Memory Semantic 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):

bash

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#

bash

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#

bash

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