PatternMemory

PatternMemory is an advanced memory system for LLMs that enables efficient pattern recognition and long-term knowledge retention.

Features

• Pattern Recognition
  • Automatic pattern detection
  • Similarity matching
  • Context-aware retrieval
• Memory Management
  • Hierarchical storage
  • Priority-based retention
  • Automatic cleanup
• Integration
  • Model-agnostic design
  • REST API support
  • Python SDK

Quick Start

from patternmemory import PatternMemory

memory = PatternMemory(
    storage_path="path/to/memory",
    max_patterns=1000,
    similarity_threshold=0.85
)

# Store a pattern
memory.store(
    pattern="User asked about refund policy",
    context="customer_support",
    metadata={"user_id": "123", "timestamp": "2024-03-20"}
)

# Retrieve similar patterns
similar = memory.retrieve(
    query="How do I get a refund?",
    context="customer_support"
)
print(similar[0].confidence)  # 0.92

Use Cases

1. Customer Support
   • Common question patterns
   • Resolution tracking
   • Knowledge base building
2. Content Generation
   • Style consistency
   • Topic coherence
   • Brand voice maintenance
3. Compliance
   • Policy pattern matching
   • Regulatory requirement tracking
   • Audit trail generation

Documentation

For detailed documentation, visit our GitHub repository.