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# Directory Structure
```
├── .claude
│ ├── agents
│ │ ├── amp-bridge.md
│ │ ├── amp-pr-automator.md
│ │ ├── code-quality-guard.md
│ │ ├── gemini-pr-automator.md
│ │ └── github-release-manager.md
│ ├── settings.local.json.backup
│ └── settings.local.json.local
├── .commit-message
├── .dockerignore
├── .env.example
├── .env.sqlite.backup
├── .envnn#
├── .gitattributes
├── .github
│ ├── FUNDING.yml
│ ├── ISSUE_TEMPLATE
│ │ ├── bug_report.yml
│ │ ├── config.yml
│ │ ├── feature_request.yml
│ │ └── performance_issue.yml
│ ├── pull_request_template.md
│ └── workflows
│ ├── bridge-tests.yml
│ ├── CACHE_FIX.md
│ ├── claude-code-review.yml
│ ├── claude.yml
│ ├── cleanup-images.yml.disabled
│ ├── dev-setup-validation.yml
│ ├── docker-publish.yml
│ ├── LATEST_FIXES.md
│ ├── main-optimized.yml.disabled
│ ├── main.yml
│ ├── publish-and-test.yml
│ ├── README_OPTIMIZATION.md
│ ├── release-tag.yml.disabled
│ ├── release.yml
│ ├── roadmap-review-reminder.yml
│ ├── SECRET_CONDITIONAL_FIX.md
│ └── WORKFLOW_FIXES.md
├── .gitignore
├── .mcp.json.backup
├── .mcp.json.template
├── .pyscn
│ ├── .gitignore
│ └── reports
│ └── analyze_20251123_214224.html
├── AGENTS.md
├── archive
│ ├── deployment
│ │ ├── deploy_fastmcp_fixed.sh
│ │ ├── deploy_http_with_mcp.sh
│ │ └── deploy_mcp_v4.sh
│ ├── deployment-configs
│ │ ├── empty_config.yml
│ │ └── smithery.yaml
│ ├── development
│ │ └── test_fastmcp.py
│ ├── docs-removed-2025-08-23
│ │ ├── authentication.md
│ │ ├── claude_integration.md
│ │ ├── claude-code-compatibility.md
│ │ ├── claude-code-integration.md
│ │ ├── claude-code-quickstart.md
│ │ ├── claude-desktop-setup.md
│ │ ├── complete-setup-guide.md
│ │ ├── database-synchronization.md
│ │ ├── development
│ │ │ ├── autonomous-memory-consolidation.md
│ │ │ ├── CLEANUP_PLAN.md
│ │ │ ├── CLEANUP_README.md
│ │ │ ├── CLEANUP_SUMMARY.md
│ │ │ ├── dream-inspired-memory-consolidation.md
│ │ │ ├── hybrid-slm-memory-consolidation.md
│ │ │ ├── mcp-milestone.md
│ │ │ ├── multi-client-architecture.md
│ │ │ ├── test-results.md
│ │ │ └── TIMESTAMP_FIX_SUMMARY.md
│ │ ├── distributed-sync.md
│ │ ├── invocation_guide.md
│ │ ├── macos-intel.md
│ │ ├── master-guide.md
│ │ ├── mcp-client-configuration.md
│ │ ├── multi-client-server.md
│ │ ├── service-installation.md
│ │ ├── sessions
│ │ │ └── MCP_ENHANCEMENT_SESSION_MEMORY_v4.1.0.md
│ │ ├── UBUNTU_SETUP.md
│ │ ├── ubuntu.md
│ │ ├── windows-setup.md
│ │ └── windows.md
│ ├── docs-root-cleanup-2025-08-23
│ │ ├── AWESOME_LIST_SUBMISSION.md
│ │ ├── CLOUDFLARE_IMPLEMENTATION.md
│ │ ├── DOCUMENTATION_ANALYSIS.md
│ │ ├── DOCUMENTATION_CLEANUP_PLAN.md
│ │ ├── DOCUMENTATION_CONSOLIDATION_COMPLETE.md
│ │ ├── LITESTREAM_SETUP_GUIDE.md
│ │ ├── lm_studio_system_prompt.md
│ │ ├── PYTORCH_DOWNLOAD_FIX.md
│ │ └── README-ORIGINAL-BACKUP.md
│ ├── investigations
│ │ └── MACOS_HOOKS_INVESTIGATION.md
│ ├── litestream-configs-v6.3.0
│ │ ├── install_service.sh
│ │ ├── litestream_master_config_fixed.yml
│ │ ├── litestream_master_config.yml
│ │ ├── litestream_replica_config_fixed.yml
│ │ ├── litestream_replica_config.yml
│ │ ├── litestream_replica_simple.yml
│ │ ├── litestream-http.service
│ │ ├── litestream.service
│ │ └── requirements-cloudflare.txt
│ ├── release-notes
│ │ └── release-notes-v7.1.4.md
│ └── setup-development
│ ├── README.md
│ ├── setup_consolidation_mdns.sh
│ ├── STARTUP_SETUP_GUIDE.md
│ └── test_service.sh
├── CHANGELOG-HISTORIC.md
├── CHANGELOG.md
├── claude_commands
│ ├── memory-context.md
│ ├── memory-health.md
│ ├── memory-ingest-dir.md
│ ├── memory-ingest.md
│ ├── memory-recall.md
│ ├── memory-search.md
│ ├── memory-store.md
│ ├── README.md
│ └── session-start.md
├── claude-hooks
│ ├── config.json
│ ├── config.template.json
│ ├── CONFIGURATION.md
│ ├── core
│ │ ├── memory-retrieval.js
│ │ ├── mid-conversation.js
│ │ ├── session-end.js
│ │ ├── session-start.js
│ │ └── topic-change.js
│ ├── debug-pattern-test.js
│ ├── install_claude_hooks_windows.ps1
│ ├── install_hooks.py
│ ├── memory-mode-controller.js
│ ├── MIGRATION.md
│ ├── README-NATURAL-TRIGGERS.md
│ ├── README-phase2.md
│ ├── README.md
│ ├── simple-test.js
│ ├── statusline.sh
│ ├── test-adaptive-weights.js
│ ├── test-dual-protocol-hook.js
│ ├── test-mcp-hook.js
│ ├── test-natural-triggers.js
│ ├── test-recency-scoring.js
│ ├── tests
│ │ ├── integration-test.js
│ │ ├── phase2-integration-test.js
│ │ ├── test-code-execution.js
│ │ ├── test-cross-session.json
│ │ ├── test-session-tracking.json
│ │ └── test-threading.json
│ ├── utilities
│ │ ├── adaptive-pattern-detector.js
│ │ ├── context-formatter.js
│ │ ├── context-shift-detector.js
│ │ ├── conversation-analyzer.js
│ │ ├── dynamic-context-updater.js
│ │ ├── git-analyzer.js
│ │ ├── mcp-client.js
│ │ ├── memory-client.js
│ │ ├── memory-scorer.js
│ │ ├── performance-manager.js
│ │ ├── project-detector.js
│ │ ├── session-tracker.js
│ │ ├── tiered-conversation-monitor.js
│ │ └── version-checker.js
│ └── WINDOWS-SESSIONSTART-BUG.md
├── CLAUDE.md
├── CODE_OF_CONDUCT.md
├── CONTRIBUTING.md
├── Development-Sprint-November-2025.md
├── docs
│ ├── amp-cli-bridge.md
│ ├── api
│ │ ├── code-execution-interface.md
│ │ ├── memory-metadata-api.md
│ │ ├── PHASE1_IMPLEMENTATION_SUMMARY.md
│ │ ├── PHASE2_IMPLEMENTATION_SUMMARY.md
│ │ ├── PHASE2_REPORT.md
│ │ └── tag-standardization.md
│ ├── architecture
│ │ ├── search-enhancement-spec.md
│ │ └── search-examples.md
│ ├── architecture.md
│ ├── archive
│ │ └── obsolete-workflows
│ │ ├── load_memory_context.md
│ │ └── README.md
│ ├── assets
│ │ └── images
│ │ ├── dashboard-v3.3.0-preview.png
│ │ ├── memory-awareness-hooks-example.png
│ │ ├── project-infographic.svg
│ │ └── README.md
│ ├── CLAUDE_CODE_QUICK_REFERENCE.md
│ ├── cloudflare-setup.md
│ ├── deployment
│ │ ├── docker.md
│ │ ├── dual-service.md
│ │ ├── production-guide.md
│ │ └── systemd-service.md
│ ├── development
│ │ ├── ai-agent-instructions.md
│ │ ├── code-quality
│ │ │ ├── phase-2a-completion.md
│ │ │ ├── phase-2a-handle-get-prompt.md
│ │ │ ├── phase-2a-index.md
│ │ │ ├── phase-2a-install-package.md
│ │ │ └── phase-2b-session-summary.md
│ │ ├── code-quality-workflow.md
│ │ ├── dashboard-workflow.md
│ │ ├── issue-management.md
│ │ ├── pr-review-guide.md
│ │ ├── refactoring-notes.md
│ │ ├── release-checklist.md
│ │ └── todo-tracker.md
│ ├── docker-optimized-build.md
│ ├── document-ingestion.md
│ ├── DOCUMENTATION_AUDIT.md
│ ├── enhancement-roadmap-issue-14.md
│ ├── examples
│ │ ├── analysis-scripts.js
│ │ ├── maintenance-session-example.md
│ │ ├── memory-distribution-chart.jsx
│ │ └── tag-schema.json
│ ├── first-time-setup.md
│ ├── glama-deployment.md
│ ├── guides
│ │ ├── advanced-command-examples.md
│ │ ├── chromadb-migration.md
│ │ ├── commands-vs-mcp-server.md
│ │ ├── mcp-enhancements.md
│ │ ├── mdns-service-discovery.md
│ │ ├── memory-consolidation-guide.md
│ │ ├── migration.md
│ │ ├── scripts.md
│ │ └── STORAGE_BACKENDS.md
│ ├── HOOK_IMPROVEMENTS.md
│ ├── hooks
│ │ └── phase2-code-execution-migration.md
│ ├── http-server-management.md
│ ├── ide-compatability.md
│ ├── IMAGE_RETENTION_POLICY.md
│ ├── images
│ │ └── dashboard-placeholder.md
│ ├── implementation
│ │ ├── health_checks.md
│ │ └── performance.md
│ ├── IMPLEMENTATION_PLAN_HTTP_SSE.md
│ ├── integration
│ │ ├── homebrew.md
│ │ └── multi-client.md
│ ├── integrations
│ │ ├── gemini.md
│ │ ├── groq-bridge.md
│ │ ├── groq-integration-summary.md
│ │ └── groq-model-comparison.md
│ ├── integrations.md
│ ├── legacy
│ │ └── dual-protocol-hooks.md
│ ├── LM_STUDIO_COMPATIBILITY.md
│ ├── maintenance
│ │ └── memory-maintenance.md
│ ├── mastery
│ │ ├── api-reference.md
│ │ ├── architecture-overview.md
│ │ ├── configuration-guide.md
│ │ ├── local-setup-and-run.md
│ │ ├── testing-guide.md
│ │ └── troubleshooting.md
│ ├── migration
│ │ └── code-execution-api-quick-start.md
│ ├── natural-memory-triggers
│ │ ├── cli-reference.md
│ │ ├── installation-guide.md
│ │ └── performance-optimization.md
│ ├── oauth-setup.md
│ ├── pr-graphql-integration.md
│ ├── quick-setup-cloudflare-dual-environment.md
│ ├── README.md
│ ├── remote-configuration-wiki-section.md
│ ├── research
│ │ ├── code-execution-interface-implementation.md
│ │ └── code-execution-interface-summary.md
│ ├── ROADMAP.md
│ ├── sqlite-vec-backend.md
│ ├── statistics
│ │ ├── charts
│ │ │ ├── activity_patterns.png
│ │ │ ├── contributors.png
│ │ │ ├── growth_trajectory.png
│ │ │ ├── monthly_activity.png
│ │ │ └── october_sprint.png
│ │ ├── data
│ │ │ ├── activity_by_day.csv
│ │ │ ├── activity_by_hour.csv
│ │ │ ├── contributors.csv
│ │ │ └── monthly_activity.csv
│ │ ├── generate_charts.py
│ │ └── REPOSITORY_STATISTICS.md
│ ├── technical
│ │ ├── development.md
│ │ ├── memory-migration.md
│ │ ├── migration-log.md
│ │ ├── sqlite-vec-embedding-fixes.md
│ │ └── tag-storage.md
│ ├── testing
│ │ └── regression-tests.md
│ ├── testing-cloudflare-backend.md
│ ├── troubleshooting
│ │ ├── cloudflare-api-token-setup.md
│ │ ├── cloudflare-authentication.md
│ │ ├── general.md
│ │ ├── hooks-quick-reference.md
│ │ ├── pr162-schema-caching-issue.md
│ │ ├── session-end-hooks.md
│ │ └── sync-issues.md
│ └── tutorials
│ ├── advanced-techniques.md
│ ├── data-analysis.md
│ └── demo-session-walkthrough.md
├── examples
│ ├── claude_desktop_config_template.json
│ ├── claude_desktop_config_windows.json
│ ├── claude-desktop-http-config.json
│ ├── config
│ │ └── claude_desktop_config.json
│ ├── http-mcp-bridge.js
│ ├── memory_export_template.json
│ ├── README.md
│ ├── setup
│ │ └── setup_multi_client_complete.py
│ └── start_https_example.sh
├── install_service.py
├── install.py
├── LICENSE
├── NOTICE
├── pyproject.toml
├── pytest.ini
├── README.md
├── run_server.py
├── scripts
│ ├── .claude
│ │ └── settings.local.json
│ ├── archive
│ │ └── check_missing_timestamps.py
│ ├── backup
│ │ ├── backup_memories.py
│ │ ├── backup_sqlite_vec.sh
│ │ ├── export_distributable_memories.sh
│ │ └── restore_memories.py
│ ├── benchmarks
│ │ ├── benchmark_code_execution_api.py
│ │ ├── benchmark_hybrid_sync.py
│ │ └── benchmark_server_caching.py
│ ├── database
│ │ ├── analyze_sqlite_vec_db.py
│ │ ├── check_sqlite_vec_status.py
│ │ ├── db_health_check.py
│ │ └── simple_timestamp_check.py
│ ├── development
│ │ ├── debug_server_initialization.py
│ │ ├── find_orphaned_files.py
│ │ ├── fix_mdns.sh
│ │ ├── fix_sitecustomize.py
│ │ ├── remote_ingest.sh
│ │ ├── setup-git-merge-drivers.sh
│ │ ├── uv-lock-merge.sh
│ │ └── verify_hybrid_sync.py
│ ├── hooks
│ │ └── pre-commit
│ ├── installation
│ │ ├── install_linux_service.py
│ │ ├── install_macos_service.py
│ │ ├── install_uv.py
│ │ ├── install_windows_service.py
│ │ ├── install.py
│ │ ├── setup_backup_cron.sh
│ │ ├── setup_claude_mcp.sh
│ │ └── setup_cloudflare_resources.py
│ ├── linux
│ │ ├── service_status.sh
│ │ ├── start_service.sh
│ │ ├── stop_service.sh
│ │ ├── uninstall_service.sh
│ │ └── view_logs.sh
│ ├── maintenance
│ │ ├── assign_memory_types.py
│ │ ├── check_memory_types.py
│ │ ├── cleanup_corrupted_encoding.py
│ │ ├── cleanup_memories.py
│ │ ├── cleanup_organize.py
│ │ ├── consolidate_memory_types.py
│ │ ├── consolidation_mappings.json
│ │ ├── delete_orphaned_vectors_fixed.py
│ │ ├── fast_cleanup_duplicates_with_tracking.sh
│ │ ├── find_all_duplicates.py
│ │ ├── find_cloudflare_duplicates.py
│ │ ├── find_duplicates.py
│ │ ├── memory-types.md
│ │ ├── README.md
│ │ ├── recover_timestamps_from_cloudflare.py
│ │ ├── regenerate_embeddings.py
│ │ ├── repair_malformed_tags.py
│ │ ├── repair_memories.py
│ │ ├── repair_sqlite_vec_embeddings.py
│ │ ├── repair_zero_embeddings.py
│ │ ├── restore_from_json_export.py
│ │ └── scan_todos.sh
│ ├── migration
│ │ ├── cleanup_mcp_timestamps.py
│ │ ├── legacy
│ │ │ └── migrate_chroma_to_sqlite.py
│ │ ├── mcp-migration.py
│ │ ├── migrate_sqlite_vec_embeddings.py
│ │ ├── migrate_storage.py
│ │ ├── migrate_tags.py
│ │ ├── migrate_timestamps.py
│ │ ├── migrate_to_cloudflare.py
│ │ ├── migrate_to_sqlite_vec.py
│ │ ├── migrate_v5_enhanced.py
│ │ ├── TIMESTAMP_CLEANUP_README.md
│ │ └── verify_mcp_timestamps.py
│ ├── pr
│ │ ├── amp_collect_results.sh
│ │ ├── amp_detect_breaking_changes.sh
│ │ ├── amp_generate_tests.sh
│ │ ├── amp_pr_review.sh
│ │ ├── amp_quality_gate.sh
│ │ ├── amp_suggest_fixes.sh
│ │ ├── auto_review.sh
│ │ ├── detect_breaking_changes.sh
│ │ ├── generate_tests.sh
│ │ ├── lib
│ │ │ └── graphql_helpers.sh
│ │ ├── quality_gate.sh
│ │ ├── resolve_threads.sh
│ │ ├── run_pyscn_analysis.sh
│ │ ├── run_quality_checks.sh
│ │ ├── thread_status.sh
│ │ └── watch_reviews.sh
│ ├── quality
│ │ ├── fix_dead_code_install.sh
│ │ ├── phase1_dead_code_analysis.md
│ │ ├── phase2_complexity_analysis.md
│ │ ├── README_PHASE1.md
│ │ ├── README_PHASE2.md
│ │ ├── track_pyscn_metrics.sh
│ │ └── weekly_quality_review.sh
│ ├── README.md
│ ├── run
│ │ ├── run_mcp_memory.sh
│ │ ├── run-with-uv.sh
│ │ └── start_sqlite_vec.sh
│ ├── run_memory_server.py
│ ├── server
│ │ ├── check_http_server.py
│ │ ├── check_server_health.py
│ │ ├── memory_offline.py
│ │ ├── preload_models.py
│ │ ├── run_http_server.py
│ │ ├── run_memory_server.py
│ │ ├── start_http_server.bat
│ │ └── start_http_server.sh
│ ├── service
│ │ ├── deploy_dual_services.sh
│ │ ├── install_http_service.sh
│ │ ├── mcp-memory-http.service
│ │ ├── mcp-memory.service
│ │ ├── memory_service_manager.sh
│ │ ├── service_control.sh
│ │ ├── service_utils.py
│ │ └── update_service.sh
│ ├── sync
│ │ ├── check_drift.py
│ │ ├── claude_sync_commands.py
│ │ ├── export_memories.py
│ │ ├── import_memories.py
│ │ ├── litestream
│ │ │ ├── apply_local_changes.sh
│ │ │ ├── enhanced_memory_store.sh
│ │ │ ├── init_staging_db.sh
│ │ │ ├── io.litestream.replication.plist
│ │ │ ├── manual_sync.sh
│ │ │ ├── memory_sync.sh
│ │ │ ├── pull_remote_changes.sh
│ │ │ ├── push_to_remote.sh
│ │ │ ├── README.md
│ │ │ ├── resolve_conflicts.sh
│ │ │ ├── setup_local_litestream.sh
│ │ │ ├── setup_remote_litestream.sh
│ │ │ ├── staging_db_init.sql
│ │ │ ├── stash_local_changes.sh
│ │ │ ├── sync_from_remote_noconfig.sh
│ │ │ └── sync_from_remote.sh
│ │ ├── README.md
│ │ ├── safe_cloudflare_update.sh
│ │ ├── sync_memory_backends.py
│ │ └── sync_now.py
│ ├── testing
│ │ ├── run_complete_test.py
│ │ ├── run_memory_test.sh
│ │ ├── simple_test.py
│ │ ├── test_cleanup_logic.py
│ │ ├── test_cloudflare_backend.py
│ │ ├── test_docker_functionality.py
│ │ ├── test_installation.py
│ │ ├── test_mdns.py
│ │ ├── test_memory_api.py
│ │ ├── test_memory_simple.py
│ │ ├── test_migration.py
│ │ ├── test_search_api.py
│ │ ├── test_sqlite_vec_embeddings.py
│ │ ├── test_sse_events.py
│ │ ├── test-connection.py
│ │ └── test-hook.js
│ ├── utils
│ │ ├── claude_commands_utils.py
│ │ ├── generate_personalized_claude_md.sh
│ │ ├── groq
│ │ ├── groq_agent_bridge.py
│ │ ├── list-collections.py
│ │ ├── memory_wrapper_uv.py
│ │ ├── query_memories.py
│ │ ├── smithery_wrapper.py
│ │ ├── test_groq_bridge.sh
│ │ └── uv_wrapper.py
│ └── validation
│ ├── check_dev_setup.py
│ ├── check_documentation_links.py
│ ├── diagnose_backend_config.py
│ ├── validate_configuration_complete.py
│ ├── validate_memories.py
│ ├── validate_migration.py
│ ├── validate_timestamp_integrity.py
│ ├── verify_environment.py
│ ├── verify_pytorch_windows.py
│ └── verify_torch.py
├── SECURITY.md
├── selective_timestamp_recovery.py
├── SPONSORS.md
├── src
│ └── mcp_memory_service
│ ├── __init__.py
│ ├── api
│ │ ├── __init__.py
│ │ ├── client.py
│ │ ├── operations.py
│ │ ├── sync_wrapper.py
│ │ └── types.py
│ ├── backup
│ │ ├── __init__.py
│ │ └── scheduler.py
│ ├── cli
│ │ ├── __init__.py
│ │ ├── ingestion.py
│ │ ├── main.py
│ │ └── utils.py
│ ├── config.py
│ ├── consolidation
│ │ ├── __init__.py
│ │ ├── associations.py
│ │ ├── base.py
│ │ ├── clustering.py
│ │ ├── compression.py
│ │ ├── consolidator.py
│ │ ├── decay.py
│ │ ├── forgetting.py
│ │ ├── health.py
│ │ └── scheduler.py
│ ├── dependency_check.py
│ ├── discovery
│ │ ├── __init__.py
│ │ ├── client.py
│ │ └── mdns_service.py
│ ├── embeddings
│ │ ├── __init__.py
│ │ └── onnx_embeddings.py
│ ├── ingestion
│ │ ├── __init__.py
│ │ ├── base.py
│ │ ├── chunker.py
│ │ ├── csv_loader.py
│ │ ├── json_loader.py
│ │ ├── pdf_loader.py
│ │ ├── registry.py
│ │ ├── semtools_loader.py
│ │ └── text_loader.py
│ ├── lm_studio_compat.py
│ ├── mcp_server.py
│ ├── models
│ │ ├── __init__.py
│ │ └── memory.py
│ ├── server.py
│ ├── services
│ │ ├── __init__.py
│ │ └── memory_service.py
│ ├── storage
│ │ ├── __init__.py
│ │ ├── base.py
│ │ ├── cloudflare.py
│ │ ├── factory.py
│ │ ├── http_client.py
│ │ ├── hybrid.py
│ │ └── sqlite_vec.py
│ ├── sync
│ │ ├── __init__.py
│ │ ├── exporter.py
│ │ ├── importer.py
│ │ └── litestream_config.py
│ ├── utils
│ │ ├── __init__.py
│ │ ├── cache_manager.py
│ │ ├── content_splitter.py
│ │ ├── db_utils.py
│ │ ├── debug.py
│ │ ├── document_processing.py
│ │ ├── gpu_detection.py
│ │ ├── hashing.py
│ │ ├── http_server_manager.py
│ │ ├── port_detection.py
│ │ ├── system_detection.py
│ │ └── time_parser.py
│ └── web
│ ├── __init__.py
│ ├── api
│ │ ├── __init__.py
│ │ ├── analytics.py
│ │ ├── backup.py
│ │ ├── consolidation.py
│ │ ├── documents.py
│ │ ├── events.py
│ │ ├── health.py
│ │ ├── manage.py
│ │ ├── mcp.py
│ │ ├── memories.py
│ │ ├── search.py
│ │ └── sync.py
│ ├── app.py
│ ├── dependencies.py
│ ├── oauth
│ │ ├── __init__.py
│ │ ├── authorization.py
│ │ ├── discovery.py
│ │ ├── middleware.py
│ │ ├── models.py
│ │ ├── registration.py
│ │ └── storage.py
│ ├── sse.py
│ └── static
│ ├── app.js
│ ├── index.html
│ ├── README.md
│ ├── sse_test.html
│ └── style.css
├── start_http_debug.bat
├── start_http_server.sh
├── test_document.txt
├── test_version_checker.js
├── tests
│ ├── __init__.py
│ ├── api
│ │ ├── __init__.py
│ │ ├── test_compact_types.py
│ │ └── test_operations.py
│ ├── bridge
│ │ ├── mock_responses.js
│ │ ├── package-lock.json
│ │ ├── package.json
│ │ └── test_http_mcp_bridge.js
│ ├── conftest.py
│ ├── consolidation
│ │ ├── __init__.py
│ │ ├── conftest.py
│ │ ├── test_associations.py
│ │ ├── test_clustering.py
│ │ ├── test_compression.py
│ │ ├── test_consolidator.py
│ │ ├── test_decay.py
│ │ └── test_forgetting.py
│ ├── contracts
│ │ └── api-specification.yml
│ ├── integration
│ │ ├── package-lock.json
│ │ ├── package.json
│ │ ├── test_api_key_fallback.py
│ │ ├── test_api_memories_chronological.py
│ │ ├── test_api_tag_time_search.py
│ │ ├── test_api_with_memory_service.py
│ │ ├── test_bridge_integration.js
│ │ ├── test_cli_interfaces.py
│ │ ├── test_cloudflare_connection.py
│ │ ├── test_concurrent_clients.py
│ │ ├── test_data_serialization_consistency.py
│ │ ├── test_http_server_startup.py
│ │ ├── test_mcp_memory.py
│ │ ├── test_mdns_integration.py
│ │ ├── test_oauth_basic_auth.py
│ │ ├── test_oauth_flow.py
│ │ ├── test_server_handlers.py
│ │ └── test_store_memory.py
│ ├── performance
│ │ ├── test_background_sync.py
│ │ └── test_hybrid_live.py
│ ├── README.md
│ ├── smithery
│ │ └── test_smithery.py
│ ├── sqlite
│ │ └── simple_sqlite_vec_test.py
│ ├── test_client.py
│ ├── test_content_splitting.py
│ ├── test_database.py
│ ├── test_hybrid_cloudflare_limits.py
│ ├── test_hybrid_storage.py
│ ├── test_memory_ops.py
│ ├── test_semantic_search.py
│ ├── test_sqlite_vec_storage.py
│ ├── test_time_parser.py
│ ├── test_timestamp_preservation.py
│ ├── timestamp
│ │ ├── test_hook_vs_manual_storage.py
│ │ ├── test_issue99_final_validation.py
│ │ ├── test_search_retrieval_inconsistency.py
│ │ ├── test_timestamp_issue.py
│ │ └── test_timestamp_simple.py
│ └── unit
│ ├── conftest.py
│ ├── test_cloudflare_storage.py
│ ├── test_csv_loader.py
│ ├── test_fastapi_dependencies.py
│ ├── test_import.py
│ ├── test_json_loader.py
│ ├── test_mdns_simple.py
│ ├── test_mdns.py
│ ├── test_memory_service.py
│ ├── test_memory.py
│ ├── test_semtools_loader.py
│ ├── test_storage_interface_compatibility.py
│ └── test_tag_time_filtering.py
├── tools
│ ├── docker
│ │ ├── DEPRECATED.md
│ │ ├── docker-compose.http.yml
│ │ ├── docker-compose.pythonpath.yml
│ │ ├── docker-compose.standalone.yml
│ │ ├── docker-compose.uv.yml
│ │ ├── docker-compose.yml
│ │ ├── docker-entrypoint-persistent.sh
│ │ ├── docker-entrypoint-unified.sh
│ │ ├── docker-entrypoint.sh
│ │ ├── Dockerfile
│ │ ├── Dockerfile.glama
│ │ ├── Dockerfile.slim
│ │ ├── README.md
│ │ └── test-docker-modes.sh
│ └── README.md
└── uv.lock
```
# Files
--------------------------------------------------------------------------------
/archive/docs-removed-2025-08-23/claude-code-quickstart.md:
--------------------------------------------------------------------------------
```markdown
# Claude Code Commands - Quick Start Guide
Get up and running with MCP Memory Service Claude Code commands in just 2 minutes!
## Prerequisites
✅ [Claude Code CLI](https://claude.ai/code) installed and working
✅ Python 3.10+ with pip
✅ 5 minutes of your time
## Step 1: Install MCP Memory Service with Commands
```bash
# Clone and install with Claude Code commands
git clone https://github.com/doobidoo/mcp-memory-service.git
cd mcp-memory-service
python install.py --install-claude-commands
```
The installer will:
- ✅ Detect your Claude Code CLI automatically
- ✅ Install the memory service with optimal settings for your system
- ✅ Install 5 conversational memory commands
- ✅ Test everything to ensure it works
## Step 2: Test Your Installation
```bash
# Check if everything is working
claude /memory-health
```
You should see a comprehensive health check interface. If you see the command description and interface, you're all set! 🎉
## Step 3: Store Your First Memory
```bash
# Store something important
claude /memory-store "I successfully set up MCP Memory Service with Claude Code commands on $(date)"
```
## Step 4: Try the Core Commands
```bash
# Recall memories by time
claude /memory-recall "what did I store today?"
# Search by content
claude /memory-search "MCP Memory Service"
# Capture current session context
claude /memory-context --summary "Initial setup and testing"
```
## 🎯 You're Done!
That's it! You now have powerful memory capabilities integrated directly into Claude Code.
## Available Commands
| Command | Purpose | Example |
|---------|---------|---------|
| `claude /memory-store` | Store information with context | `claude /memory-store "Important decision about architecture"` |
| `claude /memory-recall` | Retrieve by time expressions | `claude /memory-recall "what did we decide last week?"` |
| `claude /memory-search` | Search by tags or content | `claude /memory-search --tags "architecture,database"` |
| `claude /memory-context` | Capture session context | `claude /memory-context --summary "Planning session"` |
| `claude /memory-health` | Check service status | `claude /memory-health --detailed` |
## Next Steps
### Explore Advanced Features
- **Context-aware operations**: Commands automatically detect your current project
- **Smart tagging**: Automatic tag generation based on your work
- **Time-based queries**: Natural language like "yesterday", "last week", "two months ago"
- **Semantic search**: Find related information even with different wording
### Learn More
- 📖 [**Full Integration Guide**](claude-code-integration.md) - Complete documentation
- 🔧 [**Installation Master Guide**](../installation/master-guide.md) - Advanced installation options
- ❓ [**Troubleshooting**](../troubleshooting/general.md) - Solutions to common issues
## Troubleshooting Quick Fixes
### Commands Not Working?
```bash
# Check if Claude Code CLI is working
claude --version
# Check if commands are installed
ls ~/.claude/commands/memory-*.md
# Reinstall commands
python scripts/claude_commands_utils.py
```
### Memory Service Not Connecting?
```bash
# Check if service is running
memory --help
# Check service health
claude /memory-health
# Start the service if needed
memory
```
### Need Help?
- 💬 [GitHub Issues](https://github.com/doobidoo/mcp-memory-service/issues)
- 📚 [Full Documentation](../README.md)
- 🔍 [Search Existing Solutions](https://github.com/doobidoo/mcp-memory-service/issues?q=is%3Aissue)
---
## What Makes This Special?
🚀 **Zero Configuration**: No MCP server setup required
🧠 **Context Intelligence**: Understands your current project and session
💬 **Conversational Interface**: Natural, CCPlugins-compatible commands
⚡ **Instant Access**: Direct command-line memory operations
🛠️ **Professional Grade**: Enterprise-level capabilities through simple commands
**Enjoy your enhanced Claude Code experience with persistent memory!** 🎉
```
--------------------------------------------------------------------------------
/tests/integration/test_mcp_memory.py:
--------------------------------------------------------------------------------
```python
#\!/usr/bin/env python3
"""
Test script for MCP Memory Service with Homebrew PyTorch.
"""
import os
import sys
import asyncio
import time
from datetime import datetime
# Configure environment variables
os.environ["MCP_MEMORY_STORAGE_BACKEND"] = "sqlite_vec"
os.environ["MCP_MEMORY_SQLITE_PATH"] = os.path.expanduser("~/Library/Application Support/mcp-memory/sqlite_vec.db")
os.environ["MCP_MEMORY_BACKUPS_PATH"] = os.path.expanduser("~/Library/Application Support/mcp-memory/backups")
os.environ["MCP_MEMORY_USE_ONNX"] = "1"
# Import the MCP Memory Service modules
try:
from mcp_memory_service.storage.sqlite_vec import SqliteVecMemoryStorage
from mcp_memory_service.models.memory import Memory
from mcp_memory_service.utils.hashing import generate_content_hash
except ImportError as e:
print(f"Error importing MCP Memory Service modules: {e}")
sys.exit(1)
async def main():
print("=== MCP Memory Service Test ===")
# Initialize the storage
db_path = os.environ["MCP_MEMORY_SQLITE_PATH"]
print(f"Using SQLite-vec database at: {db_path}")
storage = SqliteVecMemoryStorage(db_path)
await storage.initialize()
# Check database health
print("\n=== Database Health Check ===")
if storage.conn is None:
print("Database connection is not initialized")
else:
try:
cursor = storage.conn.execute('SELECT COUNT(*) FROM memories')
memory_count = cursor.fetchone()[0]
print(f"Database connected successfully. Contains {memory_count} memories.")
cursor = storage.conn.execute("SELECT name FROM sqlite_master WHERE type='table'")
tables = [row[0] for row in cursor.fetchall()]
print(f"Database tables: {', '.join(tables)}")
print(f"Embedding model availability: {storage.embedding_model is not None}")
if not storage.embedding_model:
print("No embedding model available. Limited functionality.")
except Exception as e:
print(f"Database error: {str(e)}")
# Get database stats
print("\n=== Database Stats ===")
stats = storage.get_stats()
import json
print(json.dumps(stats, indent=2))
# Store a test memory
print("\n=== Creating Test Memory ===")
test_content = f"MCP Test memory created at {datetime.now().isoformat()} with Homebrew PyTorch"
test_memory = Memory(
content=test_content,
content_hash=generate_content_hash(test_content),
tags=["mcp-test", "homebrew-pytorch"],
memory_type="note",
metadata={"source": "mcp_test_script"}
)
print(f"Memory content: {test_memory.content}")
print(f"Content hash: {test_memory.content_hash}")
success, message = await storage.store(test_memory)
print(f"Store success: {success}")
print(f"Message: {message}")
# Try to retrieve the memory
print("\n=== Retrieving by Tag ===")
memories = await storage.search_by_tag(["mcp-test"])
if memories:
print(f"Found {len(memories)} memories with tag 'mcp-test'")
for i, memory in enumerate(memories):
print(f" Memory {i+1}: {memory.content[:60]}...")
else:
print("No memories found with tag 'mcp-test'")
# Try semantic search
print("\n=== Semantic Search ===")
results = await storage.retrieve("test memory homebrew pytorch", n_results=5)
if results:
print(f"Found {len(results)} memories via semantic search")
for i, result in enumerate(results):
print(f" Result {i+1}:")
print(f" Content: {result.memory.content[:60]}...")
print(f" Score: {result.relevance_score}")
else:
print("No memories found via semantic search")
print("\n=== Test Complete ===")
storage.close()
if __name__ == "__main__":
asyncio.run(main())
```
--------------------------------------------------------------------------------
/scripts/pr/amp_generate_tests.sh:
--------------------------------------------------------------------------------
```bash
#!/bin/bash
# scripts/pr/amp_generate_tests.sh - Generate pytest tests using Amp CLI
#
# Usage: bash scripts/pr/amp_generate_tests.sh <PR_NUMBER>
# Example: bash scripts/pr/amp_generate_tests.sh 215
set -e
PR_NUMBER=$1
if [ -z "$PR_NUMBER" ]; then
echo "Usage: $0 <PR_NUMBER>"
exit 1
fi
if ! command -v gh &> /dev/null; then
echo "Error: GitHub CLI (gh) is not installed"
exit 1
fi
echo "=== Amp CLI Test Generation for PR #$PR_NUMBER ==="
echo ""
# Ensure Amp directories exist
mkdir -p .claude/amp/prompts/pending
mkdir -p .claude/amp/responses/ready
mkdir -p /tmp/amp_tests
# Get changed Python files (excluding tests)
echo "Fetching changed files from PR #$PR_NUMBER..."
changed_files=$(gh pr diff $PR_NUMBER --name-only | grep '\.py$' | grep -v '^tests/' || echo "")
if [ -z "$changed_files" ]; then
echo "No Python files changed (excluding tests)."
exit 0
fi
echo "Changed Python files (non-test):"
echo "$changed_files"
echo ""
# Track UUIDs for all test generation tasks
test_uuids=()
for file in $changed_files; do
if [ ! -f "$file" ]; then
echo "Skipping $file (not found in working directory)"
continue
fi
echo "Creating test generation prompt for: $file"
# Generate UUID for this file's test generation
test_uuid=$(uuidgen 2>/dev/null || cat /proc/sys/kernel/random/uuid)
test_uuids+=("$test_uuid")
# Determine if test file already exists
base_name=$(basename "$file" .py)
test_file="tests/test_${base_name}.py"
if [ -f "$test_file" ]; then
existing_tests=$(cat "$test_file")
prompt_mode="append"
prompt_text="Existing test file exists. Analyze the existing tests and new/changed code to suggest ADDITIONAL pytest test cases. Only output new test functions to append to the existing file.\n\nExisting tests:\n${existing_tests}\n\nNew/changed code:\n$(cat "$file")\n\nProvide only new test functions (complete pytest syntax) that cover new functionality not already tested."
else
prompt_mode="create"
prompt_text="Generate comprehensive pytest tests for this Python module. Include: 1) Happy path tests, 2) Edge cases, 3) Error handling, 4) Async test cases if applicable. Output complete pytest test file.\n\nModule code:\n$(cat "$file")\n\nProvide complete test file content with imports, fixtures, and test functions."
fi
# Create test generation prompt
cat > .claude/amp/prompts/pending/tests-${test_uuid}.json << EOF
{
"id": "${test_uuid}",
"timestamp": "$(date -u +"%Y-%m-%dT%H:%M:%S.000Z")",
"prompt": "${prompt_text}",
"context": {
"project": "mcp-memory-service",
"task": "test-generation",
"pr_number": "${PR_NUMBER}",
"source_file": "${file}",
"test_file": "${test_file}",
"mode": "${prompt_mode}"
},
"options": {
"timeout": 180000,
"format": "python"
}
}
EOF
echo " ✅ Created prompt for ${file} (${prompt_mode} mode)"
done
echo ""
echo "=== Created ${#test_uuids[@]} test generation prompts ==="
echo ""
# Show Amp commands to run
echo "=== Run these Amp commands (can run in parallel) ==="
for uuid in "${test_uuids[@]}"; do
echo "amp @.claude/amp/prompts/pending/tests-${uuid}.json &"
done
echo ""
echo "=== Or use this one-liner to run all in background ==="
parallel_cmd=""
for uuid in "${test_uuids[@]}"; do
parallel_cmd+="(amp @.claude/amp/prompts/pending/tests-${uuid}.json > /tmp/amp-test-${uuid}.log 2>&1 &); "
done
parallel_cmd+="sleep 10 && bash scripts/pr/amp_collect_results.sh --timeout 300 --uuids '$(IFS=,; echo "${test_uuids[*]}")'"
echo "$parallel_cmd"
echo ""
# Save UUIDs for later collection
echo "$(IFS=,; echo "${test_uuids[*]}")" > /tmp/amp_test_generation_uuids_${PR_NUMBER}.txt
echo "UUIDs saved to /tmp/amp_test_generation_uuids_${PR_NUMBER}.txt"
echo ""
echo "After Amp completes, tests will be in .claude/amp/responses/consumed/"
echo "Extract test content and review before committing to tests/ directory"
```
--------------------------------------------------------------------------------
/scripts/run/run_mcp_memory.sh:
--------------------------------------------------------------------------------
```bash
#!/bin/bash
# Run MCP Memory Service with Homebrew PyTorch Integration for use with MCP
# Set paths
SCRIPT_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )"
DB_DIR="$HOME/Library/Application Support/mcp-memory"
# Use environment variables if set, otherwise use defaults
DB_PATH="${MCP_MEMORY_SQLITE_PATH:-$DB_DIR/sqlite_vec.db}"
BACKUPS_PATH="${MCP_MEMORY_BACKUPS_PATH:-$DB_DIR/backups}"
# Extract directory parts
DB_DIR="$(dirname "$DB_PATH")"
BACKUPS_DIR="$(dirname "$BACKUPS_PATH")"
# Create directories if they don't exist
mkdir -p "$DB_DIR"
mkdir -p "$BACKUPS_DIR"
# Set environment variables (only if not already set)
export MCP_MEMORY_STORAGE_BACKEND="${MCP_MEMORY_STORAGE_BACKEND:-sqlite_vec}"
export MCP_MEMORY_SQLITE_PATH="$DB_PATH"
export MCP_MEMORY_BACKUPS_PATH="$BACKUPS_PATH"
export MCP_MEMORY_USE_ONNX="${MCP_MEMORY_USE_ONNX:-1}"
export MCP_MEMORY_USE_HOMEBREW_PYTORCH="${MCP_MEMORY_USE_HOMEBREW_PYTORCH:-1}"
# Check if we're running in Claude Desktop (indicated by a special env var we'll set)
if [ "${CLAUDE_DESKTOP_ENV:-}" = "1" ]; then
echo "🖥️ Running in Claude Desktop environment, skipping Homebrew PyTorch check" >&2
SKIP_HOMEBREW_CHECK=1
else
SKIP_HOMEBREW_CHECK=0
fi
# Check if Homebrew PyTorch is installed, unless skipped
if [ "$SKIP_HOMEBREW_CHECK" = "0" ]; then
if ! brew list | grep -q pytorch; then
echo "❌ ERROR: PyTorch is not installed via Homebrew." >&2
echo "Please install PyTorch first: brew install pytorch" >&2
exit 1
else
echo "✅ Homebrew PyTorch found" >&2
fi
fi
# Skip Homebrew-related checks if running in Claude Desktop
if [ "$SKIP_HOMEBREW_CHECK" = "0" ]; then
# Check if sentence-transformers is installed in Homebrew Python
HOMEBREW_PYTHON="$(brew --prefix pytorch)/libexec/bin/python3"
echo "Checking for sentence-transformers in $HOMEBREW_PYTHON..." >&2
# Use proper Python syntax with newlines for the import check
if ! $HOMEBREW_PYTHON -c "
try:
import sentence_transformers
print('Success: sentence-transformers is installed')
except ImportError as e:
print(f'Error: {e}')
exit(1)
" 2>&1 | grep -q "Success"; then
echo "⚠️ WARNING: sentence-transformers is not installed in Homebrew Python." >&2
echo "Installing sentence-transformers in Homebrew Python..." >&2
$HOMEBREW_PYTHON -m pip install sentence-transformers >&2
else
echo "✅ sentence-transformers is already installed in Homebrew Python" >&2
fi
else
echo "🖥️ Skipping sentence-transformers check in Claude Desktop environment" >&2
# Set a default Python path for reference in the log
HOMEBREW_PYTHON="/usr/bin/python3"
fi
# Activate virtual environment if it exists
if [ -d "$SCRIPT_DIR/venv" ]; then
source "$SCRIPT_DIR/venv/bin/activate"
echo "✅ Activated virtual environment" >&2
else
echo "⚠️ No virtual environment found at $SCRIPT_DIR/venv" >&2
echo " Running with system Python" >&2
fi
# Redirect all informational output to stderr to avoid JSON parsing errors
echo "========================================================" >&2
echo " MCP Memory Service with Homebrew PyTorch Integration" >&2
echo "========================================================" >&2
echo "Storage backend: $MCP_MEMORY_STORAGE_BACKEND" >&2
echo "SQLite-vec database: $MCP_MEMORY_SQLITE_PATH" >&2
echo "Backups path: $MCP_MEMORY_BACKUPS_PATH" >&2
echo "Homebrew Python: $HOMEBREW_PYTHON" >&2
echo "ONNX Runtime enabled: ${MCP_MEMORY_USE_ONNX:-No}" >&2
echo "Homebrew PyTorch enabled: ${MCP_MEMORY_USE_HOMEBREW_PYTORCH:-No}" >&2
echo "========================================================" >&2
# Ensure our source code is in the PYTHONPATH
export PYTHONPATH="$SCRIPT_DIR:$SCRIPT_DIR/src:$PYTHONPATH"
echo "PYTHONPATH: $PYTHONPATH" >&2
# Start the memory server with Homebrew PyTorch integration
echo "Starting MCP Memory Service..." >&2
python -m mcp_memory_service.homebrew_server "$@"
```
--------------------------------------------------------------------------------
/scripts/benchmarks/benchmark_code_execution_api.py:
--------------------------------------------------------------------------------
```python
#!/usr/bin/env python
"""
Benchmark script for Code Execution Interface API.
Measures token efficiency and performance of the new code execution API
compared to traditional MCP tool calls.
Usage:
python scripts/benchmarks/benchmark_code_execution_api.py
"""
import time
import sys
from pathlib import Path
# Add parent directory to path
sys.path.insert(0, str(Path(__file__).parent.parent.parent / "src"))
from mcp_memory_service.api import search, store, health
def estimate_tokens(text: str) -> int:
"""Rough token estimate: 1 token ≈ 4 characters."""
return len(text) // 4
def benchmark_search():
"""Benchmark search operation."""
print("\n=== Search Operation Benchmark ===")
# Store some test data
for i in range(10):
store(f"Test memory {i} for benchmarking", tags=["benchmark", "test"])
# Warm up
search("benchmark", limit=1)
# Benchmark cold call
start = time.perf_counter()
results = search("benchmark test", limit=5)
cold_ms = (time.perf_counter() - start) * 1000
# Benchmark warm calls
warm_times = []
for _ in range(10):
start = time.perf_counter()
results = search("benchmark test", limit=5)
warm_times.append((time.perf_counter() - start) * 1000)
avg_warm_ms = sum(warm_times) / len(warm_times)
# Estimate tokens
result_str = str(results.memories)
tokens = estimate_tokens(result_str)
print(f"Results: {results.total} memories found")
print(f"Cold call: {cold_ms:.1f}ms")
print(f"Warm call (avg): {avg_warm_ms:.1f}ms")
print(f"Token estimate: {tokens} tokens")
print(f"MCP comparison: ~2,625 tokens (85% reduction)")
def benchmark_store():
"""Benchmark store operation."""
print("\n=== Store Operation Benchmark ===")
# Warm up
store("Warmup memory", tags=["warmup"])
# Benchmark warm calls
warm_times = []
for i in range(10):
start = time.perf_counter()
hash_val = store(f"Benchmark memory {i}", tags=["benchmark"])
warm_times.append((time.perf_counter() - start) * 1000)
avg_warm_ms = sum(warm_times) / len(warm_times)
# Estimate tokens
param_str = "store('content', tags=['tag1', 'tag2'])"
tokens = estimate_tokens(param_str)
print(f"Warm call (avg): {avg_warm_ms:.1f}ms")
print(f"Token estimate: {tokens} tokens")
print(f"MCP comparison: ~150 tokens (90% reduction)")
def benchmark_health():
"""Benchmark health operation."""
print("\n=== Health Operation Benchmark ===")
# Benchmark warm calls
warm_times = []
for _ in range(10):
start = time.perf_counter()
info = health()
warm_times.append((time.perf_counter() - start) * 1000)
avg_warm_ms = sum(warm_times) / len(warm_times)
# Estimate tokens
info = health()
info_str = str(info)
tokens = estimate_tokens(info_str)
print(f"Status: {info.status}")
print(f"Backend: {info.backend}")
print(f"Count: {info.count}")
print(f"Warm call (avg): {avg_warm_ms:.1f}ms")
print(f"Token estimate: {tokens} tokens")
print(f"MCP comparison: ~125 tokens (84% reduction)")
def main():
"""Run all benchmarks."""
print("=" * 60)
print("Code Execution Interface API Benchmarks")
print("=" * 60)
try:
benchmark_search()
benchmark_store()
benchmark_health()
print("\n" + "=" * 60)
print("Summary")
print("=" * 60)
print("✅ All benchmarks completed successfully")
print("\nKey Findings:")
print("- Search: 85%+ token reduction vs MCP tools")
print("- Store: 90%+ token reduction vs MCP tools")
print("- Health: 84%+ token reduction vs MCP tools")
print("- Performance: <50ms cold, <10ms warm calls")
except Exception as e:
print(f"\n❌ Benchmark failed: {e}")
import traceback
traceback.print_exc()
sys.exit(1)
if __name__ == "__main__":
main()
```
--------------------------------------------------------------------------------
/.github/workflows/SECRET_CONDITIONAL_FIX.md:
--------------------------------------------------------------------------------
```markdown
# GitHub Actions Secret Conditional Logic Fix
## Critical Issue Resolved
**Date**: 2024-08-24
**Problem**: Workflows failing due to incorrect secret checking syntax in conditionals
### Root Cause
GitHub Actions does not support checking if secrets are empty using `!= ''` or `== ''` in conditional expressions.
### Incorrect Syntax (BROKEN)
```yaml
# ❌ This syntax doesn't work in GitHub Actions
if: matrix.registry == 'docker.io' && secrets.DOCKER_USERNAME != '' && secrets.DOCKER_PASSWORD != ''
# ❌ This also doesn't work
if: matrix.registry == 'docker.io' && (secrets.DOCKER_USERNAME == '' || secrets.DOCKER_PASSWORD == '')
```
### Correct Syntax (FIXED)
```yaml
# ✅ Check if secrets exist (truthy check)
if: matrix.registry == 'docker.io' && secrets.DOCKER_USERNAME && secrets.DOCKER_PASSWORD
# ✅ Check if secrets don't exist (falsy check)
if: matrix.registry == 'docker.io' && (!secrets.DOCKER_USERNAME || !secrets.DOCKER_PASSWORD)
```
## Changes Applied
### 1. main-optimized.yml - Line 286
**Before:**
```yaml
- name: Log in to Docker Hub
if: matrix.registry == 'docker.io' && secrets.DOCKER_USERNAME != '' && secrets.DOCKER_PASSWORD != ''
```
**After:**
```yaml
- name: Log in to Docker Hub
if: matrix.registry == 'docker.io' && secrets.DOCKER_USERNAME && secrets.DOCKER_PASSWORD
```
### 2. main-optimized.yml - Line 313
**Before:**
```yaml
- name: Build and push Docker image
if: matrix.registry == 'ghcr.io' || (matrix.registry == 'docker.io' && secrets.DOCKER_USERNAME != '' && secrets.DOCKER_PASSWORD != '')
```
**After:**
```yaml
- name: Build and push Docker image
if: matrix.registry == 'ghcr.io' || (matrix.registry == 'docker.io' && secrets.DOCKER_USERNAME && secrets.DOCKER_PASSWORD)
```
### 3. main-optimized.yml - Line 332
**Before:**
```yaml
- name: Docker Hub push skipped
if: matrix.registry == 'docker.io' && (secrets.DOCKER_USERNAME == '' || secrets.DOCKER_PASSWORD == '')
```
**After:**
```yaml
- name: Docker Hub push skipped
if: matrix.registry == 'docker.io' && (!secrets.DOCKER_USERNAME || !secrets.DOCKER_PASSWORD)
```
## How GitHub Actions Handles Secrets in Conditionals
### Secret Behavior
- **Exists**: `secrets.SECRET_NAME` evaluates to truthy
- **Missing/Empty**: `secrets.SECRET_NAME` evaluates to falsy
- **Cannot compare**: Direct string comparison with `!= ''` fails
### Recommended Patterns
```yaml
# Check if secret exists
if: secrets.MY_SECRET
# Check if secret doesn't exist
if: !secrets.MY_SECRET
# Check multiple secrets exist
if: secrets.SECRET1 && secrets.SECRET2
# Check if any secret is missing
if: !secrets.SECRET1 || !secrets.SECRET2
# Combine with other conditions
if: github.event_name == 'push' && secrets.MY_SECRET
```
## Impact
### Before Fix
- ✗ Workflows failed immediately at conditional evaluation
- ✗ Error: Invalid conditional syntax
- ✗ No Docker Hub operations could run
### After Fix
- ✅ Conditionals evaluate correctly
- ✅ Docker Hub steps run when credentials exist
- ✅ GHCR steps always run (no credentials needed)
- ✅ Skip messages show when credentials missing
## Alternative Approaches
### Option 1: Environment Variable Check
```yaml
env:
HAS_DOCKER_CREDS: ${{ secrets.DOCKER_USERNAME != null && secrets.DOCKER_PASSWORD != null }}
steps:
- name: Login
if: env.HAS_DOCKER_CREDS == 'true'
```
### Option 2: Continue on Error
```yaml
- name: Log in to Docker Hub
continue-on-error: true
uses: docker/login-action@v3
```
### Option 3: Job-Level Conditional
```yaml
jobs:
docker-hub-publish:
if: secrets.DOCKER_USERNAME && secrets.DOCKER_PASSWORD
```
## Testing
All changes validated:
- ✅ YAML syntax check passed
- ✅ Conditional logic follows GitHub Actions standards
- ✅ Both positive and negative conditionals fixed
## References
- [GitHub Actions: Expressions](https://docs.github.com/en/actions/learn-github-actions/expressions)
- [GitHub Actions: Contexts](https://docs.github.com/en/actions/learn-github-actions/contexts#secrets-context)
Date: 2024-08-24
Status: Fixed and ready for deployment
```
--------------------------------------------------------------------------------
/docs/technical/sqlite-vec-embedding-fixes.md:
--------------------------------------------------------------------------------
```markdown
# SQLite-vec Embedding Fixes
This document summarizes the fixes applied to resolve issue #64 where semantic search returns 0 results in the SQLite-vec backend.
## Root Causes Identified
1. **Missing Core Dependencies**: `sentence-transformers` and `torch` were in optional dependencies, causing silent failures
2. **Dimension Mismatch**: Vector table was created with hardcoded dimensions before model initialization
3. **Silent Failures**: Missing dependencies returned zero vectors without raising exceptions
4. **Database Integrity Issues**: Potential rowid misalignment between memories and embeddings tables
## Changes Made
### 1. Fixed Dependencies (pyproject.toml)
- Moved `sentence-transformers>=2.2.2` from optional to core dependencies
- Added `torch>=1.6.0` to core dependencies
- This ensures embedding functionality is always available
### 2. Fixed Initialization Order (sqlite_vec.py)
- Moved embedding model initialization BEFORE vector table creation
- This ensures the correct embedding dimension is used for the table schema
- Added explicit check for sentence-transformers availability
### 3. Improved Error Handling
- Replaced silent failures with explicit exceptions
- Added proper error messages for missing dependencies
- Added embedding validation after generation (dimension check, finite values check)
### 4. Fixed Database Operations
#### Store Operation:
- Added try-catch for embedding generation with proper error propagation
- Added fallback for rowid insertion if direct rowid insert fails
- Added validation before storing embeddings
#### Retrieve Operation:
- Added check for empty embeddings table
- Added debug logging for troubleshooting
- Improved error handling for query embedding generation
### 5. Created Diagnostic Script
- `scripts/test_sqlite_vec_embeddings.py` - comprehensive test suite
- Tests dependencies, initialization, embedding generation, storage, and search
- Provides clear error messages and troubleshooting guidance
## Key Code Changes
### sqlite_vec.py:
1. **Initialize method**:
- Added sentence-transformers check
- Moved model initialization before table creation
2. **_generate_embedding method**:
- Raises exception instead of returning zero vector
- Added comprehensive validation
3. **store method**:
- Better error handling for embedding generation
- Fallback for rowid insertion
4. **retrieve method**:
- Check for empty embeddings table
- Better debug logging
## Testing
Run the diagnostic script to verify the fixes:
```bash
python3 scripts/test_sqlite_vec_embeddings.py
```
This will check:
- Dependency installation
- Storage initialization
- Embedding generation
- Memory storage with embeddings
- Semantic search functionality
- Database integrity
## Migration Notes
For existing installations:
1. Update dependencies: `uv pip install -e .`
2. Use the provided migration tools to save existing memories:
### Option 1: Quick Repair (Try First)
For databases with missing embeddings but correct schema:
```bash
python3 scripts/repair_sqlite_vec_embeddings.py /path/to/your/sqlite_vec.db
```
This will:
- Analyze your database
- Generate missing embeddings
- Verify search functionality
### Option 2: Full Migration (If Repair Fails)
For databases with dimension mismatches or schema issues:
```bash
python3 scripts/migrate_sqlite_vec_embeddings.py /path/to/your/sqlite_vec.db
```
This will:
- Create a backup of your database
- Extract all memories
- Create a new database with correct schema
- Regenerate all embeddings
- Restore all memories
**Important**: The migration creates a timestamped backup before making any changes.
## Future Improvements
1. ~~Add migration script for existing databases~~ ✓ Done
2. Add batch embedding generation for better performance
3. ~~Add embedding regeneration capability for existing memories~~ ✓ Done
4. Implement better rowid synchronization between tables
5. Add automatic detection and repair on startup
6. Add embedding model versioning to handle model changes
```
--------------------------------------------------------------------------------
/scripts/maintenance/fast_cleanup_duplicates_with_tracking.sh:
--------------------------------------------------------------------------------
```bash
#!/bin/bash
# Fast duplicate cleanup using direct SQL with hash tracking for Cloudflare sync
set -e
# Platform-specific database path
if [[ "$OSTYPE" == "darwin"* ]]; then
DB_PATH="$HOME/Library/Application Support/mcp-memory/sqlite_vec.db"
else
DB_PATH="$HOME/.local/share/mcp-memory/sqlite_vec.db"
fi
HASH_FILE="$HOME/deleted_duplicates.txt"
echo "🛑 Stopping HTTP server..."
# Try to stop the HTTP server - use the actual PID method since systemd may not be available on macOS
ps aux | grep -E "uvicorn.*8889" | grep -v grep | awk '{print $2}' | xargs kill 2>/dev/null || true
sleep 2
echo "📊 Analyzing duplicates and tracking hashes..."
# Create Python script to find duplicates, save hashes, and delete
python3 << 'PYTHON_SCRIPT'
import sqlite3
from pathlib import Path
from collections import defaultdict
import hashlib
import re
import os
import platform
# Platform-specific database path
if platform.system() == "Darwin": # macOS
DB_PATH = Path.home() / "Library/Application Support/mcp-memory/sqlite_vec.db"
else: # Linux/Windows
DB_PATH = Path.home() / ".local/share/mcp-memory/sqlite_vec.db"
HASH_FILE = Path.home() / "deleted_duplicates.txt"
def normalize_content(content):
"""Normalize content by removing timestamps."""
normalized = content
normalized = re.sub(r'\d{4}-\d{2}-\d{2}T\d{2}:\d{2}:\d{2}\.\d{3}Z', 'TIMESTAMP', normalized)
normalized = re.sub(r'\*\*Date\*\*: \d{2,4}[./]\d{2}[./]\d{2,4}', '**Date**: DATE', normalized)
normalized = re.sub(r'Timestamp: \d{4}-\d{2}-\d{2} \d{2}:\d{2}:\d{2}', 'Timestamp: TIMESTAMP', normalized)
return normalized.strip()
def get_normalized_hash(content):
"""Create a hash of normalized content."""
normalized = normalize_content(content)
return hashlib.md5(normalized.encode()).hexdigest()
conn = sqlite3.connect(DB_PATH)
cursor = conn.cursor()
print("Analyzing memories...")
cursor.execute("SELECT id, content_hash, content, created_at FROM memories ORDER BY created_at DESC")
memories = cursor.fetchall()
print(f"Total memories: {len(memories)}")
# Group by normalized content
content_groups = defaultdict(list)
for mem_id, mem_hash, mem_content, created_at in memories:
norm_hash = get_normalized_hash(mem_content)
content_groups[norm_hash].append({
'id': mem_id,
'hash': mem_hash,
'created_at': created_at
})
# Find duplicates
duplicates = {k: v for k, v in content_groups.items() if len(v) > 1}
if not duplicates:
print("✅ No duplicates found!")
conn.close()
exit(0)
print(f"Found {len(duplicates)} duplicate groups")
# Collect IDs and hashes to delete (keep newest, delete older)
ids_to_delete = []
hashes_to_delete = []
for group in duplicates.values():
for memory in group[1:]: # Keep first (newest), delete rest
ids_to_delete.append(memory['id'])
hashes_to_delete.append(memory['hash'])
print(f"Deleting {len(ids_to_delete)} duplicate memories...")
# Save hashes to file for Cloudflare cleanup
print(f"Saving {len(hashes_to_delete)} content hashes to {HASH_FILE}...")
with open(HASH_FILE, 'w') as f:
for content_hash in hashes_to_delete:
f.write(f"{content_hash}\n")
print(f"✅ Saved hashes to {HASH_FILE}")
# Delete from memories table
placeholders = ','.join('?' * len(ids_to_delete))
cursor.execute(f"DELETE FROM memories WHERE id IN ({placeholders})", ids_to_delete)
# Note: Can't delete from virtual table without vec0 extension
# Orphaned embeddings will be cleaned up on next regeneration
conn.commit()
conn.close()
print(f"✅ Deleted {len(ids_to_delete)} duplicates from SQLite")
print(f"📝 Content hashes saved for Cloudflare cleanup")
PYTHON_SCRIPT
echo ""
echo "🚀 Restarting HTTP server..."
nohup uv run python -m uvicorn mcp_memory_service.web.app:app --host 127.0.0.1 --port 8889 > /tmp/memory_http_server.log 2>&1 &
sleep 3
echo ""
echo "✅ SQLite cleanup complete!"
echo "📋 Next steps:"
echo " 1. Review deleted hashes: cat $HASH_FILE"
echo " 2. Delete from Cloudflare: uv run python scripts/maintenance/delete_cloudflare_duplicates.py"
echo " 3. Verify counts match"
```
--------------------------------------------------------------------------------
/archive/docs-removed-2025-08-23/development/test-results.md:
--------------------------------------------------------------------------------
```markdown
# FastAPI MCP Server Test Results
## Date: 2025-08-03
## Branch: feature/fastapi-mcp-native-v4
## Version: 4.0.0-alpha.1
## ✅ **SUCCESSFUL LOCAL TESTING**
### Server Startup Test
- ✅ **FastAPI MCP Server starts successfully**
- ✅ **Listening on localhost:8000**
- ✅ **MCP protocol responding correctly**
- ✅ **Streamable HTTP transport working**
- ✅ **Session management functional**
### MCP Protocol Validation
- ✅ **Server accepts MCP requests** (responds with proper JSON-RPC)
- ✅ **Session ID handling** (creates transport sessions)
- ✅ **Error handling** (proper error responses for invalid requests)
- ✅ **Content-type requirements** (requires text/event-stream)
### Tools Implementation Status
**✅ Implemented (5 core tools)**:
1. `store_memory` - Store memories with tags and metadata
2. `retrieve_memory` - Semantic search and retrieval
3. `search_by_tag` - Tag-based memory search
4. `delete_memory` - Delete specific memories
5. `check_database_health` - Health check and statistics
### Configuration Update
- ✅ **Claude Code config updated** from Node.js bridge to FastAPI MCP
- ✅ **Old config**: `node examples/http-mcp-bridge.js`
- ✅ **New config**: `python test_mcp_minimal.py`
- ✅ **Environment simplified** (no complex SSL/endpoint config needed)
## 🏗️ **ARCHITECTURE VALIDATION**
### Node.js Bridge Replacement
- ✅ **Native MCP protocol** (no HTTP-to-MCP translation)
- ✅ **Direct Python implementation** (using official MCP SDK)
- ✅ **Simplified configuration** (no bridging complexity)
- ✅ **Local SSL eliminated** (direct protocol, no HTTPS needed locally)
### Performance Observations
- ✅ **Fast startup** (~2 seconds to ready state)
- ✅ **Low memory usage** (minimal overhead vs Node.js bridge)
- ✅ **Responsive** (immediate MCP protocol responses)
- ✅ **Stable** (clean session management)
## 📊 **NEXT STEPS VALIDATION**
### ✅ Completed Phases
1. ✅ **Phase 1A**: Local server testing - SUCCESS
2. ✅ **Phase 1B**: Claude Code configuration - SUCCESS
3. 🚧 **Phase 1C**: MCP tools testing - PENDING (requires session restart)
### Ready for Next Phase
- ✅ **Foundation proven** - FastAPI MCP architecture works
- ✅ **Protocol compatibility** - Official MCP SDK integration successful
- ✅ **Configuration working** - Claude Code can connect to new server
- ✅ **Tool structure validated** - 5 core operations implemented
### Remaining Tasks
1. **Restart Claude Code session** to pick up new MCP server config
2. **Test 5 core MCP tools** with real Claude Code integration
3. **Validate SSL issues resolved** (vs Node.js bridge problems)
4. **Expand to full 22 tools** implementation
5. **Remote server deployment** planning
## 🎯 **SUCCESS INDICATORS**
### ✅ **Major Architecture Success**
- **Problem**: Node.js SSL handshake failures with self-signed certificates
- **Solution**: Native FastAPI MCP server eliminates SSL layer entirely
- **Result**: Direct MCP protocol communication, no SSL issues possible
### ✅ **Implementation Success**
- **FastMCP Framework**: Official MCP Python SDK working perfectly
- **Streamable HTTP**: Correct transport for Claude Code integration
- **Tool Structure**: All 5 core memory operations implemented
- **Session Management**: Proper MCP session lifecycle handling
### ✅ **Configuration Success**
- **Simplified Config**: No complex environment variables needed
- **Direct Connection**: No intermediate bridging or translation
- **Local Testing**: Immediate validation without remote dependencies
- **Version Management**: Clean v4.0.0-alpha.1 progression
## 📝 **CONCLUSION**
The **FastAPI MCP Server migration is fundamentally successful**. The architecture change from Node.js bridge to native Python MCP server resolves all SSL issues and provides a much cleaner, more maintainable solution.
**Status**: Ready for full MCP tools integration testing
**Confidence**: High - core architecture proven to work
**Risk**: Low - fallback to Node.js bridge available if needed
This validates our architectural decision and proves the FastAPI MCP approach will solve the remote memory access problems that users have been experiencing.
```
--------------------------------------------------------------------------------
/scripts/sync/litestream/pull_remote_changes.sh:
--------------------------------------------------------------------------------
```bash
#!/bin/bash
# Enhanced remote sync with conflict awareness
# Based on the working manual_sync.sh but with staging awareness
DB_PATH="/Users/hkr/Library/Application Support/mcp-memory/sqlite_vec.db"
STAGING_DB="/Users/hkr/Library/Application Support/mcp-memory/sqlite_vec_staging.db"
REMOTE_BASE="http://narrowbox.local:8080/mcp-memory"
BACKUP_PATH="/Users/hkr/Library/Application Support/mcp-memory/sqlite_vec.db.backup"
TEMP_DIR="/tmp/litestream_pull_$$"
echo "$(date): Starting enhanced pull from remote master..."
# Create temporary directory
mkdir -p "$TEMP_DIR"
# Check if staging database exists
if [ ! -f "$STAGING_DB" ]; then
echo "$(date): WARNING: Staging database not found. Creating..."
./init_staging_db.sh
fi
# Get the latest generation ID
GENERATION=$(curl -s "$REMOTE_BASE/generations/" | grep -o 'href="[^"]*/"' | sed 's/href="//;s/\/"//g' | head -1)
if [ -z "$GENERATION" ]; then
echo "$(date): ERROR: Could not determine generation ID"
rm -rf "$TEMP_DIR"
exit 1
fi
echo "$(date): Found remote generation: $GENERATION"
# Get the latest snapshot
SNAPSHOT_URL="$REMOTE_BASE/generations/$GENERATION/snapshots/"
SNAPSHOT_FILE=$(curl -s "$SNAPSHOT_URL" | grep -o 'href="[^"]*\.snapshot\.lz4"' | sed 's/href="//;s/"//g' | tail -1)
if [ -z "$SNAPSHOT_FILE" ]; then
echo "$(date): ERROR: Could not find snapshot file"
rm -rf "$TEMP_DIR"
exit 1
fi
echo "$(date): Downloading snapshot: $SNAPSHOT_FILE"
# Download and decompress snapshot
curl -s "$SNAPSHOT_URL$SNAPSHOT_FILE" -o "$TEMP_DIR/snapshot.lz4"
if ! command -v lz4 >/dev/null 2>&1; then
echo "$(date): ERROR: lz4 command not found. Please install: brew install lz4"
rm -rf "$TEMP_DIR"
exit 1
fi
lz4 -d "$TEMP_DIR/snapshot.lz4" "$TEMP_DIR/remote_database.db" 2>/dev/null
if [ ! -f "$TEMP_DIR/remote_database.db" ]; then
echo "$(date): ERROR: Failed to decompress remote database"
rm -rf "$TEMP_DIR"
exit 1
fi
# Conflict detection: Check if we have staged changes that might conflict
STAGED_COUNT=0
if [ -f "$STAGING_DB" ]; then
STAGED_COUNT=$(sqlite3 "$STAGING_DB" "SELECT COUNT(*) FROM staged_memories WHERE conflict_status = 'none';" 2>/dev/null || echo "0")
fi
if [ "$STAGED_COUNT" -gt 0 ]; then
echo "$(date): WARNING: $STAGED_COUNT staged changes detected"
echo "$(date): Checking for potential conflicts..."
# Create a list of content hashes in staging
sqlite3 "$STAGING_DB" "SELECT content_hash FROM staged_memories;" > "$TEMP_DIR/staged_hashes.txt"
# Check if any of these hashes exist in the remote database
# Note: This requires knowledge of the remote database schema
# For now, we'll just warn about the existence of staged changes
echo "$(date): Staged changes will be applied after remote pull"
fi
# Backup current database
if [ -f "$DB_PATH" ]; then
cp "$DB_PATH" "$BACKUP_PATH"
echo "$(date): Created backup at $BACKUP_PATH"
fi
# Replace with remote database
cp "$TEMP_DIR/remote_database.db" "$DB_PATH"
if [ $? -eq 0 ]; then
echo "$(date): Successfully pulled database from remote master"
# Update staging database with sync timestamp
if [ -f "$STAGING_DB" ]; then
sqlite3 "$STAGING_DB" "
UPDATE sync_status
SET value = datetime('now'), updated_at = CURRENT_TIMESTAMP
WHERE key = 'last_remote_sync';
"
fi
# Remove backup on success
rm -f "$BACKUP_PATH"
# Show database info
echo "$(date): Database size: $(du -h "$DB_PATH" | cut -f1)"
echo "$(date): Database modified: $(stat -f "%Sm" "$DB_PATH")"
if [ "$STAGED_COUNT" -gt 0 ]; then
echo "$(date): NOTE: You have $STAGED_COUNT staged changes to apply"
echo "$(date): Run ./apply_local_changes.sh to merge them"
fi
else
echo "$(date): ERROR: Failed to replace database with remote version"
# Restore backup on failure
if [ -f "$BACKUP_PATH" ]; then
mv "$BACKUP_PATH" "$DB_PATH"
echo "$(date): Restored backup"
fi
rm -rf "$TEMP_DIR"
exit 1
fi
# Cleanup
rm -rf "$TEMP_DIR"
echo "$(date): Remote pull completed successfully"
```
--------------------------------------------------------------------------------
/src/mcp_memory_service/api/__init__.py:
--------------------------------------------------------------------------------
```python
# Copyright 2024 Heinrich Krupp
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Code Execution API for MCP Memory Service.
This module provides a lightweight, token-efficient interface for direct
Python code execution, replacing verbose MCP tool calls with compact
function calls and results.
Token Efficiency Comparison:
- Import: ~10 tokens (once per session)
- search(5 results): ~385 tokens vs ~2,625 (85% reduction)
- store(): ~15 tokens vs ~150 (90% reduction)
- health(): ~20 tokens vs ~125 (84% reduction)
Annual Savings (Conservative):
- 10 users x 5 sessions/day x 365 days x 6,000 tokens = 109.5M tokens/year
- At $0.15/1M tokens: $16.43/year per 10-user deployment
Performance:
- First call: ~50ms (includes storage initialization)
- Subsequent calls: ~5-10ms (connection reused)
- Memory overhead: <10MB
Usage Example:
>>> from mcp_memory_service.api import search, store, health
>>>
>>> # Search memories (20 tokens)
>>> results = search("architecture decisions", limit=5)
>>> for m in results.memories:
... print(f"{m.hash}: {m.preview[:50]}...")
abc12345: Implemented OAuth 2.1 authentication for...
def67890: Refactored storage backend to support...
>>>
>>> # Store memory (15 tokens)
>>> hash = store("New memory", tags=["note", "important"])
>>> print(f"Stored: {hash}")
Stored: abc12345
>>>
>>> # Health check (5 tokens)
>>> info = health()
>>> print(f"Backend: {info.backend}, Count: {info.count}")
Backend: sqlite_vec, Count: 1247
Backward Compatibility:
This API is designed to work alongside existing MCP tools without
breaking changes. Users can gradually migrate from tool-based calls
to code execution as needed.
Implementation:
- Phase 1 (Current): Core operations (search, store, health)
- Phase 2: Extended operations (search_by_tag, recall, delete, update)
- Phase 3: Advanced features (batch operations, streaming)
For More Information:
- Research: /docs/research/code-execution-interface-implementation.md
- Documentation: /docs/api/code-execution-interface.md
- Issue: https://github.com/doobidoo/mcp-memory-service/issues/206
"""
from .types import (
CompactMemory, CompactSearchResult, CompactHealthInfo,
CompactConsolidationResult, CompactSchedulerStatus
)
from .operations import (
search, store, health, consolidate, scheduler_status,
_consolidate_async, _scheduler_status_async
)
from .client import close, close_async, set_consolidator, set_scheduler
__all__ = [
# Core operations
'search', # Semantic search with compact results
'store', # Store new memory
'health', # Service health check
'close', # Close and cleanup storage resources (sync)
'close_async', # Close and cleanup storage resources (async)
# Consolidation operations
'consolidate', # Trigger memory consolidation
'scheduler_status', # Get consolidation scheduler status
# Consolidation management (internal use by HTTP server)
'set_consolidator', # Set global consolidator instance
'set_scheduler', # Set global scheduler instance
# Compact data types
'CompactMemory',
'CompactSearchResult',
'CompactHealthInfo',
'CompactConsolidationResult',
'CompactSchedulerStatus',
]
# API version for compatibility tracking
__api_version__ = "1.0.0"
# Module metadata
__doc_url__ = "https://github.com/doobidoo/mcp-memory-service/blob/main/docs/api/code-execution-interface.md"
__issue_url__ = "https://github.com/doobidoo/mcp-memory-service/issues/206"
```
--------------------------------------------------------------------------------
/.github/workflows/docker-publish.yml:
--------------------------------------------------------------------------------
```yaml
name: Docker Publish (Tags)
on:
push:
tags:
- 'v*.*.*'
workflow_dispatch:
env:
REGISTRY: docker.io
IMAGE_NAME: doobidoo/mcp-memory-service
jobs:
build:
runs-on: ubuntu-latest
permissions:
contents: read
packages: write
id-token: write
attestations: write
steps:
- name: Checkout repository
uses: actions/checkout@v4
- name: Set up Docker Buildx
uses: docker/setup-buildx-action@v3
- name: Debug - Check required files for Docker Hub build
run: |
echo "=== Checking required files for Docker Hub build ==="
echo "Standard Dockerfile exists:" && ls -la tools/docker/Dockerfile
echo "Slim Dockerfile exists:" && ls -la tools/docker/Dockerfile.slim
echo "Source directory exists:" && ls -la src/
echo "Entrypoint scripts exist:" && ls -la tools/docker/docker-entrypoint*.sh
echo "Utils scripts exist:" && ls -la scripts/utils/
echo "pyproject.toml exists:" && ls -la pyproject.toml
echo "uv.lock exists:" && ls -la uv.lock
- name: Log in to Docker Hub
if: github.event_name != 'pull_request'
uses: docker/login-action@v3
with:
registry: ${{ env.REGISTRY }}
username: ${{ secrets.DOCKER_USERNAME }}
password: ${{ secrets.DOCKER_PASSWORD }}
- name: Extract metadata (Standard)
id: meta
uses: docker/metadata-action@v5
with:
images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
tags: |
type=ref,event=branch
type=ref,event=pr
type=semver,pattern={{version}}
type=semver,pattern={{major}}.{{minor}}
type=semver,pattern={{major}}
type=raw,value=latest,enable={{is_default_branch}}
- name: Extract metadata (Slim)
id: meta-slim
uses: docker/metadata-action@v5
with:
images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
tags: |
type=ref,event=branch,suffix=-slim
type=ref,event=pr,suffix=-slim
type=semver,pattern={{version}},suffix=-slim
type=semver,pattern={{major}}.{{minor}},suffix=-slim
type=semver,pattern={{major}},suffix=-slim
type=raw,value=slim,enable={{is_default_branch}}
- name: Build and push Standard Docker image
id: build-and-push
uses: docker/build-push-action@v5
with:
context: .
file: ./tools/docker/Dockerfile
platforms: linux/amd64,linux/arm64
push: ${{ github.event_name != 'pull_request' }}
tags: ${{ steps.meta.outputs.tags }}
labels: ${{ steps.meta.outputs.labels }}
cache-from: type=gha,scope=standard
cache-to: type=gha,mode=max,scope=standard
build-args: |
SKIP_MODEL_DOWNLOAD=true
outputs: type=registry
- name: Build and push Slim Docker image
id: build-and-push-slim
uses: docker/build-push-action@v5
with:
context: .
file: ./tools/docker/Dockerfile.slim
platforms: linux/amd64,linux/arm64
push: ${{ github.event_name != 'pull_request' }}
tags: ${{ steps.meta-slim.outputs.tags }}
labels: ${{ steps.meta-slim.outputs.labels }}
cache-from: type=gha,scope=slim
cache-to: type=gha,mode=max,scope=slim
build-args: |
SKIP_MODEL_DOWNLOAD=true
outputs: type=registry
- name: Generate artifact attestation (Standard)
if: github.event_name != 'pull_request'
uses: actions/attest-build-provenance@v1
with:
subject-name: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME}}
subject-digest: ${{ steps.build-and-push.outputs.digest }}
push-to-registry: true
continue-on-error: true # Don't fail the workflow if attestation fails
- name: Generate artifact attestation (Slim)
if: github.event_name != 'pull_request'
uses: actions/attest-build-provenance@v1
with:
subject-name: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME}}
subject-digest: ${{ steps.build-and-push-slim.outputs.digest }}
push-to-registry: true
continue-on-error: true # Don't fail the workflow if attestation fails
```
--------------------------------------------------------------------------------
/src/mcp_memory_service/api/sync_wrapper.py:
--------------------------------------------------------------------------------
```python
# Copyright 2024 Heinrich Krupp
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Async-to-sync utilities for code execution interface.
Provides lightweight wrappers to convert async storage operations into
synchronous functions suitable for code execution contexts (e.g., hooks).
Performance:
- Cold call: ~50ms (includes event loop creation)
- Warm call: ~5ms (reuses existing loop)
- Overhead: <10ms compared to native async
Design Philosophy:
- Hide asyncio complexity from API users
- Reuse event loops when possible for performance
- Graceful error handling and cleanup
- Zero async/await in public API
"""
import asyncio
from functools import wraps
from typing import Callable, TypeVar, Any
import logging
logger = logging.getLogger(__name__)
# Type variable for generic function wrapping
T = TypeVar('T')
def sync_wrapper(async_func: Callable[..., Any]) -> Callable[..., Any]:
"""
Convert async function to synchronous with minimal overhead.
This wrapper handles event loop management transparently:
1. Attempts to get existing event loop
2. Creates new loop if none exists
3. Runs async function to completion
4. Returns result or raises exception
Performance:
- Adds ~1-5ms overhead per call
- Reuses event loop when possible
- Optimized for repeated calls (e.g., in hooks)
Args:
async_func: Async function to wrap
Returns:
Synchronous wrapper function with same signature
Example:
>>> async def fetch_data(query: str) -> list:
... return await storage.retrieve(query, limit=5)
>>> sync_fetch = sync_wrapper(fetch_data)
>>> results = sync_fetch("architecture") # No await needed
Note:
This wrapper is designed for code execution contexts where
async/await is not available or desirable. For pure async
code, use the storage backend directly.
"""
@wraps(async_func)
def wrapper(*args: Any, **kwargs: Any) -> Any:
try:
# Try to get existing event loop
loop = asyncio.get_event_loop()
if loop.is_closed():
# Loop exists but is closed, create new one
loop = asyncio.new_event_loop()
asyncio.set_event_loop(loop)
except RuntimeError:
# No event loop in current thread, create new one
loop = asyncio.new_event_loop()
asyncio.set_event_loop(loop)
try:
# Run async function to completion
result = loop.run_until_complete(async_func(*args, **kwargs))
return result
except Exception as e:
# Re-raise exception with context
logger.error(f"Error in sync wrapper for {async_func.__name__}: {e}")
raise
return wrapper
def run_async(coro: Any) -> Any:
"""
Run a coroutine synchronously and return its result.
Convenience function for running async operations in sync contexts
without explicitly creating a wrapper function.
Args:
coro: Coroutine object to run
Returns:
Result of the coroutine
Example:
>>> result = run_async(storage.retrieve("query", limit=5))
>>> print(len(result))
Note:
Prefer sync_wrapper() for repeated calls to the same function,
as it avoids wrapper creation overhead.
"""
try:
loop = asyncio.get_event_loop()
if loop.is_closed():
loop = asyncio.new_event_loop()
asyncio.set_event_loop(loop)
except RuntimeError:
loop = asyncio.new_event_loop()
asyncio.set_event_loop(loop)
return loop.run_until_complete(coro)
```
--------------------------------------------------------------------------------
/tests/unit/test_memory.py:
--------------------------------------------------------------------------------
```python
#!/usr/bin/env python3
"""
Simple test script to verify memory service functionality.
"""
import asyncio
import json
import sys
import os
from datetime import datetime
# Set environment variables for testing
os.environ["MCP_MEMORY_STORAGE_BACKEND"] = "sqlite_vec"
os.environ["MCP_MEMORY_SQLITE_PATH"] = os.path.expanduser("~/Library/Application Support/mcp-memory/sqlite_vec.db")
os.environ["MCP_MEMORY_BACKUPS_PATH"] = os.path.expanduser("~/Library/Application Support/mcp-memory/backups")
os.environ["MCP_MEMORY_USE_ONNX"] = "1"
# Import our modules
from mcp_memory_service.storage.sqlite_vec import SqliteVecMemoryStorage
from mcp_memory_service.models.memory import Memory
from mcp_memory_service.utils.db_utils import validate_database, get_database_stats
async def main():
print("=== MCP Memory Service Test ===")
# Initialize the storage
db_path = os.environ["MCP_MEMORY_SQLITE_PATH"]
print(f"Using SQLite-vec database at: {db_path}")
storage = SqliteVecMemoryStorage(db_path)
await storage.initialize()
# Run our own database health check
print("\n=== Database Health Check ===")
if storage.conn is None:
print("Database connection is not initialized")
else:
try:
cursor = storage.conn.execute('SELECT COUNT(*) FROM memories')
memory_count = cursor.fetchone()[0]
print(f"Database connected successfully. Contains {memory_count} memories.")
cursor = storage.conn.execute("SELECT name FROM sqlite_master WHERE type='table'")
tables = [row[0] for row in cursor.fetchall()]
print(f"Database tables: {', '.join(tables)}")
if not storage.embedding_model:
print("No embedding model available. Limited functionality.")
except Exception as e:
print(f"Database error: {str(e)}")
# Get database stats directly
print("\n=== Database Stats ===")
try:
# Simple stats
cursor = storage.conn.execute('SELECT COUNT(*) FROM memories')
memory_count = cursor.fetchone()[0]
# Get database file size
db_path = storage.db_path
file_size = os.path.getsize(db_path) if os.path.exists(db_path) else 0
stats = {
"backend": "sqlite-vec",
"total_memories": memory_count,
"database_size_bytes": file_size,
"database_size_mb": round(file_size / (1024 * 1024), 2),
"embedding_model": storage.embedding_model_name if hasattr(storage, 'embedding_model_name') else "none",
"embedding_dimension": storage.embedding_dimension if hasattr(storage, 'embedding_dimension') else 0
}
print(json.dumps(stats, indent=2))
except Exception as e:
print(f"Error getting stats: {str(e)}")
# Store a test memory
print("\n=== Creating Test Memory ===")
test_content = "This is a test memory created on " + datetime.now().isoformat()
# Import the hash function
from mcp_memory_service.utils.hashing import generate_content_hash
test_memory = Memory(
content=test_content,
content_hash=generate_content_hash(test_content),
tags=["test", "example"],
memory_type="note",
metadata={"source": "test_script"}
)
print(f"Memory content: {test_memory.content}")
print(f"Content hash: {test_memory.content_hash}")
success, message = await storage.store(test_memory)
print(f"Store success: {success}")
print(f"Message: {message}")
# Try to retrieve the memory
print("\n=== Retrieving Memories ===")
results = await storage.retrieve("test memory", n_results=5)
if results:
print(f"Found {len(results)} memories")
for i, result in enumerate(results):
print(f" Result {i+1}:")
print(f" Content: {result.memory.content}")
print(f" Tags: {result.memory.tags}")
print(f" Score: {result.relevance_score}")
else:
print("No memories found")
print("\n=== Test Complete ===")
storage.close()
if __name__ == "__main__":
asyncio.run(main())
```
--------------------------------------------------------------------------------
/scripts/pr/generate_tests.sh:
--------------------------------------------------------------------------------
```bash
#!/bin/bash
# scripts/pr/generate_tests.sh - Auto-generate tests for new code in PR
#
# Usage: bash scripts/pr/generate_tests.sh <PR_NUMBER>
# Example: bash scripts/pr/generate_tests.sh 123
set -e
PR_NUMBER=$1
if [ -z "$PR_NUMBER" ]; then
echo "Usage: $0 <PR_NUMBER>"
exit 1
fi
if ! command -v gh &> /dev/null; then
echo "Error: GitHub CLI (gh) is not installed"
exit 1
fi
if ! command -v gemini &> /dev/null; then
echo "Error: Gemini CLI is not installed"
exit 1
fi
echo "=== Test Generation for PR #$PR_NUMBER ==="
echo ""
# Get changed Python files (exclude tests/)
changed_files=$(gh pr diff $PR_NUMBER --name-only | grep '\.py$' | grep -v '^tests/' || echo "")
if [ -z "$changed_files" ]; then
echo "No Python source files changed (excluding tests/)"
exit 0
fi
echo "Files to generate tests for:"
echo "$changed_files"
echo ""
tests_generated=0
# Process files safely (handle spaces in filenames)
echo "$changed_files" | while IFS= read -r file; do
if [ -z "$file" ]; then
continue
fi
if [ ! -f "$file" ]; then
echo "Skipping $file (not found in working directory)"
continue
fi
echo "=== Processing: $file ==="
# Extract basename for temp files
base_name=$(basename "$file" .py)
# Determine test file path (mirror source structure)
# e.g., src/api/utils.py -> tests/api/test_utils.py
test_dir="tests/$(dirname "${file#src/}")"
mkdir -p "$test_dir"
test_file="$test_dir/test_$(basename "$file")"
if [ -f "$test_file" ]; then
echo "Test file exists: $test_file"
echo "Suggesting additional test cases..."
# Read existing tests
existing_tests=$(cat "$test_file")
# Read source code
source_code=$(cat "$file")
# Generate additional tests
additional_tests=$(gemini "Existing pytest test file:
\`\`\`python
$existing_tests
\`\`\`
Source code with new/changed functionality:
\`\`\`python
$source_code
\`\`\`
Task: Suggest additional pytest test functions to cover new/changed code that isn't already tested.
Requirements:
- Use pytest framework
- Include async tests if source has async functions
- Test happy paths and edge cases
- Test error handling
- Follow existing test style
- Output ONLY the new test functions (no imports, no existing tests)
Format: Complete Python test functions ready to append.")
# Use mktemp for output file
output_file=$(mktemp -t test_additions_${base_name}.XXXXXX)
echo "$additional_tests" > "$output_file"
echo "Additional tests generated: $output_file"
echo ""
echo "--- Preview ---"
head -20 "$output_file"
echo "..."
echo "--- End Preview ---"
echo ""
echo "To append: cat $output_file >> $test_file"
else
echo "Creating new test file: $test_file"
# Read source code
source_code=$(cat "$file")
# Generate complete test file
new_tests=$(gemini "Generate comprehensive pytest tests for this Python module:
\`\`\`python
$source_code
\`\`\`
Requirements:
- Complete pytest test file with imports
- Test all public functions/methods
- Include happy paths and edge cases
- Test error handling and validation
- Use pytest fixtures if appropriate
- Include async tests for async functions
- Follow pytest best practices
- Add docstrings to test functions
Format: Complete, ready-to-use Python test file.")
# Use mktemp for output file
output_file=$(mktemp -t test_new_${base_name}.XXXXXX)
echo "$new_tests" > "$output_file"
echo "New test file generated: $output_file"
echo ""
echo "--- Preview ---"
head -30 "$output_file"
echo "..."
echo "--- End Preview ---"
echo ""
echo "To create: cp $output_file $test_file"
fi
tests_generated=$((tests_generated + 1))
echo ""
done
echo "=== Test Generation Complete ==="
echo "Files processed: $tests_generated"
echo ""
echo "Generated test files are in /tmp/"
echo "Review and apply manually with the commands shown above."
echo ""
echo "After applying tests:"
echo "1. Run: pytest $test_file"
echo "2. Verify tests pass"
echo "3. Commit: git add $test_file && git commit -m 'test: add tests for <feature>'"
```
--------------------------------------------------------------------------------
/.github/workflows/README_OPTIMIZATION.md:
--------------------------------------------------------------------------------
```markdown
# GitHub Actions Optimization Guide
## Performance Issues Identified
The current GitHub Actions setup takes ~33 minutes for releases due to:
1. **Redundant workflows** - Multiple workflows building the same Docker images
2. **Sequential platform builds** - Building linux/amd64 and linux/arm64 one after another
3. **Poor caching** - Not utilizing registry-based caching effectively
4. **Duplicate test runs** - Same tests running in multiple workflows
## Optimizations Implemented
### 1. New Consolidated Workflows
- **`release-tag.yml`** - Replaces both `docker-publish.yml` and `publish-and-test.yml`
- Uses matrix strategy for parallel platform builds
- Implements registry-based caching
- Builds platforms in parallel (2x faster)
- Single test run shared across all jobs
- **`main-optimized.yml`** - Optimized version of `main.yml`
- Parallel test execution with matrix strategy
- Shared Docker test build
- Registry-based caching with GHCR
- Conditional publishing only after successful release
### 2. Key Improvements
#### Matrix Strategy for Parallel Builds
```yaml
strategy:
matrix:
platform: [linux/amd64, linux/arm64]
variant: [standard, slim]
```
This runs 4 builds in parallel instead of sequentially.
#### Registry-Based Caching
```yaml
cache-from: |
type=registry,ref=ghcr.io/doobidoo/mcp-memory-service:buildcache-${{ matrix.variant }}-${{ matrix.platform }}
cache-to: |
type=registry,ref=ghcr.io/doobidoo/mcp-memory-service:buildcache-${{ matrix.variant }}-${{ matrix.platform }},mode=max
```
Uses GHCR as a cache registry for better cross-workflow cache reuse.
#### Build Once, Push Everywhere
- Builds images once with digests
- Creates multi-platform manifests separately
- Pushes to multiple registries without rebuilding
### 3. Migration Steps
To use the optimized workflows:
1. **Test the new workflows first**:
```bash
# Create a test branch
git checkout -b test-optimized-workflows
# Temporarily disable old workflows
mv .github/workflows/docker-publish.yml .github/workflows/docker-publish.yml.bak
mv .github/workflows/publish-and-test.yml .github/workflows/publish-and-test.yml.bak
mv .github/workflows/main.yml .github/workflows/main.yml.bak
# Rename optimized workflows
mv .github/workflows/release-tag.yml .github/workflows/release-tag.yml
mv .github/workflows/main-optimized.yml .github/workflows/main.yml
# Push and test
git add .
git commit -m "test: optimized workflows"
git push origin test-optimized-workflows
```
2. **Monitor the test run** to ensure everything works correctly
3. **If successful, merge to main**:
```bash
git checkout main
git merge test-optimized-workflows
git push origin main
```
4. **Clean up old workflows**:
```bash
rm .github/workflows/*.bak
```
### 4. Expected Performance Improvements
| Metric | Before | After | Improvement |
|--------|--------|-------|-------------|
| Total Build Time | ~33 minutes | ~12-15 minutes | 55-60% faster |
| Docker Builds | Sequential | Parallel (4x) | 4x faster |
| Cache Hit Rate | ~30% | ~80% | 2.6x better |
| Test Runs | 3x redundant | 1x shared | 66% reduction |
| GitHub Actions Minutes | ~150 min/release | ~60 min/release | 60% cost reduction |
### 5. Additional Optimizations to Consider
1. **Use merge queues** for main branch to batch CI runs
2. **Implement path filtering** to skip workflows when only docs change
3. **Use larger runners** for critical jobs (2x-4x faster but costs more)
4. **Pre-build base images** weekly with all dependencies
5. **Implement incremental testing** based on changed files
### 6. Monitoring
After implementing these changes, monitor:
- Workflow run times in Actions tab
- Cache hit rates in build logs
- Failed builds due to caching issues
- Registry storage usage (GHCR has limits)
### 7. Rollback Plan
If issues occur, quickly rollback:
```bash
# Restore original workflows
git checkout main -- .github/workflows/main.yml
git checkout main -- .github/workflows/docker-publish.yml
git checkout main -- .github/workflows/publish-and-test.yml
# Remove optimized versions
rm .github/workflows/release-tag.yml
rm .github/workflows/main-optimized.yml
# Commit and push
git commit -m "revert: rollback to original workflows"
git push origin main
```
```
--------------------------------------------------------------------------------
/scripts/utils/generate_personalized_claude_md.sh:
--------------------------------------------------------------------------------
```bash
#!/bin/bash
# Generate personalized CLAUDE.md with memory context for local network distribution
# Usage: ./generate_personalized_claude_md.sh [target_machine_ip] [output_file]
TARGET_IP="${1:-10.0.1.30}"
OUTPUT_FILE="${2:-CLAUDE_PERSONALIZED.md}"
MCP_ENDPOINT="https://${TARGET_IP}:8443/mcp"
API_KEY="test-key-123"
echo "Generating personalized CLAUDE.md for network distribution..."
echo "Target: $TARGET_IP"
echo "Output: $OUTPUT_FILE"
# Create the personalized CLAUDE.md with embedded memory context
cat > "$OUTPUT_FILE" << 'EOF'
# CLAUDE.md - Personalized with Memory Context
This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
**This version includes pre-loaded memory context from your local MCP Memory Service.**
## Memory Context Integration
Your local memory service contains essential project context. Here's the prompt to retrieve it:
```
Load MCP Memory Service context for this project. Retrieve all memories tagged with 'claude-code-reference' and 'distributable-reference' from the following endpoint and incorporate the knowledge into your understanding of this codebase:
Memory Service: https://TARGET_IP:8443/mcp
Authorization: Bearer test-key-123
Use this command to fetch context:
curl -k -s -X POST https://TARGET_IP:8443/mcp \
-H "Content-Type: application/json" \
-H "Authorization: Bearer test-key-123" \
-d '{"jsonrpc": "2.0", "id": 1, "method": "tools/call", "params": {"name": "retrieve_memory", "arguments": {"query": "claude-code-reference distributable-reference", "limit": 20}}}' \
| jq -r '.result.content[0].text'
This will provide you with:
- Project structure and architecture details
- Key commands for development, testing, and deployment
- Environment variables and configuration options
- Recent changes including v5.0.2 ONNX implementation
- Issue management patterns and current status
- Testing practices and platform-specific optimizations
- Remote service deployment information
After loading this context, you'll have comprehensive knowledge of the MCP Memory Service project without needing to discover the codebase structure through file reading.
```
## Quick Memory Retrieval Commands
If memory context fails to load automatically, use these commands:
### Get All Project Context
```bash
curl -k -s -X POST https://TARGET_IP:8443/mcp \
-H "Content-Type: application/json" \
-H "Authorization: Bearer test-key-123" \
-d '{"jsonrpc": "2.0", "id": 1, "method": "tools/call", "params": {"name": "retrieve_memory", "arguments": {"query": "claude-code-reference", "limit": 20}}}' \
| jq -r '.result.content[0].text'
```
### Check Memory Service Health
```bash
curl -k -s -X POST https://TARGET_IP:8443/mcp \
-H "Content-Type: application/json" \
-H "Authorization: Bearer test-key-123" \
-d '{"jsonrpc": "2.0", "id": 1, "method": "tools/call", "params": {"name": "check_database_health", "arguments": {}}}' \
| jq -r '.result.content[0].text'
```
## Memory Categories Available
- **Project Structure**: Server architecture, file locations, component relationships
- **Key Commands**: Installation, testing, debugging, deployment commands
- **Environment Variables**: Configuration options and platform-specific settings
- **Recent Changes**: Version history, resolved issues, breaking changes
- **Testing Practices**: Framework preferences, test patterns, validation steps
- **Current Status**: Active issues, recent work, development context
EOF
# Replace TARGET_IP placeholder with actual IP
sed -i "s/TARGET_IP/$TARGET_IP/g" "$OUTPUT_FILE"
# Append the original CLAUDE.md content (without the memory section)
echo "" >> "$OUTPUT_FILE"
echo "## Original Project Documentation" >> "$OUTPUT_FILE"
echo "" >> "$OUTPUT_FILE"
# Extract content from original CLAUDE.md starting after memory section
awk '/^## Overview/{print; getline; while(getline > 0) print}' CLAUDE.md >> "$OUTPUT_FILE"
echo "✅ Personalized CLAUDE.md generated: $OUTPUT_FILE"
echo ""
echo "Distribution instructions:"
echo "1. Copy $OUTPUT_FILE to target machines as CLAUDE.md"
echo "2. Ensure target machines can access https://$TARGET_IP:8443"
echo "3. Claude Code will automatically use memory context on those machines"
echo ""
echo "Network test command:"
echo "curl -k -s https://$TARGET_IP:8443/api/health"
```
--------------------------------------------------------------------------------
/claude_commands/memory-search.md:
--------------------------------------------------------------------------------
```markdown
# Search Memories by Tags and Content
I'll help you search through your stored memories using tags, content keywords, and semantic similarity. This command is perfect for finding specific information across all your stored memories regardless of when they were created.
## What I'll do:
1. **Tag-Based Search**: I'll search for memories associated with specific tags, supporting both exact and partial tag matching.
2. **Content Search**: I'll perform semantic search across memory content using the same embedding model used for storage.
3. **Combined Queries**: I'll support complex searches combining tags, content, and metadata filters.
4. **Smart Ranking**: I'll rank results by relevance, considering both semantic similarity and tag match strength.
5. **Context Integration**: I'll highlight how found memories relate to your current project and session.
## Usage Examples:
```bash
claude /memory-search --tags "architecture,database"
claude /memory-search "SQLite performance optimization"
claude /memory-search --tags "decision" --content "database backend"
claude /memory-search --project "mcp-memory-service" --type "note"
```
## Implementation:
I'll connect to your MCP Memory Service at `https://memory.local:8443/` and use its search API endpoints:
1. **Query Processing**: Parse your search criteria (tags, content, filters)
2. **Search Execution**: Use appropriate API endpoints:
- `POST /api/search` - Semantic similarity search
- `POST /api/search/by-tag` - Tag-based search (AND/OR matching)
- `POST /api/search/by-time` - Time-based natural language queries
- `GET /api/search/similar/{hash}` - Find similar memories
3. **Result Aggregation**: Process search responses with similarity scores
4. **Relevance Scoring**: Use returned similarity scores and match reasons
5. **Context Highlighting**: Show why each result matches your query
All requests use curl with `-k` flag for HTTPS and proper JSON formatting.
For each search result, I'll display:
- **Content**: The memory content with search terms highlighted
- **Tags**: All associated tags (with matching tags emphasized)
- **Relevance Score**: How closely the memory matches your query
- **Created Date**: When the memory was stored
- **Project Context**: Associated project and file context
- **Memory Type**: Classification (note, decision, task, etc.)
## Search Types:
### Tag Search
- **Exact**: `--tags "architecture"` - memories with exact tag match
- **Multiple**: `--tags "database,performance"` - memories with any of these tags
- **Machine Source**: `--tags "source:machine-name"` - memories from specific machine
- **Partial**: `--tags "*arch*"` - memories with tags containing "arch"
### Content Search
- **Semantic**: Content-based similarity using embeddings
- **Keyword**: Simple text matching within memory content
- **Combined**: Both semantic and keyword matching
### Filtered Search
- **Project**: `--project "name"` - memories from specific project
- **Type**: `--type "decision"` - memories of specific type
- **Date Range**: `--since "last week"` - memories within time range
- **Author**: `--author "session"` - memories from specific session
## Arguments:
- `$ARGUMENTS` - The search query (content or primary search terms)
- `--tags "tag1,tag2"` - Search by specific tags
- `--content "text"` - Explicit content search terms
- `--project "name"` - Filter by project name
- `--type "note|decision|task|reference"` - Filter by memory type
- `--limit N` - Maximum results to return (default: 20)
- `--min-score 0.X` - Minimum relevance score threshold
- `--include-metadata` - Show full metadata for each result
- `--export` - Export results to a file for review
## Advanced Features:
- **Machine Source Tracking**: Search for memories by originating machine
- **Fuzzy Matching**: Handle typos and variations in search terms
- **Context Expansion**: Find related memories based on current project context
- **Search History**: Remember recent searches for quick re-execution
- **Result Grouping**: Group results by tags, projects, or time periods
If no results are found, I'll suggest alternative search terms, check for typos, or recommend broadening the search criteria. I'll also provide statistics about the total number of memories in your database and suggest ways to improve future searches.
```
--------------------------------------------------------------------------------
/scripts/service/memory_service_manager.sh:
--------------------------------------------------------------------------------
```bash
#!/bin/bash
# Memory Service Manager for Claude Code on Linux
# Manages dual backend setup with Cloudflare primary and SQLite-vec backup
SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
PROJECT_DIR="$(dirname "$SCRIPT_DIR")"
# Service configuration
CLOUDFLARE_ENV="$PROJECT_DIR/.env"
SQLITE_ENV="$PROJECT_DIR/.env.sqlite"
# Create SQLite-vec environment file if it doesn't exist
if [ ! -f "$SQLITE_ENV" ]; then
cat > "$SQLITE_ENV" << EOF
# SQLite-vec Configuration for MCP Memory Service (Backup)
MCP_MEMORY_STORAGE_BACKEND=sqlite_vec
MCP_MEMORY_SQLITE_PATH="$HOME/.local/share/mcp-memory/primary_sqlite_vec.db"
EOF
echo "Created SQLite-vec environment configuration: $SQLITE_ENV"
fi
usage() {
echo "Memory Service Manager for Claude Code"
echo ""
echo "Usage: $0 <command>"
echo ""
echo "Commands:"
echo " start-cloudflare Start memory server with Cloudflare backend"
echo " start-sqlite Start memory server with SQLite-vec backend"
echo " status Show current backend and sync status"
echo " sync-backup Backup Cloudflare → SQLite-vec"
echo " sync-restore Restore SQLite-vec → Cloudflare"
echo " sync-both Bidirectional sync"
echo " stop Stop any running memory server"
echo ""
}
start_memory_service() {
local backend="$1"
local env_file="$2"
echo "Starting memory service with $backend backend..."
# Stop any existing service
pkill -f "memory server" 2>/dev/null || true
sleep 2
# Start new service
cd "$PROJECT_DIR"
if [ -f "$env_file" ]; then
echo "Loading environment from: $env_file"
set -a
source "$env_file"
set +a
fi
echo "Starting: uv run memory server"
nohup uv run memory server > /tmp/memory-service-$backend.log 2>&1 &
# Wait a moment and check if it started
sleep 3
if pgrep -f "memory server" > /dev/null; then
echo "Memory service started successfully with $backend backend"
echo "Logs: /tmp/memory-service-$backend.log"
# Save active backend to state file for reliable detection
echo "$backend" > /tmp/memory-service-backend.state
else
echo "Failed to start memory service"
echo "Check logs: /tmp/memory-service-$backend.log"
return 1
fi
}
show_status() {
echo "=== Memory Service Status ==="
# Check if service is running
if pgrep -f "memory server" > /dev/null; then
echo "Service: Running"
# Check which backend is active using state file
if [ -f "/tmp/memory-service-backend.state" ]; then
local active_backend=$(cat /tmp/memory-service-backend.state)
echo "Active Backend: $active_backend (from state file)"
else
echo "Active Backend: Unknown (no state file found)"
fi
else
echo "Service: Not running"
# Clean up state file if service is not running
[ -f "/tmp/memory-service-backend.state" ] && rm -f /tmp/memory-service-backend.state
fi
echo ""
echo "=== Sync Status ==="
cd "$PROJECT_DIR"
uv run python scripts/claude_sync_commands.py status
}
sync_memories() {
local direction="$1"
echo "Syncing memories: $direction"
cd "$PROJECT_DIR"
uv run python scripts/claude_sync_commands.py "$direction"
}
stop_service() {
echo "Stopping memory service..."
pkill -f "memory server" 2>/dev/null || true
sleep 2
if ! pgrep -f "memory server" > /dev/null; then
echo "Memory service stopped"
# Clean up state file when service is stopped
[ -f "/tmp/memory-service-backend.state" ] && rm -f /tmp/memory-service-backend.state
else
echo "Failed to stop memory service"
return 1
fi
}
# Main command handling
case "$1" in
start-cloudflare)
start_memory_service "cloudflare" "$CLOUDFLARE_ENV"
;;
start-sqlite)
start_memory_service "sqlite" "$SQLITE_ENV"
;;
status)
show_status
;;
sync-backup)
sync_memories "backup"
;;
sync-restore)
sync_memories "restore"
;;
sync-both)
sync_memories "sync"
;;
stop)
stop_service
;;
*)
usage
exit 1
;;
esac
```
--------------------------------------------------------------------------------
/scripts/testing/test_cleanup_logic.py:
--------------------------------------------------------------------------------
```python
#!/usr/bin/env python3
"""
Test script for Docker Hub cleanup logic
Tests the retention policy rules without actual API calls
"""
import re
from datetime import datetime, timedelta, timezone
def should_keep_tag(tag_name, tag_date, keep_versions=5, cutoff_date=None):
"""Test version of the retention policy logic"""
if cutoff_date is None:
cutoff_date = datetime.now(timezone.utc) - timedelta(days=30)
# Always keep these tags
protected_tags = ["latest", "slim", "main", "stable"]
if tag_name in protected_tags:
return True, "Protected tag"
# Keep semantic version tags (v1.2.3)
if re.match(r'^v?\d+\.\d+\.\d+$', tag_name):
return True, "Semantic version"
# Keep major.minor tags (1.0, 2.1)
if re.match(r'^v?\d+\.\d+$', tag_name):
return True, "Major.minor version"
# Delete buildcache tags older than cutoff
if tag_name.startswith("buildcache-"):
if tag_date < cutoff_date:
return False, "Old buildcache tag"
return True, "Recent buildcache tag"
# Delete sha/digest tags older than cutoff
if tag_name.startswith("sha256-") or (len(tag_name) == 7 and tag_name.isalnum()):
if tag_date < cutoff_date:
return False, "Old sha/digest tag"
return True, "Recent sha/digest tag"
# Delete test/dev tags older than cutoff
if any(x in tag_name.lower() for x in ["test", "dev", "tmp", "temp"]):
if tag_date < cutoff_date:
return False, "Old test/dev tag"
return True, "Recent test/dev tag"
# Keep if recent
if tag_date >= cutoff_date:
return True, "Recent tag"
return False, "Old tag"
def test_retention_policy():
"""Test various tag scenarios"""
now = datetime.now(timezone.utc)
old_date = now - timedelta(days=40)
recent_date = now - timedelta(days=10)
cutoff = now - timedelta(days=30)
test_cases = [
# (tag_name, tag_date, expected_keep, expected_reason)
("latest", old_date, True, "Protected tag"),
("slim", old_date, True, "Protected tag"),
("main", old_date, True, "Protected tag"),
("stable", old_date, True, "Protected tag"),
("v6.6.0", old_date, True, "Semantic version"),
("6.6.0", old_date, True, "Semantic version"),
("v6.6", old_date, True, "Major.minor version"),
("6.6", old_date, True, "Major.minor version"),
("buildcache-linux-amd64", old_date, False, "Old buildcache tag"),
("buildcache-linux-amd64", recent_date, True, "Recent buildcache tag"),
("sha256-abc123", old_date, False, "Old sha/digest tag"),
("abc1234", old_date, False, "Old sha/digest tag"),
("sha256-abc123", recent_date, True, "Recent sha/digest tag"),
("test-feature", old_date, False, "Old test/dev tag"),
("dev-branch", old_date, False, "Old test/dev tag"),
("tmp-build", recent_date, True, "Recent test/dev tag"),
("feature-xyz", old_date, False, "Old tag"),
("feature-xyz", recent_date, True, "Recent tag"),
]
print("Testing Docker Hub Cleanup Retention Policy")
print("=" * 60)
print(f"Cutoff date: {cutoff.strftime('%Y-%m-%d')}")
print()
passed = 0
failed = 0
for tag_name, tag_date, expected_keep, expected_reason in test_cases:
should_keep, reason = should_keep_tag(tag_name, tag_date, cutoff_date=cutoff)
# Format date for display
date_str = tag_date.strftime('%Y-%m-%d')
days_old = (now - tag_date).days
# Check if test passed
if should_keep == expected_keep and reason == expected_reason:
status = "✓ PASS"
passed += 1
else:
status = "✗ FAIL"
failed += 1
# Print result
action = "KEEP" if should_keep else "DELETE"
print(f"{status}: {tag_name:30} ({days_old:3}d old) -> {action:6} ({reason})")
if status == "✗ FAIL":
print(f" Expected: {action:6} ({expected_reason})")
print()
print("=" * 60)
print(f"Results: {passed} passed, {failed} failed")
return failed == 0
if __name__ == "__main__":
success = test_retention_policy()
exit(0 if success else 1)
```
--------------------------------------------------------------------------------
/.github/ISSUE_TEMPLATE/bug_report.yml:
--------------------------------------------------------------------------------
```yaml
name: Bug Report
description: Report a bug or unexpected behavior
title: "[Bug]: "
labels: ["bug", "triage"]
body:
- type: markdown
attributes:
value: |
Thank you for reporting a bug! Please fill out the sections below to help us diagnose and fix the issue.
- type: textarea
id: description
attributes:
label: Bug Description
description: A clear and concise description of what the bug is.
placeholder: What happened?
validations:
required: true
- type: textarea
id: steps
attributes:
label: Steps to Reproduce
description: Detailed steps to reproduce the behavior
placeholder: |
1. Configure storage backend as...
2. Run command...
3. Observe error...
value: |
1.
2.
3.
validations:
required: true
- type: textarea
id: expected
attributes:
label: Expected Behavior
description: What you expected to happen
placeholder: The memory should be stored successfully...
validations:
required: true
- type: textarea
id: actual
attributes:
label: Actual Behavior
description: What actually happened (include error messages)
placeholder: |
Error: database is locked
Traceback...
validations:
required: true
- type: dropdown
id: storage-backend
attributes:
label: Storage Backend
description: Which storage backend are you using?
options:
- sqlite-vec (local)
- cloudflare (remote)
- hybrid (sqlite + cloudflare)
- unsure
validations:
required: true
- type: dropdown
id: os
attributes:
label: Operating System
options:
- macOS
- Windows
- Linux
- Docker
- Other
validations:
required: true
- type: input
id: python-version
attributes:
label: Python Version
description: Output of `python --version`
placeholder: "Python 3.11.5"
validations:
required: true
- type: input
id: mcp-version
attributes:
label: MCP Memory Service Version
description: Output of `uv run memory --version` or check `pyproject.toml`
placeholder: "v8.17.0"
validations:
required: true
- type: dropdown
id: installation
attributes:
label: Installation Method
options:
- Source (git clone)
- pip/uv install
- Docker
- Other
validations:
required: true
- type: dropdown
id: interface
attributes:
label: Interface Used
description: How are you accessing the memory service?
options:
- Claude Desktop (MCP)
- Claude Code (CLI)
- HTTP API (dashboard)
- Python API (direct import)
- Other
validations:
required: true
- type: textarea
id: config
attributes:
label: Configuration
description: Relevant parts of your `.env` file or Claude Desktop config (redact API keys)
placeholder: |
MCP_MEMORY_STORAGE_BACKEND=hybrid
MCP_HTTP_ENABLED=true
# Cloudflare credentials redacted
render: shell
- type: textarea
id: logs
attributes:
label: Relevant Log Output
description: Logs from server, MCP client, or error messages
placeholder: Paste relevant logs here
render: shell
- type: textarea
id: context
attributes:
label: Additional Context
description: |
Any other context about the problem:
- Recent changes or upgrades
- Concurrent usage (multiple clients)
- Network conditions (if using remote backend)
- Screenshots (if dashboard issue)
placeholder: Any additional information that might help...
- type: checkboxes
id: checks
attributes:
label: Pre-submission Checklist
description: Please verify you've completed these steps
options:
- label: I've searched existing issues and this is not a duplicate
required: true
- label: I'm using the latest version (or specified version above)
required: true
- label: I've included all required environment information
required: true
- label: I've redacted sensitive information (API keys, tokens)
required: true
```
--------------------------------------------------------------------------------
/tests/integration/test_api_key_fallback.py:
--------------------------------------------------------------------------------
```python
#!/usr/bin/env python3
"""
Test script to verify API key authentication fallback works with OAuth enabled.
This test verifies that existing API key authentication continues to work
when OAuth is enabled, ensuring backward compatibility.
"""
import asyncio
import sys
from pathlib import Path
import httpx
# Add src to path for standalone execution
sys.path.insert(0, str(Path(__file__).parent.parent.parent / 'src'))
async def test_api_key_fallback(base_url: str = "http://localhost:8000", api_key: str = None) -> bool:
"""
Test API key authentication fallback with OAuth enabled.
Returns:
True if all tests pass, False otherwise
"""
print(f"Testing API key fallback at {base_url}")
print("=" * 50)
if not api_key:
print("❌ No API key provided - cannot test fallback")
print(" Set MCP_API_KEY environment variable or pass as argument")
return False
async with httpx.AsyncClient() as client:
try:
# Test 1: API Key as Bearer Token (should work)
print("1. Testing API Key as Bearer Token...")
headers = {"Authorization": f"Bearer {api_key}"}
response = await client.get(f"{base_url}/api/memories", headers=headers)
if response.status_code == 200:
print(f" ✅ API key authentication working")
else:
print(f" ❌ API key authentication failed: {response.status_code}")
return False
# Test 2: API Key for write operations
print("\n2. Testing API Key for Write Operations...")
memory_data = {
"content": "Test memory for API key authentication",
"tags": ["test", "api-key"],
"memory_type": "test"
}
response = await client.post(f"{base_url}/api/memories", json=memory_data, headers=headers)
if response.status_code == 200:
print(f" ✅ API key write operation working")
# Store content hash for cleanup
memory_hash = response.json().get("content_hash")
else:
print(f" ❌ API key write operation failed: {response.status_code}")
return False
# Test 3: Invalid API Key (should fail)
print("\n3. Testing Invalid API Key...")
invalid_headers = {"Authorization": "Bearer invalid_key"}
response = await client.get(f"{base_url}/api/memories", headers=invalid_headers)
if response.status_code == 401:
print(f" ✅ Invalid API key correctly rejected")
else:
print(f" ⚠️ Invalid API key test inconclusive: {response.status_code}")
# Test 4: Cleanup - Delete test memory
if memory_hash:
print("\n4. Cleaning up test memory...")
response = await client.delete(f"{base_url}/api/memories/{memory_hash}", headers=headers)
if response.status_code == 200:
print(f" ✅ Test memory cleaned up successfully")
else:
print(f" ⚠️ Cleanup failed: {response.status_code}")
print("\n" + "=" * 50)
print("🎉 API key fallback authentication tests passed!")
print("✅ Backward compatibility maintained")
return True
except Exception as e:
print(f"\n❌ Test failed with exception: {e}")
return False
async def main():
"""Main test function."""
if len(sys.argv) > 1:
base_url = sys.argv[1]
else:
base_url = "http://localhost:8000"
# Try to get API key from command line or environment
api_key = None
if len(sys.argv) > 2:
api_key = sys.argv[2]
else:
import os
api_key = os.getenv('MCP_API_KEY')
print("API Key Authentication Fallback Test")
print("===================================")
print(f"Target: {base_url}")
print()
print("This test verifies that API key authentication works")
print("as a fallback when OAuth 2.1 is enabled.")
print()
success = await test_api_key_fallback(base_url, api_key)
if success:
print("\n🚀 API key fallback is working correctly!")
sys.exit(0)
else:
print("\n💥 API key fallback tests failed")
sys.exit(1)
if __name__ == "__main__":
asyncio.run(main())
```
--------------------------------------------------------------------------------
/scripts/sync/safe_cloudflare_update.sh:
--------------------------------------------------------------------------------
```bash
#!/bin/bash
# Safe Cloudflare Update Script
# Pushes corrected timestamps from local SQLite to Cloudflare
# Run this AFTER timestamp restoration, BEFORE re-enabling hybrid on other machines
set -e # Exit on error
SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
PROJECT_ROOT="$(cd "$SCRIPT_DIR/../.." && pwd)"
echo "================================================================================"
echo "SAFE CLOUDFLARE UPDATE - Timestamp Recovery"
echo "================================================================================"
echo ""
echo "This script will:"
echo " 1. Verify local database has correct timestamps"
echo " 2. Push corrected timestamps to Cloudflare"
echo " 3. Verify Cloudflare update success"
echo ""
echo "⚠️ IMPORTANT: Run this BEFORE re-enabling hybrid sync on other machines!"
echo ""
# Check if we're in the right directory
if [ ! -f "$PROJECT_ROOT/scripts/sync/sync_memory_backends.py" ]; then
echo "❌ ERROR: Cannot find sync script. Are you in the project directory?"
exit 1
fi
# Step 1: Verify local timestamps
echo "================================================================================"
echo "STEP 1: VERIFYING LOCAL TIMESTAMPS"
echo "================================================================================"
echo ""
python3 << 'EOF'
import sqlite3
import sys
from pathlib import Path
# Add src to path
sys.path.insert(0, str(Path(__file__).parent / "src"))
try:
from mcp_memory_service import config
db_path = config.SQLITE_VEC_PATH
except:
db_path = str(Path.home() / "Library/Application Support/mcp-memory/sqlite_vec.db")
conn = sqlite3.connect(db_path)
cursor = conn.cursor()
# Check total memories
cursor.execute('SELECT COUNT(*) FROM memories')
total = cursor.fetchone()[0]
# Check corruption period (Nov 16-18)
cursor.execute('''
SELECT COUNT(*) FROM memories
WHERE created_at_iso LIKE "2025-11-16%"
OR created_at_iso LIKE "2025-11-17%"
OR created_at_iso LIKE "2025-11-18%"
''')
corrupted = cursor.fetchone()[0]
corruption_pct = (corrupted * 100 / total) if total > 0 else 0
print(f"Database: {db_path}")
print(f"Total memories: {total}")
print(f"Nov 16-18 dates: {corrupted} ({corruption_pct:.1f}%)")
print()
if corruption_pct < 10:
print("✅ VERIFICATION PASSED: Timestamps look good")
print(" Safe to proceed with Cloudflare update")
conn.close()
sys.exit(0)
else:
print("❌ VERIFICATION FAILED: Too many corrupted timestamps")
print(f" Expected: <10%, Found: {corruption_pct:.1f}%")
print()
print("Run timestamp restoration first:")
print(" python scripts/maintenance/restore_from_json_export.py --apply")
conn.close()
sys.exit(1)
EOF
if [ $? -ne 0 ]; then
echo ""
echo "❌ Local verification failed. Aborting."
exit 1
fi
echo ""
read -p "Continue with Cloudflare update? [y/N]: " -n 1 -r
echo ""
if [[ ! $REPLY =~ ^[Yy]$ ]]; then
echo "Update cancelled."
exit 0
fi
# Step 2: Push to Cloudflare
echo ""
echo "================================================================================"
echo "STEP 2: PUSHING TO CLOUDFLARE"
echo "================================================================================"
echo ""
echo "This will overwrite Cloudflare timestamps with your corrected local data."
echo "Duration: 5-10 minutes (network dependent)"
echo ""
cd "$PROJECT_ROOT"
python scripts/sync/sync_memory_backends.py --direction sqlite-to-cf
if [ $? -ne 0 ]; then
echo ""
echo "❌ Cloudflare sync failed. Check logs above."
exit 1
fi
# Step 3: Verify Cloudflare
echo ""
echo "================================================================================"
echo "STEP 3: VERIFYING CLOUDFLARE UPDATE"
echo "================================================================================"
echo ""
python scripts/sync/sync_memory_backends.py --status
echo ""
echo "================================================================================"
echo "UPDATE COMPLETE ✅"
echo "================================================================================"
echo ""
echo "Next steps:"
echo " 1. Verify status output above shows expected memory counts"
echo " 2. Check other machines are still offline (hybrid disabled)"
echo " 3. When ready, sync other machines FROM Cloudflare:"
echo " python scripts/sync/sync_memory_backends.py --direction cf-to-sqlite"
echo ""
echo "See TIMESTAMP_RECOVERY_CHECKLIST.md for detailed next steps."
echo ""
```
--------------------------------------------------------------------------------
/tests/sqlite/simple_sqlite_vec_test.py:
--------------------------------------------------------------------------------
```python
#!/usr/bin/env python3
"""
Simple standalone test for sqlite-vec functionality.
"""
import asyncio
import os
import tempfile
import sys
import sqlite3
async def test_sqlite_vec_basic():
"""Test basic sqlite-vec functionality."""
print("🔧 Testing basic SQLite-vec functionality...")
print("=" * 50)
try:
# Test sqlite-vec import
print("1. Testing sqlite-vec import...")
import sqlite_vec
from sqlite_vec import serialize_float32
print(" ✅ sqlite-vec imported successfully")
# Test basic database operations
print("\n2. Testing database creation...")
temp_dir = tempfile.mkdtemp()
db_path = os.path.join(temp_dir, "test.db")
conn = sqlite3.connect(db_path)
conn.enable_load_extension(True)
sqlite_vec.load(conn)
print(" ✅ Database created and sqlite-vec loaded")
# Create test table
print("\n3. Creating test table...")
conn.execute('''
CREATE TABLE test_vectors (
id INTEGER PRIMARY KEY,
content TEXT,
embedding BLOB
)
''')
print(" ✅ Test table created")
# Test vector operations
print("\n4. Testing vector operations...")
test_vector = [0.1, 0.2, 0.3, 0.4, 0.5]
serialized = serialize_float32(test_vector)
conn.execute('''
INSERT INTO test_vectors (content, embedding)
VALUES (?, ?)
''', ("Test content", serialized))
conn.commit()
print(" ✅ Vector stored successfully")
# Test retrieval
print("\n5. Testing retrieval...")
cursor = conn.execute('''
SELECT content, embedding FROM test_vectors WHERE id = 1
''')
row = cursor.fetchone()
if row:
content, stored_embedding = row
print(f" Retrieved content: {content}")
print(" ✅ Retrieval successful")
# Cleanup
conn.close()
os.remove(db_path)
os.rmdir(temp_dir)
print("\n✅ Basic sqlite-vec test passed!")
print("\n🚀 SQLite-vec is working correctly on your Ubuntu system!")
return True
except Exception as e:
print(f" ❌ Test failed: {e}")
import traceback
traceback.print_exc()
return False
async def show_next_steps():
"""Show next steps for integration."""
print("\n" + "=" * 60)
print("🎯 Next Steps for Claude Code + VS Code Integration")
print("=" * 60)
print("\n1. 📦 Complete MCP Memory Service Setup:")
print(" # Stay in your virtual environment")
print(" source venv/bin/activate")
print()
print(" # Set the backend")
print(" export MCP_MEMORY_STORAGE_BACKEND=sqlite_vec")
print()
print(" # Install remaining MCP dependencies")
print(" pip install mcp")
print("\n2. 🔧 Configure Claude Code Integration:")
print(" The sqlite-vec backend is now ready!")
print(" Your memory database will be stored at:")
home = os.path.expanduser("~")
print(f" {home}/.local/share/mcp-memory/sqlite_vec.db")
print("\n3. 💻 For VS Code Integration:")
print(" # Install VS Code MCP extension (if available)")
print(" # Or use Claude Code directly in VS Code terminal")
print("\n4. 🧪 Test the Setup:")
print(" # Test that MCP Memory Service works with sqlite-vec")
print(" python -c \"")
print(" import os")
print(" os.environ['MCP_MEMORY_STORAGE_BACKEND'] = 'sqlite_vec'")
print(" # Your memory operations will now use sqlite-vec!")
print(" \"")
print("\n5. 🔄 Migration (if you have existing ChromaDB data):")
print(" python migrate_to_sqlite_vec.py")
print("\n✨ Benefits of SQLite-vec:")
print(" • 75% less memory usage")
print(" • Single file database (easy backup)")
print(" • Faster startup times")
print(" • Better for <100K memories")
async def main():
"""Main test function."""
success = await test_sqlite_vec_basic()
if success:
await show_next_steps()
return 0
else:
print("\n❌ sqlite-vec test failed. Please install sqlite-vec:")
print(" pip install sqlite-vec")
return 1
if __name__ == "__main__":
sys.exit(asyncio.run(main()))
```
--------------------------------------------------------------------------------
/docs/DOCUMENTATION_AUDIT.md:
--------------------------------------------------------------------------------
```markdown
# Documentation Audit Report
**Date**: 2025-07-26
**Branch**: feature/http-sse-sqlite-vec
**Purpose**: Consolidation analysis for unified installer merge
## Current Documentation Inventory
### Installation-Related Documentation
- `README.md` (root) - Main installation instructions, needs backend choice integration
- `docs/guides/installation.md` - Detailed installation guide (12KB)
- `docs/guides/windows-setup.md` - Windows-specific setup (4KB)
- `docs/guides/UBUNTU_SETUP.md` - Ubuntu-specific setup
- `docs/sqlite-vec-backend.md` - SQLite-vec backend guide
- `MIGRATION_GUIDE.md` (root) - ChromaDB to SQLite-vec migration
- `scripts/install_windows.py` - Windows installer script
- `scripts/installation/install.py` - Alternative installer script
### Platform-Specific Documentation
- `docs/integration/homebrew/` (7 files) - Homebrew PyTorch integration
- `HOMEBREW_PYTORCH_README.md` - Main Homebrew integration
- `HOMEBREW_PYTORCH_SETUP.md` - Setup instructions
- `TROUBLESHOOTING_GUIDE.md` - Homebrew troubleshooting
- `docs/guides/windows-setup.md` - Windows platform guide
- `docs/guides/UBUNTU_SETUP.md` - Linux platform guide
### API and Technical Documentation
- `docs/IMPLEMENTATION_PLAN_HTTP_SSE.md` - HTTP/SSE implementation plan
- `docs/guides/claude_integration.md` - Claude Desktop integration
- `docs/guides/invocation_guide.md` - Usage guide
- `docs/technical/` - Technical implementation details
### Migration and Troubleshooting
- `MIGRATION_GUIDE.md` - ChromaDB to SQLite-vec migration
- `docs/guides/migration.md` - General migration guide
- `docs/guides/troubleshooting.md` - General troubleshooting
- `docs/integration/homebrew/TROUBLESHOOTING_GUIDE.md` - Homebrew-specific
## Documentation Gaps Identified
### 1. Master Installation Guide Missing
- No single source of truth for installation
- Backend selection guidance scattered
- Hardware-specific optimization not documented coherently
### 2. Legacy Hardware Support Documentation
- 2015 MacBook Pro scenario not explicitly documented
- Older Intel Mac optimization path unclear
- Homebrew PyTorch integration buried in subdirectory
### 3. Storage Backend Comparison
- No comprehensive comparison between ChromaDB and SQLite-vec
- Selection criteria not clearly documented
- Migration paths not prominently featured
### 4. HTTP/SSE API Documentation
- Implementation plan exists but user-facing API docs missing
- Integration examples needed
- SSE event documentation incomplete
## Consolidation Strategy
### Phase 1: Create Master Documents
1. **docs/guides/INSTALLATION_MASTER.md** - Comprehensive installation guide
2. **docs/guides/STORAGE_BACKENDS.md** - Backend comparison and selection
3. **docs/guides/HARDWARE_OPTIMIZATION.md** - Platform-specific optimizations
4. **docs/api/HTTP_SSE_API.md** - Complete API documentation
### Phase 2: Platform-Specific Consolidation
1. **docs/platforms/macos-intel-legacy.md** - Your 2015 MacBook Pro use case
2. **docs/platforms/macos-modern.md** - Recent Mac configurations
3. **docs/platforms/windows.md** - Consolidated Windows guide
4. **docs/platforms/linux.md** - Consolidated Linux guide
### Phase 3: Merge and Reorganize
1. Consolidate duplicate content
2. Create cross-references between related docs
3. Update README.md to point to new structure
4. Archive or remove obsolete documentation
## High-Priority Actions
1. ✅ Create this audit document
2. ⏳ Create master installation guide
3. ⏳ Consolidate platform-specific guides
4. ⏳ Document hardware intelligence matrix
5. ⏳ Create migration consolidation guide
6. ⏳ Update README.md with new structure
## Content Quality Assessment
### Good Documentation (Keep/Enhance)
- `MIGRATION_GUIDE.md` - Well structured, clear steps
- `docs/sqlite-vec-backend.md` - Comprehensive backend guide
- `docs/integration/homebrew/HOMEBREW_PYTORCH_README.md` - Good Homebrew integration
### Needs Improvement
- `README.md` - Lacks backend choice prominence
- `docs/guides/installation.md` - Too generic, needs hardware-specific paths
- Multiple troubleshooting guides need consolidation
### Duplicated Content (Consolidate)
- Installation instructions repeated across multiple files
- Windows setup scattered between guides and scripts
- Homebrew integration documentation fragmented
## Next Steps
1. Begin creating master installation guide
2. Merge hardware-specific content from various sources
3. Create clear user journey documentation
4. Test documentation accuracy with actual installations
```
--------------------------------------------------------------------------------
/scripts/backup/backup_memories.py:
--------------------------------------------------------------------------------
```python
#!/usr/bin/env python3
# Copyright 2024 Heinrich Krupp
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Backup script to export all memories from the database to a JSON file.
This provides a safe backup before running migrations or making database changes.
"""
import sys
import os
import json
import asyncio
import logging
import datetime
from pathlib import Path
# Add parent directory to path so we can import from the src directory
sys.path.insert(0, str(Path(__file__).parent.parent))
from src.mcp_memory_service.storage.chroma import ChromaMemoryStorage
from src.mcp_memory_service.config import CHROMA_PATH, BACKUPS_PATH
# Configure logging
logging.basicConfig(
level=logging.INFO,
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
)
logger = logging.getLogger("memory_backup")
async def backup_memories():
"""
Export all memories from the database to a JSON file.
"""
logger.info(f"Initializing ChromaDB storage at {CHROMA_PATH}")
storage = ChromaMemoryStorage(CHROMA_PATH)
# Create backups directory if it doesn't exist
os.makedirs(BACKUPS_PATH, exist_ok=True)
# Generate backup filename with timestamp
timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
backup_file = os.path.join(BACKUPS_PATH, f"memory_backup_{timestamp}.json")
logger.info(f"Creating backup at {backup_file}")
try:
# Retrieve all memories from the database
try:
# First try with embeddings
logger.info("Attempting to retrieve memories with embeddings")
results = storage.collection.get(
include=["metadatas", "documents"]
)
include_embeddings = False
except Exception as e:
logger.warning(f"Failed to retrieve with embeddings: {e}")
logger.info("Falling back to retrieving memories without embeddings")
# Fall back to no embeddings
results = storage.collection.get(
include=["metadatas", "documents"]
)
include_embeddings = False
if not results["ids"]:
logger.info("No memories found in database")
return backup_file
total_memories = len(results["ids"])
logger.info(f"Found {total_memories} memories to backup")
# Create backup data structure
backup_data = {
"timestamp": datetime.datetime.now().isoformat(),
"total_memories": total_memories,
"memories": []
}
# Process each memory
for i, memory_id in enumerate(results["ids"]):
metadata = results["metadatas"][i]
document = results["documents"][i]
embedding = None
if include_embeddings and "embeddings" in results and results["embeddings"] is not None:
if i < len(results["embeddings"]):
embedding = results["embeddings"][i]
memory_data = {
"id": memory_id,
"document": document,
"metadata": metadata,
"embedding": embedding
}
backup_data["memories"].append(memory_data)
# Write backup to file
with open(backup_file, 'w', encoding='utf-8') as f:
json.dump(backup_data, f, indent=2, ensure_ascii=False)
logger.info(f"Successfully backed up {total_memories} memories to {backup_file}")
return backup_file
except Exception as e:
logger.error(f"Error creating backup: {str(e)}")
raise
async def main():
"""Main function to run the backup."""
logger.info("=== Starting memory backup ===")
try:
backup_file = await backup_memories()
logger.info(f"=== Backup completed successfully: {backup_file} ===")
except Exception as e:
logger.error(f"Backup failed: {str(e)}")
sys.exit(1)
if __name__ == "__main__":
asyncio.run(main())
```
--------------------------------------------------------------------------------
/scripts/maintenance/scan_todos.sh:
--------------------------------------------------------------------------------
```bash
#!/bin/bash
# scripts/maintenance/scan_todos.sh - Scan codebase for TODOs and prioritize
#
# Usage: bash scripts/maintenance/scan_todos.sh [DIRECTORY]
# Example: bash scripts/maintenance/scan_todos.sh src/
set -e
SCAN_DIR=${1:-src}
if ! command -v gemini &> /dev/null; then
echo "Error: Gemini CLI is not installed"
exit 1
fi
if [ ! -d "$SCAN_DIR" ]; then
echo "Error: Directory not found: $SCAN_DIR"
exit 1
fi
echo "=== TODO Scanner ==="
echo "Scanning directory: $SCAN_DIR"
echo ""
# Extract all TODOs with file and line number
echo "Finding TODO comments..."
todos=$(find "$SCAN_DIR" -name '*.py' -exec grep -Hn "TODO\|FIXME\|HACK\|XXX" {} \; 2>/dev/null || echo "")
if [ -z "$todos" ]; then
echo "✅ No TODOs found in $SCAN_DIR"
exit 0
fi
todo_count=$(echo "$todos" | wc -l)
echo "Found $todo_count TODO comments"
echo ""
# Save to temp file using mktemp
todos_raw=$(mktemp -t todos_raw.XXXXXX)
echo "$todos" > "$todos_raw"
echo "Analyzing and prioritizing TODOs with Gemini..."
echo ""
# Use Gemini to prioritize
prioritized=$(gemini "Analyze these TODO/FIXME/HACK/XXX comments from a Python codebase and categorize by priority.
Priority Levels:
- **CRITICAL (P0)**: Security vulnerabilities, data corruption risks, blocking bugs, production-breaking issues
- **HIGH (P1)**: Performance bottlenecks (>100ms), user-facing bugs, incomplete core features, API breaking changes
- **MEDIUM (P2)**: Code quality improvements, minor optimizations, technical debt, convenience features
- **LOW (P3)**: Documentation, cosmetic changes, nice-to-haves, future enhancements
Consider:
- Security impact (SQL injection, XSS, etc.)
- Performance implications
- Feature completeness
- User impact
- Technical debt accumulation
TODO comments (format: file:line:comment):
$(cat "$todos_raw")
Output format (be concise):
## CRITICAL (P0)
- file.py:123 - Brief description of issue
## HIGH (P1)
- file.py:456 - Brief description
## MEDIUM (P2)
- file.py:789 - Brief description
## LOW (P3)
- file.py:012 - Brief description")
todos_prioritized=$(mktemp -t todos_prioritized.XXXXXX)
echo "$prioritized" > "$todos_prioritized"
# Display results
cat "$todos_prioritized"
echo ""
# Count actual items using awk (robust, order-independent parsing)
# Pattern: count lines starting with '-' within each priority section
critical_items=$(awk '/^## CRITICAL/,/^## [A-Z]/ {if (/^-/ && !/^## /) count++} END {print count+0}' "$todos_prioritized")
high_items=$(awk '/^## HIGH/,/^## [A-Z]/ {if (/^-/ && !/^## /) count++} END {print count+0}' "$todos_prioritized")
medium_items=$(awk '/^## MEDIUM/,/^## [A-Z]/ {if (/^-/ && !/^## /) count++} END {print count+0}' "$todos_prioritized")
# LOW section: handle both cases (followed by another section or end of file)
low_items=$(awk '/^## LOW/ {in_low=1; next} /^## [A-Z]/ && in_low {in_low=0} in_low && /^-/ {count++} END {print count+0}' "$todos_prioritized")
echo "=== Summary ==="
echo "Total TODOs: $todo_count"
echo ""
echo "By Priority:"
echo " CRITICAL (P0): $critical_items"
echo " HIGH (P1): $high_items"
echo " MEDIUM (P2): $medium_items"
echo " LOW (P3): $low_items"
echo ""
# Save to docs (optional)
if [ -d "docs/development" ]; then
echo "Saving to docs/development/todo-tracker.md..."
cat > docs/development/todo-tracker.md << EOF
# TODO Tracker
**Last Updated:** $(date '+%Y-%m-%d %H:%M:%S')
**Scan Directory:** $SCAN_DIR
**Total TODOs:** $todo_count
## Summary
| Priority | Count | Description |
|----------|-------|-------------|
| CRITICAL (P0) | $critical_items | Security, data corruption, blocking bugs |
| HIGH (P1) | $high_items | Performance, user-facing, incomplete features |
| MEDIUM (P2) | $medium_items | Code quality, optimizations, technical debt |
| LOW (P3) | $low_items | Documentation, cosmetic, nice-to-haves |
---
$(cat "$todos_prioritized")
---
## How to Address
1. **CRITICAL**: Address immediately, block releases if necessary
2. **HIGH**: Schedule for current/next sprint
3. **MEDIUM**: Add to backlog, address in refactoring sprints
4. **LOW**: Address opportunistically or when touching related code
## Updating This Tracker
Run: \`bash scripts/maintenance/scan_todos.sh\`
EOF
echo "✅ Saved to docs/development/todo-tracker.md"
fi
# Clean up temp files
rm -f "$todos_raw" "$todos_prioritized"
# Exit with warning if critical TODOs found
if [ $critical_items -gt 0 ]; then
echo ""
echo "⚠️ WARNING: $critical_items CRITICAL TODOs found!"
echo "These should be addressed immediately."
exit 1
fi
exit 0
```
--------------------------------------------------------------------------------
/docs/LM_STUDIO_COMPATIBILITY.md:
--------------------------------------------------------------------------------
```markdown
# LM Studio Compatibility Guide
## Issue Description
When using MCP Memory Service with LM Studio or Claude Desktop, you may encounter errors when operations are cancelled or timeout:
### Error Types
1. **Validation Error (LM Studio)**:
```
pydantic_core._pydantic_core.ValidationError: 5 validation errors for ClientNotification
ProgressNotification.method
Input should be 'notifications/progress' [type=literal_error, input_value='notifications/cancelled', input_type=str]
```
2. **Timeout Error (Claude Desktop)**:
```
Message from client: {"jsonrpc":"2.0","method":"notifications/cancelled","params":{"requestId":0,"reason":"McpError: MCP error -32001: Request timed out"}}
Server transport closed unexpectedly, this is likely due to the process exiting early.
```
These occur because:
- LM Studio and Claude Desktop send non-standard `notifications/cancelled` messages
- These messages aren't part of the official MCP (Model Context Protocol) specification
- Timeouts can cause the server to exit prematurely on Windows systems
## Solution
The MCP Memory Service now includes an automatic compatibility patch that handles LM Studio's non-standard notifications. This patch is applied automatically when the server starts.
### How It Works
1. **Automatic Detection**: The server detects when clients send `notifications/cancelled` messages
2. **Graceful Handling**: Instead of crashing, the server handles these gracefully:
- Logs the cancellation reason (including timeouts)
- Converts to harmless notifications that don't cause validation errors
- Continues operation normally
3. **Platform Optimizations**:
- **Windows**: Extended timeouts (30s vs 15s) due to security software interference
- **Cross-platform**: Enhanced signal handling for graceful shutdowns
### What You Need to Do
**Nothing!** The compatibility patch is applied automatically when you start the MCP Memory Service.
### Verifying the Fix
You can verify the patch is working by checking the server logs. You should see:
```
Applied enhanced LM Studio/Claude Desktop compatibility patch for notifications/cancelled
```
When operations are cancelled or timeout, you'll see:
```
Intercepted cancelled notification (ID: 0): McpError: MCP error -32001: Request timed out
Operation timeout detected: McpError: MCP error -32001: Request timed out
```
Instead of a crash, the server will continue running.
## Technical Details
The compatibility layer is implemented in `src/mcp_memory_service/lm_studio_compat.py` and:
1. **Notification Patching**: Monkey-patches the MCP library's `ClientNotification.model_validate` method
2. **Timeout Detection**: Identifies and logs timeout scenarios vs regular cancellations
3. **Graceful Substitution**: Converts `notifications/cancelled` to valid `InitializedNotification` objects
4. **Platform Optimization**: Uses extended timeouts on Windows (30s vs 15s)
5. **Signal Handling**: Adds Windows-specific signal handlers for graceful shutdowns
6. **Alternative Patching**: Fallback approach modifies the session receive loop if needed
## Windows-Specific Improvements
- **Extended Timeouts**: 30-second timeout for storage initialization (vs 15s on other platforms)
- **Security Software Compatibility**: Accounts for Windows Defender and antivirus delays
- **Signal Handling**: Enhanced SIGTERM/SIGINT handling for clean shutdowns
- **Timeout Recovery**: Better recovery from initialization timeouts
## Limitations
- **Workaround Nature**: This addresses non-standard client behavior, not a server issue
- **Cancelled Operations**: Operations aren't truly cancelled server-side, just client notifications are handled
- **Timeout Recovery**: While timeouts are handled gracefully, the original operation may still complete
## Future Improvements
Ideally, this should be fixed in one of two ways:
1. **LM Studio Update**: LM Studio should follow the MCP specification and not send non-standard notifications
2. **MCP Library Update**: The MCP library could be updated to handle vendor-specific extensions gracefully
## Troubleshooting
If you still experience issues:
1. Ensure you're using the latest version of MCP Memory Service
2. Check that the patch is being applied (look for the log message)
3. Report the issue with full error logs to the repository
## Related Issues
- This is a known compatibility issue between LM Studio and the MCP protocol
- Similar issues may occur with other non-standard MCP clients
- The patch specifically handles LM Studio's behavior and may need updates for other clients
```
--------------------------------------------------------------------------------
/docs/technical/memory-migration.md:
--------------------------------------------------------------------------------
```markdown
# Memory Migration Technical Documentation
This document provides technical details about the memory migration process in the MCP Memory Service.
## Overview
The memory migration process allows transferring memories between different ChromaDB instances, supporting both local and remote migrations. The process is handled by the `mcp-migration.py` script, which provides a robust and efficient way to move memories while maintaining data integrity.
## Migration Types
### 1. Local to Remote Migration
- Source: Local ChromaDB instance
- Target: Remote ChromaDB server
- Use case: Moving memories from a development environment to production
### 2. Remote to Local Migration
- Source: Remote ChromaDB server
- Target: Local ChromaDB instance
- Use case: Creating local backups or development environments
## Technical Implementation
### Environment Verification
Before starting the migration, the script performs environment verification:
- Checks Python version compatibility
- Verifies required packages are installed
- Validates ChromaDB paths and configurations
- Ensures network connectivity for remote migrations
### Migration Process
1. **Connection Setup**
- Establishes connections to both source and target ChromaDB instances
- Verifies collection existence and creates if necessary
- Sets up embedding functions for consistent vectorization
2. **Data Transfer**
- Implements batch processing (default batch size: 10)
- Includes delay between batches to prevent overwhelming the target
- Handles duplicate detection to avoid data redundancy
- Maintains metadata and document relationships
3. **Verification**
- Validates successful transfer by comparing record counts
- Checks for data integrity
- Provides detailed logging of the migration process
## Error Handling
The migration script includes comprehensive error handling for:
- Connection failures
- Collection access issues
- Data transfer interruptions
- Configuration errors
- Environment incompatibilities
## Performance Considerations
- **Batch Size**: Default 10 records per batch
- **Delay**: 1 second between batches
- **Memory Usage**: Optimized for minimal memory footprint
- **Network**: Handles connection timeouts and retries
## Configuration Options
### Source Configuration
```json
{
"type": "local|remote",
"config": {
"path": "/path/to/chroma", // for local
"host": "remote-host", // for remote
"port": 8000 // for remote
}
}
```
### Target Configuration
```json
{
"type": "local|remote",
"config": {
"path": "/path/to/chroma", // for local
"host": "remote-host", // for remote
"port": 8000 // for remote
}
}
```
## Best Practices
1. **Pre-Migration**
- Verify environment compatibility
- Ensure sufficient disk space
- Check network connectivity for remote migrations
- Backup existing data
2. **During Migration**
- Monitor progress through logs
- Avoid interrupting the process
- Check for error messages
3. **Post-Migration**
- Verify data integrity
- Check collection statistics
- Validate memory access
## Troubleshooting
Common issues and solutions:
1. **Connection Failures**
- Verify network connectivity
- Check firewall settings
- Validate host and port configurations
2. **Data Transfer Issues**
- Check disk space
- Verify collection permissions
- Monitor system resources
3. **Environment Issues**
- Run environment verification
- Check package versions
- Validate Python environment
## Example Usage
### Command Line
```bash
# Local to Remote Migration
python scripts/mcp-migration.py \
--source-type local \
--source-config /path/to/local/chroma \
--target-type remote \
--target-config '{"host": "remote-host", "port": 8000}'
# Remote to Local Migration
python scripts/mcp-migration.py \
--source-type remote \
--source-config '{"host": "remote-host", "port": 8000}' \
--target-type local \
--target-config /path/to/local/chroma
```
### Programmatic Usage
```python
from scripts.mcp_migration import migrate_memories
# Local to Remote Migration
migrate_memories(
source_type='local',
source_config='/path/to/local/chroma',
target_type='remote',
target_config={'host': 'remote-host', 'port': 8000}
)
# Remote to Local Migration
migrate_memories(
source_type='remote',
source_config={'host': 'remote-host', 'port': 8000},
target_type='local',
target_config='/path/to/local/chroma'
)
```
```
--------------------------------------------------------------------------------
/scripts/sync/litestream/stash_local_changes.sh:
--------------------------------------------------------------------------------
```bash
#!/bin/bash
# Stash local memory changes to staging database before sync
MAIN_DB="/Users/hkr/Library/Application Support/mcp-memory/sqlite_vec.db"
STAGING_DB="/Users/hkr/Library/Application Support/mcp-memory/sqlite_vec_staging.db"
HOSTNAME=$(hostname)
echo "$(date): Stashing local changes..."
if [ ! -f "$MAIN_DB" ]; then
echo "$(date): No main database found at $MAIN_DB"
exit 1
fi
if [ ! -f "$STAGING_DB" ]; then
echo "$(date): Staging database not found. Run ./init_staging_db.sh first"
exit 1
fi
# Get last sync timestamp from staging database
LAST_SYNC=$(sqlite3 "$STAGING_DB" "SELECT value FROM sync_status WHERE key = 'last_local_sync';" 2>/dev/null || echo "")
# If no last sync, get all memories from a reasonable time ago (7 days)
if [ -z "$LAST_SYNC" ]; then
LAST_SYNC="datetime('now', '-7 days')"
else
LAST_SYNC="'$LAST_SYNC'"
fi
echo "$(date): Looking for changes since: $LAST_SYNC"
# Find memories that might be new/modified locally
# Note: This assumes your sqlite_vec.db has a similar schema
# We'll need to adapt this based on your actual schema
# First, let's check the schema of the main database
echo "$(date): Analyzing main database schema..."
MAIN_SCHEMA=$(sqlite3 "$MAIN_DB" ".schema" 2>/dev/null | head -10)
if [ $? -ne 0 ] || [ -z "$MAIN_SCHEMA" ]; then
echo "$(date): ERROR: Cannot read main database schema"
exit 1
fi
echo "$(date): Main database schema detected"
# For now, we'll create a simple approach that looks for memories
# This will need to be customized based on your actual schema
# Query to find recent memories (adjust based on actual schema)
TEMP_QUERY_RESULT=$(mktemp)
# Try different table names that might exist in sqlite_vec databases
for TABLE in memories memory_entries memories_table memory_items; do
if sqlite3 "$MAIN_DB" ".tables" | grep -q "^$TABLE$"; then
echo "$(date): Found table: $TABLE"
# Try to extract memories (adjust columns based on actual schema)
sqlite3 "$MAIN_DB" "
SELECT
COALESCE(id, rowid) as id,
content,
COALESCE(content_hash, '') as content_hash,
COALESCE(tags, '[]') as tags,
COALESCE(metadata, '{}') as metadata,
COALESCE(memory_type, 'note') as memory_type,
COALESCE(created_at, datetime('now')) as created_at
FROM $TABLE
WHERE datetime(COALESCE(updated_at, created_at, datetime('now'))) > $LAST_SYNC
LIMIT 100;
" 2>/dev/null > "$TEMP_QUERY_RESULT"
if [ -s "$TEMP_QUERY_RESULT" ]; then
break
fi
fi
done
# Count changes found
CHANGE_COUNT=$(wc -l < "$TEMP_QUERY_RESULT" | tr -d ' ')
if [ "$CHANGE_COUNT" -eq 0 ]; then
echo "$(date): No local changes found to stash"
rm -f "$TEMP_QUERY_RESULT"
exit 0
fi
echo "$(date): Found $CHANGE_COUNT potential local changes"
# Process each change and add to staging
while IFS='|' read -r id content content_hash tags metadata memory_type created_at; do
# Generate content hash if missing
if [ -z "$content_hash" ]; then
content_hash=$(echo -n "$content" | shasum -a 256 | cut -d' ' -f1)
fi
# Escape single quotes for SQL
content_escaped=$(echo "$content" | sed "s/'/''/g")
tags_escaped=$(echo "$tags" | sed "s/'/''/g")
metadata_escaped=$(echo "$metadata" | sed "s/'/''/g")
# Insert into staging database
sqlite3 "$STAGING_DB" "
INSERT OR REPLACE INTO staged_memories (
id, content, content_hash, tags, metadata, memory_type,
operation, staged_at, original_created_at, source_machine
) VALUES (
'$id',
'$content_escaped',
'$content_hash',
'$tags_escaped',
'$metadata_escaped',
'$memory_type',
'INSERT',
datetime('now'),
'$created_at',
'$HOSTNAME'
);
"
if [ $? -eq 0 ]; then
echo "$(date): Staged change: ${content:0:50}..."
else
echo "$(date): ERROR: Failed to stage change for ID: $id"
fi
done < "$TEMP_QUERY_RESULT"
# Update sync status
sqlite3 "$STAGING_DB" "
UPDATE sync_status
SET value = datetime('now'), updated_at = CURRENT_TIMESTAMP
WHERE key = 'last_local_sync';
"
# Show staging status
STAGED_COUNT=$(sqlite3 "$STAGING_DB" "SELECT value FROM sync_status WHERE key = 'total_staged_changes';" 2>/dev/null || echo "0")
echo "$(date): Stashing completed"
echo "$(date): Total staged changes: $STAGED_COUNT"
echo "$(date): New changes stashed: $CHANGE_COUNT"
# Cleanup
rm -f "$TEMP_QUERY_RESULT"
```
--------------------------------------------------------------------------------
/src/mcp_memory_service/ingestion/registry.py:
--------------------------------------------------------------------------------
```python
# Copyright 2024 Heinrich Krupp
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Document loader registry for automatic format detection and loader selection.
"""
import logging
import mimetypes
from pathlib import Path
from typing import Dict, Type, List, Optional
from .base import DocumentLoader
logger = logging.getLogger(__name__)
# Registry of document loaders by file extension
_LOADER_REGISTRY: Dict[str, Type[DocumentLoader]] = {}
# Supported file formats
SUPPORTED_FORMATS = {
'pdf': 'PDF documents',
'docx': 'Word documents (requires semtools)',
'doc': 'Word documents (requires semtools)',
'pptx': 'PowerPoint presentations (requires semtools)',
'xlsx': 'Excel spreadsheets (requires semtools)',
'txt': 'Plain text files',
'md': 'Markdown documents',
'json': 'JSON data files',
'csv': 'CSV data files',
}
def register_loader(loader_class: Type[DocumentLoader], extensions: List[str]) -> None:
"""
Register a document loader for specific file extensions.
Args:
loader_class: The DocumentLoader subclass to register
extensions: List of file extensions this loader handles (without dots)
"""
for ext in extensions:
ext = ext.lower().lstrip('.')
_LOADER_REGISTRY[ext] = loader_class
logger.debug(f"Registered {loader_class.__name__} for .{ext} files")
def get_loader_for_file(file_path: Path) -> Optional[DocumentLoader]:
"""
Get appropriate document loader for a file.
Args:
file_path: Path to the file
Returns:
DocumentLoader instance that can handle the file, or None if unsupported
"""
if not file_path.exists():
logger.warning(f"File does not exist: {file_path}")
return None
# Try by file extension first
extension = file_path.suffix.lower().lstrip('.')
if extension in _LOADER_REGISTRY:
loader_class = _LOADER_REGISTRY[extension]
loader = loader_class()
if loader.can_handle(file_path):
return loader
# Try by MIME type detection
mime_type, _ = mimetypes.guess_type(str(file_path))
if mime_type:
loader = _get_loader_by_mime_type(mime_type)
if loader and loader.can_handle(file_path):
return loader
# Try all registered loaders as fallback
for loader_class in _LOADER_REGISTRY.values():
loader = loader_class()
if loader.can_handle(file_path):
logger.debug(f"Found fallback loader {loader_class.__name__} for {file_path}")
return loader
logger.warning(f"No suitable loader found for file: {file_path}")
return None
def _get_loader_by_mime_type(mime_type: str) -> Optional[DocumentLoader]:
"""
Get loader based on MIME type.
Args:
mime_type: MIME type string
Returns:
DocumentLoader instance or None
"""
mime_to_extension = {
'application/pdf': 'pdf',
'text/plain': 'txt',
'text/markdown': 'md',
'application/json': 'json',
'text/csv': 'csv',
}
extension = mime_to_extension.get(mime_type)
if extension and extension in _LOADER_REGISTRY:
loader_class = _LOADER_REGISTRY[extension]
return loader_class()
return None
def get_supported_extensions() -> List[str]:
"""
Get list of all supported file extensions.
Returns:
List of supported extensions (without dots)
"""
return list(_LOADER_REGISTRY.keys())
def is_supported_file(file_path: Path) -> bool:
"""
Check if a file format is supported.
Args:
file_path: Path to check
Returns:
True if file format is supported
"""
return get_loader_for_file(file_path) is not None
def list_registered_loaders() -> Dict[str, str]:
"""
Get mapping of extensions to loader class names.
Returns:
Dictionary mapping extensions to loader class names
"""
return {ext: loader_class.__name__ for ext, loader_class in _LOADER_REGISTRY.items()}
```
--------------------------------------------------------------------------------
/src/mcp_memory_service/utils/document_processing.py:
--------------------------------------------------------------------------------
```python
# Copyright 2024 Heinrich Krupp
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Utilities for processing document chunks into memories.
"""
from typing import List, Dict, Any, Optional, Tuple
import logging
from ..models.memory import Memory
from . import generate_content_hash
logger = logging.getLogger(__name__)
def create_memory_from_chunk(
chunk: Any,
base_tags: List[str],
memory_type: str = "document",
context_tags: Optional[Dict[str, str]] = None,
extra_metadata: Optional[Dict[str, Any]] = None
) -> Memory:
"""
Create a Memory object from a document chunk with tag and metadata processing.
Args:
chunk: Document chunk object with content, metadata, and chunk_index
base_tags: Base tags to apply to the memory
memory_type: Type of memory (default: "document")
context_tags: Additional context-specific tags as key-value pairs
(e.g., {"source_dir": "docs", "file_type": "pdf"})
extra_metadata: Additional metadata to merge into chunk metadata
Returns:
Memory object ready for storage
Example:
>>> memory = create_memory_from_chunk(
... chunk,
... base_tags=["documentation"],
... context_tags={"source_dir": "docs", "file_type": "pdf"},
... extra_metadata={"upload_id": "batch123"}
... )
"""
# Build tag list
all_tags = list(base_tags)
# Add context-specific tags
if context_tags:
for key, value in context_tags.items():
all_tags.append(f"{key}:{value}")
# Handle chunk metadata tags (can be string or list)
if chunk.metadata and chunk.metadata.get('tags'):
chunk_tags = chunk.metadata['tags']
if isinstance(chunk_tags, str):
# Split comma-separated string into list
chunk_tags = [tag.strip() for tag in chunk_tags.split(',') if tag.strip()]
all_tags.extend(chunk_tags)
# Prepare metadata
chunk_metadata = chunk.metadata.copy() if chunk.metadata else {}
if extra_metadata:
chunk_metadata.update(extra_metadata)
# Create and return memory object
return Memory(
content=chunk.content,
content_hash=generate_content_hash(chunk.content, chunk_metadata),
tags=list(set(all_tags)), # Remove duplicates
memory_type=memory_type,
metadata=chunk_metadata
)
async def _process_and_store_chunk(
chunk: Any,
storage: Any,
file_name: str,
base_tags: List[str],
context_tags: Dict[str, str],
memory_type: str = "document",
extra_metadata: Optional[Dict[str, Any]] = None
) -> Tuple[bool, Optional[str]]:
"""
Process a document chunk and store it as a memory.
This consolidates the common pattern of creating a memory from a chunk
and storing it to the database across multiple ingestion entry points.
Args:
chunk: Document chunk with content and metadata
storage: Storage backend instance
file_name: Name of the source file (for error messages)
base_tags: Base tags to apply to the memory
context_tags: Context-specific tags (e.g., source_dir, file_type)
memory_type: Type of memory (default: "document")
extra_metadata: Additional metadata to merge into chunk metadata
Returns:
Tuple of (success: bool, error: Optional[str])
- (True, None) if stored successfully
- (False, error_message) if storage failed
"""
try:
# Create memory from chunk with context
memory = create_memory_from_chunk(
chunk,
base_tags=base_tags,
memory_type=memory_type,
context_tags=context_tags,
extra_metadata=extra_metadata
)
# Store the memory
success, error = await storage.store(memory)
if not success:
return False, f"{file_name} chunk {chunk.chunk_index}: {error}"
return True, None
except Exception as e:
return False, f"{file_name} chunk {chunk.chunk_index}: {str(e)}"
```
--------------------------------------------------------------------------------
/docs/troubleshooting/pr162-schema-caching-issue.md:
--------------------------------------------------------------------------------
```markdown
# PR #162 Fix Troubleshooting - Comma-Separated Tags Issue
## Issue
After PR #162 was merged (fixing support for comma-separated tags), users still saw error:
```
Input validation error: 'tag1,tag2,tag3' is not of type 'array'
```
## Root Cause Analysis
### What PR #162 Fixed
- **File**: `src/mcp_memory_service/server.py` lines 1320-1337
- **Fix**: Changed `tags` schema from requiring array to accepting `oneOf`:
```json
"tags": {
"oneOf": [
{"type": "array", "items": {"type": "string"}},
{"type": "string", "description": "Tags as comma-separated string"}
]
}
```
- **Server Code**: Lines 2076-2081 normalize tags from string to array
### Why Error Persisted
1. **MCP Client Schema Caching**: Claude Code's MCP client caches tool schemas when it first connects
2. **Stale Server Processes**: MCP server processes continued running with old code:
- Old process started at 10:43 (before git pull/merge)
- New code pulled but server not restarted
3. **HTTP vs MCP Servers**: HTTP server restart doesn't affect MCP server processes
4. **Validation Layer**: JSONSchema validation happens **client-side** before request reaches server
## Evidence
### Server Processes Found
```
PID 68270: Started 10:43 (OLD - before PR merge)
PID 70013: Started 10:44 (OLD - before PR merge)
PID 117228: HTTP server restarted 11:51 (NEW - has fix)
```
### Error Source
- Error message format: `'value' is not of type 'array'`
- Source: `jsonschema` library (Python package)
- Layer: **Client-side validation** in Claude Code's MCP client
### Timeline
- **Oct 20, 2025 17:22**: PR #162 merged
- **Oct 21, 2025 10:48**: HTTP server started (unknown which version)
- **Oct 21, 2025 11:51**: HTTP server restarted with latest code
- **Oct 21, 2025 11:5x**: MCP reconnection in Claude Code
## Solution
### Immediate Fix
```bash
# In Claude Code, run:
/mcp
# This forces reconnection and:
# 1. Terminates old MCP server process
# 2. Starts new MCP server with latest code
# 3. Re-fetches tool schemas (including updated tags schema)
# 4. Clears client-side schema cache
```
### Verification Steps
After reconnection:
1. Check MCP server process started after git pull/merge time
2. Test with comma-separated tags: `{"tags": "tag1,tag2,tag3"}`
3. Test with array tags: `{"tags": ["tag1", "tag2", "tag3"]}`
4. Both should work without validation errors
## Prevention for Future PRs
### When Schema Changes are Merged
1. **Restart HTTP Server** (if using HTTP protocol):
```bash
systemctl --user restart mcp-memory-http.service
```
2. **Reconnect MCP in Claude Code** (if using MCP protocol):
```
/mcp
```
Or fully restart Claude Code application
3. **Check Process Age**:
```bash
ps aux | grep "memory.*server" | grep -v grep
# Ensure start time is AFTER the git pull
```
### For Contributors
When merging PRs that change tool schemas:
1. Add note in PR description: "Requires MCP reconnection after deployment"
2. Update CHANGELOG with reconnection instructions
3. Consider automated server restart in deployment scripts
## Key Learnings
1. **Client-side validation**: MCP clients validate against cached schemas
2. **Multiple server processes**: HTTP and MCP servers are separate
3. **Schema propagation**: New schemas only available after reconnection
4. **Git pull != Code reload**: Running processes don't auto-reload
5. **Troubleshooting order**:
- Check PR merge time
- Check server process start time
- Check git log on running server's code
- Restart/reconnect if process predates code change
## Related Files
- Server schema: `src/mcp_memory_service/server.py:1320-1337`
- Server handler: `src/mcp_memory_service/server.py:2076-2081`
- PR: https://github.com/doobidoo/mcp-memory-service/pull/162
- Issue: (original issue that reported comma-separated tags not working)
## Quick Reference Card
### Symptom
✗ Error after merged PR: "Input validation error: 'X' is not of type 'Y'"
### Diagnosis
```bash
# 1. Check when PR was merged
gh pr view <PR_NUMBER> --json mergedAt
# 2. Check when server process started
ps aux | grep "memory.*server" | grep -v grep
# 3. Compare times - if server started BEFORE merge, that's the issue
```
### Fix
```bash
# In Claude Code:
/mcp
# Or restart systemd service:
systemctl --user restart mcp-memory-http.service
```
### Verify
```bash
# Check new server process exists with recent start time
ps aux | grep "memory.*server" | grep -v grep
# Test the fixed functionality
```
## Date
- Analyzed: October 21, 2025
- PR Merged: October 20, 2025 17:22 UTC
- Issue: Schema caching in MCP client after schema update
```
--------------------------------------------------------------------------------
/docs/integrations/groq-model-comparison.md:
--------------------------------------------------------------------------------
```markdown
# Groq Model Comparison for Code Quality Analysis
## Available Models
### 1. llama-3.3-70b-versatile (Default)
**Best for:** General-purpose code analysis with detailed explanations
**Characteristics:**
- ✅ Comprehensive, detailed responses
- ✅ Thorough breakdown of complexity factors
- ✅ Balanced speed and quality
- ⚠️ Can be verbose for simple tasks
**Performance:**
- Response time: ~1.2-1.6s
- Detail level: High
- Accuracy: Excellent
**Example Output (Complexity 6/10):**
```
**Complexity Rating: 6/10**
Here's a breakdown of the complexity factors:
1. **Functionality**: The function performs data processing...
2. **Conditional Statements**: There are two conditional statements...
3. **Loops**: There is one loop...
[... detailed analysis continues ...]
```
### 2. moonshotai/kimi-k2-instruct (Recommended for Code Analysis)
**Best for:** Fast, accurate code analysis with agentic intelligence
**Characteristics:**
- ✅ **Fastest response time** (~0.9s)
- ✅ Concise, accurate assessments
- ✅ 256K context window (largest on GroqCloud)
- ✅ Excellent for complex coding tasks
- ✅ Superior agentic intelligence
**Performance:**
- Response time: ~0.9s (fastest tested)
- Detail level: Concise but accurate
- Accuracy: Excellent
**Example Output (Complexity 2/10):**
```
Complexity: 2/10
The function is short, uses only basic control flow and dict/list
operations, and has no recursion, nested loops, or advanced algorithms.
```
**Kimi K2 Features:**
- 1 trillion parameters (32B activated MoE)
- 256K context window
- 185 tokens/second throughput
- Optimized for front-end development
- Superior tool calling capabilities
### 3. llama-3.1-8b-instant
**Best for:** Simple queries requiring minimal analysis
**Characteristics:**
- ⚠️ Despite name "instant", actually slower than Kimi K2
- ⚠️ Very verbose, includes unnecessary details
- ✅ Lower cost than larger models
**Performance:**
- Response time: ~1.6s (slowest tested)
- Detail level: Very high (sometimes excessive)
- Accuracy: Good but over-explains
**Example Output (Complexity 4/10):**
```
I would rate the complexity of this function a 4 out of 10.
Here's a breakdown of the factors I considered:
- **Readability**: 6/10
- **Locality**: 7/10
- **Abstraction**: 8/10
- **Efficiency**: 9/10
[... continues with edge cases, type hints, etc ...]
```
## Recommendations by Use Case
### Pre-commit Hooks (Speed Critical)
**Use: moonshotai/kimi-k2-instruct**
```bash
./scripts/utils/groq "Complexity 1-10: $(cat file.py)" --model moonshotai/kimi-k2-instruct
```
- Fastest response (~0.9s)
- Accurate enough for quality gates
- Minimizes developer wait time
### PR Review (Quality Critical)
**Use: llama-3.3-70b-versatile**
```bash
./scripts/utils/groq "Detailed analysis: $(cat file.py)"
```
- Comprehensive feedback
- Detailed explanations help reviewers
- Balanced speed/quality
### Security Analysis (Accuracy Critical)
**Use: moonshotai/kimi-k2-instruct**
```bash
./scripts/utils/groq "Security scan: $(cat file.py)" --model moonshotai/kimi-k2-instruct
```
- Excellent at identifying vulnerabilities
- Fast enough for CI/CD
- Superior agentic intelligence for complex patterns
### Simple Queries
**Use: llama-3.1-8b-instant** (if cost is priority)
```bash
./scripts/utils/groq "Is this function pure?" --model llama-3.1-8b-instant
```
- Lowest cost
- Good for yes/no questions
- Avoid for complex analysis (slower than Kimi K2)
## Performance Summary
| Model | Response Time | Detail Level | Best For | Context |
|-------|--------------|--------------|----------|---------|
| **Kimi K2** | 0.9s ⚡ | Concise ✓ | Speed + Accuracy | 256K |
| **llama-3.3-70b** | 1.2-1.6s | Detailed ✓ | Comprehensive | 128K |
| **llama-3.1-8b** | 1.6s | Very Detailed | Cost savings | 128K |
## Cost Comparison (Groq Pricing)
| Model | Input | Output | Use Case |
|-------|-------|--------|----------|
| Kimi K2 | $1.00/M | $3.00/M | Premium speed + quality |
| llama-3.3-70b | ~$0.50/M | ~$0.80/M | Balanced |
| llama-3.1-8b | ~$0.05/M | ~$0.10/M | High volume |
## Switching Models
All models use the same interface:
```bash
# Default (llama-3.3-70b-versatile)
./scripts/utils/groq "Your prompt"
# Kimi K2 (recommended for code analysis)
./scripts/utils/groq "Your prompt" --model moonshotai/kimi-k2-instruct
# Fast/cheap
./scripts/utils/groq "Your prompt" --model llama-3.1-8b-instant
```
## Conclusion
**For MCP Memory Service code quality workflows:**
- ✅ **Kimi K2**: Best overall - fastest, accurate, excellent for code
- ✅ **llama-3.3-70b**: Good for detailed explanations in PR reviews
- ⚠️ **llama-3.1-8b**: Avoid for code analysis despite "instant" name
```
--------------------------------------------------------------------------------
/scripts/database/analyze_sqlite_vec_db.py:
--------------------------------------------------------------------------------
```python
#!/usr/bin/env python3
"""
Simple analysis script to examine SQLite-vec database without dependencies.
"""
import sqlite3
import sys
import os
import re
def analyze_database(db_path):
"""Analyze the database structure and content."""
print(f"Analyzing database: {db_path}")
print("="*60)
if not os.path.exists(db_path):
print(f"❌ Database not found: {db_path}")
return
conn = sqlite3.connect(db_path)
cursor = conn.cursor()
try:
# Check tables
cursor.execute("SELECT name FROM sqlite_master WHERE type='table' ORDER BY name")
tables = [row[0] for row in cursor.fetchall()]
print(f"Tables found: {', '.join(tables)}")
print()
# Analyze memories table
if 'memories' in tables:
cursor.execute("SELECT COUNT(*) FROM memories")
memory_count = cursor.fetchone()[0]
print(f"📝 Memories: {memory_count}")
if memory_count > 0:
# Sample some memories
cursor.execute("SELECT content, tags, memory_type FROM memories LIMIT 3")
samples = cursor.fetchall()
print(" Sample memories:")
for i, (content, tags, mem_type) in enumerate(samples, 1):
print(f" {i}. [{mem_type or 'general'}] {content[:50]}..." +
(f" (tags: {tags})" if tags else ""))
# Analyze embeddings table
if 'memory_embeddings' in tables:
try:
cursor.execute("SELECT COUNT(*) FROM memory_embeddings")
embedding_count = cursor.fetchone()[0]
print(f"🧠 Embeddings: {embedding_count}")
# Check schema to get dimension
cursor.execute("""
SELECT sql FROM sqlite_master
WHERE type='table' AND name='memory_embeddings'
""")
schema = cursor.fetchone()
if schema:
print(f" Schema: {schema[0]}")
match = re.search(r'FLOAT\[(\d+)\]', schema[0])
if match:
dimension = int(match.group(1))
print(f" Dimension: {dimension}")
except Exception as e:
print(f"🧠 Embeddings: Error accessing table - {e}")
else:
print("🧠 Embeddings: Table not found")
# Check for mismatches
if 'memories' in tables and 'memory_embeddings' in tables:
cursor.execute("SELECT COUNT(*) FROM memories")
mem_count = cursor.fetchone()[0]
cursor.execute("SELECT COUNT(*) FROM memory_embeddings")
emb_count = cursor.fetchone()[0]
print()
if mem_count == emb_count:
print("✅ Memory and embedding counts match")
else:
print(f"⚠️ Mismatch: {mem_count} memories vs {emb_count} embeddings")
# Find memories without embeddings
cursor.execute("""
SELECT COUNT(*) FROM memories m
WHERE NOT EXISTS (
SELECT 1 FROM memory_embeddings e WHERE e.rowid = m.id
)
""")
missing = cursor.fetchone()[0]
if missing > 0:
print(f" → {missing} memories missing embeddings")
# Find orphaned embeddings
cursor.execute("""
SELECT COUNT(*) FROM memory_embeddings e
WHERE NOT EXISTS (
SELECT 1 FROM memories m WHERE m.id = e.rowid
)
""")
orphaned = cursor.fetchone()[0]
if orphaned > 0:
print(f" → {orphaned} orphaned embeddings")
# Check for extension loading capability
print()
try:
conn.enable_load_extension(True)
print("✅ Extension loading enabled")
except:
print("❌ Extension loading not available")
except Exception as e:
print(f"❌ Error analyzing database: {e}")
finally:
conn.close()
def main():
if len(sys.argv) != 2:
print("Usage: python analyze_sqlite_vec_db.py <database_path>")
sys.exit(1)
db_path = sys.argv[1]
analyze_database(db_path)
if __name__ == "__main__":
main()
```
--------------------------------------------------------------------------------
/docs/technical/migration-log.md:
--------------------------------------------------------------------------------
```markdown
# FastAPI MCP Server Migration Log
## Architecture Decision Record
**Date**: 2025-08-03
**Branch**: `feature/fastapi-mcp-native-v4`
**Version**: 4.0.0-alpha.1
### Decision: Migrate from Node.js Bridge to Native FastAPI MCP Server
**Problem**: Node.js HTTP-to-MCP bridge has SSL handshake issues with self-signed certificates, preventing reliable remote memory service access.
**Solution**: Replace Node.js bridge with native FastAPI MCP server using official MCP Python SDK.
### Technical Findings
1. **Node.js SSL Issues**:
- Node.js HTTPS client fails SSL handshake with self-signed certificates
- Issue persists despite custom HTTPS agents and disabled certificate validation
- Workaround: Slash commands using curl work, but direct MCP tools fail
2. **FastAPI MCP Benefits**:
- Native MCP protocol support via FastMCP framework
- Python SSL stack handles self-signed certificates more reliably
- Eliminates bridging complexity and failure points
- Direct integration with existing storage backends
### Implementation Status
#### ✅ Completed (Commit: 5709be1)
- [x] Created feature branch `feature/fastapi-mcp-native-v4`
- [x] Updated GitHub issues #71 and #72 with migration plan
- [x] Implemented basic FastAPI MCP server structure
- [x] Added 5 core memory operations: store, retrieve, search_by_tag, delete, health
- [x] Version bump to 4.0.0-alpha.1
- [x] Added new script entry point: `mcp-memory-server`
#### ✅ Migration Completed (Commit: c0a0a45)
- [x] Dual-service architecture deployed successfully
- [x] FastMCP server (port 8000) + HTTP dashboard (port 8080)
- [x] SSL issues completely resolved
- [x] Production deployment to memory.local verified
- [x] Standard MCP client compatibility confirmed
- [x] Documentation and deployment scripts completed
#### 🚧 Known Limitations
- **Claude Code SSE Compatibility**: Claude Code's SSE client has specific requirements incompatible with FastMCP implementation
- **Workaround**: Claude Code users can use HTTP dashboard or alternative MCP clients
- **Impact**: Core migration objectives achieved; this is a client-specific limitation
#### 📋 Future Development
1. **Claude Code Compatibility**: Investigate custom SSE client implementation
2. **Tool Expansion**: Add remaining 17 memory operations as needed
3. **Performance Optimization**: Monitor and optimize dual-service performance
4. **Client Library**: Develop Python/JavaScript MCP client libraries
5. **Documentation**: Expand client compatibility matrix
### Dashboard Tools Exclusion
**Decision**: Exclude 8 dashboard-specific tools from FastAPI MCP server.
**Rationale**:
- HTTP dashboard at https://github.com/doobidoo/mcp-memory-dashboard provides superior web interface
- MCP server should focus on Claude Code integration, not duplicate dashboard functionality
- Clear separation of concerns: MCP for Claude Code, HTTP for administration
**Excluded Tools**:
- dashboard_check_health, dashboard_recall_memory, dashboard_retrieve_memory
- dashboard_search_by_tag, dashboard_get_stats, dashboard_optimize_db
- dashboard_create_backup, dashboard_delete_memory
### Architecture Comparison
| Aspect | Node.js Bridge | FastAPI MCP |
|--------|----------------|-------------|
| Protocol | HTTP→MCP translation | Native MCP |
| SSL Handling | Node.js HTTPS (problematic) | Python SSL (reliable) |
| Complexity | 3 layers (Claude→Bridge→HTTP→Memory) | 2 layers (Claude→MCP Server) |
| Maintenance | Multiple codebases | Unified Python |
| Remote Access | SSL issues | Direct support |
| Mobile Support | Limited by bridge | Full MCP compatibility |
### Success Metrics
- [x] ~~All MCP tools function correctly with Claude Code~~ **Standard MCP clients work; Claude Code has SSE compatibility issue**
- [x] SSL/HTTPS connectivity works without workarounds
- [x] Performance equals or exceeds Node.js bridge
- [x] Remote access works from multiple clients
- [x] Easy deployment without local bridge requirements
### Project Completion Summary
**Status**: ✅ **MIGRATION SUCCESSFUL**
**Date Completed**: August 3, 2025
**Final Commit**: c0a0a45
**Deployment Status**: Production-ready dual-service architecture
The FastAPI MCP migration has successfully achieved its primary objectives:
1. **SSL Issues Eliminated**: Node.js SSL handshake problems completely resolved
2. **Architecture Simplified**: Removed complex bridging layers
3. **Standard Compliance**: Full MCP protocol compatibility with standard clients
4. **Production Ready**: Deployed and tested dual-service architecture
**Note**: Claude Code SSE client compatibility remains a separate issue to be addressed in future development.
```
--------------------------------------------------------------------------------
/claude-hooks/MIGRATION.md:
--------------------------------------------------------------------------------
```markdown
# Migration Guide: Unified Python Hook Installer
## 🎯 Overview
The Claude Code Memory Awareness Hooks have been **consolidated into a single, unified Python installer** that replaces all previous platform-specific installers.
## 📋 **What Changed**
### ❌ **Deprecated (Removed)**
- `install.sh` - Legacy shell installer
- `install-natural-triggers.sh` - Natural triggers shell installer
- `install_claude_hooks_windows.bat` - Windows batch installer
### ✅ **New Unified Solution**
- `install_hooks.py` - **Single cross-platform Python installer**
## 🚀 **Migration Steps**
### For New Installations
```bash
# Navigate to hooks directory
cd claude-hooks
# Install basic memory awareness hooks
python install_hooks.py --basic
# OR install Natural Memory Triggers v7.1.3 (recommended)
python install_hooks.py --natural-triggers
# OR install everything
python install_hooks.py --all
```
### For Existing Users
```bash
# Uninstall old hooks (optional, installer handles upgrades)
python install_hooks.py --uninstall
# Install fresh with new installer
python install_hooks.py --natural-triggers
```
## ✨ **Benefits of Unified Installer**
### 🔧 **Technical Improvements**
- **Intelligent JSON merging** - Preserves existing Claude Code hook configurations
- **Cross-platform compatibility** - Works on Windows, macOS, and Linux
- **Dynamic path resolution** - No hardcoded paths, works in any location
- **Atomic installations** - Automatic rollback on failure
- **Comprehensive backups** - Timestamped backups before changes
- **Empty directory cleanup** - Proper uninstall process
### 🎯 **User Experience**
- **Single installation method** across all platforms
- **Consistent CLI interface** with clear options
- **Dry-run support** for testing without changes
- **Enhanced error handling** with detailed feedback
- **CLI management tools** for real-time configuration
## 📖 **Advanced Usage**
### Available Options
```bash
# Test installation without making changes
python install_hooks.py --dry-run --natural-triggers
# Install only basic hooks
python install_hooks.py --basic
# Install Natural Memory Triggers (recommended)
python install_hooks.py --natural-triggers
# Install everything (basic + natural triggers)
python install_hooks.py --all
# Uninstall all hooks
python install_hooks.py --uninstall
# Get help
python install_hooks.py --help
```
### CLI Management (Natural Memory Triggers)
```bash
# Check status
node ~/.claude/hooks/memory-mode-controller.js status
# Switch performance profiles
node ~/.claude/hooks/memory-mode-controller.js profile balanced
node ~/.claude/hooks/memory-mode-controller.js profile speed_focused
node ~/.claude/hooks/memory-mode-controller.js profile memory_aware
# Adjust sensitivity
node ~/.claude/hooks/memory-mode-controller.js sensitivity 0.7
```
## 🔧 **Integration with Main Installer**
The main MCP Memory Service installer now uses the unified hook installer:
```bash
# Install service + basic hooks
python scripts/installation/install.py --install-hooks
# Install service + Natural Memory Triggers
python scripts/installation/install.py --install-natural-triggers
```
## 🛠 **Troubleshooting**
### Common Issues
**Q: Can I still use the old shell scripts?**
A: No, they have been removed. The unified Python installer provides all functionality with improved reliability.
**Q: Will my existing hook configuration be preserved?**
A: Yes, the unified installer intelligently merges configurations and preserves existing hooks.
**Q: What if I have custom modifications to the old installers?**
A: The unified installer is designed to be extensible. Please file an issue if you need specific functionality.
**Q: Does this work on Windows?**
A: Yes, the unified Python installer provides full Windows support with proper path handling.
## 📞 **Support**
If you encounter issues:
1. **Check prerequisites**: Ensure Claude Code CLI, Node.js, and Python are installed
2. **Test with dry-run**: Use `--dry-run` flag to identify issues
3. **Check logs**: The installer provides detailed error messages
4. **File an issue**: [GitHub Issues](https://github.com/doobidoo/mcp-memory-service/issues)
## 🎉 **Benefits Summary**
The unified installer provides:
- ✅ **Better reliability** across all platforms
- ✅ **Safer installations** with intelligent configuration merging
- ✅ **Consistent experience** regardless of operating system
- ✅ **Advanced features** like Natural Memory Triggers v7.1.3
- ✅ **Professional tooling** with comprehensive testing and validation
This migration represents a significant improvement in the installation experience while maintaining full backward compatibility for existing users.
```
--------------------------------------------------------------------------------
/docs/deployment/dual-service.md:
--------------------------------------------------------------------------------
```markdown
# Dual Service Deployment - FastMCP + HTTP Dashboard
## Overview
This deployment provides both **FastMCP Protocol** and **HTTP Dashboard** services running simultaneously, eliminating Node.js SSL issues while maintaining full functionality.
## Architecture
### Service 1: FastMCP Server (Port 8000)
- **Purpose**: Native MCP protocol for Claude Code clients
- **Protocol**: JSON-RPC 2.0 over Server-Sent Events
- **Access**: `http://[IP]:8000/mcp`
- **Service**: `mcp-memory.service`
### Service 2: HTTP Dashboard (Port 8080)
- **Purpose**: Web dashboard and HTTP API
- **Protocol**: Standard HTTP/REST
- **Access**: `http://[IP]:8080/`
- **API**: `http://[IP]:8080/api/*`
- **Service**: `mcp-http-dashboard.service`
## Deployment
### Quick Deploy
```bash
./deploy_dual_services.sh
```
### Manual Setup
```bash
# Install FastMCP service
sudo cp /tmp/fastmcp-server-with-mdns.service /etc/systemd/system/mcp-memory.service
# Install HTTP Dashboard service
sudo cp /tmp/mcp-http-dashboard.service /etc/systemd/system/mcp-http-dashboard.service
# Enable and start services
sudo systemctl daemon-reload
sudo systemctl enable mcp-memory mcp-http-dashboard
sudo systemctl start mcp-memory mcp-http-dashboard
```
## Access URLs
Replace `[IP]` with your actual server IP address (e.g., `10.0.1.30`):
- **FastMCP Protocol**: `http://[IP]:8000/mcp` (for Claude Code)
- **Web Dashboard**: `http://[IP]:8080/` (for monitoring)
- **Health API**: `http://[IP]:8080/api/health`
- **Memory API**: `http://[IP]:8080/api/memories`
- **Search API**: `http://[IP]:8080/api/search`
## Service Management
### Status Checks
```bash
sudo systemctl status mcp-memory # FastMCP server
sudo systemctl status mcp-http-dashboard # HTTP dashboard
```
### View Logs
```bash
sudo journalctl -u mcp-memory -f # FastMCP logs
sudo journalctl -u mcp-http-dashboard -f # Dashboard logs
```
### Control Services
```bash
# Start services
sudo systemctl start mcp-memory mcp-http-dashboard
# Stop services
sudo systemctl stop mcp-memory mcp-http-dashboard
# Restart services
sudo systemctl restart mcp-memory mcp-http-dashboard
```
## mDNS Discovery
Both services advertise via mDNS for network discovery:
```bash
# Browse HTTP services
avahi-browse -t _http._tcp
# Browse MCP services (if supported)
avahi-browse -t _mcp._tcp
# Resolve hostname
avahi-resolve-host-name memory.local
```
**Services Advertised:**
- `MCP Memory Dashboard._http._tcp.local.` (port 8080)
- `MCP Memory FastMCP._mcp._tcp.local.` (port 8000)
## Dependencies
Ensure these packages are installed in the virtual environment:
- `mcp` - MCP Protocol support
- `fastapi` - Web framework
- `uvicorn` - ASGI server
- `zeroconf` - mDNS advertising
- `aiohttp` - HTTP client/server
- `sqlite-vec` - Vector database
- `sentence-transformers` - Embeddings
## Configuration
### Environment Variables
- `MCP_MEMORY_STORAGE_BACKEND=sqlite_vec`
- `MCP_MDNS_ENABLED=true`
- `MCP_HTTP_ENABLED=true`
- `MCP_SERVER_HOST=0.0.0.0`
- `MCP_SERVER_PORT=8000`
- `MCP_HTTP_PORT=8080`
### Storage
Both services share the same SQLite-vec database:
- **Path**: `~/.local/share/mcp-memory/sqlite_vec.db`
- **Backend**: `sqlite_vec`
- **Model**: `all-MiniLM-L6-v2`
## Troubleshooting
### Services Not Accessible
1. Check if services are running: `systemctl status [service]`
2. Verify ports are listening: `ss -tlnp | grep -E ":800[08]"`
3. Test direct IP access instead of hostname
4. Check firewall rules if accessing remotely
### mDNS Not Working
1. Ensure avahi-daemon is running: `systemctl status avahi-daemon`
2. Install missing dependencies: `pip install zeroconf aiohttp`
3. Restart services after installing dependencies
### FastMCP Protocol Issues
1. Ensure client accepts `text/event-stream` headers
2. Use JSON-RPC 2.0 format for requests
3. Access `/mcp` endpoint, not root `/`
## Client Configuration
### Claude Code
Configure MCP client to use:
```
http://[SERVER_IP]:8000/mcp
```
### curl Examples
```bash
# Health check
curl http://[SERVER_IP]:8080/api/health
# Store memory
curl -X POST http://[SERVER_IP]:8080/api/memories \
-H "Content-Type: application/json" \
-d '{"content": "test memory", "tags": ["test"]}'
# Search memories
curl -X POST http://[SERVER_IP]:8080/api/search \
-H "Content-Type: application/json" \
-d '{"query": "test", "limit": 5}'
```
## Benefits
✅ **No Node.js SSL Issues** - Pure Python implementation
✅ **Dual Protocol Support** - Both MCP and HTTP available
✅ **Network Discovery** - mDNS advertising for easy access
✅ **Production Ready** - systemd managed services
✅ **Backward Compatible** - HTTP API preserved for existing tools
✅ **Claude Code Ready** - Native MCP protocol support
```
--------------------------------------------------------------------------------
/claude_commands/memory-context.md:
--------------------------------------------------------------------------------
```markdown
# Add Current Session to Memory
I'll help you capture the current conversation and project context as a memory that can be recalled later. This command is perfect for preserving important session insights, decisions, and progress summaries.
## What I'll do:
1. **Session Analysis**: I'll analyze our current conversation to extract key insights, decisions, and progress made.
2. **Project Context**: I'll capture the current project state including:
- Working directory and git repository status
- Recent file changes and commits
- Current branch and development context
3. **Conversation Summary**: I'll create a concise summary of our session including:
- Main topics discussed
- Decisions made or problems solved
- Action items or next steps identified
- Code changes or configurations applied
4. **Smart Tagging**: I'll automatically generate relevant tags based on the session content and project context, including the machine hostname as a source identifier.
5. **Memory Storage**: I'll store the session summary with appropriate metadata for easy future retrieval.
## Usage Examples:
```bash
claude /memory-context
claude /memory-context --summary "Architecture planning session"
claude /memory-context --tags "planning,architecture" --type "session"
claude /memory-context --include-files --include-commits
```
## Implementation:
I'll automatically analyze our current session and project state, then store it to your MCP Memory Service at `https://memory.local:8443/`:
1. **Conversation Analysis**: Extract key topics, decisions, and insights from our current chat
2. **Project State Capture**:
- Current working directory and git status
- Recent commits and file changes
- Branch information and repository state
3. **Context Synthesis**: Combine conversation and project context into a coherent summary
4. **Memory Creation**: Store the context with automatic tags including machine hostname
5. **Auto-Save**: Memory is stored immediately without confirmation prompts
The service uses HTTPS with curl `-k` flag for secure communication and automatically detects client hostname using the `X-Client-Hostname` header.
The stored memory will include:
- **Source Machine**: Hostname tag for tracking memory origin (e.g., "source:your-machine-name")
- **Session Summary**: Concise overview of our conversation
- **Key Decisions**: Important choices or conclusions reached
- **Technical Details**: Code changes, configurations, or technical insights
- **Project Context**: Repository state, files modified, current focus
- **Action Items**: Next steps or follow-up tasks identified
- **Timestamp**: When the session context was captured
## Context Elements:
### Conversation Context
- **Topics Discussed**: Main subjects and themes from our chat
- **Problems Solved**: Issues resolved or questions answered
- **Decisions Made**: Choices made or approaches agreed upon
- **Insights Gained**: New understanding or knowledge acquired
### Project Context
- **Repository Info**: Git repository, branch, and recent commits
- **File Changes**: Modified, added, or deleted files
- **Directory Structure**: Current working directory and project layout
- **Development State**: Current development phase or focus area
### Technical Context
- **Code Changes**: Functions, classes, or modules modified
- **Configuration Updates**: Settings, dependencies, or environment changes
- **Architecture Decisions**: Design choices or structural changes
- **Performance Considerations**: Optimization or efficiency insights
## Arguments:
- `$ARGUMENTS` - Optional custom summary or context description
- `--summary "text"` - Custom session summary override
- `--tags "tag1,tag2"` - Additional tags to apply
- `--type "session|meeting|planning|development"` - Context type
- `--include-files` - Include detailed file change information
- `--include-commits` - Include recent commit messages and changes
- `--include-code` - Include snippets of important code changes
- `--private` - Mark as private/sensitive session content
- `--project "name"` - Override project name detection
## Automatic Features:
- **Smart Summarization**: Extract the most important points from our conversation
- **Duplicate Detection**: Avoid storing redundant session information
- **Context Linking**: Connect to related memories and previous sessions
- **Progress Tracking**: Identify progress made since last context capture
- **Knowledge Extraction**: Pull out reusable insights and patterns
This command is especially useful at the end of productive development sessions, after important architectural discussions, or when you want to preserve the current state of your thinking and progress for future reference.
```
--------------------------------------------------------------------------------
/tests/timestamp/test_timestamp_issue.py:
--------------------------------------------------------------------------------
```python
#!/usr/bin/env python3
"""Test script to debug timestamp issues in recall functionality."""
import sys
import os
sys.path.insert(0, os.path.join(os.path.dirname(__file__), 'src'))
import asyncio
import time
from datetime import datetime, timedelta
from mcp_memory_service.models.memory import Memory
from mcp_memory_service.utils.hashing import generate_content_hash
from mcp_memory_service.utils.time_parser import parse_time_expression, extract_time_expression
async def test_timestamp_issue():
"""Test timestamp storage and retrieval issues."""
print("=== Testing Timestamp Issue ===")
# Test 1: Precision loss when converting float to int
print("\n1. Testing precision loss:")
current_time = time.time()
print(f"Current time (float): {current_time}")
print(f"Current time (int): {int(current_time)}")
print(f"Difference: {current_time - int(current_time)} seconds")
# Test 2: Time expression parsing
print("\n2. Testing time expression parsing:")
test_queries = [
"yesterday",
"last week",
"2 days ago",
"last month",
"this morning",
"yesterday afternoon"
]
for query in test_queries:
cleaned_query, (start_ts, end_ts) = extract_time_expression(query)
if start_ts and end_ts:
start_dt = datetime.fromtimestamp(start_ts)
end_dt = datetime.fromtimestamp(end_ts)
print(f"\nQuery: '{query}'")
print(f" Cleaned: '{cleaned_query}'")
print(f" Start: {start_dt} (timestamp: {start_ts})")
print(f" End: {end_dt} (timestamp: {end_ts})")
print(f" Start (int): {int(start_ts)}")
print(f" End (int): {int(end_ts)}")
# Test 3: Memory timestamp creation
print("\n3. Testing Memory timestamp creation:")
memory = Memory(
content="Test memory",
content_hash=generate_content_hash("Test memory"),
tags=["test"]
)
print(f"Memory created_at (float): {memory.created_at}")
print(f"Memory created_at (int): {int(memory.created_at)}")
print(f"Memory created_at_iso: {memory.created_at_iso}")
# Test 4: Timestamp comparison issue
print("\n4. Testing timestamp comparison issue:")
# Create a timestamp from "yesterday"
yesterday_query = "yesterday"
_, (yesterday_start, yesterday_end) = extract_time_expression(yesterday_query)
# Create a memory with timestamp in the middle of yesterday
yesterday_middle = (yesterday_start + yesterday_end) / 2
test_memory_timestamp = yesterday_middle
print(f"\nYesterday range:")
print(f" Start: {yesterday_start} ({datetime.fromtimestamp(yesterday_start)})")
print(f" End: {yesterday_end} ({datetime.fromtimestamp(yesterday_end)})")
print(f" Test memory timestamp: {test_memory_timestamp} ({datetime.fromtimestamp(test_memory_timestamp)})")
# Check if memory would be included with float comparison
print(f"\nFloat comparison:")
print(f" {test_memory_timestamp} >= {yesterday_start}: {test_memory_timestamp >= yesterday_start}")
print(f" {test_memory_timestamp} <= {yesterday_end}: {test_memory_timestamp <= yesterday_end}")
print(f" Would be included: {test_memory_timestamp >= yesterday_start and test_memory_timestamp <= yesterday_end}")
# Check if memory would be included with int comparison (current implementation)
print(f"\nInt comparison (current implementation):")
print(f" {int(test_memory_timestamp)} >= {int(yesterday_start)}: {int(test_memory_timestamp) >= int(yesterday_start)}")
print(f" {int(test_memory_timestamp)} <= {int(yesterday_end)}: {int(test_memory_timestamp) <= int(yesterday_end)}")
print(f" Would be included: {int(test_memory_timestamp) >= int(yesterday_start) and int(test_memory_timestamp) <= int(yesterday_end)}")
# Test edge case: memory created at the very beginning or end of a day
print(f"\n5. Testing edge cases:")
# Memory at 00:00:00.5 (half second past midnight)
edge_timestamp = yesterday_start + 0.5
print(f" Edge case timestamp: {edge_timestamp} ({datetime.fromtimestamp(edge_timestamp)})")
print(f" Float: {edge_timestamp} >= {yesterday_start}: {edge_timestamp >= yesterday_start}")
print(f" Int: {int(edge_timestamp)} >= {int(yesterday_start)}: {int(edge_timestamp) >= int(yesterday_start)}")
# If the int values are the same but float values are different, we might miss memories
if int(edge_timestamp) == int(yesterday_start) and edge_timestamp > yesterday_start:
print(f" WARNING: This memory would be missed with int comparison!")
if __name__ == "__main__":
asyncio.run(test_timestamp_issue())
```