Memory Semantic Search

Semantic search over workspace markdown files using embedding API + SQLite vector store. Use when: (1) searching workspace notes/memory by meaning rather than exact keywords, (2) finding related markdown content across files, (3) recalling past decisions, context, or notes semantically. Requires an OpenAI-compatible embedding API. NOT for: searching non-markdown files, web search, or code search.

Standalone semantic search over workspace .md files. Uses an OpenAI-compatible embedding API and SQLite for vector storage. No external dependencies beyond Python 3 stdlib + the embedding API.

Setup

Set these environment variables (or pass as CLI args):

export EMBEDDING_API_KEY="sk-xxx"
export EMBEDDING_API_BASE="https://api.openai.com/v1"   # any OpenAI-compatible endpoint
export EMBEDDING_MODEL="text-embedding-3-small"          # optional, this is the default

Usage

Index workspace

python3 scripts/index.py /path/to/workspace

Options:

• --force — full reindex (clear existing data)
• --db PATH — custom SQLite path (default: memory_search.sqlite in skill dir)
• --api-base, --api-key, --model — override env vars

Incremental: only new/changed chunks are embedded. Deleted files are cleaned up automatically.

Search

python3 scripts/search.py "your query here"

Options:

• --top-k N — number of results (default: 5)
• --min-score F — minimum cosine similarity threshold (default: 0.3)
• --json — output as JSON
• --db, --api-base, --api-key, --model — same as index

Typical agent workflow

1. Run index.py on the workspace (once, or after file changes)
2. Run search.py "query" to find relevant snippets
3. Use read tool to load full context from the returned file paths and line numbers