Dynamically setting redirect_true on tools

[tyadav7]

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Example Code

class CustomCalculatorTool(BaseTool):
    name = "Calculator"
    description = "useful for when you need to answer questions about math"
    args_schema: Type[BaseModel] = CalculatorInput
    return_direct: bool = True

    def _run(
        self, a: int, b: int, run_manager: Optional[CallbackManagerForToolRun] = None
    ) -> str:
        """Use the tool."""
        return a * b

    async def _arun(
        self,
        a: int,
        b: int,
        run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
    ) -> str:
        """Use the tool asynchronously."""
        raise NotImplementedError("Calculator does not support async")


#want to set return_direct dynamically

Description

I am trying to build a tool and I want to dynamically set the return_dircect flag, since in some response I want the LLM to fetch data from the tool and then return insights on the data otherwise when the data is sufficient enough to be shown directly to the user i want the return directly.

System Info

System Information

OS: Windows
OS Version: 10.0.22621
Python Version: 3.10.0 (tags/v3.10.0:b494f59, Oct 4 2021, 19:00:18) [MSC v.1929 64 bit (AMD64)]
Package Information

langchain_core: 0.2.34
langchain: 0.2.14
langchain_community: 0.2.12
langsmith: 0.1.103
langchain_chroma: 0.1.3
langchain_cli: 0.0.30
langchain_openai: 0.1.22
langchain_text_splitters: 0.2.2
langserve: 0.2.2

Optional packages not installed

langgraph

[dosubot[bot]]

To dynamically set the return_direct flag in your CustomCalculatorTool class, you can modify the return_direct attribute based on the response within the _run method. Here's an example of how you can achieve this:

from typing import Optional, Type
from langchain.pydantic_v1 import BaseModel, Field
from langchain_core.callbacks import (
    AsyncCallbackManagerForToolRun,
    CallbackManagerForToolRun,
)
from langchain_core.tools import BaseTool

class CalculatorInput(BaseModel):
    a: int = Field(description="first number")
    b: int = Field(description="second number")

class CustomCalculatorTool(BaseTool):
    name = "Calculator"
    description = "useful for when you need to answer questions about math"
    args_schema: Type[BaseModel] = CalculatorInput
    return_direct: bool = False  # Default value

    def _run(
        self, a: int, b: int, run_manager: Optional[CallbackManagerForToolRun] = None
    ) -> str:
        """Use the tool."""
        result = a * b
        # Dynamically set return_direct based on the result
        if result > 100:  # Example condition
            self.return_direct = True
        else:
            self.return_direct = False
        return result

    async def _arun(
        self,
        a: int,
        b: int,
        run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
    ) -> str:
        """Use the tool asynchronously."""
        result = self._run(a, b, run_manager=run_manager.get_sync())
        return result

# Example usage
multiply = CustomCalculatorTool()
print(multiply.invoke({"a": 2, "b": 3}))  # Should set return_direct to False
print(multiply.return_direct)
print(multiply.invoke({"a": 50, "b": 3}))  # Should set return_direct to True
print(multiply.return_direct)

In this example, the return_direct flag is set dynamically based on the result of the multiplication. If the result is greater than 100, return_direct is set to True; otherwise, it is set to False ^[1].

To continue talking to Dosu, mention @dosu.

[tyadav7]

Is it possible if i am using an implementation like

@tool
def multiply(a: int, b: int) -> int:
"""Multiply two numbers."""
return a * b

[dosubot[bot]]

To dynamically set the return_direct flag using the @tool decorator, you can create a wrapper function that handles the logic for setting the flag. Here's an example:

from langchain_core.tools import tool

def dynamic_return_direct_tool(func):
    def wrapper(*args, **kwargs):
        result = func(*args, **kwargs)
        if result > 100:  # Example condition
            wrapper.return_direct = True
        else:
            wrapper.return_direct = False
        return result
    wrapper.return_direct = False  # Default value
    return wrapper

@dynamic_return_direct_tool
@tool
def multiply(a: int, b: int) -> int:
    """Multiply two numbers."""
    return a * b

# Example usage
print(multiply(2, 3))  # Should set return_direct to False
print(multiply.return_direct)
print(multiply(50, 3))  # Should set return_direct to True
print(multiply.return_direct)

In this example, the dynamic_return_direct_tool function is a decorator that wraps the multiply function and sets the return_direct attribute based on the result of the multiplication.

To continue talking to Dosu, mention @dosu.

[tyadav7]

I cannot pass this tool to agentExecutor like this see below

AgentExecutor(agent=agent, llm=llm, tools=tools, verbose=True)

[dosubot[bot]]

To pass a tool with a dynamically set return_direct flag to AgentExecutor, you can create the tool with the return_direct flag set to True and then initialize the agent with this tool. Here is an example:

from langchain_core.tools import Tool
from langchain_core.agents import AgentExecutor, initialize_agent, AgentType
from langchain_openai import ChatOpenAI

# Define the tool with dynamic return_direct logic
def dynamic_return_direct_tool(func):
    def wrapper(*args, **kwargs):
        result = func(*args, **kwargs)
        if result > 100:  # Example condition
            wrapper.return_direct = True
        else:
            wrapper.return_direct = False
        return result
    wrapper.return_direct = False  # Default value
    return wrapper

@dynamic_return_direct_tool
@tool
def multiply(a: int, b: int) -> int:
    """Multiply two numbers."""
    return a * b

# Create the tool instance
multiply_tool = Tool(
    name="Multiply",
    func=multiply,
    description="Multiply two numbers",
    return_direct=multiply.return_direct,
)

# Initialize the agent with the tool
llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0)
tools = [multiply_tool]
agent = initialize_agent(
    tools,
    llm,
    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
)

# Create the AgentExecutor
agent_executor = AgentExecutor(agent=agent, llm=llm, tools=tools, verbose=True)

# Example usage
output = agent_executor.run("Multiply 50 and 3")
print(output)

In this example, the multiply function is wrapped with the dynamic_return_direct_tool decorator, which sets the return_direct attribute based on the result. The Tool instance is then created with the return_direct flag set to the current value of multiply.return_direct. This tool is passed to the initialize_agent function to create the agent, which is subsequently used with AgentExecutor ^[1][2].

To continue talking to Dosu, mention @dosu.

[tyadav7]

Okay this works but one additional question unrelated to the above my python debugger now fails to stop inside my function any reason why here is my launch.json snippet

{
"name": "Python Debugger: FastAPI",
"type": "debugpy",
"request": "launch",
"module": "uvicorn",
"justMyCode": false,
"args": [
"app.server:app"
],
"jinja": true,
"envFile": "${workspaceFolder}/.env"
}

[dosubot[bot]]

Ensure that the app.server:app module is correctly configured to run with uvicorn in the launch.json file. The app instance is created in the app/server.py file and is ready to be served by uvicorn:

from fastapi import FastAPI
from fastapi.responses import RedirectResponse
from langserve import add_routes

app = FastAPI()

@app.get("/")
async def redirect_root_to_docs():
    return RedirectResponse("/docs")

# Edit this to add the chain you want to add
add_routes(app, NotImplemented)

if __name__ == "__main__":
    import uvicorn

    uvicorn.run(app, host="0.0.0.0", port=8000)

To run this with uvicorn, you would use the command:

uvicorn app.server:app --host 0.0.0.0 --port 8000

Make sure that the justMyCode setting in the launch.json file is set to false, which you have already done. This setting allows the debugger to stop at breakpoints in all code, not just your own.

If the debugger still fails to stop inside your function, consider the following:

1. Check Breakpoints: Ensure that breakpoints are set correctly in your code.
2. Debugpy Installation: Verify that the debugpy package is installed and up-to-date in your development environment.
3. Custom Middleware or Exception Handlers: Check if there are any custom exception handlers or middleware in app.server:app that might interfere with the debugger.
4. Environment Variables: Ensure that there are no additional settings or configurations in the .env file that might impact the debugger's behavior.

If none of these steps resolve the issue, there might be specific configurations or middleware in app.server:app that could affect the debugger, but I couldn't find specific information on this within the repository ^[1].

To continue talking to Dosu, mention @dosu.