Performance Tuning

Note

This documentation section is currently under development. The content below is a placeholder and will be expanded in future releases.

Overview

This section provides guidance on tuning your Memories-Dev deployment for optimal performance across various workloads and environments.

Topics to be Covered

Memory tier configuration
Query optimization parameters
Storage backend tuning
Vector database optimization
Network and IO tuning
Hardware recommendations
Workload-specific optimizations

Configuration Parameters

# Example configuration for performance tuning
config = Config(
    vector_db_params={
        "index_type": "HNSW",
        "ef_construction": 200,
        "M": 16
    },
    memory_params={
        "hot_memory_size": 10000,
        "warm_memory_size": 50000,
        "batch_size": 1024
    },
    storage_params={
        "compression_level": 6,
        "chunk_size": "128MB"
    }
)

Coming Soon

Detailed documentation with performance benchmarks, tuning recommendations for different hardware configurations, and advanced optimization techniques will be added in upcoming releases.