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Fix Google Generative AI: 400 Request contains an invalid argument (#120741)
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tronikos
GitHub
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commit
d2a457c24f
@ -95,9 +95,12 @@ def _format_tool(
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) -> dict[str, Any]:
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"""Format tool specification."""
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parameters = _format_schema(
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convert(tool.parameters, custom_serializer=custom_serializer)
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)
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if tool.parameters.schema:
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parameters = _format_schema(
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convert(tool.parameters, custom_serializer=custom_serializer)
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)
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else:
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parameters = None
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return protos.Tool(
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{
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@ -409,3 +409,169 @@
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),
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])
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# ---
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# name: test_function_call
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list([
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tuple(
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'',
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tuple(
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),
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dict({
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'generation_config': dict({
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'max_output_tokens': 150,
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'temperature': 1.0,
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'top_k': 64,
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'top_p': 0.95,
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}),
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'model_name': 'models/gemini-1.5-flash-latest',
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'safety_settings': dict({
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'DANGEROUS': 'BLOCK_MEDIUM_AND_ABOVE',
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'HARASSMENT': 'BLOCK_MEDIUM_AND_ABOVE',
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'HATE': 'BLOCK_MEDIUM_AND_ABOVE',
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'SEXUAL': 'BLOCK_MEDIUM_AND_ABOVE',
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}),
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'system_instruction': '''
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Current time is 05:00:00. Today's date is 2024-05-24.
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You are a voice assistant for Home Assistant.
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Answer questions about the world truthfully.
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Answer in plain text. Keep it simple and to the point.
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Only if the user wants to control a device, tell them to expose entities to their voice assistant in Home Assistant.
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''',
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'tools': list([
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function_declarations {
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name: "test_tool"
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description: "Test function"
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parameters {
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type_: OBJECT
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properties {
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key: "param1"
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value {
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type_: ARRAY
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description: "Test parameters"
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items {
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type_: STRING
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format_: "lower"
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}
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}
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}
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}
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}
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,
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]),
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}),
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),
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tuple(
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'().start_chat',
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tuple(
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),
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dict({
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'history': list([
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]),
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}),
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),
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tuple(
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'().start_chat().send_message_async',
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tuple(
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'Please call the test function',
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),
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dict({
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}),
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),
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tuple(
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'().start_chat().send_message_async',
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tuple(
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parts {
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function_response {
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name: "test_tool"
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response {
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fields {
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key: "result"
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value {
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string_value: "Test response"
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}
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}
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}
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}
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}
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,
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),
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dict({
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}),
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),
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])
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# ---
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# name: test_function_call_without_parameters
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list([
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tuple(
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'',
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tuple(
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),
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dict({
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'generation_config': dict({
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'max_output_tokens': 150,
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'temperature': 1.0,
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'top_k': 64,
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'top_p': 0.95,
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}),
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'model_name': 'models/gemini-1.5-flash-latest',
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'safety_settings': dict({
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'DANGEROUS': 'BLOCK_MEDIUM_AND_ABOVE',
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'HARASSMENT': 'BLOCK_MEDIUM_AND_ABOVE',
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'HATE': 'BLOCK_MEDIUM_AND_ABOVE',
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'SEXUAL': 'BLOCK_MEDIUM_AND_ABOVE',
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}),
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'system_instruction': '''
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Current time is 05:00:00. Today's date is 2024-05-24.
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You are a voice assistant for Home Assistant.
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Answer questions about the world truthfully.
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Answer in plain text. Keep it simple and to the point.
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Only if the user wants to control a device, tell them to expose entities to their voice assistant in Home Assistant.
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''',
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'tools': list([
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function_declarations {
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name: "test_tool"
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description: "Test function"
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}
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,
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]),
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}),
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),
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tuple(
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'().start_chat',
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tuple(
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),
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dict({
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'history': list([
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]),
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}),
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),
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tuple(
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'().start_chat().send_message_async',
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tuple(
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'Please call the test function',
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),
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dict({
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}),
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),
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tuple(
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'().start_chat().send_message_async',
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tuple(
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parts {
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function_response {
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name: "test_tool"
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response {
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fields {
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key: "result"
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value {
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string_value: "Test response"
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}
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}
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}
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}
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}
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,
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),
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dict({
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}),
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),
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])
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# ---
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@ -172,6 +172,7 @@ async def test_function_call(
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hass: HomeAssistant,
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mock_config_entry_with_assist: MockConfigEntry,
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mock_init_component,
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snapshot: SnapshotAssertion,
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) -> None:
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"""Test function calling."""
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agent_id = mock_config_entry_with_assist.entry_id
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@ -256,6 +257,7 @@ async def test_function_call(
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device_id="test_device",
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),
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)
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assert [tuple(mock_call) for mock_call in mock_model.mock_calls] == snapshot
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# Test conversating tracing
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traces = trace.async_get_traces()
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@ -272,6 +274,87 @@ async def test_function_call(
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assert "Answer in plain text" in detail_event["data"]["prompt"]
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@patch(
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"homeassistant.components.google_generative_ai_conversation.conversation.llm.AssistAPI._async_get_tools"
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)
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async def test_function_call_without_parameters(
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mock_get_tools,
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hass: HomeAssistant,
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mock_config_entry_with_assist: MockConfigEntry,
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mock_init_component,
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snapshot: SnapshotAssertion,
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) -> None:
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"""Test function calling without parameters."""
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agent_id = mock_config_entry_with_assist.entry_id
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context = Context()
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mock_tool = AsyncMock()
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mock_tool.name = "test_tool"
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mock_tool.description = "Test function"
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mock_tool.parameters = vol.Schema({})
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mock_get_tools.return_value = [mock_tool]
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with patch("google.generativeai.GenerativeModel") as mock_model:
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mock_chat = AsyncMock()
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mock_model.return_value.start_chat.return_value = mock_chat
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chat_response = MagicMock()
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mock_chat.send_message_async.return_value = chat_response
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mock_part = MagicMock()
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mock_part.function_call = FunctionCall(name="test_tool", args={})
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def tool_call(hass, tool_input, tool_context):
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mock_part.function_call = None
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mock_part.text = "Hi there!"
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return {"result": "Test response"}
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mock_tool.async_call.side_effect = tool_call
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chat_response.parts = [mock_part]
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result = await conversation.async_converse(
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hass,
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"Please call the test function",
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None,
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context,
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agent_id=agent_id,
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device_id="test_device",
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)
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assert result.response.response_type == intent.IntentResponseType.ACTION_DONE
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assert result.response.as_dict()["speech"]["plain"]["speech"] == "Hi there!"
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mock_tool_call = mock_chat.send_message_async.mock_calls[1][1][0]
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mock_tool_call = type(mock_tool_call).to_dict(mock_tool_call)
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assert mock_tool_call == {
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"parts": [
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{
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"function_response": {
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"name": "test_tool",
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"response": {
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"result": "Test response",
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},
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},
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},
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],
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"role": "",
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}
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mock_tool.async_call.assert_awaited_once_with(
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hass,
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llm.ToolInput(
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tool_name="test_tool",
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tool_args={},
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),
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llm.LLMContext(
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platform="google_generative_ai_conversation",
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context=context,
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user_prompt="Please call the test function",
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language="en",
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assistant="conversation",
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device_id="test_device",
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),
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)
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assert [tuple(mock_call) for mock_call in mock_model.mock_calls] == snapshot
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@patch(
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"homeassistant.components.google_generative_ai_conversation.conversation.llm.AssistAPI._async_get_tools"
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)
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