pyaskit 1.0.1

AskIt: Unified programming interface for programming with large language models (GPT-3.5, GPT-4)

AskIt (pyaskit)

Introduction

AskIt serves as a dedicated library or domain-specific language designed to streamline the utilization of Large Language Models (LLMs) such as GPT-4. It simplifies the complexities of prompt engineering and eradicates the requirement for parsing responses from LLMs, making programming tasks smoother.

Using AskIt, you can deploy LLMs for a multitude of tasks, such as:

• Natural Language Processing: including translation, paraphrasing, and sentiment analysis.
• Problem Solving: resolving mathematical problems.
• Code Generation: creating new codes, and more.

AskIt operates through the OpenAI API for backend LLM functions. Besides Python, AskIt has also been implemented in TypeScript. You can access the TypeScript version, ts-askit.

Key Features

• Type-Guided Output Control: Enhance LLM precision with types.

• Template-Based Function Definition: Define functions using a prompt template.

• Code Generation: Generate functions from the unified interface.

• Programming by Example (PBE): Define functions using examples. Refer to the Programming by Example (PBE) section for further details.

Installation

To install AskIt, run this command in your terminal:

pip install pyaskit

or

pip install git+https://github.com/katsumiok/pyaskit.git

Before using AskIt, you need to set your OpenAI API key as an environment variable OPENAI_API_KEY and ASKIT_MODEL:

export OPENAI_API_KEY=<your OpenAI API key>

<your OpenAI API key> is a string that looks like this: sk-<your key>. You can find your API key in the OpenAI dashboard.

You can also specify the model name as an environment variable ASKIT_MODEL:

export ASKIT_MODEL=<model name>

<model name> is the name of the model you want to use. The latest AskIt is tested with gpt-4 and gpt-3.5-turbo-16k. You can find the list of available models in the OpenAI API documentation.

Getting Started

Here are some basic examples to help you familiarize yourself with AskIt:

Hello World

import pyaskit as ai
import pyaskit.types as t

s = ai.ask(t.str, 'Paraphrase "Hello World!"')
print(s)

To utilize AskIt, start by importing the pyaskit and pyaskit.types modules. The ask API, which takes two arguments - the output type and the prompt - produces the LLM's output in the designated format. In this case, the output type is str and the prompt is Paraphrase "Hello World!". A comprehensive explanation of types in AskIt is provided in the Types section. Executing this code will yield a paraphrase of the prompt, such as:

Greetings, Planet!

Defining a Function from a Template Prompt

The define API allows for prompt parameterization using template syntax:

import pyaskit as ai
import pyaskit.types as t

paraphrase = ai.define(t.str, 'Paraphrase {{text}}')
s = paraphrase(text='Hello World!')
# s = paraphrase('Hello World!') # This is also valid
print(s)

In this instance, the define API creates a templated function that instructs the LLM to paraphrase specified text. Invoking the paraphrase function with 'Hello World!' will return a paraphrased version of this text. Running this code might output something like "Greetings, Planet!".

The define API allows for straightforward creation of custom functions to harness the capabilities of large language models for diverse tasks. Further examples can be found in the examples directory.

Translating a Codable Task

Certain tasks, such as those requiring real-time data, external resources like network access, file access, or database access, are unsuitable for LLM execution. However, AskIt can handle these tasks by converting the prompt into a Python program in the background.

The subsequent example demonstrates using AskIt to tackle a task necessitating network access:

import pyaskit as ai
import pyaskit.types as t

get_html = ai.define(t.str, 'Get the webpage from {{url}}').compile()
html = get_html(url='https://csail.mit.edu')
print(html)

In this scenario, you only need to call compile() on the function returned by the define API. The compile function transforms the prompt into a Python program and returns a function that executes this code, behaving just like a regular Python function.

Programming by Example (PBE)

Function Definition Using Examples

Language Learning Models (LLMs) offer the advantage of few-shot learning, a capability that AskIt utilizes in programming tasks. AskIt enables you to solve tasks using the Programming by Example (PBE) technique, where you provide examples of the desired input and output.

Let's consider creating a function to add two binary numbers (represented as strings). This function accepts two binary numbers and returns their sum, also in binary form. The following code demonstrates defining such a function using illustrative examples.

from pyaskit import define
import pyaskit.types as t

training_examples = [
    {"input": {"x": "1", "y": "0"}, "output": "1"},
    {"input": {"x": "1", "y": "1"}, "output": "10"},
    {"input": {"x": "101", "y": "11"}, "output": "1000"},
    {"input": {"x": "1001", "y": "110"}, "output": "1111"},
    {"input": {"x": "1111", "y": "1"}, "output": "10000"},
]

add_binary_numbers = define(t.str, "Add {{x}} and {{y}}", training_examples=training_examples)
sum_binary = add_binary_numbers(x="101", y="11")
print(sum_binary)  # Output: "1000"

In this example, the define API takes three arguments: the output type, the prompt, and the training examples. Each entry in the training examples list is a dictionary containing an 'input' dictionary (with variable names and values) and an 'output' representing the expected function output given the input. The define API then returns a function that accepts input variables as keyword arguments and outputs the LLM's output in the specified type.

The add_binary_numbers function, which adds two binary numbers, behaves like any regular Python function.

Testing the Translated Function

You can use the compile function to test the translated function using an optional list of test examples.

The following code demonstrates how to test the function defined above with new test examples:

test_examples = [
    {"input": {"x": "0", "y": "1"}, "output": "1"},
    {"input": {"x": "10", "y": "0"}, "output": "10"},
    {"input": {"x": "110", "y": "10"}, "output": "1000"},
]
f = add_binary_numbers.compile(test_examples=test_examples)
sum_binary = f(x="101", y="11")
print(sum_binary)  # Output: "1000"

Here, f is the translated function that operates similarly to add_binary_numbers. By comparing the output of the translated function with the expected output for each test example, AskIt ensures the translated function behaves as expected. If any discrepancy arises, AskIt re-attempts the translation. After multiple unsuccessful translation attempts, AskIt raises an exception.

Specifying Types in AskIt

AskIt offers APIs to designate the output types for Language Learning Models (LLMs). By supplying these types as the first argument to the ask and define APIs, you can manage the LLM's output format.

The following table describes the various types supported by AskIt:

Type	Description	Type Example	Value Example
int	Integer	t.int	123
float	Floating Point Number	t.float	1.23
bool	Boolean	t.bool	True
str	String	t.str	"Hello World!"
literal	Literal	t.literal(123)	123
list	List	t.list(t.int)	[1, 2, 3]
dict	Dictionary	t.dict({  'a': t.int,  'b': t.int })	{'a': 1, 'b': 2}
union	Union (Multiple Possible Values)	t.union(t.literal('yes'), t.literal('no'))	"yes" or "no"

Note that each type declaration aids AskIt in parsing and understanding the desired output, ensuring your LLM returns data in the precise format you require.

Prompt Template Usage

The prompt template is a string composed of placeholders for the parameters of the function being defined. Placeholders are denoted by double curly braces {{ and }} and can only contain a variable name. This variable name is then used as a parameter in the defined function.

Function parameters can be defined in two ways: either by keyword arguments or by positional arguments. For keyword arguments, the variable name within the placeholder serves as the keyword argument's name. For positional arguments, the sequence in which placeholders appear defines the order of the positional arguments.

Consider the following example which demonstrates how to define a function, add, that accepts two arguments x and y and returns their sum:

from pyaskit import define
import pyaskit.types as t

add = define(t.int, '{{x}} + {{y}}')
print(add(x=1, y=2))  # keyword arguments
print(add(1, 2))  # positional arguments

In this case, the add function can be invoked using either keyword or positional arguments, with the sum of x and y returned as the output.

Notably, if the same variable name is used multiple times in the prompt template, subsequent uses are mapped to the initial occurrence. Observe this behavior in the following example:

from pyaskit import define
import pyaskit.types as t

add = define(t.int, '{{x}} + {{y}} + {{x}} + {{z}}')
print(add(x=1, y=2, z=3))
print(add(1, 2, 3))

Here, {{x}} appears twice in the prompt template. The second occurrence of {{x}} maps back to the first. Hence, even though {{z}} is the fourth placeholder in the template, it aligns with the third argument of the function.

Contributing

See CONTRIBUTING.md for details on our code of conduct, and the process for submitting pull requests to us.

License

This project is licensed under the MIT License - see the LICENSE.md file for details

Citation

If you use our software in your research, please cite our paper:

@misc{okuda2023askit,
      title={AskIt: Unified Programming Interface for Programming with Large Language Models}, 
      author={Katsumi Okuda and Saman Amarasinghe},
      year={2023},
      eprint={2308.15645},
      archivePrefix={arXiv},
      primaryClass={cs.PL}
}