Gymnasium framework for training language model agents on constructive tasks.
To install aviary (note fh stands for FutureHouse):
pip install fhaviaryTo install aviary with the bundled environments, please see the Environments section below.
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.
For local development, please see the CONTRIBUTING.md.
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.
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.
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 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 = TrueThe 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()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: ExampleStateWe do not have other variables aside from state for this environment. We could have things like configuration, a name,
tasks, etc. attached to it.
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")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 = TrueThere 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.
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 = TrueNow 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.toolsNow 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, FalseYou will probably often use this specific syntax for calling the tools - calling exec_tool_calls with the action.
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]},
)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.
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 |