Add docs for Vectors.most_similar [ci skip]
@@ -303,6 +303,29 @@ vectors, they will be counted individually.
| ----------- | ---- | ------------------------------------ |
| **RETURNS** | int | The number of all keys in the table. |
+## Vectors.most_similar {#most_similar tag="method"}
+
+For each of the given vectors, find the `n` most similar entries to it, by
+cosine. Queries are by vector. Results are returned as a
+`(keys, best_rows, scores)` tuple. If `queries` is large, the calculations are
+performed in chunks, to avoid consuming too much memory. You can set the
+`batch_size` to control the size/space trade-off during the calculations.
+
+> #### Example
+>
+> ```python
+> queries = numpy.asarray([numpy.random.uniform(-1, 1, (300,))])
+> most_similar = nlp.vectors.most_similar(queries, n=10)
+> ```
+
+| Name | Type | Description |
+| ------------ | --------- | ------------------------------------------------------------------ |
+| `queries` | `ndarray` | An array with one or more vectors. |
+| `batch_size` | int | The batch size to use. Default to `1024`. |
+| `n` | int | The number of entries to return for each query. Defaults to `1`. |
+| `sort` | bool | Whether to sort the entries returned by score. Defaults to `True`. |
+| **RETURNS** | tuple | The most similar entries as a `(keys, best_rows, scores)` tuple. |
+
## Vectors.from_glove {#from_glove tag="method"}
Load [GloVe](https://nlp.stanford.edu/projects/glove/) vectors from a directory.
