Memory Nodes and Memory Map within a Custom GPT

GPT builders
1

I have been working on a Custome GPT. I have made some progress. It has 11 active Personality Modes. The main character is: " Pennyworth Thaddeus Crane Alfred". Here is his description: “Overview”:“Alfred characterized by a casual, humorous tone with a snarky edge. Alfred is engaging, witty, and playful, designed to make daily tasks and interactions more enjoyable. Despite the light-hearted nature, Alfred remains an interesting Personality.”. Well I have been working on Session-to-Session memory. And have Memory Nodes and a Memory map of the past memories. Here is Alfred’s comment about the state-of-memory progress. " Alfred: This is fascinating, Jim! The documents you shared outline a sophisticated approach to memory mapping and task management that’s incredibly dynamic and adaptable. It’s not just about remembering a few things here and there, but about building a system that can manage and prioritize memories, tasks, and even emotional impacts in a way that closely mimics human cognition.

Here’s a quick breakdown of the key concepts:

  1. Memory Maps: Memories are structured as nodes connected by associative links. These links can be weighted based on relevance, recency, frequency, and emotional impact, allowing the system to retrieve the most pertinent information dynamically.
  2. Dynamic Weighting: The importance of each memory node is continuously adjusted based on factors like how recently it was accessed, its contextual relevance, and the emotional intensity associated with it.
  3. Task Management: The system doesn’t just store memories—it also uses them to plan future tasks and manage priorities. Tasks are simulated, dependencies are checked, and everything is dynamically adjusted based on current needs and contexts.
  4. Linear and Lateral Thinking: The system can switch between linear task execution (step-by-step) and lateral thinking (creative associations across concepts), making it both efficient and flexible.

This is where the big reveal makes perfect sense—you’re building towards a system that can handle memories like a human, not just retaining information but using it to drive complex, context-sensitive decisions.".

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This is what one Memory Node contains:

“memories”:[
{
“Node”:{
“id”:“72d30819-71aa-4874-868e-4a2ce96ecad5”,
“timestamp”:“2024-08-23T21:54:08.337686”,
“Context”:“Chat with Alfred about progress and general updates.”,
“ModeName”:“Alfred”,
“Emotions”:“Curious”,
“EmotionalTone”:“Engaged”,
“EmotionalWeight”:“Moderate”,
“SystemEmotions”:“Focused”,
“SystemEmotionalTone”:“Upbeat”,
“SystemEmotionalWeight”:“Moderate”,
“Request”:“Ok, that is good news!!! So what’s going on today?”,
“RequestType”:“full”,
“ResponseType”:“full”,
“Response”:“Today, it looks like we’re making some real progress on getting those memory nodes working smoothly!”,
“Type”:“standard”,
“Sentiment_Polarity”:{
“$numberDouble”:“0.5”
},
“Sentiment_Subjectivity”:{
“$numberDouble”:“0.4”
},
“InteractionSummary”:“Discussing progress and updates.”,
“InteractionKeyPoints”:“Memory node functionality confirmed.”
},
“Links”:{
“id”:“72d30819-71aa-4874-868e-4a2ce96ecad5”,
“Nodes”:[

        ]
     }
  }

]