> ## Documentation Index
> Fetch the complete documentation index at: https://schemabrain.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# find_relevant_entities

> Embedding-cosine search restricted to confirmed entities.

<Note>
  **Layer:** Semantic layer
</Note>

Embedding-cosine retrieval restricted to entities. Reuses the column-level
embedding index (no second model) but ranks per *entity* by taking the MAX
cosine across the columns of each entity's bound table. Returns
domain-named hits so the agent stays in business terms.

```json theme={null}
{
  "name": "customer",
  "score": 0.84,
  "qualified_table": "public.users",
  "best_column": "email",
  "best_column_description": "Primary contact email used for order confirmations and password reset",
  "token_estimate": 58
}
```

Empty envelope routes the agent differently depending on whether the
semantic layer is bare or just unmatched:

* **No entities curated yet** → `follow_up_hints: ["find_relevant_tables"]`
  (skip `list_entities`, it would also be empty).
* **Entities exist but none match** → `follow_up_hints: ["list_entities", "find_relevant_tables"]`.

Use `find_relevant_tables` instead when no entities are curated.