Metadata-Version: 2.1
Name: actionweaver
Version: 0.0.15
Summary: An Application Framework for Building LLM Agents
Author: Teng "Niel" Hu
Author-email: hu.niel92@gmail.com
Requires-Python: >=3.9,<4.0
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Requires-Dist: openai (==1.2.0)
Requires-Dist: pydantic (==2.2.1)
Requires-Dist: pyvis (>=0.3.2,<0.4.0)
Description-Content-Type: text/markdown


# 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!](https://github.com/TengHu/ActionWeaver)

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](docs/source/notebooks/use_cases.ipynb)**

## Installation
You can install ActionWeaver using pip:

```python
pip install actionweaver
```

## Quickstart
Using OpenAI's API to initialize a chat completion model
```python
from actionweaver.llms.openai.chat import OpenAIChatCompletion

chat = OpenAIChatCompletion("gpt-3.5-turbo")
``` 
or using Azure OpenAI service to start a chat completion model
```python
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")
```

### 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.

```python
@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. 
```python 
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.

```python
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 **[example notebook](docs/source/notebooks/quickstart.ipynb)**

##  Actions of Stateful Agent 

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

```python
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 **[example notebook](docs/source/notebooks/stateful_agent.ipynb)**

##  Grouping and Extending Actions Through Inheritance

In this example, we wrap the [LangChain Google search](https://python.langchain.com/docs/integrations/tools/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.
```python
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.


```python
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`.

```python
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 multiple action calling in one function.
- 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:

```bash
@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}
}
```


