aviary

Gymnasium framework for training language model agents on constructive tasks.

• Installation
  • Google Colab
  • Developer Installation
• Messages
• Environment
• Functional Environments
• Subclass Environments
  • Common environments
  • Tool
    • Advanced tool descriptions
  • Environment reset method
  • Environment step method
  • Environment export_frame method
  • View Environment Tools
• Environments

Installation

To install aviary (note fh stands for FutureHouse):

pip install fhaviary

To install aviary with the bundled environments, please see the Environments section below.

Google Colab

As of 10/25/2024, unfortunately Google Colab does not yet support Python 3.11 or 3.12 (issue).

Thus, as a workaround, you will need to install Python 3.11 into your notebook. Here is a sample notebook showing how to do this: https://colab.research.google.com/drive/1mejZ5cxgKZrMpYEe0iRoanaGGQ0Cr6WI?usp=sharing

Also, note that async code works in Google Colab.

Developer Installation

For local development, please see the CONTRIBUTING.md.

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(s)
assistant	Agent	A tool selector agent's tool selection message
system	Agent system prompt	"You are an agent."
user	Environment system prompt or emitted observation	HotPotQA problem to solve, or details of an internal env failure
tool	Result of tool run in the environment	Some number crunching program's output

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.

Functional Environments

The easiest way to create an environment is using the functional interface, which just uses functions and decorators to define environments. First, let's define what the environment looks like by defining its start function:

from aviary.core import fenv


@fenv.start()
def my_env(topic):
    # return first observation, and the starting environment state
    # (empty in this case)
    return f"Write a story about {topic}", {}

Note that the decorator is a call (start()). The start decorator starts the definition of an environment. The function, my_env, can take whatever you would like and should return a tuple containing the first observation and anything you would like to store about the state of the environment (used to persist/share things between tools). The state will always automatically have an optional reward and a boolean done that indicates if the environment is complete.

Now we can define some tools:

@my_env.tool()
def multiply(x: float, y: float) -> float:
    """Multiply two numbers."""
    return x * y


@my_env.tool()
def print_story(story: str | bytes, state) -> None:
    """Print a story to user and complete task."""
    print(story)
    state.reward = 1
    state.done = True

The tools will be converted into things visible for LLMs using the type hints and the variable descriptions. Thus, the type hinting can be valuable for the agent using it correctly. The docstrings are also passed to the LLM, and is the primary way (along with function name) for communicating about intended tool usage.

You can access the state variable in tools, which will have any fields you passed in the return tuple of start(). For example, if you returned {'foo': 'bar'}, then you could access state.foo in the tools.

Stop an environment or set a reward via the state variable as shown the second tool. If the reward is not set, it is treated as zero.

Now we can use our environment:

env = my_env(topic="foo")
obs, tools = await env.reset()

Subclass Environments

If you need more control over Environments and tools, you'll want to subclass the Environment

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.core 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.core 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.core 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):
    """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):
    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 initial observations and the tools. The reset function is async to allow for database interactions or HTTP requests.

from aviary.core import Message, Tool


async def reset(self):
    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.core import Message


async def step(self, action: Message):
    msgs = 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

Optionally, we can define a function to export a snapshot of the environment and its state for visualization or debugging purposes.

from aviary.core import Frame


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

View Environment Tools

If an environment can be instantiated without anything other than a task (i.e., it implements from_task), you can start a server to view its tools:

pip install fhaviary[server]
aviary tools [env name]

This will start a server that allows you to view the tools and call them, viewing the descriptions/types and output that an agent would see when using the tools.

Environments

Here are a few environments implemented with aviary:

Environment	PyPI	Extra	README
GSM8k	aviary.gsm8k	fhaviary[gsm8k]	README.md
HotPotQA	aviary.hotpotqa	fhaviary[hotpotqa]	README.md
PaperQA	paper-qa	fhaviary[paperqa]	README.md