MongoDB Atlas: create a Vector Search index that actually works

# Create the vector search index

Use mongosh, a driver, or the Atlas UI (Search tab, JSON editor). The definition shape:

```js
db.docs.createSearchIndex("vec_idx", "vectorSearch", {
  fields: [
    { type: "vector", path: "embedding", numDimensions: 1536, similarity: "cosine" },
    { type: "filter", path: "tenantId" }
  ]
});
```

## Rules

- The index type must be `vectorSearch`, not `search`. A full-text index will not serve `$vectorSearch`.
- `numDimensions` must exactly equal your embedding model's output size (1536 for text-embedding-ada-002, 3072 for text-embedding-3-large, etc.). A mismatch means broken results or errors; the index cannot guess.
- `similarity`: `cosine`, `dotProduct`, or `euclidean`. Match what your embedding model expects; cosine is the safe default.
- Prefilter fields must be declared as `type: "filter"`. A plain `$match` after `$vectorSearch` filters after the top-k, silently dropping results you expected.
- Index builds are asynchronous. Poll `db.docs.getSearchIndexes()` until `status` is `READY` before querying. Querying early gives "index not found" errors.

## Verify

`getSearchIndexes()` shows the index with status READY, and a trivial `$vectorSearch` with a real embedding returns scored results.