2.4. Core Workflow Concepts

This document illustrates the key workflows and concepts in the Memories-Dev framework.

2.4.1. Memory Processing Workflow

sequenceDiagram participant App as Application participant MM as Memory Manager participant EM as Earth Memory participant LM as Local Memory participant AI as AI Processing App->>MM: Request Memory Access MM->>LM: Check Local Cache alt Cache Hit LM-->>MM: Return Cached Data MM-->>App: Return Result else Cache Miss MM->>EM: Query Earth Memory EM->>AI: Process Query AI-->>EM: Enhanced Results EM-->>MM: Return Results MM->>LM: Update Cache MM-->>App: Return Result end

2.4.2. Key Concepts

  1. Memory Management * Local caching strategies * Earth memory synchronization * Cache invalidation policies

  2. Data Processing * Pattern recognition * Semantic analysis * Context preservation

  3. AI Integration * Real-time processing * Learning capabilities * Adaptive responses

2.4.3. Implementation Guidelines

from memories_dev import MemoryManager, EarthMemory, LocalCache

# Initialize components
memory_manager = MemoryManager()
earth_memory = EarthMemory()
local_cache = LocalCache()

# Example workflow
def process_memory_request(query):
    # Check local cache first
    result = local_cache.get(query)
    if result:
        return result

    # Query earth memory if not in cache
    result = earth_memory.query(query)
    local_cache.set(query, result)
    return result

Note

The workflow shown above is a simplified version. Actual implementations may include additional steps for error handling, validation, and optimization.