keep/PLAN.md

Refactoring Plan to Reduce Code Duplication

1. Create Core Service Layer

Files: src/core/ (new directory)

• src/core/item_service.rs - Main service for item operations (synchronous)
• src/core/async_item_service.rs - Async wrapper for API use
• src/core/compression_service.rs - Compression handling
• src/core/meta_service.rs - Metadata processing
• src/core/mod.rs - Module exports
• src/core/error.rs - Unified error handling

Reason: Extract common business logic from modes and APIs into reusable services Implementation:

• Move logic from modes (get, save, list, info) and API handlers into service functions
• Services should return structured data, not format output
• Handle compression, metadata, and database operations
• Keep core services synchronous for CLI performance
• Provide async wrappers using tokio::task::spawn_blocking for API use
• Use streaming for pipeline efficiency (process data in chunks)

2. Create Common Data Structures

Files: src/core/types.rs Reason: Standardize data structures used across modes and APIs Implementation:

• Define structs for Item, ItemWithContent, ItemWithMeta, Response<T>
• Include conversion functions from database types (From<db::Item>)
• Add serialization/deserialization support for JSON/YAML
• Ensure all fields are properly documented
• Use zero-copy patterns where possible (slicing instead of copying)

3. Refactor CLI Modes to Use Services

Files:

• src/modes/get.rs
• src/modes/save.rs
• src/modes/list.rs
• src/modes/info.rs
• src/modes/delete.rs
• src/modes/diff.rs
• src/modes/status.rs

Reason: Remove direct database and file system access from modes Implementation:

• Replace current implementations with calls to core services
• Keep only CLI-specific formatting and output logic
• Handle command-line argument parsing and validation
• Use synchronous services directly
• Implement streaming for stdin/stdout to maintain pipeline performance

4. Refactor REST API to Use Async Services

Files:

• src/modes/server/api/item.rs
• src/modes/server/api/status.rs

Reason: Remove business logic from HTTP handlers Implementation:

• Convert handlers to call async core services
• Keep only HTTP-specific code (status codes, headers, etc.)
• Use common error handling with conversions to HTTP responses
• Wrap synchronous service calls in tokio::task::spawn_blocking

5. Refactor MCP Tools to Use Services

Files: src/modes/server/mcp/tools.rs

Reason: Remove duplication with REST API and CLI modes Implementation:

• Replace current implementation with calls to core services
• Keep only MCP protocol-specific logic
• Use synchronous services directly (MCP is typically local/short-lived)
• Standardize response format to match API/CLI

6. Create Common Error Handling

Files: src/core/error.rs Reason: Standardize error types across the application Implementation:

• Define comprehensive error enum with conversions:
  • From database errors
  • From I/O errors
  • From compression errors
  • From validation errors
• Implement conversions to:
  • anyhow::Error (for CLI)
  • axum::http::StatusCode (for API)
  • ToolError (for MCP)
• Provide user-friendly error messages
• Include error codes for programmatic handling

7. Update Database Layer for Batch Operations

Files: src/db.rs Reason: Support efficient batch operations needed by services Implementation:

• Add functions to get multiple items with their metadata and tags
• Add batch insertion/updates for tags and metadata
• Add transaction support for atomic operations
• Optimize queries for common access patterns
• Ensure all batch operations are properly documented

8. Add Integration Tests

Files: tests/integration/ (new directory) Reason: Ensure refactored code maintains functionality and performance Implementation:

• Test core services independently
• Test CLI modes and APIs through their public interfaces
• Verify compression, metadata, and database operations
• Include performance benchmarks for critical paths
• Use in-memory databases and tempfiles for isolation
• Test both sync and async service implementations

9. Performance Optimization Guidelines

Reason: Ensure the refactored version doesn't slow down pipelines Implementation:

• Use streaming for stdin/stdout processing (chunked I/O)
• Minimize buffering and memory copies
• Offload CPU-bound work (compression, plugins) to thread pools
• Provide fast-path options (e.g., --fast flag to skip metadata plugins)
• Benchmark critical operations before/after refactoring
• Document performance characteristics

Implementation Order:

1. Create core module structure and error types (core/error.rs, core/types.rs)
2. Implement core services with basic functionality (sync first)
3. Add async wrappers for API use
4. Refactor one mode (e.g., get) to use services and validate approach
5. Refactor corresponding API endpoints to use async services
6. Repeat for other modes and APIs
7. Refactor MCP tools to use services
8. Add comprehensive tests and benchmarks
9. Clean up removed code from original files
10. Document performance characteristics and tradeoffs

Benefits:

• Reduced code duplication between CLI, API, and MCP
• Easier maintenance with clear separation of concerns
• Consistent behavior across all interfaces
• Better testability with isolated service layer
• Maintained or improved pipeline performance
• Flexible architecture supporting both sync and async use cases

Key Risks and Mitigations:

1. Performance Regression:

   • Risk: Refactoring could slow down pipeline operations
   • Mitigation: Benchmark before/after, use streaming, minimize overhead

2. Increased Complexity:

   • Risk: Adding service layer could make code harder to understand
   • Mitigation: Clear documentation, gradual refactoring, maintain simple interfaces

3. Async/Sync Boundaries:

   • Risk: Mixing sync/async could lead to deadlocks or inefficiencies
   • Mitigation: Clear boundaries, use spawn_blocking for sync work in async context

4. Breaking Changes:

   • Risk: Refactoring could change behavior in subtle ways
   • Mitigation: Comprehensive tests, gradual rollout, maintain backward compatibility

Design Principles:

1. Zero-Copy Where Possible: Use slicing instead of copying data
2. Streaming Processing: Handle data in chunks for memory efficiency
3. Clear Boundaries: Separate core logic from interface-specific code
4. Performance First: Optimize for common pipeline use cases
5. Consistent Errors: Unified error handling across all interfaces
6. Backward Compatibility: Maintain existing CLI/API behavior