Enact is an in-development python framework for building generative software, i.e., software that integrates with machine learning models or APIs that generate distributions of outputs.
More specifically, enact can be understood as a framework for working with scaffolding programs, that is, software scripts that orchestrate calls to generative subsystems by, for example, by generating context-dependent prompts, by sampling and choosing generation candidates, by retrying failed generations, etc.
Scaffolding programs have unique requirements that make them different from traditional software.
- They represent complex distributions: Each run may yield a different result.
- They require optimization through trial and error: Prompts require tuning to produce good results, different models or APIs may be more or less well suited to a given task, additional sampling or search strategies can sometimes improve performance (e.g., chain of thought prompting, MCTS).
- They may need to support higher-order generativity: A generative subsystem may produce code that is executed to produce another generative output (see, e.g., Voyager, which samples programs from an LLM and then samples executions of those programs in a game environment)
Enact's focus is on offering core framework functionality required for developing, inspecting and improving software used to interact with generative subsystems.
To this end, enact boosts your existing python programs by giving you the ability to
- track and persist execution traces
- explore the space of possible executions by rewinding and replaying a previous execution of a scaffolding program.
- persist programs and their associated data in run-time accessible versioned storage.
Enact represents a first-principles approach to supporting development of generative software close to the level of the programming language. It is therefore different from and complementary to libraries that focus on unified integrations (e.g., API wrappers) or on particular algorithm schemas for orchestrating generative flows (e.g., chain/tree-of-thought based approaches).
See why-enact for a more in-depth discussion.
Enact is available as a pypi package and can be installed via:
pip install enactEnact recursively tracks inputs and outputs of functions registered with the
framework. A record of an execution is called an Invocation, and can be
persisted to a store. This is useful for tracking the behavior generative
software.
import enact
import random
@enact.register
def roll_die(sides: int) -> int:
"""Roll a die."""
return random.randint(1, sides)
@enact.register
def roll_sum(num_rolls: int) -> int:
"""Roll dice."""
return sum(roll_die(6) for _ in range(num_rolls))
with enact.InMemoryStore() as store:
invocation = enact.invoke(roll_sum, (2,))
print(enact.invocation_summary(invocation))Output:
->roll_sum(2) = 7
->roll_die(6) = 2
->roll_die(6) = 5
Invocations can be rewound and replayed:
# Rewind the last roll and replay it.
with store:
first_roll = invocation.rewind(1) # Rewind by one roll.
print('Partial invocation: ')
print(enact.invocation_summary(first_roll))
reroll_second = first_roll.replay() # Replay the second die roll.
print('\nReplayed invocation: ')
print(enact.invocation_summary(reroll_second))Output:
Partial invocation:
->roll_sum(2) incomplete
->roll_die(6) = 2
Replayed invocation:
->roll_sum(2) = 8
->roll_die(6) = 2
->roll_die(6) = 6Enact components are structurally hashed and persisted in a store. This makes it possible to access ephemeral configurations of software associated only with a particular run:
import dataclasses
@enact.register
@dataclasses.dataclass
class DiceRoll(enact.Invokable[None, int]):
num_rolls: int
sides_per_die: int
def call(self) -> int:
return sum(roll_die(self.sides_per_die) for _ in range(self.num_rolls))
with enact.InMemoryStore() as store:
dice_roll = DiceRoll(2, 6)
invocation = enact.invoke(DiceRoll(2, 6))
# modify the diceroll object
dice_roll.num_rolls = 3
dice_roll.sides_per_die = 8
# Fetch the version associated with the first roll:
print(enact.invocation_summary(invocation))
print(f'Dice roll object associated with the invocation: '
f'{invocation.request().invokable()}')
print(f'Dice roll object after modification: {dice_roll}')Output:
->DiceRoll(num_rolls=2, sides_per_die=6)(None) = 7
-><function roll_die at 0x7fa6e1e42ee0>(6) = 4
-><function roll_die at 0x7fa6e1e42ee0>(6) = 3
Dice roll object associated with the invocation: DiceRoll(num_rolls=2, sides_per_die=6)
Dice roll object after modification: DiceRoll(num_rolls=3, sides_per_die=8)Enact has built-in support for basic python datatypes such as primitives, lists, sets, dictionaries and tuples.
If you need support for additional types, you can either define them as
Resource dataclasses in the framework or define a wrapper object that
is used internally by enact to represent you custom python type.
import dataclasses
@enact.register
@dataclasses.dataclass
class MyResource(enact.Resource):
x: int
y: list = dataclasses.field(default_factory=list)
with store:
# Commit your resource to the store, obtain a reference.
ref = enact.commit(MyResource(x=1, y=[2, 3]))
# Check out your reference.
print(ref.checkout()) # Equivalent to "print(ref())".Output:
MyResource(x=1, y=[2, 3])For existing types, it can be more convenient to wrap them rather than to
redefine them as enact Resource objects.
This can be accomplished by registering a TypeWrapper subclass.
class Die:
"""Non-enact python type."""
def __init__(self, sides):
self.sides = sides
@enact.register
def roll(self):
return random.randint(1, self.sides)
@enact.register
@dataclasses.dataclass
class DieWrapper(enact.TypeWrapper[Die]):
"""Wrapper for Die."""
sides: int
@classmethod
def wrapped_type(cls):
return Die
@classmethod
def wrap(cls, value: Die):
"""Translate to enact wrapper."""
return DieWrapper(value.sides)
def unwrap(self) -> Die:
"""Translate to basic python type."""
return Die(self.sides)
with store:
die = Die(sides=6)
invocation = enact.invoke(die.roll)
print(enact.invocation_summary(invocation))Output:
-><__main__.Die object at 0x7f3464398340>.roll() = 4
Full documentation is work in progress. See enact concepts for more information, take a look at the examples, e.g., how enact can be used to instantiate generic MCTS for scaffolding programs.
Generative AI is transforming the software development process.
Most traditional software relies on functional buildings blocks in which inputs
and system state directly determine outputs: output = program(input). In
contrast, software that accesses generative AI subsystems represents a
conditional distribution: p(output | input).
This small change in emphasis - from functions to conditional distributions - implies a shift across multiple dimensions of the engineering process, summarized in the table below.
| Generative | Traditional | |
|---|---|---|
| Building blocks | Conditional distributions | Deterministic functions |
| Engineering | Improve output distributions | Add features, debug errors |
| Subsystems | Unique quality characteristics | Interchangeable |
| Code sharing | Components | Frameworks |
| Execution traces | Training data | Debugging |
| Interactivity | Within system components | At system boundaries |
| Code vs data | Overlapping | Distinct |
Generative AI can be used to quickly sketch out impressive end-to-end experiences. Evolving prototypes into production-ready systems can be non-trivial though, as they may show undesirable behaviors a certain percentage of the time, may fail to respond correctly to unusual user inputs or may simply fail to meet a softly defined quality target.
Where traditional engineering focuses solely on implementing features and fixing bugs, generative software engineering requires tuning the output distribution of the system as a whole. This can be accomplished in many different ways: Running experiments, e.g., using different LLMs or prompts, fine-tuning the underlying machine learning models, or making changes to the scaffolding program.
Enact aims to provide the core infrastructure necessary to build, maintain and optimize generative software.
In generative software, individual components produce distributions that may be more or less well-suited to the system's overall goal: An LLM that performs well on tabular data may perform less well when used as a chat agent. One fine-tuned diffusion model may produce images that are closer to the target style than another. This is distinct from the the case of traditional software engineering, where the choice between two correct, API-identical implementations of a module is of little importance to the behavior of the system as a whole.
Fitting a piece of a generative software involves optimizing the output distribution towards some quality target. In some cases this can be achieved using machine learning techniques such as gradient descent, but in cases where this is not possible, it involves searching for high performing solutions in the space of software and software configurations.
This search can be manual or automatic, and it can involve changes to parameters such as LLM prompts or to the scaffolding program itself, e.g., searching over multiple candidate responses.
The profusion of base models and possible algorithmic elaborations of base models suggests that gradient-free search methods (i.e., search) will take a central place in generative software development. This could be as simple as running an experiment in which one component is compared against another or as complex as searching the space of software, e.g., using LLM-boosted genetic algorithms.
As algorithms in the AlphaZero family have shown us, a particularly fruitful approach is combining search with machine learning techniques in such a way that search improves the reasoning power of the core ML model, whereas the ML model can be improved with the experience gained through search.
By offering in-depth program telemetry and execution search over scaffolding programs, enact aims to provide the basic infrastructure to enable these kinds of approaches.
There are many generative AI components that are mutually API-compatible (e.g., text-to-image generation, LLMs) but that cannot be directly ranked in terms of quality since they represent a unique take on the problem domain. The combination of API compatibility and variation lends itself to a more evolutionary, collaborative style than traditional software, where shared effort tends to centralize into fewer, lower-level frameworks.
This new collaborative approach is already evidenced by both widespread sharing of prompt templates and the existence of public databases of fine-tuned models.
Enact aims to simplify collaboration using a content-addressable memory approach to storage. A program and its executions can be uniquely identified by its hash, which provides a shared ontology for collaborative program search.
Many popular programming languages offer capacity for reflection, wherein the structure of code is programmatically interpretable by code. Generative software additionally requires the ability for code to reflect on code execution.
Sampling a generative model is computationally intensive and provides valuable information that may be used as system feedback or training data. Since generative software may include complex, recursive algorithms over generative components, this suggests a need for not simply tracking inputs and outputs at system boundaries, but at every level of execution.
This is particularly relevant to higher order generative flows, in which the output of one generative step structures the execution of the next: Such a system can use call traces as feedback to improve its output, in the same way that a human may use a debugger to step through an execution to debug an issue.
Enact provides the concept of invocations, and can support higher-order invocations: That is, provide in-depth telemetry for software that reflects on executions of other software.
Unlike traditional systems, where user interactions happen at system boundaries, AI and humans are often equal participants in generative computations. An example is Reinforcement Learning from Human Feedback (RLHF), where humans provide feedback on AI output, only to be replaced by an AI model that mimics their feedback behavior during the training process. In other cases, critical generative flows (e.g., the drafting of legal documents) may require a human verification step, without which the system provides subpar results.
The choice between sampling human input and sampling a generative model involves considerations such as cost and quality, and may be subject to change during the development of a system. For example, an early deployment of a system may heavily sample outputs or feedback from human participants, until there is enough data to bootstrap the target distribution using automated generative components.
Enact's execution search capablities allow humans to alter or inject data into program executions: A partial execution that failed due to requiring user attention can be persisted and replayed at a later time.
Traditional software systems tend to allow a clear distinction between code (written by developers) and data (generated by the user and the system). In generative systems this distinction breaks down: Approaches such as AutoGPT or Voyager use generative AI to generate programs (specified in code or plain text), which in turn may be interpreted by generative AI systems; prompts for chat-based generative AI could equally be considered code or data.
The framework is open source and Apache licensed. We are actively looking for contributors that are excited about the vision. If you're interested in the project, feel free to reach out to leo@agentic.ai.
You can download the source code, report issues and create pull requests at https://github.com/agentic-ai/enact.