Bagatur 5fd1e67808 core[minor], integrations...[patch]: Support ToolCall as Tool input and ToolMessage as Tool output (#24038) ... Changes: - ToolCall, InvalidToolCall and ToolCallChunk can all accept a "type" parameter now - LLM integration packages add "type" to all the above - Tool supports ToolCall inputs that have "type" specified - Tool outputs ToolMessage when a ToolCall is passed as input - Tools can separately specify ToolMessage.content and ToolMessage.raw_output - Tools emit events for validation errors (using on_tool_error and on_tool_end) Example: ```python @tool("structured_api", response_format="content_and_raw_output") def _mock_structured_tool_with_raw_output( arg1: int, arg2: bool, arg3: Optional[dict] = None ) -> Tuple[str, dict]: """A Structured Tool""" return f"{arg1} {arg2}", {"arg1": arg1, "arg2": arg2, "arg3": arg3} def test_tool_call_input_tool_message_with_raw_output() -> None: tool_call: Dict = { "name": "structured_api", "args": {"arg1": 1, "arg2": True, "arg3": {"img": "base64string..."}}, "id": "123", "type": "tool_call", } expected = ToolMessage("1 True", raw_output=tool_call["args"], tool_call_id="123") tool = _mock_structured_tool_with_raw_output actual = tool.invoke(tool_call) assert actual == expected tool_call.pop("type") with pytest.raises(ValidationError): tool.invoke(tool_call) actual_content = tool.invoke(tool_call["args"]) assert actual_content == expected.content ``` --------- Co-authored-by: Erick Friis <erick@langchain.dev>		2024-07-11 14:54:02 -07:00
..
integration_tests	core, anthropic[patch]: support streaming tool calls when function has no arguments (#23915)	2024-07-05 18:57:41 +00:00
unit_tests	core[minor], integrations...[patch]: Support ToolCall as Tool input and ToolMessage as Tool output (#24038)	2024-07-11 14:54:02 -07:00
__init__.py