--- name: sqlite-vectordb description: SQLite vector DB for work log storage and semantic search. Use for indexing work logs, generating embeddings, semantic search, and DB maintenance. --- # SQLITE VECTOR DB SKILL Store work logs in a searchable vector database and provide semantic search infrastructure. **When to use**: - **Add entry**: After `\/log-work` execution (integrated with work-logger) - **Search**: When previous work context is needed (called by work-context-finder) - **Delete**: To clean up incorrect\/outdated logs **Note**: DB initializes automatically. No need to run `init_db.py` manually. <\/objective> ## Add Entry Index a markdown log into the vector DB. ```bash uv run .claude\/skills\/sqlite-vectordb\/scripts\/add_entry.py \ --file "private-docs\/work-logs\/YYYY-MM-DD-slug.md" \ --summary "One-line summary" \ --tags "tag1,tag2" ``` - `--file`, `-f` (required): Work log file path - `--summary`, `-s` (required): One-line summary for search indexing - `--tags`, `-t` (required): Comma-separated tags ## Search Semantic similarity search in work logs. ```bash uv run .claude\/skills\/sqlite-vectordb\/scripts\/search.py \ --query "search terms" \ --limit 5 ``` - `--query`, `-q` (required): Search query - `--limit`, `-l`: Max results (default: 5) - `--tag`, `-t`: Filter by tag - `--type`, `-T`: Filter by log type - `--json`, `-j`: JSON output ## Delete Entry Remove a work log entry from the database. ```bash uv run .claude\/skills\/sqlite-vectordb\/scripts\/delete_entry.py \ --file "private-docs\/work-logs\/YYYY-MM-DD-slug.md" ``` ## Initialize DB (Optional) Manual schema creation. Usually not needed - other scripts auto-initialize. ```bash uv run .claude\/skills\/sqlite-vectordb\/scripts\/init_db.py ``` <\/commands> - **DB location**: `private-docs\/work-logs\/.vector-db\/work-logs.db` - **Engine**: SQLite + `sqlite-vec` extension - **Embedding model**: `all-MiniLM-L6-v2` (384-dim) - **Chunk types**: summary, details, challenges, other - **Execution**: `uv run` with PEP 723 inline deps - **Schema reference**: [references\/schema.md](references\/schema.md) <\/technical_specs> **Language requirement**: All data stored in the vector DB MUST be written in English. - **Summary**: English only (for consistent embedding quality) - **Tags**: English only (e.g., `auth`, `refactor`, not `인증`, `리팩토링`) - **Query**: English preferred for optimal search accuracy - **Rationale**: Embedding model (`all-MiniLM-L6-v2`) performs best with English text <\/standards> **Exit codes**: - 0: Success - 1: File not found - 2: DB error **Common fixes**: - DB corrupted: Delete `private-docs\/work-logs\/.vector-db\/work-logs.db` and re-run (auto-recreates) - Missing deps: Run `uv sync` <\/troubleshooting> **Valid usage**: ```bash # Add entry (auto-initializes DB if missing) uv run .claude\/skills\/sqlite-vectordb\/scripts\/add_entry.py \ --file "private-docs\/work-logs\/2026-01-16-auth-feature.md" \ --summary "Implement OAuth2 authentication" \ --tags "auth,oauth,security" # Search with filters uv run .claude\/skills\/sqlite-vectordb\/scripts\/search.py \ --query "authentication login" --limit 3 --tag auth ``` **Invalid usage**: ```bash # WRONG: Non-existent file → Exit code 1 uv run ...\/add_entry.py --file "missing.md" ... # WRONG: Empty summary → Poor search quality uv run ...\/add_entry.py --file "..." --summary "" --tags "..." ``` <\/examples>