# Copyright 2025 Google LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the Licens import json from unittest.mock import AsyncMock from unittest.mock import Mock import warnings from google.adk.models.lite_llm import _build_function_declaration_log from google.adk.models.lite_llm import _content_to_message_param from google.adk.models.lite_llm import _FINISH_REASON_MAPPING from google.adk.models.lite_llm import _function_declaration_to_tool_param from google.adk.models.lite_llm import _get_completion_inputs from google.adk.models.lite_llm import _get_content from google.adk.models.lite_llm import _message_to_generate_content_response from google.adk.models.lite_llm import _model_response_to_chunk from google.adk.models.lite_llm import _parse_tool_calls_from_text from google.adk.models.lite_llm import _split_message_content_and_tool_calls from google.adk.models.lite_llm import _to_litellm_response_format from google.adk.models.lite_llm import _to_litellm_role from google.adk.models.lite_llm import FunctionChunk from google.adk.models.lite_llm import LiteLlm from google.adk.models.lite_llm import LiteLLMClient from google.adk.models.lite_llm import TextChunk from google.adk.models.lite_llm import UsageMetadataChunk from google.adk.models.llm_request import LlmRequest from google.genai import types from litellm import ChatCompletionAssistantMessage from litellm import ChatCompletionMessageToolCall from litellm import Function from litellm.types.utils import ChatCompletionDeltaToolCall from litellm.types.utils import Choices from litellm.types.utils import Delta from litellm.types.utils import ModelResponse from litellm.types.utils import StreamingChoices from pydantic import BaseModel from pydantic import Field import pytest LLM_REQUEST_WITH_FUNCTION_DECLARATION = LlmRequest( contents=[ types.Content( role="user", parts=[types.Part.from_text(text="Test prompt")] ) ], config=types.GenerateContentConfig( tools=[ types.Tool( function_declarations=[ types.FunctionDeclaration( name="test_function", description="Test function description", parameters=types.Schema( type=types.Type.OBJECT, properties={ "test_arg": types.Schema( type=types.Type.STRING ), "array_arg": types.Schema( type=types.Type.ARRAY, items={ "type": types.Type.STRING, }, ), "nested_arg": types.Schema( type=types.Type.OBJECT, properties={ "nested_key1": types.Schema( type=types.Type.STRING ), "nested_key2": types.Schema( type=types.Type.STRING ), }, ), }, ), ) ] ) ], ), ) FILE_URI_TEST_CASES = [ pytest.param("gs://bucket/document.pdf", "application/pdf", id="pdf"), pytest.param("gs://bucket/data.json", "application/json", id="json"), pytest.param("gs://bucket/data.txt", "text/plain", id="txt"), ] FILE_BYTES_TEST_CASES = [ pytest.param( b"test_pdf_data", "application/pdf", "data:application/pdf;base64,dGVzdF9wZGZfZGF0YQ==", id="pdf", ), pytest.param( b'{"hello":"world"}', "application/json", "data:application/json;base64,eyJoZWxsbyI6IndvcmxkIn0=", id="json", ), ] STREAMING_MODEL_RESPONSE = [ ModelResponse( model="test_model", choices=[ StreamingChoices( finish_reason=None, delta=Delta( role="assistant", content="zero, ", ), ) ], ), ModelResponse( model="test_model", choices=[ StreamingChoices( finish_reason=None, delta=Delta( role="assistant", content="one, ", ), ) ], ), ModelResponse( model="test_model", choices=[ StreamingChoices( finish_reason=None, delta=Delta( role="assistant", content="two:", ), ) ], ), ModelResponse( model="test_model", choices=[ StreamingChoices( finish_reason=None, delta=Delta( role="assistant", tool_calls=[ ChatCompletionDeltaToolCall( type="function", id="test_tool_call_id", function=Function( name="test_function", arguments='{"test_arg": "test_', ), index=0, ) ], ), ) ], ), ModelResponse( model="test_model", choices=[ StreamingChoices( finish_reason=None, delta=Delta( role="assistant", tool_calls=[ ChatCompletionDeltaToolCall( type="function", id=None, function=Function( name=None, arguments='value"}', ), index=0, ) ], ), ) ], ), ModelResponse( model="test_model", choices=[ StreamingChoices( finish_reason="tool_use", ) ], ), ] class _StructuredOutput(BaseModel): value: int = Field(description="Value to emit") class _ModelDumpOnly: """Test helper that mimics objects exposing only model_dump.""" def __init__(self): self._schema = { "type": "object", "properties": {"foo": {"type": "string"}}, } def model_dump(self, *, exclude_none=True, mode="json"): # The method signature matches pydantic BaseModel.model_dump to simulate # google.genai schema-like objects. del exclude_none del mode return self._schema def test_get_completion_inputs_formats_pydantic_schema_for_litellm(): llm_request = LlmRequest( config=types.GenerateContentConfig(response_schema=_StructuredOutput) ) _, _, response_format, _ = _get_completion_inputs(llm_request) assert response_format == { "type": "json_object", "response_schema": _StructuredOutput.model_json_schema(), } def test_to_litellm_response_format_passes_preformatted_dict(): response_format = { "type": "json_object", "response_schema": { "type": "object", "properties": {"foo": {"type": "string"}}, }, } assert _to_litellm_response_format(response_format) == response_format def test_to_litellm_response_format_wraps_json_schema_dict(): schema = { "type": "object", "properties": {"foo": {"type": "string"}}, } formatted = _to_litellm_response_format(schema) assert formatted["type"] == "json_object" assert formatted["response_schema"] == schema def test_to_litellm_response_format_handles_model_dump_object(): schema_obj = _ModelDumpOnly() formatted = _to_litellm_response_format(schema_obj) assert formatted["type"] == "json_object" assert formatted["response_schema"] == schema_obj.model_dump() def test_to_litellm_response_format_handles_genai_schema_instance(): schema_instance = types.Schema( type=types.Type.OBJECT, properties={"foo": types.Schema(type=types.Type.STRING)}, required=["foo"], ) formatted = _to_litellm_response_format(schema_instance) assert formatted["type"] == "json_object" assert formatted["response_schema"] == schema_instance.model_dump( exclude_none=True, mode="json" ) MULTIPLE_FUNCTION_CALLS_STREAM = [ ModelResponse( choices=[ StreamingChoices( finish_reason=None, delta=Delta( role="assistant", tool_calls=[ ChatCompletionDeltaToolCall( type="function", id="call_1", function=Function( name="function_1", arguments='{"arg": "val', ), index=0, ) ], ), ) ] ), ModelResponse( choices=[ StreamingChoices( finish_reason=None, delta=Delta( role="assistant", tool_calls=[ ChatCompletionDeltaToolCall( type="function", id=None, function=Function( name=None, arguments='ue1"}', ), index=0, ) ], ), ) ] ), ModelResponse( choices=[ StreamingChoices( finish_reason=None, delta=Delta( role="assistant", tool_calls=[ ChatCompletionDeltaToolCall( type="function", id="call_2", function=Function( name="function_2", arguments='{"arg": "val', ), index=1, ) ], ), ) ] ), ModelResponse( choices=[ StreamingChoices( finish_reason=None, delta=Delta( role="assistant", tool_calls=[ ChatCompletionDeltaToolCall( type="function", id=None, function=Function( name=None, arguments='ue2"}', ), index=1, ) ], ), ) ] ), ModelResponse( choices=[ StreamingChoices( finish_reason="tool_calls", ) ] ), ] STREAM_WITH_EMPTY_CHUNK = [ ModelResponse( choices=[ StreamingChoices( finish_reason=None, delta=Delta( role="assistant", tool_calls=[ ChatCompletionDeltaToolCall( type="function", id="call_abc", function=Function( name="test_function", arguments='{"test_arg":', ), index=0, ) ], ), ) ] ), ModelResponse( choices=[ StreamingChoices( finish_reason=None, delta=Delta( role="assistant", tool_calls=[ ChatCompletionDeltaToolCall( type="function", id=None, function=Function( name=None, arguments=' "value"}', ), index=0, ) ], ), ) ] ), # This is the problematic empty chunk that should be ignored. ModelResponse( choices=[ StreamingChoices( finish_reason=None, delta=Delta( role="assistant", tool_calls=[ ChatCompletionDeltaToolCall( type="function", id=None, function=Function( name=None, arguments="", ), index=0, ) ], ), ) ] ), ModelResponse( choices=[StreamingChoices(finish_reason="tool_calls", delta=Delta())] ), ] @pytest.fixture def mock_response(): return ModelResponse( model="test_model", choices=[ Choices( message=ChatCompletionAssistantMessage( role="assistant", content="Test response", tool_calls=[ ChatCompletionMessageToolCall( type="function", id="test_tool_call_id", function=Function( name="test_function", arguments='{"test_arg": "test_value"}', ), ) ], ) ) ], ) # Test case reflecting litellm v1.71.2, ollama v0.9.0 streaming response # no tool call ids # indices all 0 # finish_reason stop instead of tool_calls NON_COMPLIANT_MULTIPLE_FUNCTION_CALLS_STREAM = [ ModelResponse( choices=[ StreamingChoices( finish_reason=None, delta=Delta( role="assistant", tool_calls=[ ChatCompletionDeltaToolCall( type="function", id=None, function=Function( name="function_1", arguments='{"arg": "val', ), index=0, ) ], ), ) ] ), ModelResponse( choices=[ StreamingChoices( finish_reason=None, delta=Delta( role="assistant", tool_calls=[ ChatCompletionDeltaToolCall( type="function", id=None, function=Function( name=None, arguments='ue1"}', ), index=0, ) ], ), ) ] ), ModelResponse( choices=[ StreamingChoices( finish_reason=None, delta=Delta( role="assistant", tool_calls=[ ChatCompletionDeltaToolCall( type="function", id=None, function=Function( name="function_2", arguments='{"arg": "val', ), index=0, ) ], ), ) ] ), ModelResponse( choices=[ StreamingChoices( finish_reason=None, delta=Delta( role="assistant", tool_calls=[ ChatCompletionDeltaToolCall( type="function", id=None, function=Function( name=None, arguments='ue2"}', ), index=0, ) ], ), ) ] ), ModelResponse( choices=[ StreamingChoices( finish_reason="stop", ) ] ), ] @pytest.fixture def mock_acompletion(mock_response): return AsyncMock(return_value=mock_response) @pytest.fixture def mock_completion(mock_response): return Mock(return_value=mock_response) @pytest.fixture def mock_client(mock_acompletion, mock_completion): return MockLLMClient(mock_acompletion, mock_completion) @pytest.fixture def lite_llm_instance(mock_client): return LiteLlm(model="test_model", llm_client=mock_client) class MockLLMClient(LiteLLMClient): def __init__(self, acompletion_mock, completion_mock): self.acompletion_mock = acompletion_mock self.completion_mock = completion_mock async def acompletion(self, model, messages, tools, **kwargs): if kwargs.get("stream", False): kwargs_copy = dict(kwargs) kwargs_copy.pop("stream", None) async def stream_generator(): stream_data = self.completion_mock( model=model, messages=messages, tools=tools, stream=True, **kwargs_copy, ) for item in stream_data: yield item return stream_generator() else: return await self.acompletion_mock( model=model, messages=messages, tools=tools, **kwargs ) def completion(self, model, messages, tools, stream, **kwargs): return self.completion_mock( model=model, messages=messages, tools=tools, stream=stream, **kwargs ) def test_build_function_declaration_log(): """Test that _build_function_declaration_log formats function declarations correctly.""" # Test case 1: Function with parameters and response func_decl1 = types.FunctionDeclaration( name="test_func1", description="Test function 1", parameters=types.Schema( type=types.Type.OBJECT, properties={ "param1": types.Schema( type=types.Type.STRING, description="param1 desc" ) }, ), response=types.Schema(type=types.Type.BOOLEAN, description="return bool"), ) log1 = _build_function_declaration_log(func_decl1) assert log1 == ( "test_func1: {'param1': {'description': 'param1 desc', 'type':" " }} -> {'description': 'return bool', 'type':" " }" ) # Test case 2: Function with JSON schema parameters and response func_decl2 = types.FunctionDeclaration( name="test_func2", description="Test function 2", parameters_json_schema={ "type": "object", "properties": {"param2": {"type": "integer"}}, }, response_json_schema={"type": "string"}, ) log2 = _build_function_declaration_log(func_decl2) assert log2 == ( "test_func2: {'type': 'object', 'properties': {'param2': {'type':" " 'integer'}}} -> {'type': 'string'}" ) # Test case 3: Function with no parameters and no response func_decl3 = types.FunctionDeclaration( name="test_func3", description="Test function 3", ) log3 = _build_function_declaration_log(func_decl3) assert log3 == "test_func3: {} -> None" @pytest.mark.asyncio async def test_generate_content_async(mock_acompletion, lite_llm_instance): async for response in lite_llm_instance.generate_content_async( LLM_REQUEST_WITH_FUNCTION_DECLARATION ): assert response.content.role == "model" assert response.content.parts[0].text == "Test response" assert response.content.parts[1].function_call.name == "test_function" assert response.content.parts[1].function_call.args == { "test_arg": "test_value" } assert response.content.parts[1].function_call.id == "test_tool_call_id" assert response.model_version == "test_model" mock_acompletion.assert_called_once() _, kwargs = mock_acompletion.call_args assert kwargs["model"] == "test_model" assert kwargs["messages"][0]["role"] == "user" assert kwargs["messages"][0]["content"] == "Test prompt" assert kwargs["tools"][0]["function"]["name"] == "test_function" assert ( kwargs["tools"][0]["function"]["description"] == "Test function description" ) assert ( kwargs["tools"][0]["function"]["parameters"]["properties"]["test_arg"][ "type" ] == "string" ) @pytest.mark.asyncio async def test_generate_content_async_with_model_override( mock_acompletion, lite_llm_instance ): llm_request = LlmRequest( model="overridden_model", contents=[ types.Content( role="user", parts=[types.Part.from_text(text="Test prompt")] ) ], ) async for response in lite_llm_instance.generate_content_async(llm_request): assert response.content.role == "model" assert response.content.parts[0].text == "Test response" mock_acompletion.assert_called_once() _, kwargs = mock_acompletion.call_args assert kwargs["model"] == "overridden_model" assert kwargs["messages"][0]["role"] == "user" assert kwargs["messages"][0]["content"] == "Test prompt" @pytest.mark.asyncio async def test_generate_content_async_without_model_override( mock_acompletion, lite_llm_instance ): llm_request = LlmRequest( model=None, contents=[ types.Content( role="user", parts=[types.Part.from_text(text="Test prompt")] ) ], ) async for response in lite_llm_instance.generate_content_async(llm_request): assert response.content.role == "model" mock_acompletion.assert_called_once() _, kwargs = mock_acompletion.call_args assert kwargs["model"] == "test_model" @pytest.mark.asyncio async def test_generate_content_async_adds_fallback_user_message( mock_acompletion, lite_llm_instance ): llm_request = LlmRequest( contents=[ types.Content( role="user", parts=[], ) ] ) async for _ in lite_llm_instance.generate_content_async(llm_request): pass mock_acompletion.assert_called_once() _, kwargs = mock_acompletion.call_args user_messages = [ message for message in kwargs["messages"] if message["role"] == "user" ] assert any( message.get("content") == "Handle the requests as specified in the System Instruction." for message in user_messages ) assert ( sum(1 for content in llm_request.contents if content.role == "user") == 1 ) assert llm_request.contents[-1].parts[0].text == ( "Handle the requests as specified in the System Instruction." ) litellm_append_user_content_test_cases = [ pytest.param( LlmRequest( contents=[ types.Content( role="developer", parts=[types.Part.from_text(text="Test prompt")], ) ] ), 2, id="litellm request without user content", ), pytest.param( LlmRequest( contents=[ types.Content( role="user", parts=[types.Part.from_text(text="user prompt")], ) ] ), 1, id="litellm request with user content", ), pytest.param( LlmRequest( contents=[ types.Content( role="model", parts=[types.Part.from_text(text="model prompt")], ), types.Content( role="user", parts=[types.Part.from_text(text="user prompt")], ), types.Content( role="model", parts=[types.Part.from_text(text="model prompt")], ), ] ), 4, id="user content is not the last message scenario", ), ] @pytest.mark.parametrize( "llm_request, expected_output", litellm_append_user_content_test_cases ) def test_maybe_append_user_content( lite_llm_instance, llm_request, expected_output ): lite_llm_instance._maybe_append_user_content(llm_request) assert len(llm_request.contents) == expected_output function_declaration_test_cases = [ ( "simple_function", types.FunctionDeclaration( name="test_function", description="Test function description", parameters=types.Schema( type=types.Type.OBJECT, properties={ "test_arg": types.Schema(type=types.Type.STRING), "array_arg": types.Schema( type=types.Type.ARRAY, items=types.Schema( type=types.Type.STRING, ), ), "nested_arg": types.Schema( type=types.Type.OBJECT, properties={ "nested_key1": types.Schema(type=types.Type.STRING), "nested_key2": types.Schema(type=types.Type.STRING), }, required=["nested_key1"], ), }, required=["nested_arg"], ), ), { "type": "function", "function": { "name": "test_function", "description": "Test function description", "parameters": { "type": "object", "properties": { "test_arg": {"type": "string"}, "array_arg": { "items": {"type": "string"}, "type": "array", }, "nested_arg": { "properties": { "nested_key1": {"type": "string"}, "nested_key2": {"type": "string"}, }, "type": "object", "required": ["nested_key1"], }, }, "required": ["nested_arg"], }, }, }, ), ( "no_description", types.FunctionDeclaration( name="test_function_no_description", parameters=types.Schema( type=types.Type.OBJECT, properties={ "test_arg": types.Schema(type=types.Type.STRING), }, ), ), { "type": "function", "function": { "name": "test_function_no_description", "description": "", "parameters": { "type": "object", "properties": { "test_arg": {"type": "string"}, }, }, }, }, ), ( "empty_parameters", types.FunctionDeclaration( name="test_function_empty_params", parameters=types.Schema(type=types.Type.OBJECT, properties={}), ), { "type": "function", "function": { "name": "test_function_empty_params", "description": "", "parameters": { "type": "object", "properties": {}, }, }, }, ), ( "nested_array", types.FunctionDeclaration( name="test_function_nested_array", parameters=types.Schema( type=types.Type.OBJECT, properties={ "array_arg": types.Schema( type=types.Type.ARRAY, items=types.Schema( type=types.Type.OBJECT, properties={ "nested_key": types.Schema( type=types.Type.STRING ) }, ), ), }, ), ), { "type": "function", "function": { "name": "test_function_nested_array", "description": "", "parameters": { "type": "object", "properties": { "array_arg": { "items": { "properties": { "nested_key": {"type": "string"} }, "type": "object", }, "type": "array", }, }, }, }, }, ), ( "nested_properties", types.FunctionDeclaration( name="test_function_nested_properties", parameters=types.Schema( type=types.Type.OBJECT, properties={ "array_arg": types.Schema( type=types.Type.ARRAY, items=types.Schema( type=types.Type.OBJECT, properties={ "nested_key": types.Schema( type=types.Type.OBJECT, properties={ "inner_key": types.Schema( type=types.Type.STRING, ) }, ) }, ), ), }, ), ), { "type": "function", "function": { "name": "test_function_nested_properties", "description": "", "parameters": { "type": "object", "properties": { "array_arg": { "items": { "type": "object", "properties": { "nested_key": { "type": "object", "properties": { "inner_key": {"type": "string"}, }, }, }, }, "type": "array", }, }, }, }, }, ), ( "no_parameters", types.FunctionDeclaration( name="test_function_no_params", description="Test function with no parameters", ), { "type": "function", "function": { "name": "test_function_no_params", "description": "Test function with no parameters", "parameters": { "type": "object", "properties": {}, }, }, }, ), ( "parameters_without_required", types.FunctionDeclaration( name="test_function_no_required", description="Test function with parameters but no required field", parameters=types.Schema( type=types.Type.OBJECT, properties={ "optional_arg": types.Schema(type=types.Type.STRING), }, ), ), { "type": "function", "function": { "name": "test_function_no_required", "description": ( "Test function with parameters but no required field" ), "parameters": { "type": "object", "properties": { "optional_arg": {"type": "string"}, }, }, }, }, ), ] @pytest.mark.parametrize( "_, function_declaration, expected_output", function_declaration_test_cases, ids=[case[0] for case in function_declaration_test_cases], ) def test_function_declaration_to_tool_param( _, function_declaration, expected_output ): assert ( _function_declaration_to_tool_param(function_declaration) == expected_output ) def test_function_declaration_to_tool_param_without_required_attribute(): """Ensure tools without a required field attribute don't raise errors.""" class SchemaWithoutRequired: """Mimics a Schema object that lacks the required attribute.""" def __init__(self): self.properties = { "optional_arg": types.Schema(type=types.Type.STRING), } func_decl = types.FunctionDeclaration( name="function_without_required_attr", description="Function missing required attribute", ) func_decl.parameters = SchemaWithoutRequired() expected = { "type": "function", "function": { "name": "function_without_required_attr", "description": "Function missing required attribute", "parameters": { "type": "object", "properties": { "optional_arg": {"type": "string"}, }, }, }, } assert _function_declaration_to_tool_param(func_decl) == expected def test_function_declaration_to_tool_param_with_parameters_json_schema(): """Ensure function declarations using parameters_json_schema are handled. This verifies that when a FunctionDeclaration includes a raw `parameters_json_schema` dict, it is used directly as the function parameters in the resulting tool param. """ func_decl = types.FunctionDeclaration( name="fn_with_json", description="desc", parameters_json_schema={ "type": "object", "properties": { "a": {"type": "string"}, "b": {"type": "array", "items": {"type": "string"}}, }, "required": ["a"], }, ) expected = { "type": "function", "function": { "name": "fn_with_json", "description": "desc", "parameters": { "type": "object", "properties": { "a": {"type": "string"}, "b": {"type": "array", "items": {"type": "string"}}, }, "required": ["a"], }, }, } assert _function_declaration_to_tool_param(func_decl) == expected @pytest.mark.asyncio async def test_generate_content_async_with_system_instruction( lite_llm_instance, mock_acompletion ): mock_response_with_system_instruction = ModelResponse( choices=[ Choices( message=ChatCompletionAssistantMessage( role="assistant", content="Test response", ) ) ] ) mock_acompletion.return_value = mock_response_with_system_instruction llm_request = LlmRequest( contents=[ types.Content( role="user", parts=[types.Part.from_text(text="Test prompt")] ) ], config=types.GenerateContentConfig( system_instruction="Test system instruction" ), ) async for response in lite_llm_instance.generate_content_async(llm_request): assert response.content.role == "model" assert response.content.parts[0].text == "Test response" mock_acompletion.assert_called_once() _, kwargs = mock_acompletion.call_args assert kwargs["model"] == "test_model" assert kwargs["messages"][0]["role"] == "developer" assert kwargs["messages"][0]["content"] == "Test system instruction" assert kwargs["messages"][1]["role"] == "user" assert kwargs["messages"][1]["content"] == "Test prompt" @pytest.mark.asyncio async def test_generate_content_async_with_tool_response( lite_llm_instance, mock_acompletion ): mock_response_with_tool_response = ModelResponse( choices=[ Choices( message=ChatCompletionAssistantMessage( role="tool", content='{"result": "test_result"}', tool_call_id="test_tool_call_id", ) ) ] ) mock_acompletion.return_value = mock_response_with_tool_response llm_request = LlmRequest( contents=[ types.Content( role="user", parts=[types.Part.from_text(text="Test prompt")] ), types.Content( role="tool", parts=[ types.Part.from_function_response( name="test_function", response={"result": "test_result"}, ) ], ), ], config=types.GenerateContentConfig( system_instruction="test instruction", ), ) async for response in lite_llm_instance.generate_content_async(llm_request): assert response.content.role == "model" assert response.content.parts[0].text == '{"result": "test_result"}' mock_acompletion.assert_called_once() _, kwargs = mock_acompletion.call_args assert kwargs["model"] == "test_model" assert kwargs["messages"][2]["role"] == "tool" assert kwargs["messages"][2]["content"] == '{"result": "test_result"}' @pytest.mark.asyncio async def test_generate_content_async_with_usage_metadata( lite_llm_instance, mock_acompletion ): mock_response_with_usage_metadata = ModelResponse( choices=[ Choices( message=ChatCompletionAssistantMessage( role="assistant", content="Test response", ) ) ], usage={ "prompt_tokens": 10, "completion_tokens": 5, "total_tokens": 15, "cached_tokens": 8, }, ) mock_acompletion.return_value = mock_response_with_usage_metadata llm_request = LlmRequest( contents=[ types.Content( role="user", parts=[types.Part.from_text(text="Test prompt")] ), ], config=types.GenerateContentConfig( system_instruction="test instruction", ), ) async for response in lite_llm_instance.generate_content_async(llm_request): assert response.content.role == "model" assert response.content.parts[0].text == "Test response" assert response.usage_metadata.prompt_token_count == 10 assert response.usage_metadata.candidates_token_count == 5 assert response.usage_metadata.total_token_count == 15 assert response.usage_metadata.cached_content_token_count == 8 mock_acompletion.assert_called_once() def test_content_to_message_param_user_message(): content = types.Content( role="user", parts=[types.Part.from_text(text="Test prompt")] ) message = _content_to_message_param(content) assert message["role"] == "user" assert message["content"] == "Test prompt" @pytest.mark.parametrize("file_uri,mime_type", FILE_URI_TEST_CASES) def test_content_to_message_param_user_message_with_file_uri( file_uri, mime_type ): file_part = types.Part.from_uri(file_uri=file_uri, mime_type=mime_type) content = types.Content( role="user", parts=[ types.Part.from_text(text="Summarize this file."), file_part, ], ) message = _content_to_message_param(content) assert message["role"] == "user" assert isinstance(message["content"], list) assert message["content"][0]["type"] == "text" assert message["content"][0]["text"] == "Summarize this file." assert message["content"][1]["type"] == "file" assert message["content"][1]["file"]["file_id"] == file_uri assert "format" not in message["content"][1]["file"] @pytest.mark.parametrize("file_uri,mime_type", FILE_URI_TEST_CASES) def test_content_to_message_param_user_message_file_uri_only( file_uri, mime_type ): file_part = types.Part.from_uri(file_uri=file_uri, mime_type=mime_type) content = types.Content( role="user", parts=[ file_part, ], ) message = _content_to_message_param(content) assert message["role"] == "user" assert isinstance(message["content"], list) assert message["content"][0]["type"] == "file" assert message["content"][0]["file"]["file_id"] == file_uri assert "format" not in message["content"][0]["file"] def test_content_to_message_param_multi_part_function_response(): part1 = types.Part.from_function_response( name="function_one", response={"result": "result_one"}, ) part1.function_response.id = "tool_call_1" part2 = types.Part.from_function_response( name="function_two", response={"value": 123}, ) part2.function_response.id = "tool_call_2" content = types.Content( role="tool", parts=[part1, part2], ) messages = _content_to_message_param(content) assert isinstance(messages, list) assert len(messages) == 2 assert messages[0]["role"] == "tool" assert messages[0]["tool_call_id"] == "tool_call_1" assert messages[0]["content"] == '{"result": "result_one"}' assert messages[1]["role"] == "tool" assert messages[1]["tool_call_id"] == "tool_call_2" assert messages[1]["content"] == '{"value": 123}' def test_content_to_message_param_assistant_message(): content = types.Content( role="assistant", parts=[types.Part.from_text(text="Test response")] ) message = _content_to_message_param(content) assert message["role"] == "assistant" assert message["content"] == "Test response" def test_content_to_message_param_function_call(): content = types.Content( role="assistant", parts=[ types.Part.from_text(text="test response"), types.Part.from_function_call( name="test_function", args={"test_arg": "test_value"} ), ], ) content.parts[1].function_call.id = "test_tool_call_id" message = _content_to_message_param(content) assert message["role"] == "assistant" assert message["content"] == "test response" tool_call = message["tool_calls"][0] assert tool_call["type"] == "function" assert tool_call["id"] == "test_tool_call_id" assert tool_call["function"]["name"] == "test_function" assert tool_call["function"]["arguments"] == '{"test_arg": "test_value"}' def test_content_to_message_param_multipart_content(): """Test handling of multipart content where final_content is a list with text objects.""" content = types.Content( role="assistant", parts=[ types.Part.from_text(text="text part"), types.Part.from_bytes(data=b"test_image_data", mime_type="image/png"), ], ) message = _content_to_message_param(content) assert message["role"] == "assistant" # When content is a list and the first element is a text object with type "text", # it should extract the text (for providers like ollama_chat that don't handle lists well) # This is the behavior implemented in the fix assert message["content"] == "text part" assert message["tool_calls"] is None def test_content_to_message_param_single_text_object_in_list(): """Test extraction of text from single text object in list (for ollama_chat compatibility).""" from unittest.mock import patch # Mock _get_content to return a list with single text object with patch("google.adk.models.lite_llm._get_content") as mock_get_content: mock_get_content.return_value = [{"type": "text", "text": "single text"}] content = types.Content( role="assistant", parts=[types.Part.from_text(text="single text")], ) message = _content_to_message_param(content) assert message["role"] == "assistant" # Should extract the text from the single text object assert message["content"] == "single text" assert message["tool_calls"] is None def test_message_to_generate_content_response_text(): message = ChatCompletionAssistantMessage( role="assistant", content="Test response", ) response = _message_to_generate_content_response(message) assert response.content.role == "model" assert response.content.parts[0].text == "Test response" def test_message_to_generate_content_response_tool_call(): message = ChatCompletionAssistantMessage( role="assistant", content=None, tool_calls=[ ChatCompletionMessageToolCall( type="function", id="test_tool_call_id", function=Function( name="test_function", arguments='{"test_arg": "test_value"}', ), ) ], ) response = _message_to_generate_content_response(message) assert response.content.role == "model" assert response.content.parts[0].function_call.name == "test_function" assert response.content.parts[0].function_call.args == { "test_arg": "test_value" } assert response.content.parts[0].function_call.id == "test_tool_call_id" def test_message_to_generate_content_response_inline_tool_call_text(): message = ChatCompletionAssistantMessage( role="assistant", content=( '{"id":"inline_call","name":"get_current_time",' '"arguments":{"timezone_str":"Asia/Taipei"}} <|im_end|>system' ), ) response = _message_to_generate_content_response(message) assert len(response.content.parts) == 2 text_part = response.content.parts[0] tool_part = response.content.parts[1] assert text_part.text == "<|im_end|>system" assert tool_part.function_call.name == "get_current_time" assert tool_part.function_call.args == {"timezone_str": "Asia/Taipei"} assert tool_part.function_call.id == "inline_call" def test_message_to_generate_content_response_with_model(): message = ChatCompletionAssistantMessage( role="assistant", content="Test response", ) response = _message_to_generate_content_response( message, model_version="gemini-2.5-pro" ) assert response.content.role == "model" assert response.content.parts[0].text == "Test response" assert response.model_version == "gemini-2.5-pro" def test_parse_tool_calls_from_text_multiple_calls(): text = ( '{"name":"alpha","arguments":{"value":1}}\n' "Some filler text " '{"id":"custom","name":"beta","arguments":{"timezone":"Asia/Taipei"}} ' "ignored suffix" ) tool_calls, remainder = _parse_tool_calls_from_text(text) assert len(tool_calls) == 2 assert tool_calls[0].function.name == "alpha" assert json.loads(tool_calls[0].function.arguments) == {"value": 1} assert tool_calls[1].id == "custom" assert tool_calls[1].function.name == "beta" assert json.loads(tool_calls[1].function.arguments) == { "timezone": "Asia/Taipei" } assert remainder == "Some filler text ignored suffix" def test_parse_tool_calls_from_text_invalid_json_returns_remainder(): text = 'Leading {"unused": "payload"} trailing text' tool_calls, remainder = _parse_tool_calls_from_text(text) assert tool_calls == [] assert remainder == 'Leading {"unused": "payload"} trailing text' def test_split_message_content_and_tool_calls_inline_text(): message = { "role": "assistant", "content": ( 'Intro {"name":"alpha","arguments":{"value":1}} trailing content' ), } content, tool_calls = _split_message_content_and_tool_calls(message) assert content == "Intro trailing content" assert len(tool_calls) == 1 assert tool_calls[0].function.name == "alpha" assert json.loads(tool_calls[0].function.arguments) == {"value": 1} def test_split_message_content_prefers_existing_structured_calls(): tool_call = ChatCompletionMessageToolCall( type="function", id="existing", function=Function( name="existing_call", arguments='{"arg": "value"}', ), ) message = { "role": "assistant", "content": "ignored", "tool_calls": [tool_call], } content, tool_calls = _split_message_content_and_tool_calls(message) assert content == "ignored" assert tool_calls == [tool_call] def test_get_content_text(): parts = [types.Part.from_text(text="Test text")] content = _get_content(parts) assert content == "Test text" def test_get_content_text_inline_data_single_part(): parts = [ types.Part.from_bytes( data="Inline text".encode("utf-8"), mime_type="text/plain" ) ] content = _get_content(parts) assert content == "Inline text" def test_get_content_text_inline_data_multiple_parts(): parts = [ types.Part.from_bytes( data="First part".encode("utf-8"), mime_type="text/plain" ), types.Part.from_text(text="Second part"), ] content = _get_content(parts) assert content[0]["type"] == "text" assert content[0]["text"] == "First part" assert content[1]["type"] == "text" assert content[1]["text"] == "Second part" def test_get_content_text_inline_data_fallback_decoding(): parts = [ types.Part.from_bytes(data=b"\xff", mime_type="text/plain"), ] content = _get_content(parts) assert content == "ΓΏ" def test_get_content_image(): parts = [ types.Part.from_bytes(data=b"test_image_data", mime_type="image/png") ] content = _get_content(parts) assert content[0]["type"] == "image_url" assert ( content[0]["image_url"]["url"] == "data:image/png;base64,dGVzdF9pbWFnZV9kYXRh" ) assert "format" not in content[0]["image_url"] def test_get_content_video(): parts = [ types.Part.from_bytes(data=b"test_video_data", mime_type="video/mp4") ] content = _get_content(parts) assert content[0]["type"] == "video_url" assert ( content[0]["video_url"]["url"] == "data:video/mp4;base64,dGVzdF92aWRlb19kYXRh" ) assert "format" not in content[0]["video_url"] @pytest.mark.parametrize( "file_data,mime_type,expected_base64", FILE_BYTES_TEST_CASES ) def test_get_content_file_bytes(file_data, mime_type, expected_base64): parts = [types.Part.from_bytes(data=file_data, mime_type=mime_type)] content = _get_content(parts) assert content[0]["type"] == "file" assert content[0]["file"]["file_data"] == expected_base64 assert "format" not in content[0]["file"] @pytest.mark.parametrize("file_uri,mime_type", FILE_URI_TEST_CASES) def test_get_content_file_uri(file_uri, mime_type): parts = [types.Part.from_uri(file_uri=file_uri, mime_type=mime_type)] content = _get_content(parts) assert content[0]["type"] == "file" assert content[0]["file"]["file_id"] == file_uri assert "format" not in content[0]["file"] def test_get_content_audio(): parts = [ types.Part.from_bytes(data=b"test_audio_data", mime_type="audio/mpeg") ] content = _get_content(parts) assert content[0]["type"] == "audio_url" assert ( content[0]["audio_url"]["url"] == "data:audio/mpeg;base64,dGVzdF9hdWRpb19kYXRh" ) assert "format" not in content[0]["audio_url"] def test_to_litellm_role(): assert _to_litellm_role("model") == "assistant" assert _to_litellm_role("assistant") == "assistant" assert _to_litellm_role("user") == "user" assert _to_litellm_role(None) == "user" @pytest.mark.parametrize( "response, expected_chunks, expected_usage_chunk, expected_finished", [ ( ModelResponse( choices=[ { "message": { "content": "this is a test", } } ] ), [TextChunk(text="this is a test")], UsageMetadataChunk( prompt_tokens=0, completion_tokens=0, total_tokens=0 ), "stop", ), ( ModelResponse( choices=[ { "message": { "content": "this is a test", } } ], usage={ "prompt_tokens": 3, "completion_tokens": 5, "total_tokens": 8, }, ), [TextChunk(text="this is a test")], UsageMetadataChunk( prompt_tokens=3, completion_tokens=5, total_tokens=8 ), "stop", ), ( ModelResponse( choices=[ StreamingChoices( finish_reason=None, delta=Delta( role="assistant", tool_calls=[ ChatCompletionDeltaToolCall( type="function", id="1", function=Function( name="test_function", arguments='{"key": "va', ), index=0, ) ], ), ) ] ), [FunctionChunk(id="1", name="test_function", args='{"key": "va')], UsageMetadataChunk( prompt_tokens=0, completion_tokens=0, total_tokens=0 ), None, ), ( ModelResponse(choices=[{"finish_reason": "tool_calls"}]), [None], UsageMetadataChunk( prompt_tokens=0, completion_tokens=0, total_tokens=0 ), "tool_calls", ), ( ModelResponse(choices=[{}]), [None], UsageMetadataChunk( prompt_tokens=0, completion_tokens=0, total_tokens=0 ), "stop", ), ( ModelResponse( choices=[{ "finish_reason": "tool_calls", "message": { "role": "assistant", "content": ( '{"id":"call_1","name":"get_current_time",' '"arguments":{"timezone_str":"Asia/Taipei"}}' ), }, }], usage={ "prompt_tokens": 7, "completion_tokens": 9, "total_tokens": 16, }, ), [ FunctionChunk( id="call_1", name="get_current_time", args='{"timezone_str": "Asia/Taipei"}', index=0, ), ], UsageMetadataChunk( prompt_tokens=7, completion_tokens=9, total_tokens=16 ), "tool_calls", ), ( ModelResponse( choices=[{ "finish_reason": "tool_calls", "message": { "role": "assistant", "content": ( 'Intro {"id":"call_2","name":"alpha",' '"arguments":{"foo":"bar"}} wrap' ), }, }], usage={ "prompt_tokens": 11, "completion_tokens": 13, "total_tokens": 24, }, ), [ TextChunk(text="Intro wrap"), FunctionChunk( id="call_2", name="alpha", args='{"foo": "bar"}', index=0, ), ], UsageMetadataChunk( prompt_tokens=11, completion_tokens=13, total_tokens=24 ), "tool_calls", ), ], ) def test_model_response_to_chunk( response, expected_chunks, expected_usage_chunk, expected_finished ): result = list(_model_response_to_chunk(response)) observed_chunks = [] usage_chunk = None for chunk, finished in result: if isinstance(chunk, UsageMetadataChunk): usage_chunk = chunk continue observed_chunks.append((chunk, finished)) assert len(observed_chunks) == len(expected_chunks) for (chunk, finished), expected_chunk in zip( observed_chunks, expected_chunks ): if expected_chunk is None: assert chunk is None else: assert isinstance(chunk, type(expected_chunk)) assert chunk == expected_chunk assert finished == expected_finished if expected_usage_chunk is None: assert usage_chunk is None else: assert usage_chunk is not None assert usage_chunk == expected_usage_chunk @pytest.mark.asyncio async def test_acompletion_additional_args(mock_acompletion, mock_client): lite_llm_instance = LiteLlm( # valid args model="test_model", llm_client=mock_client, api_key="test_key", api_base="some://url", api_version="2024-09-12", # invalid args (ignored) stream=True, messages=[{"role": "invalid", "content": "invalid"}], tools=[{ "type": "function", "function": { "name": "invalid", }, }], ) async for response in lite_llm_instance.generate_content_async( LLM_REQUEST_WITH_FUNCTION_DECLARATION ): assert response.content.role == "model" assert response.content.parts[0].text == "Test response" assert response.content.parts[1].function_call.name == "test_function" assert response.content.parts[1].function_call.args == { "test_arg": "test_value" } assert response.content.parts[1].function_call.id == "test_tool_call_id" mock_acompletion.assert_called_once() _, kwargs = mock_acompletion.call_args assert kwargs["model"] == "test_model" assert kwargs["messages"][0]["role"] == "user" assert kwargs["messages"][0]["content"] == "Test prompt" assert kwargs["tools"][0]["function"]["name"] == "test_function" assert "stream" not in kwargs assert "llm_client" not in kwargs assert kwargs["api_base"] == "some://url" @pytest.mark.asyncio async def test_acompletion_with_drop_params(mock_acompletion, mock_client): lite_llm_instance = LiteLlm( model="test_model", llm_client=mock_client, drop_params=True ) async for _ in lite_llm_instance.generate_content_async( LLM_REQUEST_WITH_FUNCTION_DECLARATION ): pass mock_acompletion.assert_called_once() _, kwargs = mock_acompletion.call_args assert kwargs["drop_params"] is True @pytest.mark.asyncio async def test_completion_additional_args(mock_completion, mock_client): lite_llm_instance = LiteLlm( # valid args model="test_model", llm_client=mock_client, api_key="test_key", api_base="some://url", api_version="2024-09-12", # invalid args (ignored) stream=False, messages=[{"role": "invalid", "content": "invalid"}], tools=[{ "type": "function", "function": { "name": "invalid", }, }], ) mock_completion.return_value = iter(STREAMING_MODEL_RESPONSE) responses = [ response async for response in lite_llm_instance.generate_content_async( LLM_REQUEST_WITH_FUNCTION_DECLARATION, stream=True ) ] assert len(responses) == 4 mock_completion.assert_called_once() _, kwargs = mock_completion.call_args assert kwargs["model"] == "test_model" assert kwargs["messages"][0]["role"] == "user" assert kwargs["messages"][0]["content"] == "Test prompt" assert kwargs["tools"][0]["function"]["name"] == "test_function" assert kwargs["stream"] assert "llm_client" not in kwargs assert kwargs["api_base"] == "some://url" @pytest.mark.asyncio async def test_completion_with_drop_params(mock_completion, mock_client): lite_llm_instance = LiteLlm( model="test_model", llm_client=mock_client, drop_params=True ) mock_completion.return_value = iter(STREAMING_MODEL_RESPONSE) responses = [ response async for response in lite_llm_instance.generate_content_async( LLM_REQUEST_WITH_FUNCTION_DECLARATION, stream=True ) ] assert len(responses) == 4 mock_completion.assert_called_once() _, kwargs = mock_completion.call_args assert kwargs["drop_params"] is True @pytest.mark.asyncio async def test_generate_content_async_stream( mock_completion, lite_llm_instance ): mock_completion.return_value = iter(STREAMING_MODEL_RESPONSE) responses = [ response async for response in lite_llm_instance.generate_content_async( LLM_REQUEST_WITH_FUNCTION_DECLARATION, stream=True ) ] assert len(responses) == 4 assert responses[0].content.role == "model" assert responses[0].content.parts[0].text == "zero, " assert responses[0].model_version == "test_model" assert responses[1].content.role == "model" assert responses[1].content.parts[0].text == "one, " assert responses[1].model_version == "test_model" assert responses[2].content.role == "model" assert responses[2].content.parts[0].text == "two:" assert responses[2].model_version == "test_model" assert responses[3].content.role == "model" assert responses[3].content.parts[-1].function_call.name == "test_function" assert responses[3].content.parts[-1].function_call.args == { "test_arg": "test_value" } assert responses[3].content.parts[-1].function_call.id == "test_tool_call_id" assert responses[3].model_version == "test_model" mock_completion.assert_called_once() _, kwargs = mock_completion.call_args assert kwargs["model"] == "test_model" assert kwargs["messages"][0]["role"] == "user" assert kwargs["messages"][0]["content"] == "Test prompt" assert kwargs["tools"][0]["function"]["name"] == "test_function" assert ( kwargs["tools"][0]["function"]["description"] == "Test function description" ) assert ( kwargs["tools"][0]["function"]["parameters"]["properties"]["test_arg"][ "type" ] == "string" ) @pytest.mark.asyncio async def test_generate_content_async_stream_with_usage_metadata( mock_completion, lite_llm_instance ): streaming_model_response_with_usage_metadata = [ *STREAMING_MODEL_RESPONSE, ModelResponse( usage={ "prompt_tokens": 10, "completion_tokens": 5, "total_tokens": 15, }, choices=[ StreamingChoices( finish_reason=None, ) ], ), ] mock_completion.return_value = iter( streaming_model_response_with_usage_metadata ) responses = [ response async for response in lite_llm_instance.generate_content_async( LLM_REQUEST_WITH_FUNCTION_DECLARATION, stream=True ) ] assert len(responses) == 4 assert responses[0].content.role == "model" assert responses[0].content.parts[0].text == "zero, " assert responses[1].content.role == "model" assert responses[1].content.parts[0].text == "one, " assert responses[2].content.role == "model" assert responses[2].content.parts[0].text == "two:" assert responses[3].content.role == "model" assert responses[3].content.parts[-1].function_call.name == "test_function" assert responses[3].content.parts[-1].function_call.args == { "test_arg": "test_value" } assert responses[3].content.parts[-1].function_call.id == "test_tool_call_id" assert responses[3].usage_metadata.prompt_token_count == 10 assert responses[3].usage_metadata.candidates_token_count == 5 assert responses[3].usage_metadata.total_token_count == 15 mock_completion.assert_called_once() _, kwargs = mock_completion.call_args assert kwargs["model"] == "test_model" assert kwargs["messages"][0]["role"] == "user" assert kwargs["messages"][0]["content"] == "Test prompt" assert kwargs["tools"][0]["function"]["name"] == "test_function" assert ( kwargs["tools"][0]["function"]["description"] == "Test function description" ) assert ( kwargs["tools"][0]["function"]["parameters"]["properties"]["test_arg"][ "type" ] == "string" ) @pytest.mark.asyncio async def test_generate_content_async_stream_with_usage_metadata( mock_completion, lite_llm_instance ): """Tests that cached prompt tokens are propagated in streaming mode.""" streaming_model_response_with_usage_metadata = [ *STREAMING_MODEL_RESPONSE, ModelResponse( usage={ "prompt_tokens": 10, "completion_tokens": 5, "total_tokens": 15, "cached_tokens": 8, }, choices=[ StreamingChoices( finish_reason=None, ) ], ), ] mock_completion.return_value = iter( streaming_model_response_with_usage_metadata ) responses = [ response async for response in lite_llm_instance.generate_content_async( LLM_REQUEST_WITH_FUNCTION_DECLARATION, stream=True ) ] assert len(responses) == 4 assert responses[3].usage_metadata.prompt_token_count == 10 assert responses[3].usage_metadata.candidates_token_count == 5 assert responses[3].usage_metadata.total_token_count == 15 assert responses[3].usage_metadata.cached_content_token_count == 8 @pytest.mark.asyncio async def test_generate_content_async_multiple_function_calls( mock_completion, lite_llm_instance ): """Test handling of multiple function calls with different indices in streaming mode. This test verifies that: 1. Multiple function calls with different indices are handled correctly 2. Arguments and names are properly accumulated for each function call 3. The final response contains all function calls with correct indices """ mock_completion.return_value = MULTIPLE_FUNCTION_CALLS_STREAM llm_request = LlmRequest( contents=[ types.Content( role="user", parts=[types.Part.from_text(text="Test multiple function calls")], ) ], config=types.GenerateContentConfig( tools=[ types.Tool( function_declarations=[ types.FunctionDeclaration( name="function_1", description="First test function", parameters=types.Schema( type=types.Type.OBJECT, properties={ "arg": types.Schema(type=types.Type.STRING), }, ), ), types.FunctionDeclaration( name="function_2", description="Second test function", parameters=types.Schema( type=types.Type.OBJECT, properties={ "arg": types.Schema(type=types.Type.STRING), }, ), ), ] ) ], ), ) responses = [] async for response in lite_llm_instance.generate_content_async( llm_request, stream=True ): responses.append(response) # Verify we got the final response with both function calls assert len(responses) > 0 final_response = responses[-1] assert final_response.content.role == "model" assert len(final_response.content.parts) == 2 # Verify first function call assert final_response.content.parts[0].function_call.name == "function_1" assert final_response.content.parts[0].function_call.id == "call_1" assert final_response.content.parts[0].function_call.args == {"arg": "value1"} # Verify second function call assert final_response.content.parts[1].function_call.name == "function_2" assert final_response.content.parts[1].function_call.id == "call_2" assert final_response.content.parts[1].function_call.args == {"arg": "value2"} @pytest.mark.asyncio async def test_generate_content_async_non_compliant_multiple_function_calls( mock_completion, lite_llm_instance ): """Test handling of multiple function calls with same 0 indices in streaming mode. This test verifies that: 1. Multiple function calls with same indices (0) are handled correctly 2. Arguments and names are properly accumulated for each function call 3. The final response contains all function calls with correct incremented indices """ mock_completion.return_value = NON_COMPLIANT_MULTIPLE_FUNCTION_CALLS_STREAM llm_request = LlmRequest( contents=[ types.Content( role="user", parts=[types.Part.from_text(text="Test multiple function calls")], ) ], config=types.GenerateContentConfig( tools=[ types.Tool( function_declarations=[ types.FunctionDeclaration( name="function_1", description="First test function", parameters=types.Schema( type=types.Type.OBJECT, properties={ "arg": types.Schema(type=types.Type.STRING), }, ), ), types.FunctionDeclaration( name="function_2", description="Second test function", parameters=types.Schema( type=types.Type.OBJECT, properties={ "arg": types.Schema(type=types.Type.STRING), }, ), ), ] ) ], ), ) responses = [] async for response in lite_llm_instance.generate_content_async( llm_request, stream=True ): responses.append(response) # Verify we got the final response with both function calls assert len(responses) > 0 final_response = responses[-1] assert final_response.content.role == "model" assert len(final_response.content.parts) == 2 # Verify first function call assert final_response.content.parts[0].function_call.name == "function_1" assert final_response.content.parts[0].function_call.id == "0" assert final_response.content.parts[0].function_call.args == {"arg": "value1"} # Verify second function call assert final_response.content.parts[1].function_call.name == "function_2" assert final_response.content.parts[1].function_call.id == "1" assert final_response.content.parts[1].function_call.args == {"arg": "value2"} @pytest.mark.asyncio async def test_generate_content_async_stream_with_empty_chunk( mock_completion, lite_llm_instance ): """Tests that empty tool call chunks in a stream are ignored.""" mock_completion.return_value = iter(STREAM_WITH_EMPTY_CHUNK) responses = [ response async for response in lite_llm_instance.generate_content_async( LLM_REQUEST_WITH_FUNCTION_DECLARATION, stream=True ) ] assert len(responses) == 1 final_response = responses[0] assert final_response.content.role == "model" # Crucially, assert that only ONE tool call was generated, # proving the empty chunk was ignored. assert len(final_response.content.parts) == 1 function_call = final_response.content.parts[0].function_call assert function_call.name == "test_function" assert function_call.id == "call_abc" assert function_call.args == {"test_arg": "value"} @pytest.mark.asyncio def test_get_completion_inputs_generation_params(): # Test that generation_params are extracted and mapped correctly req = LlmRequest( contents=[ types.Content(role="user", parts=[types.Part.from_text(text="hi")]), ], config=types.GenerateContentConfig( temperature=0.33, max_output_tokens=123, top_p=0.88, top_k=7, stop_sequences=["foo", "bar"], presence_penalty=0.1, frequency_penalty=0.2, ), ) from google.adk.models.lite_llm import _get_completion_inputs _, _, _, generation_params = _get_completion_inputs(req) assert generation_params["temperature"] == 0.33 assert generation_params["max_completion_tokens"] == 123 assert generation_params["top_p"] == 0.88 assert generation_params["top_k"] == 7 assert generation_params["stop"] == ["foo", "bar"] assert generation_params["presence_penalty"] == 0.1 assert generation_params["frequency_penalty"] == 0.2 # Should not include max_output_tokens assert "max_output_tokens" not in generation_params assert "stop_sequences" not in generation_params @pytest.mark.asyncio def test_get_completion_inputs_empty_generation_params(): # Test that generation_params is None when no generation parameters are set req = LlmRequest( contents=[ types.Content(role="user", parts=[types.Part.from_text(text="hi")]), ], config=types.GenerateContentConfig(), ) from google.adk.models.lite_llm import _get_completion_inputs _, _, _, generation_params = _get_completion_inputs(req) assert generation_params is None @pytest.mark.asyncio def test_get_completion_inputs_minimal_config(): # Test that generation_params is None when config has no generation parameters req = LlmRequest( contents=[ types.Content(role="user", parts=[types.Part.from_text(text="hi")]), ], config=types.GenerateContentConfig( system_instruction="test instruction" # Non-generation parameter ), ) from google.adk.models.lite_llm import _get_completion_inputs _, _, _, generation_params = _get_completion_inputs(req) assert generation_params is None @pytest.mark.asyncio def test_get_completion_inputs_partial_generation_params(): # Test that generation_params is correctly built even with only some parameters req = LlmRequest( contents=[ types.Content(role="user", parts=[types.Part.from_text(text="hi")]), ], config=types.GenerateContentConfig( temperature=0.7, # Only temperature is set, others are None/default ), ) from google.adk.models.lite_llm import _get_completion_inputs _, _, _, generation_params = _get_completion_inputs(req) assert generation_params is not None assert generation_params["temperature"] == 0.7 # Should only contain the temperature parameter assert len(generation_params) == 1 def test_function_declaration_to_tool_param_edge_cases(): """Test edge cases for function declaration conversion that caused the original bug.""" from google.adk.models.lite_llm import _function_declaration_to_tool_param # Test function with None parameters (the original bug scenario) func_decl = types.FunctionDeclaration( name="test_function_none_params", description="Function with None parameters", parameters=None, ) result = _function_declaration_to_tool_param(func_decl) expected = { "type": "function", "function": { "name": "test_function_none_params", "description": "Function with None parameters", "parameters": { "type": "object", "properties": {}, }, }, } assert result == expected # Verify no 'required' field is added when parameters is None assert "required" not in result["function"]["parameters"] @pytest.mark.parametrize( "usage, expected_tokens", [ ({"prompt_tokens_details": {"cached_tokens": 123}}, 123), ( { "prompt_tokens_details": [ {"cached_tokens": 50}, {"cached_tokens": 25}, ] }, 75, ), ({"cached_prompt_tokens": 45}, 45), ({"cached_tokens": 67}, 67), ({"prompt_tokens": 100}, 0), ({}, 0), ("not a dict", 0), (None, 0), ({"prompt_tokens_details": {"cached_tokens": "not a number"}}, 0), (json.dumps({"cached_tokens": 89}), 89), (json.dumps({"some_key": "some_value"}), 0), ], ) def test_extract_cached_prompt_tokens(usage, expected_tokens): from google.adk.models.lite_llm import _extract_cached_prompt_tokens assert _extract_cached_prompt_tokens(usage) == expected_tokens def test_gemini_via_litellm_warning(monkeypatch): """Test that Gemini via LiteLLM shows warning.""" # Ensure environment variable is not set monkeypatch.delenv("ADK_SUPPRESS_GEMINI_LITELLM_WARNINGS", raising=False) with warnings.catch_warnings(record=True) as w: warnings.simplefilter("always") # Test with Google AI Studio Gemini via LiteLLM LiteLlm(model="gemini/gemini-2.5-pro-exp-03-25") assert len(w) == 1 assert issubclass(w[0].category, UserWarning) assert "[GEMINI_VIA_LITELLM]" in str(w[0].message) assert "better performance" in str(w[0].message) assert "gemini-2.5-pro-exp-03-25" in str(w[0].message) assert "ADK_SUPPRESS_GEMINI_LITELLM_WARNINGS" in str(w[0].message) def test_gemini_via_litellm_warning_vertex_ai(monkeypatch): """Test that Vertex AI Gemini via LiteLLM shows warning.""" # Ensure environment variable is not set monkeypatch.delenv("ADK_SUPPRESS_GEMINI_LITELLM_WARNINGS", raising=False) with warnings.catch_warnings(record=True) as w: warnings.simplefilter("always") # Test with Vertex AI Gemini via LiteLLM LiteLlm(model="vertex_ai/gemini-1.5-flash") assert len(w) == 1 assert issubclass(w[0].category, UserWarning) assert "[GEMINI_VIA_LITELLM]" in str(w[0].message) assert "vertex_ai/gemini-1.5-flash" in str(w[0].message) def test_gemini_via_litellm_warning_suppressed(monkeypatch): """Test that Gemini via LiteLLM warning can be suppressed.""" monkeypatch.setenv("ADK_SUPPRESS_GEMINI_LITELLM_WARNINGS", "true") with warnings.catch_warnings(record=True) as w: warnings.simplefilter("always") LiteLlm(model="gemini/gemini-2.5-pro-exp-03-25") assert len(w) == 0 def test_non_gemini_litellm_no_warning(): """Test that non-Gemini models via LiteLLM don't show warning.""" with warnings.catch_warnings(record=True) as w: warnings.simplefilter("always") # Test with non-Gemini model LiteLlm(model="openai/gpt-4o") assert len(w) == 0 @pytest.mark.parametrize( "finish_reason,response_content,expected_content,has_tool_calls", [ ("length", "Test response", "Test response", False), ("stop", "Complete response", "Complete response", False), ( "tool_calls", "", "", True, ), ("content_filter", "", "", False), ], ids=["length", "stop", "tool_calls", "content_filter"], ) @pytest.mark.asyncio async def test_finish_reason_propagation( mock_acompletion, lite_llm_instance, finish_reason, response_content, expected_content, has_tool_calls, ): """Test that finish_reason is properly propagated from LiteLLM response.""" tool_calls = None if has_tool_calls: tool_calls = [ ChatCompletionMessageToolCall( type="function", id="test_id", function=Function( name="test_function", arguments='{"arg": "value"}', ), ) ] mock_response = ModelResponse( choices=[ Choices( message=ChatCompletionAssistantMessage( role="assistant", content=response_content, tool_calls=tool_calls, ), finish_reason=finish_reason, ) ] ) mock_acompletion.return_value = mock_response llm_request = LlmRequest( contents=[ types.Content( role="user", parts=[types.Part.from_text(text="Test prompt")] ) ], ) async for response in lite_llm_instance.generate_content_async(llm_request): assert response.content.role == "model" # Verify finish_reason is mapped to FinishReason enum assert isinstance(response.finish_reason, types.FinishReason) # Verify correct enum mapping using the actual mapping from lite_llm assert response.finish_reason == _FINISH_REASON_MAPPING[finish_reason] if expected_content: assert response.content.parts[0].text == expected_content if has_tool_calls: assert len(response.content.parts) > 0 assert response.content.parts[-1].function_call.name == "test_function" mock_acompletion.assert_called_once() @pytest.mark.asyncio async def test_finish_reason_unknown_maps_to_other( mock_acompletion, lite_llm_instance ): """Test that unknown finish_reason values map to FinishReason.OTHER.""" mock_response = ModelResponse( choices=[ Choices( message=ChatCompletionAssistantMessage( role="assistant", content="Test response", ), finish_reason="unknown_reason_type", ) ] ) mock_acompletion.return_value = mock_response llm_request = LlmRequest( contents=[ types.Content( role="user", parts=[types.Part.from_text(text="Test prompt")] ) ], ) async for response in lite_llm_instance.generate_content_async(llm_request): assert response.content.role == "model" # Unknown finish_reason should map to OTHER assert isinstance(response.finish_reason, types.FinishReason) assert response.finish_reason == types.FinishReason.OTHER mock_acompletion.assert_called_once()