Collaborative Memory Hu

Attempting a larger system ai for learning testing and fun.

1. Set basic file structure and placeholders
├── A-P-I
│   └── apiHandler.j
├── Assets
│   └── style.css
├── DB
│   └── dbConfig.js
├── Gui
│   └── app.js
│   └── index.html
├── PreProcessors
│   └── textPreprocess.js
├── Scripts
│   └── utils.js
├── Transformers
│   └── transformer.js
└── listFiles.bat
└── main.js

2. Possible libraries to use

• Tracery
• sentiment
• franc
• compromise
• ml5
• brainjs
• tfjs
• natural
• taffdb

Usgage Plans

1. Tracery

Purpose: Procedural text generation.
Use Case: Creating dynamic templates for generating responses, dialogue trees, or creative writing outputs.

2. Sentiment

Purpose: Sentiment analysis.
Use Case: Analysing user input or generated text to determine emotional tone and adjusting responses accordingly.

3. Franc

Purpose: Language detection.
Use Case: Detecting the language of user inputs to handle multilingual interactions seamlessly.

4. Compromise

Purpose: Lightweight NLP parsing.
Use Case: Parsing and extracting key information from user inputs for better understanding and contextual responses.

5. ML5.js

Purpose: Machine learning for the web.
Use Case: Simplifying the integration of ML models for image recognition, pose detection, or basic neural networks.

6. Brain.js

Purpose: Neural networks in JavaScript.
Use Case: Creating and training simple neural networks for tasks like classification or pattern recognition.

7. TensorFlow.js

Purpose: Advanced ML and deep learning.
Use Case: Running more complex machine learning models, such as transformers, directly in the browser.

8. Natural

Purpose: NLP toolkit.
Use Case: Tokenization, stemming, and classification to preprocess and analyse text data.

9. Taffdb

Purpose: Base rewrite
Use Case: Rewriting from a further standpoint, End-goal creating a more practical usage friendly DB.

Integration Plan

Preprocessing: Use Franc and Natural for language detection, tokenization, and stemming.
Sentiment Analysis: Use Sentiment to gauge emotional tone.
Dynamic Responses: Combine Tracery and Compromise to generate context-aware text outputs.
Machine Learning: Integrate ML5.js, Brain.js, and TensorFlow.js for tasks like image recognition or chatbot intelligence.
Neural Networks: Use Brain.js for simple tasks and TensorFlow.js for complex models.

Looking at self flat file storage and methods of splitting sentences,s e.g..

function processText(sentence) {
  const words = sentence.match(/bw+(-w+)*b/g) // Tokenize words
  const pairs = words
    .map((_, i) => (words[i + 1] ? `${words[i]} ${words[i + 1]}` : null))
    .filter(Boolean)

  return { words, pairs }
}

const sentence = "This could lead to a self-evolving, context-aware database — something truly new and adaptable. What do you think, buddy? Does this blend spark any new thoughts?"
const result = processText(sentence)
console.log(result)

With new objects like:

{
  "root": "This could lead to a self-evolving, context-aware database — something truly new and adaptable.",
  "words": [
    "This", "could", "lead", "to", "a", "self-evolving", "context-aware", "database", "something", "truly", "new", "adaptable"
  ],
  "pairs": [
    "This could", "could lead", "self-evolving context-aware", "context-aware database", "truly new", "new adaptable"
  ],
  "intent": "educate",
  "tone": "friendly"
}

Credits to Lexx he is a great teacher

Using the earlier mentioned ML5 (Machine learning 5) we can identify what each word in the sentence is thereby improving their meaning

{
  "root": "This could lead to a self-evolving, context-aware database — something truly new and adaptable.",
  "words": [
    { "word": "This", "POS": "Determiner" },
    { "word": "could", "POS": "Modal", "context": ["might", "possibly"] },
    { "word": "lead", "POS": "Verb", "context": ["guide", "result in"] },
    { "word": "self-evolving", "POS": "Adjective", "context": ["adaptive", "self-improving"] },
    { "word": "context-aware", "POS": "Adjective", "context": ["situational", "responsive"] },
    { "word": "database", "POS": "Noun", "context": ["data store", "repository"] },
    { "word": "new", "POS": "Adjective", "context": ["innovative", "fresh"] },
    { "word": "adaptable", "POS": "Adjective", "context": ["flexible", "versatile"] }
  ],
  "pairs": [
    { "pair": "This could", "relation": "possibility" },
    { "pair": "could lead", "relation": "cause-effect" },
    { "pair": "self-evolving context-aware", "relation": "description" },
    { "pair": "context-aware database", "relation": "object-description" },
    { "pair": "truly new", "relation": "emphasis" },
    { "pair": "new adaptable", "relation": "quality" }
  ],
  "intent": "educate",
  "tone": "friendly"
}

Current Proposed todo

1. .Refine Data Flow: Ensure the AI processes data efficiently (parsing, storing, retrieving).
2. .Expand Context Awareness: Implement deeper semantic layers for understanding phrases.
3. .Integrate ML Models: Use ml5.js or TensorFlow.js for real-time analysis.
4. .Prototype: Start coding initial preprocessing modules.

Building a AIO(all in one) input system “Continue to AIO System Development: Next Steps