actionweaver 0.0.6

An Application Framework for Building LLM Agents

ActionWeaver

🪡 An Application Framework for Building LLM Agents 🪡

ActionWeaver empowers developers to build robust and flexible tools using agents that leverage OpenAI's functions. With just a simple decorator, developers can transform ANY vanilla Python code into a powerful addition to their LLM agent. ActionWeaver unlocks a new type of programs by seamlessly integrating traditional programming with LLM powerful capabilities.

Features:

• Easily integrate ANY Python code into your agent's toolbox with just a single line.
• Leverage the features provided by other ecosystems like LangChain and incorporate them into your agent.
• Build complex orchestration of OpenAI functions and perform intricate hierarchies and chains of OpenAI function callings.
• ActionWeaver adopts structured logging, making the developer experience more efficient.

Demo notebook

Installation

You can install ActionWeaver using pip:

pip install actionweaver

LLM Agent with Action

Developers can construct an agent using OpenAI's LLM, and further enhance it using ActionWeaver's Action decorators. For instance, to enable the get_current_time function below to be invoked by an LLM, simply decorate it with the GetCurrentTime action:

import logging
from typing import List
from actionweaver import ActionHandlerMixin, action
from actionweaver.llms.openai.chat import OpenAIChatCompletion
from actionweaver.llms.openai.tokens import TokenUsageTracker

logger = logging.getLogger(__name__)

class AgentV0(ActionHandlerMixin):
    def __init__(self, logger):
        self.logger = logger
        self.token_tracker = TokenUsageTracker(budget=None, logger=logger)
        self.llm = OpenAIChatCompletion("gpt-3.5-turbo", token_usage_tracker = self.token_tracker, logger=logger)
        
        self.messages = [{"role": "system", "content": "You are a resourceful assistant."}]
        self.buffer = [] 
    
    def __call__(self, text):
        self.messages += [{"role": "user", "content":text}]
        return self.llm.create(messages=self.messages)
        
    @action(name="GetCurrentTime")
    def get_current_time(self) -> str:
        """
        Use this for getting the current time in the specified time zone.
        
        :return: A string representing the current time in the specified time zone.
        """
        import datetime
        current_time = datetime.datetime.now()
        
        return f"The current time is {current_time}"

agent = AgentV0(logger)

ActionWeaver utilizes the decorated method's signature and docstring as a description, passing them along to OpenAI's function API. The Action decorator is also highly adaptable and can be combined with other decorators, provided that the original signature is preserved.

You can invoke actions just like regular instance methods

agent.get_current_time() # Output: 'The current time is 20:34.'

You can also interact with the agent by asking questions, and the agent will dispatch the corresponding action using OpenAI functions

agent("what time is it") # Output: 'The current time is 20:40:30.'

Grouping and Extending Actions Through Inheritance

In this example, we wrap the LangChain Google search as a method, creating a powerful and extensible design pattern. By defining a new agent that inherits from the previous agent and LangChainTools, the new agent will inherit actions from both classes. This approach leverages object-oriented principles to enable rapid development and easy expansion of the agent's capabilities.

In the example below, through inheritance, the new agent can utilize the Google search tool method as well as any other actions defined in the parent classes. This structure makes it simple to build upon existing code.

class LangChainTools(ActionHandlerMixin):
    @action(name="GoogleSearch")
    def google_search(self, query: str) -> str:
        """
        Perform a Google search using the provided query. 
        
        This action requires `langchain` and `google-api-python-client` installed, and GOOGLE_API_KEY, GOOGLE_CSE_ID environment variables.
        See https://python.langchain.com/docs/integrations/tools/google_search.

        :param query: The search query to be used for the Google search.
        :return: The search results as a string.
        """
        from langchain.utilities import GoogleSearchAPIWrapper

        search = GoogleSearchAPIWrapper()
        return search.run(query)
    
class AgentV1(AgentV0, LangChainTools):
    pass

agent = AgentV1(logger)
agent("what happened today")

"""
Output: Here are some events that happened or are scheduled for today (August 23, 2023):\n\n1. Agreement State Event: Event Number 56678 - Maine Radiation Control Program.\n2. Childbirth Class - August 23, 2023, at 6:00 pm.\n3. No events scheduled for August 23, 2023, at Ambassador.\n4. Fine Arts - Late Start.\n5. Millersville University events.\n6. Regular City Council Meeting - August 23, 2023, at 10:00 AM.\n\nPlease note that these are just a few examples, and there may be other events happening as well.
"""

Orchestration of Actions

ActionWeaver enables the design of hierarchies and chains of actions with following features:

Scope: Each action is confined to its own visibility scope.

Orchestration expression:

1. SelectOne(['a1', 'a2', 'a3]): Prompting the llm to choose either 'a2' or 'a3' after 'a1' has been invoked, or to take no action.

2. RequireNext(['a1', 'a2', 'a3]): Mandating the language model to execute 'a2' immediately following 'a1', followed by 'a3'.

Example: Hierarchy of Actions

Instead of overwhelming OpenAI with an extensive list of functions, we can design a hierarchy of actions. In this example, we introduce a new class that defines three specific actions, reflecting a hierarchical approach:

• FileHandler with default scope: This action serves as the entry point for all file-manipulating actions, with orchestration logic SelectOne(["FileHandler", "ListFiles", "ReadFile"]).

• ListFiles with file scope.

• ReadFile with file scope.

class FileUtility(AgentV0):
    @action(name="FileHandler", orch_expr = SelectOne(["FileHandler", "ListFiles", "ReadFile"]))
    def handle_file(self, instruction: str) -> str:
        """
        Handles user instructions related to file operations. Put every context in the instruction only!
    
        Args:
            instruction (str): The user's instruction about file handling.
    
        Returns:
            str: The response to the user's question.
        """
        return instruction
        

    @action(name="ListFiles", scope="file")
    def list_all_files_in_repo(self, repo_path: str ='.') -> List:
        """
        Lists all the files in the given repository.
    
        :param repo_path: Path to the repository. Defaults to the current directory.
        :return: List of file paths.
        """

        logger.info(f"list_all_files_in_repo: {repo_path}")
        
        file_list = []
        for root, _, files in os.walk(repo_path):
            for file in files:
                file_list.append(os.path.join(root, file))
            break
        return file_list

    @action(name="ReadFile", scope="file")
    def read_from_file(self, file_path: str) -> str:
        """
        Reads the content of a file and returns it as a string.
    
        :param file_path: The path to the file that needs to be read.
        :return: A string containing the content of the file.
        """
        logger.info(f"read_from_file: {file_path}")
        
        with open(file_path, 'r') as file:
            content = file.read()
        return f"The file content: \n{content}"

agent = FileUtility(logger)

Example: Chains of Actions

We can also force LLM to ask for current time after read a file by setting orchestration in ReadFile.

class FileUtility(AgentV0):
    @action(name="FileHandler", orch_expr = SelectOne(["FileHandler", "ListFiles", "ReadFile"]))
    def handle_file(self, instruction: str) -> str:
        """
        Handles user instructions related to file operations. Put every context in the instruction only!
    
        Args:
            instruction (str): The user's instruction about file handling.
    
        Returns:
            str: The response to the user's question.
        """
        return instruction
        

    @action(name="ListFiles", scope="file")
    def list_all_files_in_repo(self, repo_path: str ='.') -> List:
        """
        Lists all the files in the given repository.
    
        :param repo_path: Path to the repository. Defaults to the current directory.
        :return: List of file paths.
        """

        logger.info(f"list_all_files_in_repo: {repo_path}")
        
        file_list = []
        for root, _, files in os.walk(repo_path):
            for file in files:
                file_list.append(os.path.join(root, file))
            break
        return file_list

    @action(name="ReadFile", scope="file", orch_expr = RequireNext(["ReadFile", "GetCurrentTime"]))
    def read_from_file(self, file_path: str) -> str:
        """
        Reads the content of a file and returns it as a string.
    
        :param file_path: The path to the file that needs to be read.
        :return: A string containing the content of the file.
        """
        logger.info(f"read_from_file: {file_path}")
        
        with open(file_path, 'r') as file:
            content = file.read()
        return f"The file content: \n{content}"

agent = FileUtility(logger)

Contributing

Contributions in the form of bug fixes, new features, documentation improvements, and pull requests are VERY welcomed.

📔 Citation & Acknowledgements

If you find ActionWeaver useful, please consider citing the project:

@software{Teng_Hu_ActionWeaver_2023,
    author = {Teng Hu},
    license = {Apache-2.0},
    month = Aug,
    title = {ActionWeaver: Application Framework for LLMs},
    url = {https://github.com/TengHu/ActionWeaver},
    year = {2023}
}