Domain of Thought: A New Era in Chatbot Interactions

Prompt for Senior Program Managers, Project Managers, and Strategic Development:

Let's think step by step about designing a new, large-scale project. Begin by defining the project scope and objectives. Next, analyze potential risks and challenges, and plan mitigation strategies. Then, consider resource allocation, including team roles and budgeting. After that, devise a detailed timeline with key milestones. Finally, develop a communication plan to keep all stakeholders informed and engaged throughout the project lifecycle.

Prompt for CIOs, Cyber Security Managers, and IT Strategists:

Let's think step by step about implementing a robust cybersecurity strategy for a large organization. Start by assessing the current IT infrastructure and identifying potential vulnerabilities. Next, research the latest cybersecurity trends and threats. Then, develop a multi-layered security approach, including both hardware and software solutions. After that, plan employee training sessions on security best practices. Finally, create a response plan for potential cyber incidents.

Prompt for Technology and Software Engineering Leaders:

Let's think step by step about developing a new software application. Begin by identifying the user needs and market gap. Next, design a prototype and gather feedback. Then, choose the appropriate technology stack and start the development process. After that, plan regular testing phases to ensure quality and functionality. Finally, develop a launch and marketing strategy to bring the product to market.

Prompt for AI Innovators, Data Scientists, and Educators:

Let's think step by step about creating an AI-based educational platform for personalized learning. Start by identifying the key learning outcomes and student profiles. Next, research the AI technologies suitable for adaptive learning. Then, design the user interface with a focus on user experience. After that, develop the content delivery mechanism using AI algorithms. Finally, plan a pilot test with a group of students to gather feedback and make improvements.

Prompt for Financial Analysts, Sales & Marketing Professionals, and Business Transformation Experts:

Let's think step by step about launching a new product in a competitive market. Begin with market research to understand customer needs and competitor analysis. Next, develop the product with unique features that address these needs. Then, create a compelling marketing and branding strategy. After that, plan the distribution channels for maximum reach. Finally, set up metrics to evaluate the product's performance post-launch.

Introduction to the World of ChatGPT and Prompt Engineering

In the ever-evolving landscape of artificial intelligence, a groundbreaking technique known as Domain of Thought (DoT) is setting new benchmarks. For those new to this realm, ChatGPT stands as a beacon of advanced AI, capable of conversing and solving complex problems with a finesse that blurs the line between human and machine intelligence. At the core of this marvel lies 'Prompt Engineering' – the art of skillfully crafting questions or 'prompts' to guide ChatGPT in delivering more accurate and contextually relevant responses.

The Game Changer: Domain of Thought Prompting

So, what makes Domain of Thought prompting a game changer in the world of AI and ChatGPT? To appreciate its significance, imagine ChatGPT as a highly skilled but literal-minded assistant. The way you phrase your questions or tasks – your 'prompt' – can mean the difference between a response that's merely adequate and one that's spot-on.

In traditional prompting, you might ask a direct question or give a straightforward task. DoT, however, introduces an element of structured, sequential reasoning – it’s like giving ChatGPT a roadmap for its thoughts. This method encourages the AI to break down complex queries into smaller, more manageable parts, leading to a more thorough and nuanced understanding of the task at hand.

Why DoT Stands Out

For non-technical readers, the beauty of DoT lies in its simplicity and effectiveness. This approach is akin to solving a jigsaw puzzle – by tackling one piece at a time, ChatGPT can construct a more complete and accurate picture of the solution. DoT prompting is particularly beneficial in cases where a question involves multiple layers of reasoning or when a detailed explanation is more valuable than a direct answer.

Practical Applications and Benefits

The real-world applications of DoT are vast and varied. From helping you plan a complex project step-by-step, offering detailed programming assistance, to providing in-depth explanations of scientific concepts, DoT enhances ChatGPT's ability to handle intricate requests across numerous domains.

For users, this translates to more effective interactions with AI, particularly in professional settings where precision and detail are paramount. It's like having a virtual expert at your fingertips, one that can dissect and address multifaceted problems with remarkable accuracy.

Conclusion: The Dawn of a New AI Interaction Paradigm

The introduction of Domain of Thought is more than just an advancement in AI technology; it's a paradigm shift in how we interact with and harness the power of AI. For those just stepping into the world of ChatGPT and prompt engineering, DoT offers an exciting and user-friendly gateway to unlock the full potential of AI conversations. Whether you're a seasoned tech enthusiast or a curious newcomer, the DoT technique opens up a world where the boundaries of AI assistance are continually expanding, making every interaction with ChatGPT an adventure in discovery and problem-solving.

Elevating DSPy with Domain of Thought: A Leap in Language Model Programming

Introduction to Domain of Thought in DSPy

In the realm of programming language models (LMs), the DSPy framework has emerged as a beacon of innovation. The introduction of LM Assertions marked a significant leap, enabling DSPy to enforce computational constraints effectively. As we stand on the cusp of another breakthrough, we're excited to introduce the "Domain of Thought" (DoT) implementation in DSPy, designed to further enhance the capabilities of language model pipelines.

DoT in DSPy: The Concept and Integration

The essence of DoT is in structuring the reasoning process of LMs into more manageable and logical sequences, akin to a human expert breaking down a complex problem into simpler sub-questions. This implementation in DSPy is particularly groundbreaking, as it aligns seamlessly with the existing framework of LM Assertions, ensuring that each step in the reasoning chain not only adheres to computational constraints but also contributes effectively towards the final output.

Enhancing Computational Constraints with DoT

DoT's integration into DSPy enables a more nuanced and controlled approach to imposing computational constraints. This method allows DSPy to guide LMs through a series of logically connected steps, ensuring compliance with set rules and guidelines at each stage. This step-by-step refinement aligns perfectly with DSPy's objective of building reliable and accurate LM systems.

Case Studies and Performance Gains

Early case studies in complex question-answering scenarios have demonstrated the efficacy of integrating DoT into DSPy. In tasks requiring multi-hop information retrieval and synthesis of long-form answers, DoT's structured approach significantly enhances LM performance. Preliminary results have shown intrinsic and extrinsic gains, with improvements up to 35.7% and 13.3%, respectively, highlighting the potential of DoT in elevating LMs to new heights of efficiency and accuracy.

Conclusion and Future Prospects

The integration of Domain of Thought into DSPy represents not just a technical advancement but a paradigm shift in LM programming. As we prepare for its release next week, we anticipate this feature to be a game-changer in how LMs are programmed and utilized. The combination of DSPy's LM Assertions and DoT's structured reasoning promises to open up new horizons in AI and language modeling, driving forward the development of more sophisticated, reliable, and user-friendly AI systems. We invite the DSPy community to explore this new frontier and contribute to the ongoing evolution of language model programming.

import logging  # Import the logging module
from dspy import Module, OpenAI, settings, ChainOfThought, Assert

logger = logging.getLogger(__name__)  # Create a logger instance
logger.setLevel(logging.ERROR)  # Set the logger's level to ERROR or the appropriate level


class GenModule(Module):
    def __init__(self, output_key, input_keys: list[str] = None, lm=None):
        if lm is None:
            lm = OpenAI(max_tokens=500)
            settings.configure(lm=lm)

        if input_keys is None:
            self.input_keys = ["prompt"]

        super().__init__()

        self.output_key = output_key

        # Define the generation and correction queries based on generation_type
        self.signature = ', '.join(self.input_keys) + f" -> {self.output_key}"
        self.correction_signature = ', '.join(self.input_keys) + f", error -> {self.output_key}"

        # DSPy modules for generation and correction
        self.generate = ChainOfThought(self.signature)
        self.correct_generate = ChainOfThought(self.correction_signature)

    def forward(self, **kwargs):
        # Generate the output using provided inputs
        gen_result = self.generate(**kwargs)
        output = gen_result.get(self.output_key)

        # Try validating the output
        try:
            return self.validate_output(output)
        except (AssertionError, ValueError) as error:
            logger.error(error)
            # Correction attempt
            corrected_result = self.correct_generate(**kwargs, error=str(error))
            corrected_output = corrected_result.get(self.output_key)
            return self.validate_output(corrected_output)

    def validate_output(self, output):
        # Implement validation logic or override in subclass
        raise NotImplementedError("Validation logic should be implemented in subclass")

import ast

from dspy import Assert

from rdddy.generators.gen_module import GenModule


def is_primitive_type(data_type):
    primitive_types = {
        int, float, str, bool, list, tuple, dict, set
    }

    return data_type in primitive_types


class GenPythonPrimitive(GenModule):
    def __init__(self, primitive_type, lm=None):
        if not is_primitive_type(primitive_type):
            raise ValueError(f'primitive type {primitive_type.__name__} must be a Python primitive type')
        super().__init__(f"{primitive_type.__name__}_python_primitive_string", lm)
        self.primitive_type = primitive_type

    def validate_primitive(self, output) -> bool:
        try:
            return isinstance(ast.literal_eval(output), self.primitive_type)
        except SyntaxError as error:
            return False

    def validate_output(self, output):
        Assert(
            self.validate_primitive(output),
            f"You need to create a valid python {self.primitive_type.__name__} "
            f"primitive type for \n{self.output_key}\n"
            f"You will be penalized for not returning only a {self.primitive_type.__name__} for "
            f"{self.output_key}",
        )
        data = ast.literal_eval(output)

        if self.primitive_type is set:
            data = set(data)
        return data

    def __call__(self, prompt):
        return self.forward(prompt=prompt)


class GenDict(GenPythonPrimitive):
    def __init__(self):
        super().__init__(primitive_type=dict)


class GenList(GenPythonPrimitive):
    def __init__(self):
        super().__init__(primitive_type=list)


class GenBool(GenPythonPrimitive):
    def __init__(self):
        super().__init__(primitive_type=bool)


class GenInt(GenPythonPrimitive):
    def __init__(self):
        super().__init__(primitive_type=int)


class GenFloat(GenPythonPrimitive):
    def __init__(self):
        super().__init__(primitive_type=float)


class GenTuple(GenPythonPrimitive):
    def __init__(self):
        super().__init__(primitive_type=tuple)


class GenSet(GenPythonPrimitive):
    def __init__(self):
        super().__init__(primitive_type=set)


def main():
    result = GenTuple()("Create a list of planets in our solar system sorted by largest to smallest")

    assert result == ('Jupiter', 'Saturn', 'Uranus', 'Neptune', 'Earth', 'Venus', 'Mars', 'Mercury')

    print(f"The planets of the solar system are {result}")


if __name__ == '__main__':
    main()

import inspect

from jinja2 import Environment, FileSystemLoader


from pydantic import BaseModel, Field
from typing import List
from rdddy.generators.gen_python_primitive import GenPythonPrimitive


class Command(BaseModel):
    name: str = Field(..., min_length=1, description="The name of the command")
    help: str = Field(..., min_length=1, description="The help text for the command")


class TyperCLI(BaseModel):
    name: str = Field(..., min_length=1, description="The name of the CLI application")
    commands: List[Command] = Field(
        ..., description="The commands of the CLI application"
    )


# Example description for testing
cli_description = f"""
{inspect.getsource(Command)}
{inspect.getsource(TyperCLI)}

We are building a Typer CLI application named 'DSPyGenerator'. It should include the following commands:

1. Command Name: generate_module
   Help: Generates a new DSPy module with a specified name.

2. Command Name: generate_signature
   Help: Creates a new signature class for defining input-output behavior in DSPy modules.

3. Command Name: generate_chainofthought
   Help: Generates a ChainOfThought module with a standard question-answering signature.

4. Command Name: generate_retrieve
   Help: Generates a Retrieve module for use in information retrieval tasks within DSPy.

5. Command Name: generate_teleprompter
   Help: Creates a teleprompter setup for optimizing DSPy programs.

6. Command Name: generate_example
   Help: Generates an example structure for use in training and testing DSPy modules.

7. Command Name: generate_assertion
   Help: Generates a template for creating LM Assertions in DSPy programs.

"""

module = GenPythonPrimitive(primitive_type=dict)

results = module.forward(cli_description)


# Example CLI data
cli_data = TyperCLI(**results)


# --- Jinja Templates ---
cli_template = """
import typer
app = typer.Typer()

{% for command in cli_data.commands %}
@app.command(name="{{ command.name }}")
def {{ command.name }}():
    \"\"\"{{ command.help }}\"\"\"
    # Command logic goes here
    print("This is the {{ command.name }} command.")

{% endfor %}

if __name__ == "__main__":
    app()


"""

pytest_template = """
import pytest
from typer.testing import CliRunner
from metadspy.cli import app  # Updated import statement

runner = CliRunner()

{% for command in cli_data.commands %}
def test_{{ command.name }}():
    result = runner.invoke(app, ["{{ command.name }}"])
    assert result.exit_code == 0
    assert "This is the {{ command.name }} command." in result.output  # Replace with specific expected output

{% endfor %}
"""

# --- Render Templates ---
env = Environment(loader=FileSystemLoader("."))
env.from_string(cli_template).stream(cli_data=cli_data.model_dump()).dump("gen_cli.py")
env.from_string(pytest_template).stream(cli_data=cli_data.model_dump()).dump(
    "test_cli.py"
)

print("CLI application and tests generated.")