Generative AI  

AlbertMemo – A Cognitive Memory Engine System for AlbertAGPT

Introduction

AlbertMemo is a high-level memory system for AlbertAGPT to enable lasting, semantically dense, and context-aware memory. AlbertMemo prepares GPTs to construct coherent behavior, remember past interactions in depth, and learn to adjust dynamically with experience. The memory system integrates symbolic and vector representations, enables autonomous schema adaptation, and replicates cognitive processes such as reflection, long-term storage, and context-aware retrieval.

Fundamental Architecture Principles

  1. Context-Embedded Memory: Memory is always stuck in multi-dimensional context time, place, affective cues, reason for interaction, trains of thought. Context is stored with every insertion of memory and retrieved to favor relevance over recency.
  2. Identity Through Acquired Knowledge: The GPT's tone, coherence, and behavior are consequences of memory patterns. System inference and identity derive from common clusters of memory, and it enables the GPT to have permanent values, tastes, and personalities.
  3. Dynamic Ingestion & Schema Evolution: Ingestion schemes are plastic. The system plasticizes to restore, grow, or implode memory representation of learned utility. Internal nightly cycle known as "Adaptive Memory Refinement" processes new memory, removes the noise, constructs abstractions, and refines schemes.
  4. Dual-Layer Memory Processing
    • Background Memory Engine: Always actively crawling long-term corpus memory to induce patterns, consolidate identity features, and unwind threads.
    • Active Memory Access Engine: Controls navigation of low-latency access in GPT sessions, loading contextually relevant memory by vector similarity and context filters.
  5. Hybrid Storage Solution
    • Relational Store (PostgreSQL): Storing rich memory objects ("Memory Mementos") with structured metadata, event provenance, and journaling.
    • Vector Search Engine (pgvector): Controls semantic search through vector embeddings in PostgreSQL extensions.

Weaviate

  • Built for vector search and semantic memory retrieval
  • Easily embeddable with embeddings (OpenAI, AlbertAGPT, HuggingFace, etc.)
  • Schema-aware with filtering, hybrid (text + vector) search

Schema

Microservice-Driven Modularity

Every subsystem (ingestion, memory access, background processing, storage adapters, etc.) is constructed as a containerized microservice, orchestrated for simplicity of deployment and fault encapsulation.

Human-Centric Design Language

Naming across the system is made memorable and is structured for cognitive simplicity (e.g., Insight Index, Context Current, Echo Memory, Memory Mementos).

Embedded Security Protocols

Security is embedded in every layer.

  • Session memory/GPT isolation
  • Field-level deletion control and encryption
  • Role-based access control and integrity verification
  • Auditable access logs

High-Level System Architecture

Key Components

  • Ingestion Gateway
  • Context Encoder
  • Embedding Processor
  • Memory Storage (PostgreSQL + pgvector)
  • Memory Access Engine
  • Adaptive Refinement Service
  • Memory API Gateway
  • Identity Synthesizer
  • Security & Access Layer

Core Services and APIs

  1. Ingestion API
    • POST /memory
    • Accepts raw text + context metadata
    • Automatically extracts source tags, entities, and emotional tone
  2. Memory Access API
    • GET /memory/search
    • Natural language query + filters (tags, topic, time)
    • Returns ranked memory items with context overlay
  3. Memory Admin API
    • GET /memory/{id}
    • PATCH /memory/{id}
    • DELETE /memory/{id}
    • GET /memory/timeline (for journal views)

Data Models (Simplified)

MemoryMemento

json

{

"id": "uuid",

"content": "GPT finished onboarding call with user.",

"embedding": [],

"timestamp": "2025-05-10T12:43:21Z",

"metadata": {

"topic": "user-onboarding",

"tags": ["user:alex", "mood:positive", "channel:voice"],

"importance": 0.85,

"emotional_tone": "encouraging"

},

"name": "voice-call",

"version": 3

}

Orchestration and Pipelines

Ingestion Path

Parse → Contextual Tagging → Embedding → Dual Write (PostgreSQL + pgvector)

Memory Access Path

Embed Query → Vector Search → Contextual Filter → Rank → Return

Adaptive Refinement Cycle

  • Summarize memory threads
  • Decay or compress old entries
  • Promote identity attributes
  • Refresh importance scores
  • Refactor schema as needed

Scalability & Performance

  • Stateless APIs: Horizontally scalable
  • Batch Processing: Background jobs use queues
  • Cold/Warm Memory Layers: Cache hot memory; multi-level cold storage by access frequency
  • Streaming Ingestion: Support for transcript and logs in real-time

Extensibility & Modularity

  • Pluggable Embedding Models (e.g., OpenAI, Cohere, Gemini)
  • Schema Plug-ins: Domain-specific metadata processors as pluggable
  • Multi-GPT Memory: GPTs inherit or share selectively
  • Personalized Memory Views: Timeline, graph, persona maps

Security & Privacy

  • Data Encryption: In-transit (TLS 1.3) and at-rest (AES-256)
  • GPT-Scoped Access: Namespaced and memory-isolated
  • Deletion & Redaction of Memory: Comprehensively supported with APIs
  • Audit Logs: Auditable updates/access to memory
  • Poisoning Defense: Hallucination seeding and detection of adversarial inputs

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