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Refining Tool Schemas, Inputs, & Outputs

In many scenarios, the schemas, inputs, or outputs of tools may benefit from additional processing. This refinement step can significantly improve the quality and usability of your data. Here are three key use cases:
  • Modifying Schema: Modify the tool schema like the description of the parameters or the default values. For example, if youโ€™re manually providing certain values in input (like project_id), you can mark these fields as โ€œnot requiredโ€ in the schema so the LLM knows it doesnโ€™t need to ask for them.
  • Modifying Inputs: Add values as inputs to avoid specifying them in the prompt. e.g., passing project_id & team_id to the LINEAR_CREATE_LINEAR_ISSUE action.
  • Modifying Outputs: Modify outputs to get the desired data. e.g., extracting execution_id & issue_id from the response of LINEAR_CREATE_LINEAR_ISSUE action. Doing this can help keep the LLM context clean.
Composio empowers you with the ability to define custom functions as schema modifiers, input modifiers, or output modifiers. These can be applied at two levels:
  1. App-level: Affects all actions within a specific tool.
  2. Action-level: Tailored processing for individual actions.
1

Install required libraries

Python
pip install langchain langchain-openai composio-langchain
2

Import required libraries

Python
from langchain.agents import create_openai_functions_agent, AgentExecutor
from langchain import hub
from langchain_openai import ChatOpenAI
from composio_langchain import ComposioToolSet, Action, App
3

Import Prompt template & Initialize ChatOpenAI & composio toolset client

Python
prompt = hub.pull("hwchase17/openai-functions-agent")

llm = ChatOpenAI()
composio_toolset = ComposioToolSet()
4

Define a Custom Function to Modify Schema

This function will be used to modify the schema of the LINEAR_CREATE_LINEAR_ISSUE action, we get rid of the parameters project_id and team_id, later in the program we will pass these values as inputs to the action manually. The technical term for this is Action-level Schema Processing.
Python
def linear_schema_processor(schema: dict) -> dict:
    # This way the agent doesn't expect a project and team ID to run the action
    del schema['project_id']
    del schema['team_id']
    return schema
5

Define a Custom Function to Modify Input

This function will be used to modify the input data for the LINEAR_CREATE_LINEAR_ISSUE action. Here we have added the values for project_id and team_id parameters to the input data. By doing this, we can avoid specifying these values in the prompt and be sure that the agent uses the correct values. The technical term for this is Action-level Pre-Processing.
Python
def linear_pre_processor(input_data: dict) -> dict:
    input_data['project_id'] = 'e708162b-9b1a-4901-ab93-0f0149f9d805'  
    input_data['team_id'] = '249ee4cc-7bbb-4ff1-adbe-d3ef2f3df94e'
    return input_data
6

Define a Custom Function to Modify Output

This function will be used to modify the output data for the LINEAR_CREATE_LINEAR_ISSUE action. Here we are modifying the output to just return the action execution status successful & the issue_id, by doing this can keep the LLM context clean. The technical term for this is Action-level Post-Processing.
Python
def linear_post_processor(output_data: dict) -> dict:
    output_data = {
        'success': output_data['successfull'],
        'issue_id': output_data['id'],
    }
    return output_data
7

Get Linear Action from Composio

When getting tools using the get_tools() method, we need to pass the processors parameter to specify the schema, pre-processing, and post-processing functions. In this example, weโ€™re setting up an Action-level preprocessor by mapping the LINEAR_CREATE_LINEAR_ISSUE action to our linear_schema_processor, linear_pre_processor and linear_post_processor functions defined above respectively in schema, pre, and post processors.
Python
tools = composio_toolset.get_tools(
    processors={
        "schema": {
            Action.LINEAR_CREATE_LINEAR_ISSUE: linear_schema_processor,
        },
        "pre": {
            Action.LINEAR_CREATE_LINEAR_ISSUE: linear_pre_processor,
        },
        "post": {
            Action.LINEAR_CREATE_LINEAR_ISSUE: linear_post_processor,
        }
    },
    actions=[Action.LINEAR_CREATE_LINEAR_ISSUE]
)
8

Invoke the agent

Python
task = "Create a Linear Issue to update the frontend"

agent = create_openai_functions_agent(llm, tools, prompt)
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)

agent_executor.invoke({"input": task})

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How to use processors at App-level?

Above we saw how to use processors at the Action-level, below is an example of how to use them at the App-level. 
tools = composio_toolset.get_tools(
    processors={
        "pre": {
            App.<app_name>: processor_function,
        },
        "post": {
            App.<app_name>: processor_function,
        },
        "schema": {
            App.<app_name>: processor_function,
        },
    },
    apps=[App.<app_name>]
)
Ensure that your schema processing, preprocessing, and postprocessing functions are efficient and donโ€™t introduce significant latency.