Self-hosted Supermemory uses the same embedding provider stack as the hosted platform: local ONNX models, OpenAI, Gemini, or any OpenAI-compatible embeddings endpoint (including Ollama). LLM keys power extraction and summarization; embeddings are configured separately.
Defaults
| |
|---|
| Provider | local |
| Model | Xenova/bge-base-en-v1.5 |
| Dimensions | 768 |
| API key | None — runs on your machine |
The default local model is English-only. Non-English content can ingest successfully while dense semantic recall stays weak. See Multilingual. First-time setup (interactive)
On first boot with a TTY, Supermemory asks for an LLM API key (required), then optionally which embedding model to use.
- Choose or paste an LLM provider key (OpenAI, Anthropic, Gemini, Groq, or OpenAI-compatible).
- Optionally pick an embedding provider/model. Press Enter to keep the local English model.
- Choices are saved encrypted under your data directory (
$SUPERMEMORY_DATA_DIR, typically ./.supermemory / ~/.supermemory).
Boot order is intentional: LLM keys load first so remote embedding options can reuse them (for example OpenAI or Gemini embeddings with the same key).
First boot (terminal): Supermemory asks for an LLM API key (required), then optionally which embedding model to use. Press Enter to keep the local English model. Choices are saved encrypted under your data directory.
Configuration (env)
For Docker, CI, or any non-interactive deploy, set env vars — there is no interactive prompt without a TTY.
| Variable | Purpose | Default |
|---|
SUPERMEMORY_EMBEDDING_PROVIDER | Embedding backend: local, openai, gemini, or an OpenAI-compatible remote (ollama / custom base URL) | local |
SUPERMEMORY_EMBEDDING_MODEL | Model id for the chosen provider | Xenova/bge-base-en-v1.5 (local) |
SUPERMEMORY_EMBEDDING_DIMENSIONS | Vector size; must match the model and any already-stored data | 768 (local default) |
SUPERMEMORY_EMBEDDING_BASE_URL | Base URL for OpenAI-compatible embedding APIs (Ollama, vLLM, etc.) | unset |
OPENAI_API_KEY | Used when provider is openai (or compatible) if not otherwise supplied | unset |
GEMINI_API_KEY | Used when provider is gemini | unset |
| Variable | Purpose | Default |
|---|
SUPERMEMORY_LOCAL_EMBEDDING_POOL_SIZE | Number of embedding workers | 1 |
SUPERMEMORY_LOCAL_EMBEDDING_WASM_THREADS | Compute threads per worker | 1 |
SUPERMEMORY_LOCAL_EMBEDDING_BATCH_SIZE | Texts per worker dispatch | 8 |
SUPERMEMORY_LOCAL_EMBEDDING_IDLE_TIMEOUT_MS | Idle time before workers shut down | 120000 |
SUPERMEMORY_SKIP_EMBEDDING_PREWARM | Skip startup prewarm, load on first use | unset |
SUPERMEMORY_EMBEDDING_RAM_LIMIT — see Memory limits & ingestion queue.
Docker / production: Set at least one LLM key and, if you don’t want local embeddings, set SUPERMEMORY_EMBEDDING_PROVIDER / SUPERMEMORY_EMBEDDING_MODEL / SUPERMEMORY_EMBEDDING_DIMENSIONS (and base URL or API key as needed). There is no interactive prompt without a TTY.
Multilingual
The default Xenova/bge-base-en-v1.5 model is trained for English. For German, Dutch, and other non-English corpora, dense recall can fail even when hybrid keyword search still finds rare tokens.
For multilingual or non-English deployments, switch before large backfills:
Or use a remote multilingual embedding API (OpenAI, Gemini, or Ollama with a multilingual embed model). Set provider, model, and dimensions together. Changing them later requires a fresh data directory or full re-ingestion — see below.
Remote providers
Local (default)
OpenAI
Gemini
Ollama (OpenAI-compatible)
Use the dimension published for your chosen model. A mismatch with vectors already in the store fails boot.
Changing models later
Not supported in place. Embeddings from different models (or different dimensions) are not comparable. Start from a fresh data directory or re-ingest all content so vectors stay in one space. If configured dimensions disagree with stored data, the server refuses to boot.
