Unable to Pass Custom Component Result to Agent

[JeraldPeter]

Hi,

I created a custom Component to execute a "curl" commmand. If I execute as a "Component" I am able to get response in output but If I convert the same custom Componentas a "Tool" to agent. The result is not getting pass through. I am gettting below error;

Error Screenshot:-

If I understood correctly the response_format of the tool is coming as "Content" type. (Refer below Screenshot). I am not sure how to change to "String" or Any other datatype .

Langflow Workflow :- (A Simple Agent to execute a curl command)

"ExecuteCurlCommand" Component

'# from langflow.field_typing import Data
from langflow.custom import Component
from langflow.io import MessageTextInput, Output
from langflow.schema import Data
from langflow.schema.dotdict import dotdict
from langflow.base.curl.parse import parse_context
from loguru import logger

import subprocess

class ExecuteCurlCommand(Component):
display_name = "CurlCommandExec"
description = (
"This component allows you to make HTTP requests supplied by curl commands. "
"You need to provide fully qualifed pre populated values including headers , method , body & url"
)
icon = "Globe"
name = "ExecuteCurlCommand"
verbose = True
response_format = Message

inputs = [
    MessageTextInput(
        name="curl",
        display_name="cURL",
        info="Paste a curl command to populate the fields. "
        "This will fill in the dictionary fields for headers and body.",
        tool_mode=True,
    ),
]

outputs = [
    Output(display_name="Output", name="output", method="execute_curl"),
]

def execute_curl(self) -> Data:
    curl_command=self.curl;
    # Execute the command
    print("***** Incoming URL  :- ",curl_command)
    
    result = subprocess.run(curl_command, capture_output=True, text=True)

    # Print the output
    print("Status Code:", result.returncode)
    print("Response:", result.stdout)
    print("Error:", result.stderr)
    
    data = Data(value=result.stdout)
    self.status = data
    return data`

Response as a running component:-

Let me know right way to pass the result to Agent .

[dosubot[bot]]

Hey @JeraldPeter! I'm here to help you out with any bugs, questions, or contributions. Let's tackle this issue together!

To resolve the issue of passing the result from your custom ExecuteCurlCommand component to an agent, you need to ensure that the output is formatted correctly for the agent to interpret it. The error message indicates that the 'content' field must be a string or an array of objects, which suggests that the output from your component might not be in the expected format.

Here are a few steps you can take to address this:

1. Ensure Output is a String: The Data class should encapsulate the output as a string. In your execute_curl method, you are already capturing the output as result.stdout, which is a string. Ensure that this is correctly wrapped in the Data class and returned.

2. Check response_format: The response_format attribute in your component is set to Message. If the agent expects a string, you might need to adjust this to ensure compatibility. You can try setting response_format to a string type if possible.

3. Modify _build_results Function: If you have access to the _build_results function in base.py, ensure that it correctly handles the conversion of the Data object to a string or an array of objects. This might involve modifying how the content field is processed.

4. Debugging: Add logging or print statements to verify the type and content of the data being passed to the agent. This can help identify if the data is being transformed incorrectly at any point.

5. Agent Configuration: Ensure that the agent is configured to accept the output format you are providing. This might involve adjusting the agent's input schema or processing logic.

By ensuring that the output from your component is a string and matches the expected format of the agent, you should be able to pass the result successfully. If the issue persists, reviewing the agent's input handling logic might provide further insights.

To continue talking to Dosu, mention @dosu.

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[JeraldPeter]

@dosu

How to change response_format from content to string ?

[dosubot[bot]]

To change the response_format from content to string, you need to adjust the definition of your custom component. In your ExecuteCurlCommand class, ensure that the response_format attribute is set to a string type. Here's how you can modify it:

class ExecuteCurlCommand(Component):
    display_name = "CurlCommandExec"
    description = (
        "This component allows you to make HTTP requests supplied by curl commands. "
        "You need to provide fully qualified pre-populated values including headers, method, body & url."
    )
    icon = "Globe"
    name = "ExecuteCurlCommand"
    verbose = True
    response_format = "string"  # Change this line to set the response format to string

    inputs = [
        MessageTextInput(
            name="curl",
            display_name="cURL",
            info="Paste a curl command to populate the fields. "
            "This will fill in the dictionary fields for headers and body.",
            tool_mode=True,
        ),
    ]

    outputs = [
        Output(display_name="Output", name="output", method="execute_curl"),
    ]

    def execute_curl(self) -> Data:
        curl_command = self.curl
        result = subprocess.run(curl_command, capture_output=True, text=True)
        data = Data(value=result.stdout)
        self.status = data
        return data

By setting response_format to "string", you ensure that the output is interpreted as a string, which should align with the agent's expectations. Make sure to test this change to confirm that it resolves the issue.

To continue talking to Dosu, mention @dosu.

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[JeraldPeter]

@dosu

I tried but its not working.

[dosubot[bot]]

Ensure that the response_format is correctly set to "string" in your custom component. Additionally, verify that the Data object returned by the execute_curl method is structured correctly. The Data class should encapsulate the output as a string, and you should ensure that the value attribute is used correctly.

Here's a revised version of your execute_curl method:

def execute_curl(self) -> Data:
    curl_command = self.curl
    result = subprocess.run(curl_command, capture_output=True, text=True)
    data = Data(text=result.stdout)  # Ensure the text key is used
    self.status = data
    return data

Make sure that the Data object uses the text key to store the output, as this is typically expected for text data. If the issue persists, check the agent's input handling logic to ensure it is configured to accept the output format you are providing.

To continue talking to Dosu, mention @dosu.

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[JeraldPeter]

@dosu

I tried this too didn't work. I believe there is something we need to do with Tool definition with langchain.

https://api.python.langchain.com/en/latest/tools/langchain_core.tools.StructuredTool.html

If we refer this documenation we have ablity to modify the response output also to define a Tool for agent requries different implementation it seems.

[dosubot[bot]]

To address the issue with the tool definition in Langchain, you should ensure that your custom component is correctly defined as a StructuredTool. This involves using the StructuredTool.from_function method to wrap your function with the necessary schema and metadata. Here's how you can modify your tool definition:

1. Define the Tool Function: Ensure your function is defined correctly and returns the expected output.

2. Use StructuredTool.from_function: Wrap your function using this method to create a structured tool. This method allows you to specify the function, argument schema, and other metadata.

Here's an example of how you might define your tool:

from langchain_core.tools import StructuredTool

def execute_curl_command(curl_command: str) -> str:
    result = subprocess.run(curl_command, capture_output=True, text=True)
    return result.stdout

# Define the tool using StructuredTool.from_function
curl_tool = StructuredTool.from_function(
    func=execute_curl_command,
    args_schema={"curl_command": str},  # Define the argument schema
    name="CurlCommandExec",
    description="Executes a curl command and returns the response as a string.",
    return_direct=True  # Set to True if you want the output returned directly
)

3. Integrate with the Agent: Ensure that this tool is added to the agent's list of tools. The agent should be configured to use this tool correctly.

By defining your tool using StructuredTool.from_function, you ensure that the response output is correctly formatted and passed to the agent. This setup should help resolve the issue with passing the result from your custom component to the agent.

To continue talking to Dosu, mention @dosu.

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[JeraldPeter]

@dosu

The solution also didnt work. Actually the problem lies on return type; In a tool chain method. The response will be accepted as "String" or "Object". In the current implemenation I am sending the data as json string which is unable to parse it using "Tool Chain" Implemention.

To over come this issue we need to implement "LCToolCompontent" from "langchain_utilities" and redirect the output as is to Agent.

Here is the new fix for "curlExecutorCompontent"

`# from langflow.field_typing import Data
from langflow.custom import Component
from langflow.io import MessageTextInput, Output
from langflow.schema import Data
from langflow.schema.dotdict import dotdict
from langflow.base.curl.parse import parse_context
from loguru import logger

from langflow.base.langchain_utilities.model import LCToolComponent

import subprocess

class ExecuteCurlSchema(BaseModel):
curl: str = Field(..., description="The curl field to get execute curl command")

class ExecuteCurlCommand(LCToolComponent):
display_name = "CurlCommandExec"
description = (
"This component allows you to make HTTP requests supplied by curl commands. "
)
icon = "Globe"
name = "ExecuteCurlCommand"

inputs = [
    MessageTextInput(
        name="curl",
        display_name="cURL",
        info="Paste a curl command to populate the fields. "
        "This will fill in the dictionary fields for headers and body.",
        tool_mode=True,
    ),
]

def run_model(self) -> Message:
    return self._execute_curl(
        self.curl
        )

def build_tool(self) -> Tool:
    return StructuredTool.from_function(
        name="curl_executor",
        description="This component allows you to make HTTP requests supplied by curl commands.",
        func=self._execute_curl,
        args_schema=ExecuteCurlSchema,
        return_direct = True
    )

def _execute_curl(
    self,
    curl : str
    ) -> Message:
    curl_command=curl;
    # Execute the command
    print("***** Incoming URL  :- ",curl_command)
    
    result = subprocess.run(curl_command, capture_output=True, text=True)

    # Print the output
    print("Status Code:", result.returncode)
    print("Response:", result.stdout)
    print("Error:", result.stderr)
    self.status = result.stdout
    return result.stdout 

`

Here is output from Agent :-

[dosubot[bot]]

It looks like you've successfully implemented the LCToolComponent to handle the execution of your curl command and pass the output directly to the agent. By using the LCToolComponent, you ensure that the tool is properly integrated with the Langflow framework, allowing the output to be correctly interpreted by the agent.

The key changes you made include:

1. Subclassing LCToolComponent: This ensures that your component is recognized as a tool within the Langflow framework, with the necessary methods (run_model and build_tool) implemented.

2. Using StructuredTool.from_function: This method wraps your _execute_curl function, specifying the argument schema and ensuring the output is returned directly.

3. Returning the Output as a String: The _execute_curl method returns the result of the curl command as a string, which aligns with the expected input format for the agent.

These changes should resolve the issue of passing the result to the agent, as the output is now correctly formatted and integrated into the tool chain. If the output is being displayed correctly in the agent, it indicates that the implementation is working as intended.

To continue talking to Dosu, mention @dosu.

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