fhaviary 0.7.2

Gymnasium framework for training language model agents on constructive tasks

aviary

Gymnasium framework for training language model agents on constructive tasks.

Installation

To install aviary:

pip install -e .

To install aviary and the provided environments:

pip install -e . -e packages/gsm8k -e packages/hotpotqa

To run test suites you will need to set the OPENAI_API_KEY and ANTHROPIC_API_KEY environment variables. In ~/.bashrc you can add:

export OPENAI_API_KEY=your_openai_api_key
export ANTHROPIC_API_KEY=your_anthropic_api_key

Messages

Communication between the agent and environment is done through messages. Messages have two attributes:

msg = Message(content="Hello, world!", role="assistant")

For the meaning of role, see the table below. You can change around roles as desired, except for tool which has a special meaning in aviary.

Role	Host	Example
assistant	AI	ChatGPT
system	AI system prompt	You are an AI assistant
user	User	You, using ChatGPT
tool	Tool in the environment	Some custom number crunching program

The content is a string that can be anything, or a null value.

Environment

An environment should have two functions:

obs_msgs, tools = await env.reset()
new_obs_msgs, reward, done, truncated = await env.step(action_msg)

where messages are how communication is passed. The action_msg should be ToolRequestMessage which is 1 or more calls to tools provided by the reset. The obs_msgs returned from the environment are ToolResponseMessage or other general messages that are observations. The reward is a scalar value. The done is a boolean value. The truncated is a boolean value.

Let's see a complete example for building an environment.

Environment subclass and state

First we define an environment by subclassing the Environment and defining a state. The state is all variables that change per step and we want to keep together. It will be accessible in your tools, so you can use it to store information that you want to persist between steps and between tools.

from pydantic import BaseModel
from aviary.env import Environment

class ExampleState(BaseModel):
    reward: float = 0
    done: bool = False

class ExampleEnv(Environment[ExampleState]):
    state: ExampleState

We do not have other variables aside from state for this environment. We could have things like configuration, a name, tasks, etc. attached to it.

Common environments

We expose a simple interface to some commonly-used environments that are included in the aviary codebase. You can instantiate one by referring to its name and passing keyword arguments:

from aviary.env import Environment

env = Environment.from_name(
    "calculator",
    problem_id="example-problem",
    problem="What is 2+3?",
    answer=5,
)

Included with some environments are collections of problems that define training or evaluation datasets. We refer to these as TaskDatasets, and expose them with a similar interface:

from aviary.env import TaskDataset

dataset = TaskDataset.from_name("hotpotqa", split="dev")

Tool

Now let's define our functions that will make up our tools. We'll just have one tool. Tools can optionally have their last argument be state which is the environment state. This is how you can access the state. This argument will not be exposed to the agent as a possible parameter and will be injected by the environment (if part of the function signature).

def print_story(story: str, state: ExampleState) -> None:
    """Print a story.

    Args:
        story: Story to print.
        state: Environment state (hidden from agent - can put this string to shutup linter).
    """
    print(story)
    state.reward = 1
    state.done = True

There is special syntax we use for defining a tool. The tool is built from the following parts of the function: its name, its arguments names, the arguments types, and the docstring. The docstring is parsed to get a description of the function and its arguments, so match the syntax carefully.

Setting the state.done = True is how we indicate completion. This example terminates immediately. You can use other ways to decide to terminate.

You can make the function async - the environment will account for that when the tool is called.

Advanced tool descriptions

We support more sophisticated signatures, for those who want to use them:

• Multiline docstrings
• Non-primitive type hints (e.g. type unions)
• Default values
• Exclusion of info below \f (see below)

If you have summary-level information that belongs in the docstring, but you don't want it part of the Tool.info.description, add a r prefix to the docstring and inject \f before the summary information to exclude. This convention was created by FastAPI (docs).

def print_story(story: str | bytes, state: ExampleState) -> None:
    r"""Print a story.

    Extra information that is part of the tool description.

    \f

    This sentence is excluded because it's an implementation detail.

    Args:
        story: Story to print, either as a string or bytes.
        state: Environment state.
    """
    print(story)
    state.reward = 1
    state.done = True

Environment reset method

Now we'll define the reset function which should set-up the tools and return one or more observations and the tools.

from aviary.message import Message
from aviary.tools import Tool

def reset(self) -> tuple[list[Message], list[Tool]]:
    self.tools = [Tool.from_function(ExampleEnv.print_story)]

    start = Message(content="Write a 5 word story and call print")

    return [start], self.tools

Environment step method

Now we can define the step function which should take an action and return the next observation, reward, done, and if the episode was truncated.

from aviary.message import Message

async def step(self, action: Message) -> tuple[list[Message], float, bool, bool]:
    msgs: list[Message] = await self.exec_tool_calls(action, state=self.state)
    return msgs, self.state.reward, self.state.done, False

You will probably often use this specific syntax for calling the tools - calling exec_tool_calls with the action.

Environment export_frame method

Lastly, we can define a function to export the state for visualization or debugging purposes. This is optional.

from aviary.env import Frame

def export_frame(self) -> Frame:
    return Frame(
        state={"done": self.state.done, "reward": self.state.reward},
        info={"tool_names": [t.info.name for t in self.tools]},
    )

Environments

GSM8k Environment

What it does

The GSM8k environment allows agents to solve math word problems from the GSM8k dataset.

Installation

To install the GSM8k environment, run the following command:

pip install fhaviary[gsm8k]

HotPotQA Environment

What it does

The HotPotQA environment allows agents to perform multi-hop question answering on the HotPotQA dataset.

Installation

To install the HotPotQA environment, run the following command:

pip install fhaviary[hotpotqa]

PaperQA Environment

What it does

The PaperQA environment allows agents to perform question answering on the PaperQA dataset.

Installation

To install the PaperQA environment, follow the instructions in the PaperQA repository.