> ## Documentation Index
> Fetch the complete documentation index at: https://docs.10et.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Embeddings

> Semantic search with auto-backfill

TENET uses `text-embedding-3-small` (1536 dimensions) for semantic search. Embeddings are computed at index time and auto-backfilled if keys were missing.

## Provider Fallback

```
1. Try OPENAI_API_KEY → text-embedding-3-small
2. If fails → try OPENROUTER_API_KEY → openai/text-embedding-3-small
3. If both fail → memory stored without embedding (BM25 only)
4. Auto-backfill runs periodically to catch up
```

## Auto-Backfill

The periodic indexer automatically backfills missing embeddings:

* Runs on first tick after hub startup
* Runs whenever new entries are added
* Truncates content >28K chars (model limit \~8K tokens)
* Uses consecutive-null counter (3 strikes before stopping)

## Manual Backfill

```bash theme={null}
curl -X POST http://localhost:4360/api/memory/index \
  -H "Authorization: Bearer $TOKEN" \
  -d '{"backfill": true}'
```

## Stats

```bash theme={null}
tenet memory status
# embeddings: {available: true, count: 349, model: "openrouter/text-embedding-3-small"}
```