sqlite-vectordb

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• Delete: To clean up incorrect/outdated logs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.

Add Entry

Index a markdown log into the vector DB.

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.

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.

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.

uv run .claude/skills/sqlite-vectordb/scripts/init_db.py

<technical_specs>

• 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

</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

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

Valid usage:

# 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:

# 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 "..."