Spaces:
Configuration error
Configuration error
File size: 8,833 Bytes
447ebeb |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 |
import os
import sys
import pytest
from litellm.utils import supports_url_context
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_url_context(self):
from litellm.utils import supports_url_context
os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
litellm.model_cost = litellm.get_model_cost_map(url="")
litellm._turn_on_debug()
base_completion_call_args = self.get_base_completion_call_args()
if not supports_url_context(base_completion_call_args["model"], None):
pytest.skip("Model does not support url context")
response = self.completion_function(
**base_completion_call_args,
messages=[{"role": "user", "content": "Summarize the content of this URL: https://en.wikipedia.org/wiki/Artificial_intelligence"}],
tools=[{"urlContext": {}}],
)
assert response is not None
assert response.model_extra['vertex_ai_url_context_metadata'] is not None, "URL context metadata should be present"
print(f"response={response}")
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
def test_gemini_thinking_budget_0():
litellm._turn_on_debug()
from litellm.types.utils import Message, CallTypes
from litellm.utils import return_raw_request
import json
raw_request = return_raw_request(
endpoint=CallTypes.completion,
kwargs={
"model": "gemini/gemini-2.5-flash-preview-04-17",
"messages": [{"role": "user", "content": "Explain the concept of Occam's Razor and provide a simple, everyday example"}],
"thinking": {"type": "enabled", "budget_tokens": 0}
}
)
print(raw_request)
assert "0" in json.dumps(raw_request["raw_request_body"])
def test_gemini_finish_reason():
import os
from litellm import completion
litellm._turn_on_debug()
response = completion(model="gemini/gemini-1.5-pro", messages=[{"role": "user", "content": "give me 3 random words"}], max_tokens=2)
print(response)
assert response.choices[0].finish_reason is not None
assert response.choices[0].finish_reason == "length"
def test_gemini_url_context():
from litellm import completion
litellm._turn_on_debug()
url = "https://ai.google.dev/gemini-api/docs/models"
prompt = f"""
Summarize this document:
{url}
"""
response = completion(
model="gemini/gemini-2.0-flash",
messages=[{"role": "user", "content": prompt}],
tools=[{"urlContext": {}}],
)
print(response)
message = response.choices[0].message.content
assert message is not None
url_context_metadata = response.model_extra['vertex_ai_url_context_metadata']
assert url_context_metadata is not None
urlMetadata = url_context_metadata[0]['urlMetadata'][0]
assert urlMetadata['retrievedUrl'] == url
assert urlMetadata['urlRetrievalStatus'] == 'URL_RETRIEVAL_STATUS_SUCCESS'
def test_gemini_with_grounding():
from litellm import completion, Usage, stream_chunk_builder
litellm._turn_on_debug()
litellm.set_verbose = True
tools = [{"googleSearch": {}}]
# response = completion(model="gemini/gemini-2.0-flash", messages=[{"role": "user", "content": "What is the capital of France?"}], tools=tools)
# print(response)
# usage: Usage = response.usage
# assert usage.prompt_tokens_details.web_search_requests is not None
# assert usage.prompt_tokens_details.web_search_requests > 0
## Check streaming
response = completion(model="gemini/gemini-2.0-flash", messages=[{"role": "user", "content": "What is the capital of France?"}], tools=tools, stream=True, stream_options={"include_usage": True})
chunks = []
for chunk in response:
chunks.append(chunk)
print(f"chunks before stream_chunk_builder: {chunks}")
assert len(chunks) > 0
complete_response = stream_chunk_builder(chunks)
print(complete_response)
assert complete_response is not None
usage: Usage = complete_response.usage
assert usage.prompt_tokens_details.web_search_requests is not None
assert usage.prompt_tokens_details.web_search_requests > 0
def test_gemini_with_empty_function_call_arguments():
from litellm import completion
litellm._turn_on_debug()
tools = [
{
"type": "function",
"function": {
"name": "get_current_weather",
"parameters": "",
},
}
]
response = completion(model="gemini/gemini-2.0-flash", messages=[{"role": "user", "content": "What is the capital of France?"}], tools=tools)
print(response)
assert response.choices[0].message.content is not None |