm.types.utils import Message, CallTypes
from litellm.utils import return_raw_request
import json
messages = [
{"role": "user", "content": "Explain the concept of Occam's Razor and provide a simple, everyday example"}
]
reasoning_content = "I'm thinking about Occam's Razor."
assistant_message = Message(content='Okay, let\'s break down Occam\'s Razor.', reasoning_content=reasoning_content, role='assistant', tool_calls=None, function_call=None, provider_specific_fields=None)
messages.append(assistant_message)
raw_request = return_raw_request(
endpoint=CallTypes.completion,
kwargs={
"model": "gemini/gemini-2.5-flash-preview-04-17",
"messages": messages,
}
)
assert reasoning_content in json.dumps(raw_request)
response = completion(
model="gemini/gemini-2.5-flash-preview-04-17",
messages=messages, # make sure call works
)
print(response.choices[0].message)
assert response.choices[0].message.content is not None