actionweaver 0.0.16

An Application Framework for Building LLM Agents

ActionWeaver

🪡 AI application framework that puts function-calling as a first-class feature 🪡

Supporting both OpenAI API and Azure OpenAI service!

---

Star us on Github!

ActionWeaver is an AI application framework that is designed based on the concept of LLM function calling, while popular frameworks like Langchain and Haystack are built around the concept of pipelines. ActionWeaver strives to be the most reliable, user-friendly, high-speed, and cost-effective function-calling framework for AI engineers.

Our vision is to enable seamlessly merging traditional computing systems with the powerful reasoning capabilities of Language Model Models.

Features:

• Function Calling as First-Class Citizen: Put function-calling at the core of the framework.
• Extensibility: Integrate ANY Python code into your agent's toolbox with a single line of code, or combining tools from other ecosystems like LangChain or Llama Index.
• Function Orchestration: Build complex orchestration of function callings. including intricate hierarchies or chains.
• Debuggability: structured logging improves the developer experience.

More use cases and demos

Installation

You can install ActionWeaver using pip:

pip install actionweaver

Quickstart

Using OpenAI's API to initialize a chat completion model

from actionweaver.llms.openai.chat import OpenAIChatCompletion

chat = OpenAIChatCompletion("gpt-3.5-turbo")

or using Azure OpenAI service to start a chat completion model

from actionweaver.llms.azure.chat import ChatCompletion

chat = ChatCompletion(
    model=YOUR_MODEL, 
    azure_endpoint = os.getenv("AZURE_OPENAI_ENDPOINT"), 
    api_key=os.getenv("AZURE_OPENAI_KEY"),  
    api_version="2023-10-01-preview")

or using the LATEST OpenAI API that supports parallel function calling !

from actionweaver.llms.openai.tools.chat import OpenAIChatCompletion

chat = OpenAIChatCompletion("gpt-3.5-turbo-1106")

Add ANY Python function as a tool to the Large Language Model.

Developers can attach ANY Python function as a tool with a simple decorator. In the following example, we introduce action GetCurrentTime, and then proceed to use the OpenAI API to invoke it.

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.

@action(name="GetCurrentTime")
def get_current_time() -> 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}"

# Ask LLM what time is it
chat.create([{"role": "user", "content": "what time is it now"}], actions = [get_current_time])

Force execution of an action

You can also force the language model to execute the action.

get_current_time.invoke(chat, [{"role": "user", "content": "what time is it now"}])

Structured extraction

You can create a Pydantic model to define the structure of the data you want to extract, and then force the language model to extract structured data from information in the prompt.

from pydantic import BaseModel
from actionweaver.actions.factories.pydantic_model_to_action import action_from_model

class User(BaseModel):
    name: str
    age: int

action_from_model(User).invoke(chat, [{"role": "user", "content": "Tom is 31 years old"}])

See openai api notebook

See azure openai ai service notebook

Actions of Stateful Agent

Developers also could create a class and enhance its functionality using ActionWeaver's action decorators.

class AgentV0:
    def __init__(self):
        self.llm = OpenAIChatCompletion("gpt-3.5-turbo")
        self.messages = []
        self.times = []
    
    def __call__(self, text):
        self.messages += [{"role": "user", "content":text}]
        return self.llm.create(messages=self.messages, actions = [self.get_current_time])
        
    @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()

        self.times += [str(current_time)]
        
        return f"The current time is {current_time}"

agent = AgentV0()

agent("what time is it") # Output: 'The current time is 20:34.'

# You can invoke actions just like regular instance methods
agent.get_current_time() # Output: 'The current time is 20:34.'

See Simple agent

See ReAct/Multi-Step Planning agent

Grouping and Extending Actions Through Inheritance

In this example, we wrap the LangChain Google search as a method, and define a new agent that inherits the previous agent and LangChain search tool. 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.

class LangChainTools:
    @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):
    def __call__(self, text):
        self.messages += [{"role": "user", "content":text}]
        return self.llm.create(messages=self.messages, actions = [self.google_search, self.get_current_time])

agent = AgentV1()
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 (Experimental)

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}"

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="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)

Roadmap

• support parallel function calling.
• support async function calling.
• function server with open source language model.

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}
}