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https://github.com/hwchase17/langchain.git
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975b6129f6
Open to other thoughts on UX.
string input:
```python
as_tool = retriever.as_tool()
as_tool.invoke("cat") # [Document(...), ...]
```
typed dict input:
```python
class Args(TypedDict):
key: int
def f(x: Args) -> str:
return str(x["key"] * 2)
as_tool = RunnableLambda(f).as_tool(
name="my tool",
description="description", # name, description are inferred if not supplied
)
as_tool.invoke({"key": 3}) # "6"
```
for untyped dict input, allow specification of parameters + types
```python
def g(x: Dict[str, Any]) -> str:
return str(x["key"] * 2)
as_tool = RunnableLambda(g).as_tool(arg_types={"key": int})
result = as_tool.invoke({"key": 3}) # "6"
```
Passing the `arg_types` is slightly awkward but necessary to ensure tool
calls populate parameters correctly:
```python
from typing import Any, Dict
from langchain_core.runnables import RunnableLambda
from langchain_openai import ChatOpenAI
def f(x: Dict[str, Any]) -> str:
return str(x["key"] * 2)
runnable = RunnableLambda(f)
as_tool = runnable.as_tool(arg_types={"key": int})
llm = ChatOpenAI().bind_tools([as_tool])
result = llm.invoke("Use the tool on 3.")
tool_call = result.tool_calls[0]
args = tool_call["args"]
assert args == {"key": 3}
as_tool.run(args)
```
Contrived (?) example with langgraph agent as a tool:
```python
from typing import List, Literal
from typing_extensions import TypedDict
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent
llm = ChatOpenAI(temperature=0)
def magic_function(input: int) -> int:
"""Applies a magic function to an input."""
return input + 2
agent_1 = create_react_agent(llm, [magic_function])
class Message(TypedDict):
role: Literal["human"]
content: str
agent_tool = agent_1.as_tool(
arg_types={"messages": List[Message]},
name="Jeeves",
description="Ask Jeeves.",
)
agent_2 = create_react_agent(llm, [agent_tool])
```
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
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||
|---|---|---|
| .. | ||
| __init__.py | ||
| tool.py | ||
| unparse.py | ||