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DSPyGen: Streamlining AI Development

Welcome to DSPyGen, a powerful command-line interface (CLI) designed to revolutionize AI development by leveraging DSPy modules. Inspired by the efficiency and modularity of frameworks like Ruby on Rails, DSPyGen simplifies the process of creating, developing, and deploying language model (LM) pipelines.

Table of Contents

  1. Introduction
  2. Features
  3. Getting Started
  4. Usage
  5. Module Creation
  6. Best Practices
  7. Structuring Code: A New Class of Digital Assets (by Dr Holger Vogel)
  8. Contributing
  9. License

Introduction

DSPyGen, influenced by the pioneering work of Sean Chatman and James I. Chatman, embodies a structured approach to AI development. This tool is designed to streamline your workflow, enhance productivity, and ensure you stay ahead in the rapidly evolving tech landscape.

Features

  • Quick Initialization: Set up your DSPyGen project in seconds, echoing the ease of starting new projects with Ruby on Rails.
  • Modular Approach: Inspired by Ruby on Rails' modular design, DSPyGen allows for the easy generation and enhancement of DSPy modules.
  • Intuitive Command Structure: Manage your AI development workflow with straightforward commands.

Quick Start

Initialize a new DSPyGen project:

dspygen init my_project
cd my_project

Generate a new module:

dspygen module new -cn TextSummarizer -i "text" -o "summary"

Run the module:

dspygen module text_summarizer call "Gettysburg address"

Serve the REST API:

docker-compose up app

Module Creation

The dspygen module new command is the cornerstone of DSPyGen, enabling users to create new modules efficiently. This section highlights the usage and provides common use cases for language models.

Usage

dspygen module new [OPTIONS]

Options

  • --class-name, -cn TEXT: The name of the module class (required).
  • --inputs, -i TEXT: A comma-separated list of input names.
  • --output, -o TEXT: Output name for the module.
  • --help: Show this message and exit.

Common Use Cases

  1. Text Summarization:

    dspygen module new -cn TextSummarizer -i "text" -o "summary"

  2. Sentiment Analysis:

    dspygen module new -cn SentimentAnalyzer -i "text" -o "sentiment"

  3. YouTube Comment Generation:

    dspygen module new -cn Comment -i "vid_title,words" -o "viral_comment"

  4. Machine Translation:

    dspygen module new -cn Translator -i "source_text,target_language" -o "translated_text"

  5. Code Generation:

    dspygen module new -cn CodeGenerator -i "prompt" -o "generated_code"

Getting Started

Using OpenAI:

from dspygen.utils.dspy_tools import init_dspy
from dspygen.modules.text_summarizer_module import text_summarizer_call


init_dspy(model="gpt-4o", max_tokens=500)
text_summarizer_call("Gettysburg address")

Using Groq: Obtain your API key from Groq and modify your .env file as demonstrated in .envDemo. Don't forget to initialize:

from dspygen.utils.dspy_tools import init_dspy
from dspygen.lm.groq_lm import Groq
from dspygen.modules.text_summarizer_module import text_summarizer_call


init_dspy(model="llama3-70b-8192", lm_class=Groq, max_tokens=8000)
text_summarizer_call("Gettysburg address")

Privacy and Data Protection: For privacy and data loss protection, we recommend initializing DSPyGen with Ollama.

Install Ollama: Visit Ollama to install the necessary tools.

from dspygen.utils.dspy_tools import init_ol
from dspygen.modules.text_summarizer_module import text_summarizer_call

init_ol(model="llama3", max_tokens=2000)

text_summarizer_call("Gettysburg address")

By following these steps, you can integrate the concept of structured commodities into your code generation workflow, ensuring compliance and fair compensation for creators.

By understanding and utilizing the dspygen module new command, you can harness the full potential of DSPyGen to create powerful and flexible AI development workflows.

Production Module

The following example demonstrates a production module that generates a mock Pytest module for a given Python source code. This module is designed to create comprehensive and robust mock tests that simulate possible unit tests based on the functions and methods defined within the source code.

import dspy

class GenerateMockPytest(dspy.Signature):
    """
    Generates a mocked pytest module for the provided Python source code.
    This class aims to create comprehensive and robust mock tests that simulate
    possible unit tests based on the functions and methods defined within the source code.
    Write the test like a FAANG Python architect at Meta.
    Only reply within ```python``` block. All other text needs to be in docstrings or comments.
    """
    source_code = dspy.InputField(desc="Python source code for which to generate a mock test.")
    mocked_pytest = dspy.OutputField(desc="Generated mock pytest code. Within triple backticks", 
                                     prefix="```python\n")

class PytestModule(dspy.Module):
    """PytestModule"""

    def __init__(self, **forward_args):
        super().__init__()
        self.forward_args = forward_args
        self.output = None

    def forward(self, source_code):
        pred = dspy.Predict(GenerateMockPytest)
        self.output = pred(source_code=source_code).mocked_pytest
        return self.output

def pytest_call(source_code):
    pytest = PytestModule()
    return pytest.forward(source_code=source_code)

example_code = """def fetch_user_name(user_id):
    import requests
    response = requests.get(f'https://api.example.com/users/{user_id}')
    return response.json()['name']
"""

def main():
    from dspygen.utils.dspy_tools import init_ol
    lm = init_ol()
    source_code = example_code
    result = pytest_call(source_code=source_code)
    from dspygen.utils.file_tools import extract_code
    print(extract_code(result))
    print(lm.inspect_history(n=1))


if __name__ == "__main__":
    main()

Example: Generated Pytest Module (Continued)

import pytest
from your_module import fetch_user_name

@pytest.fixture
def mocker():
    return pytest.mockito()

def test_fetch_user_name(mocker):
    mocked_requests_get = mocker.patch('requests.get')
    response_json = {'name': 'John Doe'}
    mocked_requests_get.return_value.json.return_value = response_json
    
    result = fetch_user_name(123)
    assert result == 'John Doe'
    
    # Verify that the requests.get call was not made
    assert not mocked_requests_get.called

Example: Running the Test

Initial state: ANALYZING_REQUIREMENTS
Test Failed: ============================= test session starts ==============================
platform darwin -- Python 3.12.3, pytest-8.2.0, pluggy-1.5.0 -- /Users/sac/Library/Caches/pypoetry/virtualenvs/soc-FgW3JNy9-py3.12/bin/python
cachedir: .pytest_cache
rootdir: /var/folders/s6/jqyw48zs39z38b_3f6f_x2sc0000gn/T
plugins: anyio-4.3.0, clarity-1.0.1, Faker-23.3.0, asyncio-0.23.6, mock-3.14.0, xdist-3.6.1
asyncio: mode=Mode.STRICT
collecting ... collected 1 item

../../../../../../../var/folders/s6/jqyw48zs39z38b_3f6f_x2sc0000gn/T/tmp880863oe_test.py::test_fetch_user_name ERROR [100%]

==================================== ERRORS ====================================
____________________ ERROR at setup of test_fetch_user_name ____________________

    @pytest.fixture
    def mocker():
>       return pytest.mockito()
E       AttributeError: module 'pytest' has no attribute 'mockito'

/var/folders/s6/jqyw48zs39z38b_3f6f_x2sc0000gn/T/tmp880863oe_test.py:6: AttributeError
=========================== short test summary info ============================
ERROR ../../../../../../../var/folders/s6/jqyw48zs39z38b_3f6f_x2sc0000gn/T/tmp880863oe_test.py::test_fetch_user_name
=============================== 1 error in 0.04s ===============================

Best Practices

Daily Productivity Process

  1. Set Clear Goals: Use the Ivy Lee Method to prioritize your daily tasks.
  2. Use Pomodoro Technique: Work in focused intervals to maintain productivity.
  3. Regular Reviews: Reflect on progress and plan for the next day.

Embrace Continuous Learning

Stay updated with the latest advancements in AI technology and share your insights with the community to enhance collective productivity.

Structuring Code: A New Class of Digital Assets

Dematerialized Commodity Concept

Inspired by financial engineering and the structuring of financial products, we aim to bring the same level of compliance and innovation to code generation systems. In today's AI-driven world, it is crucial to ensure that creators of valuable, new code receive appropriate compensation whenever their code is analyzed, cloned, or used, especially at the enterprise level.

NFTs as Structured Commodities

The foundation for all valuable, useful code should be a new form of NFT – a structured commodity of code. This concept is akin to a dematerialized asset, similar to the Meta-Bricks repository we previously created. This would involve a massive store of runnable and easily pluggable/composable elements of code, paired with terms and conditions familiar from classical structured products (e.g., Ricardian Contracts).

Legal Compliance and Revenue Sharing

To minimize legal risks and ensure proper compensation, retrievers should use these structured commodities for code generation workflows. They should always send payments or share revenues from new creations derived from these meta-bricks to the original creators. While many current LLMs do not reference the source of the code, this is an area that can and should be improved.

By Dr Holger Vogel (LinkedIn)

Contributing

We welcome contributions to improve DSPyGen. Please follow the guidelines in the CONTRIBUTING.md file.

License

This project is licensed under the MIT License. See the LICENSE file for more details.

By leveraging the structured approach and productivity principles from the Sean Chatman and James I. Chatman Methods, DSPyGen aims to enhance your AI development experience. Get started today and streamline your workflow with ease!

For more information, visit our GitHub repository.