MinimalChainable

MinimalChainable is a library designed to facilitate the execution and chaining of prompts, allowing dynamic substitution of context and references to previous results. This guide will help you get started from scratch with the project.

Using MinimalChainable

Installation

To begin, install MinimalChainable from GitHub using npm:

npm install https://github.com/EsteveSegura/MinimalChainable

Basic Usage

Importing: Import the MinimalChainable class into your JavaScript file:
```
const MinimalChainable = require('minimalchainable');
```

Defining Context and Prompts: Define a context and a model you'll use, along with a gpt function that will make calls to GPT to process prompts:

const context = { var1: 'value1', var2: 'value2' };
const model = {}; // Model to use

// Function to call GPT
async function gpt(model, prompt) {
  // Logic to call GPT and get response
  return 'GPT response';
}

const prompts = [
  'Prompt with {{var1}} and {{var2}}',
  'Next prompt with {{output[-1]}}',
];

Executing Prompts: Use the static run method of MinimalChainable to execute prompts with the defined context and the gpt function:

async function runPrompts() {
  try {
    const [output, contextFilledPrompts] = await MinimalChainable.run(context, model, gpt, prompts);

    // Handle context-filled prompts and obtained results here
    console.log('Context-filled prompts:');
    console.log(contextFilledPrompts);

    console.log('Obtained results:');
    console.log(output);

  } catch (error) {
    console.error('Error:', error);
  }
}

runPrompts();

Using {{output}} and References

The {{output[-1]}} reference in prompts allows you to dynamically insert the result of the immediately preceding prompt into subsequent prompts. This feature is powerful for creating a chain of prompts where each step relies on the output of the previous step.

Example:

Suppose you have the following prompts defined:

const prompts = [
  'Generate a title for an article about {{topic}}.',
  'Now, summarize the article: {{output}}',
];

In this example:

The first prompt asks for a title related to a specified topic.
The second prompt uses {{output}} to reference the title generated in the first prompt.

When executed with MinimalChainable, the prompts might resolve as follows:

Prompt 1: Generate a title for an article about AI Ethics.
Prompt 2: Now, summarize the article: "The Ethics of Artificial Intelligence explores..."

Output: ["The Ethics of Artificial Intelligence explores...", "Summary of the article"]

Objects in {{output}}

If a prompt result is an object, you can access its properties using dot notation within the prompt. This allows for fine-grained control over how you utilize complex data structures returned from previous prompts.

Example:

Consider a scenario where the first prompt returns an object:

const prompts = [
  'Generate a blog post with the title: {{output.title}}',
  'Add a hook for the blog post: {{output.hook}}',
];

Assume the first prompt outputs the following object:

{ title: 'Understanding Machine Learning Algorithms', hook: 'Explore the intricacies of ML algorithms.' }

When processed by MinimalChainable, the prompts would result in:

Prompt 1: Generate a blog post with the title: Understanding Machine Learning Algorithms
Prompt 2: Add a hook for the blog post: Explore the intricacies of ML algorithms.

Output: [
  { title: 'Understanding Machine Learning Algorithms', hook: 'Explore the intricacies of ML algorithms.' },
  "Hook added to the blog post"
]

When to Use Prompt Chaining

Prompt chaining with MinimalChainable can significantly enhance your workflow by breaking down complex tasks and ensuring high performance and accuracy. Here are four guiding questions to determine when to use prompt chaining:

Complex Tasks: Are you asking your AI to accomplish multiple distantly related tasks in a single prompt?
Performance and Error Reduction: Do you need to maximize prompt performance and minimize errors?
Previous Output as Input: Do subsequent prompts require the output of a previous prompt as input?
Adaptive Workflow: Do you need an adaptive workflow that adjusts based on prompt outcomes?

Prompt chaining addresses these needs by structuring interactions in a logical sequence, enhancing clarity, and ensuring each step builds upon the last effectively.

Summary

MinimalChainable equips you with practical examples to harness the power of dynamic prompt chaining and output referencing. By leveraging {{output}} and handling objects within prompts, you can automate complex text generation workflows and integrate decision-making processes seamlessly into your applications.

Additional Considerations

When integrating MinimalChainable into your projects, remember:

Stay close to the prompt to maintain clarity and control.
Avoid unnecessary abstractions to maintain simplicity and debugging efficiency.
Start with minimal, purposeful code that accomplishes specific tasks effectively.

Examples

Summary of Prompts and Results

Calling the Reddit API

The code makes a request to the Reddit API to fetch posts from the /r/artificial subreddit. It filters posts that have content in selftext and returns a list with titles, texts, and URLs.

First Prompt: Post Selection

The first prompt takes the fetched Reddit posts and asks to select the 5 most interesting ones. The prompt is filled with a list of post titles.

Prompt:

Please select the 5 most interesting posts of this artificial intelligence subreddit to generate a newsletter
Reddit Posts:
- Title 1
- Title 2
...

Result:

1. AI-sludge site creates zombified versions of real technology writers
2. ChatGPT might rule the AI chatbots — but it can't beat Google Search
3. Musicians are in trouble
4. What AI tools are creators you using in the Sound/Gfx/3D area?
5. My favorite painter got ripped off on Instagram, and I’m grieving the end of an era

Second Prompt: Generating Post Bodies

Using the selected titles from the first prompt, the second prompt asks to generate the body text for each selected post.

Prompt:

Now, let's generate the body for each selected post to include in the newsletter
1. Title 1
2. Title 2
...

Result:

1. AI-sludge site creates zombified versions of real technology writers
   In an unsettling development, a notorious "AI-sludge" website has begun...
2. ChatGPT might rule the AI chatbots — but it can't beat Google Search
   While ChatGPT has become a dominant force in the realm of AI chatbots...
...

Third Prompt: Summary and CTA

Using the generated bodies from the second prompt, the third prompt asks for a summary in one sentence, including a call to action (CTA) to visit https://girlazo.com.

Prompt:

Finalize the newsletter by providing a summary of the selected posts the summary has to be less than the length of a tweet, and it has to be in one sentence, and a call to action: CTA should be go to https://girlazo.com. Content to summarize: 
1. Body 1
2. Body 2
...

Result:

1. AI-generated articles mimic tech writers but lack human depth, raising ethical concerns about journalism's future. Go to https://girlazo.com.
2. ChatGPT excels in conversation but can't surpass Google Search's accuracy and breadth in providing relevant information. Go to https://girlazo.com.
3. Musicians face financial and creative challenges from streaming royalties and AI-generated music, necessitating industry adaptation. Go to https://girlazo.com.
4. AI tools like AIVA, Sensei, and Dreamcatcher are revolutionizing sound, graphics, and 3D design, empowering creators. Go to https://girlazo.com.
5. Art theft on Instagram threatens artists' integrity and livelihood, emphasizing the need for better intellectual property protections. Go to https://girlazo.com.

Inspired by daikeren prompt chain gist in python