SKILL.md

name	local-embedding
description	Run embedding on-device with ONNX Runtime. Build from source, model selection, offline mode. Use when setting up local embedding without an API key.

Build from Source

Pre-built binaries do NOT include local embedding.

cd Memoria
make build-local
sudo cp memoria/target/release/memoria /usr/local/bin/

Binary is ~50-80MB (bundles ONNX Runtime). Expected.

Configure

memoria init --tool kiro    # No --embedding-* flags needed

Leave EMBEDDING_* env vars empty in mcp.json → local embedding is the default.

How It Works

1. First query → model downloads to ~/.cache/fastembed/ (~30MB default)
2. Model loads via ONNX Runtime (~3-5s)
3. Subsequent queries are fast (in-process)

Models

Model	Dim	Size	Notes
all-MiniLM-L6-v2	384	~30MB	Default. Fast, English
BAAI/bge-m3	1024	~1.2GB	Best quality, multilingual

Change model in mcp.json env block:

{ "EMBEDDING_MODEL": "BAAI/bge-m3", "EMBEDDING_DIM": "1024" }

⚠️ Choose BEFORE first startup. Dimension is locked into schema.

When to Use

	Local	Remote (OpenAI/SiliconFlow)
Privacy	✅ Offline	⚠️ Text sent to API
Cost	Free	API key
First query	~3-5s	Fast
Build	From source	Pre-built works
Offline	✅	❌

Recommendation: Use remote unless you need offline/strict privacy.

Troubleshooting

Problem	Fix
"compiled without local-embedding"	Build from source: make build-local
Model download fails	Set HF_ENDPOINT for mirror, or manually download to ~/.cache/fastembed/
High memory	Default ~100MB. bge-m3 ~1-2GB. Choose based on available RAM