agentaction 0.1.7

Action chaining and history for agents

Action chaining and history for agents

Why Use This?

This package helps manage and simplify the task of handling actions for an agent, especially a looping agent with chained functions. Actions can be anything, but the intended purpose is to work with openai function calling or other JSON/function calling LLM completion paradigms.

This package facilitates action creation, retrieval, and management, all while supporting vector search powered by chromadb to efficiently locate relevant actions.

Installation

pip install agentaction

Quickstart

Create a directory for your action modules:

mkdir actions

In this directory, you can create Python files (.py) that define your actions. Each file should define a get_actions function that returns a list of action dictionaries. Here is a sample action file sample_action.py:

def sample_function(args):
    # Your function logic here
    return "Hello, " + args["name"]

def get_actions():
    return [
        {
            "prompt": "Say hello",
            "builder": None,
            "handler": sample_function,
            "suggestion_after_actions": [],
            "never_after_actions": [],
            "function": {
                "name": "sample_function",
                "description": "Says hello to a person",
                "args": ["name"]
            }
        }
    ]

Now you can use the action manager in your agent. Here's a simple example:

from actions_manager import import_actions, use_action

# Import the actions
import_actions("./actions")

# Use an action
result = use_action("sample_function", {"name": "John"})
actions = search_actions("hello")
print(result)  # Should print: {"success": True, "output": "Hello, John"}

You can use the get_available_actions and get_action functions to search for and retrieve actions, respectively. And, don't forget to use the add_to_action_history function to keep track of which actions your agent has performed.

Usage Guide

Action Creation and Addition

from actions_manager import add_action

action = {
    "prompt": "Action Prompt",
    "builder": None, # the function that is called to build the action prompt
    "handler": your_function_name, # the function that is called when the action is executed
    "suggestion_after_actions": ["other_action_name1", "other_action_name2"],
    "never_after_actions": ["action_name3", "action_name4"],
    "function": {
        "name": "your_function_name",
        "description": "Your function description",
        "args": ["arg1", "arg2"]
    }
}

add_action("your_function_name", action)

Action Execution

from actions_manager import use_action

result = use_action("your_function_name", {"arg1": "value1", "arg2": "value2"})

Search for Relevant Actions

from actions_manager import get_available_actions

actions = get_available_actions("query_text")

API Documentation

compose_action_prompt(action: dict, values: dict) -> str

Generates a prompt for a given action based on provided values.

get_actions() -> dict

Retrieves all the actions present in the global actions dictionary.

add_to_action_history(action_name: str, action_arguments: dict={}, success: bool=True)

Adds an executed action to the action history.

get_action_history(n_results: int=20) -> list

Retrieves the most recent executed actions.

get_last_action() -> str or None

Retrieves the last executed action from the action history.

get_available_actions(search_text: str) -> list

Retrieves the available actions based on relevance and last action.

get_formatted_actions(search_text: str) -> list

Retrieve a dict containing the available actions in several formats

get_action_from_memory(action_name) -> dict or None

Retrieve an action from memory based on the action's name.

search_actions(search_text: str, n_results: int=5) -> list

Searches for actions based on a query text.

use_action(function_name: str, arguments: dict) -> dict

Executes a specific action by its function name.

add_action(name: str, action: dict)

Adds an action to the actions dictionary and 'actions' collection in memory.

get_action(name: str) -> dict or None

Retrieves a specific action by its name from the 'actions' dictionary.

remove_action(name: str) -> bool

Removes a specific action by name.

import_actions(actions_dir: str)

Imports all the actions present in the 'actions_dir' directory. The actions returned are then added to the 'actions' dictionary.

clear_actions()

Wipes the 'actions' collection in memory and resets the 'actions' dictionary.

Contributions Welcome

If you like this library and want to contribute in any way, please feel free to submit a PR and I will review it. Please note that the goal here is simplicity and accesibility, using common language and few dependencies.