metadata
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")