| 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
Leave EMBEDDING_* env vars empty in mcp.json → local embedding is the default.
How It Works
- First query → model downloads to
~/.cache/fastembed/ (~30MB default)
- Model loads via ONNX Runtime (~3-5s)
- 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 |
