mirror of
https://github.com/hwchase17/langchain.git
synced 2025-08-31 02:11:09 +00:00
5fd1e67808
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>
114 lines
2.9 KiB
Python
114 lines
2.9 KiB
Python
from typing import Any, List, Literal
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from langchain_core.messages import AIMessage
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from langchain_core.outputs import ChatGeneration
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from langchain_core.pydantic_v1 import BaseModel
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from langchain_anthropic.output_parsers import ToolsOutputParser
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_CONTENT: List = [
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{
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"type": "text",
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"text": "thought",
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},
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{"type": "tool_use", "input": {"bar": 0}, "id": "1", "name": "_Foo1"},
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{
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"type": "text",
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"text": "thought",
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},
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{"type": "tool_use", "input": {"baz": "a"}, "id": "2", "name": "_Foo2"},
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]
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_RESULT: List = [ChatGeneration(message=AIMessage(_CONTENT))] # type: ignore[misc]
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class _Foo1(BaseModel):
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bar: int
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class _Foo2(BaseModel):
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baz: Literal["a", "b"]
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def test_tools_output_parser() -> None:
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output_parser = ToolsOutputParser()
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expected = [
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{
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"name": "_Foo1",
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"args": {"bar": 0},
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"id": "1",
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"index": 1,
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"type": "tool_call",
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},
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{
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"name": "_Foo2",
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"args": {"baz": "a"},
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"id": "2",
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"index": 3,
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"type": "tool_call",
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},
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]
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actual = output_parser.parse_result(_RESULT)
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assert expected == actual
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def test_tools_output_parser_args_only() -> None:
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output_parser = ToolsOutputParser(args_only=True)
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expected = [
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{"bar": 0},
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{"baz": "a"},
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]
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actual = output_parser.parse_result(_RESULT)
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assert expected == actual
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expected = []
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actual = output_parser.parse_result([ChatGeneration(message=AIMessage(""))]) # type: ignore[misc]
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assert expected == actual
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def test_tools_output_parser_first_tool_only() -> None:
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output_parser = ToolsOutputParser(first_tool_only=True)
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expected: Any = {
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"name": "_Foo1",
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"args": {"bar": 0},
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"id": "1",
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"index": 1,
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"type": "tool_call",
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}
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actual = output_parser.parse_result(_RESULT)
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assert expected == actual
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expected = None
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actual = output_parser.parse_result([ChatGeneration(message=AIMessage(""))]) # type: ignore[misc]
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assert expected == actual
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def test_tools_output_parser_pydantic() -> None:
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output_parser = ToolsOutputParser(pydantic_schemas=[_Foo1, _Foo2])
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expected = [_Foo1(bar=0), _Foo2(baz="a")]
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actual = output_parser.parse_result(_RESULT)
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assert expected == actual
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def test_tools_output_parser_empty_content() -> None:
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class ChartType(BaseModel):
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chart_type: Literal["pie", "line", "bar"]
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output_parser = ToolsOutputParser(
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first_tool_only=True, pydantic_schemas=[ChartType]
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)
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message = AIMessage(
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"",
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tool_calls=[
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{
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"name": "ChartType",
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"args": {"chart_type": "pie"},
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"id": "foo",
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"type": "tool_call",
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}
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],
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)
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actual = output_parser.invoke(message)
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expected = ChartType(chart_type="pie")
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assert expected == actual
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