---
license: apache-2.0
language:
  - en
base_model:
  - mradermacher/oh-dcft-v3.1-claude-3-5-sonnet-20241022-GGUF
  - openai/whisper-large-v3-turbo
pipeline_tag: memory-management
inference_api: true
title: Adaptive Memory Architecture (AMA)
description: >
  A biomimetic, multi-tier memory management system designed to
  revolutionize  how AI systems process, store, and retrieve information.
  Featuring dynamic  semantic embedding, intelligent relationship tracking, and
  adaptive memory compression.
key_features:
  - Multi-tier memory management
  - Semantic embedding integration
  - Dynamic relationship inference
  - Intelligent memory compression
  - Contextually aware information processing
technical_details:
  memory_tiers:
    - volatile_short_term:
        capacity: 10 items
        characteristics:
          - High-speed access
          - Recent interactions
          - Cache-like implementation
    - persistent_long_term:
        capacity: unlimited
        characteristics:
          - Important concept storage
          - Hierarchical knowledge representation
    - context_working_memory:
        capacity: 5 items
        characteristics:
          - Current conversation state
          - Active task parameters
performance_metrics:
  retrieval_speed: O(log n)
  semantic_similarity_calculation: cosine distance
  memory_compression_ratio: adaptive
research_potential:
  - Neuromorphic memory modeling
  - Adaptive learning systems
  - Cognitive architecture development
ethical_considerations:
  - Transparent memory tracking
  - Configurable confidence scoring
  - Relationship type inference
code_structure:
  classes:
    - name: MemoryItem
      responsibilities:
        - Represent individual memory units
        - Track memory metadata
        - Manage relationships
    - name: MemoryTier
      responsibilities:
        - Manage memory storage
        - Implement pruning strategies
        - Provide retrieval mechanisms
    - name: MemoryManager
      responsibilities:
        - Coordinate memory tiers
        - Handle memory insertion
        - Perform semantic searches
    - name: SemanticEmbedding
      responsibilities:
        - Generate vector representations
        - Calculate semantic similarities
        - Manage embedding cache
dependencies:
  - natural
  - tensorflow
  - crypto
usage_example: |
  ```python
  memory_manager = MemoryManager()
  memory_manager.insert("AI ethics are crucial")
  results = memory_manager.retrieve("ethical AI")
---