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Performance Documentation

This directory contains documentation related to optimizing and tuning the performance of the LLM-MCP (LLM Platform Model Context Protocol) platform.

Performance Guides​

Performance Topics​

The performance guides cover the following key areas:

Hardware Recommendations​

  • Development environment requirements
  • Production environment sizing
  • Resource allocation recommendations

Server Performance Optimization​

  • Node.js configuration
  • Process management
  • Thread pool optimization
  • Memory management

Database Performance Optimization​

  • MongoDB optimization techniques
  • Qdrant vector database tuning
  • Connection pooling strategies
  • Query optimization

Vector Operations Optimization​

  • Vector storage efficiency
  • Vector search parameter tuning
  • Batch operations
  • Hybrid search implementation

Tool Execution Performance​

  • Concurrent execution management
  • Timeouts and retries
  • Resource isolation
  • Worker pool implementation

Network Optimization​

  • HTTP/2 configuration
  • Connection pooling
  • gRPC optimization
  • Compression settings

Caching Strategies​

  • Multi-level caching
  • Intelligent cache invalidation
  • Cache warming
  • Adaptive caching

Load Testing and Benchmarking​

  • Load testing methodologies
  • Performance benchmarking
  • Stress testing
  • Capacity planning

Scaling Strategies​

  • Horizontal scaling
  • Kubernetes deployment
  • Load balancing
  • High availability configuration

Monitoring Performance​

  • Prometheus metrics
  • Grafana dashboards
  • Performance alerts
  • Real-time monitoring

Best Practices​

For optimal performance in production environments, we recommend:

  • Regularly conducting load tests to identify bottlenecks
  • Implementing a comprehensive monitoring solution
  • Tuning configuration parameters based on workload characteristics
  • Following a methodical approach to optimization with measurements before and after changes
  • Implementing proper caching strategies at multiple levels
  • Optimizing database queries and indexes
  • Configuring appropriate resource limits and scaling policies