Support registered vectors (#12492)

* Support registered vectors

* Format

* Auto-fill [nlp] on load from config and from bytes/disk

* Only auto-fill [nlp]

* Undo all changes to Language.from_disk

* Expand BaseVectors

These methods are needed in various places for training and vector
similarity.

* isort

* More linting

* Only fill [nlp.vectors]

* Update spacy/vocab.pyx

Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>

* Revert changes to test related to auto-filling [nlp]

* Add vectors registry

* Rephrase error about vocab methods for vectors

* Switch to dummy implementation for BaseVectors.to_ops

* Add initial draft of docs

* Remove example from BaseVectors docs

* Apply suggestions from code review

Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>

* Update website/docs/api/basevectors.mdx

Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>

* Fix type and lint bpemb example

* Update website/docs/api/basevectors.mdx

---------

Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>

spacy/default_config.cfg

@@ -26,6 +26,9 @@ batch_size = 1000
 [nlp.tokenizer]
 @tokenizers = "spacy.Tokenizer.v1"
 
+[nlp.vectors]
+@vectors = "spacy.Vectors.v1"
+
 # The pipeline components and their models
 [components]
 

spacy/errors.py

@@ -553,6 +553,8 @@ class Errors(metaclass=ErrorsWithCodes):
             "during training, make sure to include it in 'annotating components'")
 
     # New errors added in v3.x
+    E849 = ("The vocab only supports {method} for vectors of type "
+            "spacy.vectors.Vectors, not {vectors_type}.")
     E850 = ("The PretrainVectors objective currently only supports default or "
             "floret vectors, not {mode} vectors.")
     E851 = ("The 'textcat' component labels should only have values of 0 or 1, "

spacy/language.py

@@ -65,6 +65,7 @@ from .util import (
     registry,
     warn_if_jupyter_cupy,
 )
+from .vectors import BaseVectors
 from .vocab import Vocab, create_vocab
 
 PipeCallable = Callable[[Doc], Doc]
@@ -158,6 +159,7 @@ class Language:
         max_length: int = 10**6,
         meta: Dict[str, Any] = {},
         create_tokenizer: Optional[Callable[["Language"], Callable[[str], Doc]]] = None,
+        create_vectors: Optional[Callable[["Vocab"], BaseVectors]] = None,
         batch_size: int = 1000,
         **kwargs,
     ) -> None:
@@ -198,6 +200,10 @@ class Language:
         if vocab is True:
             vectors_name = meta.get("vectors", {}).get("name")
             vocab = create_vocab(self.lang, self.Defaults, vectors_name=vectors_name)
+            if not create_vectors:
+                vectors_cfg = {"vectors": self._config["nlp"]["vectors"]}
+                create_vectors = registry.resolve(vectors_cfg)["vectors"]
+            vocab.vectors = create_vectors(vocab)
         else:
             if (self.lang and vocab.lang) and (self.lang != vocab.lang):
                 raise ValueError(Errors.E150.format(nlp=self.lang, vocab=vocab.lang))
@@ -1765,6 +1771,10 @@ class Language:
             ).merge(config)
         if "nlp" not in config:
             raise ValueError(Errors.E985.format(config=config))
+        # fill in [nlp.vectors] if not present (as a narrower alternative to
+        # auto-filling [nlp] from the default config)
+        if "vectors" not in config["nlp"]:
+            config["nlp"]["vectors"] = {"@vectors": "spacy.Vectors.v1"}
         config_lang = config["nlp"].get("lang")
         if config_lang is not None and config_lang != cls.lang:
             raise ValueError(
@@ -1796,6 +1806,7 @@ class Language:
             filled["nlp"], validate=validate, schema=ConfigSchemaNlp
         )
         create_tokenizer = resolved_nlp["tokenizer"]
+        create_vectors = resolved_nlp["vectors"]
         before_creation = resolved_nlp["before_creation"]
         after_creation = resolved_nlp["after_creation"]
         after_pipeline_creation = resolved_nlp["after_pipeline_creation"]
@@ -1816,7 +1827,12 @@ class Language:
         # inside stuff like the spacy train function. If we loaded them here,
         # then we would load them twice at runtime: once when we make from config,
         # and then again when we load from disk.
-        nlp = lang_cls(vocab=vocab, create_tokenizer=create_tokenizer, meta=meta)
+        nlp = lang_cls(
+            vocab=vocab,
+            create_tokenizer=create_tokenizer,
+            create_vectors=create_vectors,
+            meta=meta,
+        )
         if after_creation is not None:
             nlp = after_creation(nlp)
             if not isinstance(nlp, cls):

spacy/ml/staticvectors.py

@@ -9,7 +9,7 @@ from thinc.util import partial
 from ..attrs import ORTH
 from ..errors import Errors, Warnings
 from ..tokens import Doc
-from ..vectors import Mode
+from ..vectors import Mode, Vectors
 from ..vocab import Vocab
 
 
@@ -48,11 +48,14 @@ def forward(
     key_attr: int = getattr(vocab.vectors, "attr", ORTH)
     keys = model.ops.flatten([cast(Ints1d, doc.to_array(key_attr)) for doc in docs])
     W = cast(Floats2d, model.ops.as_contig(model.get_param("W")))
-    if vocab.vectors.mode == Mode.default:
+    if isinstance(vocab.vectors, Vectors) and vocab.vectors.mode == Mode.default:
         V = model.ops.asarray(vocab.vectors.data)
         rows = vocab.vectors.find(keys=keys)
         V = model.ops.as_contig(V[rows])
-    elif vocab.vectors.mode == Mode.floret:
+    elif isinstance(vocab.vectors, Vectors) and vocab.vectors.mode == Mode.floret:
+        V = vocab.vectors.get_batch(keys)
+        V = model.ops.as_contig(V)
+    elif hasattr(vocab.vectors, "get_batch"):
         V = vocab.vectors.get_batch(keys)
         V = model.ops.as_contig(V)
     else:
@@ -61,7 +64,7 @@ def forward(
         vectors_data = model.ops.gemm(V, W, trans2=True)
     except ValueError:
         raise RuntimeError(Errors.E896)
-    if vocab.vectors.mode == Mode.default:
+    if isinstance(vocab.vectors, Vectors) and vocab.vectors.mode == Mode.default:
         # Convert negative indices to 0-vectors
         # TODO: more options for UNK tokens
         vectors_data[rows < 0] = 0

spacy/schemas.py

@@ -397,6 +397,7 @@ class ConfigSchemaNlp(BaseModel):
     after_creation: Optional[Callable[["Language"], "Language"]] = Field(..., title="Optional callback to modify nlp object after creation and before the pipeline is constructed")
     after_pipeline_creation: Optional[Callable[["Language"], "Language"]] = Field(..., title="Optional callback to modify nlp object after the pipeline is constructed")
     batch_size: Optional[int] = Field(..., title="Default batch size")
+    vectors: Callable = Field(..., title="Vectors implementation")
     # fmt: on
 
     class Config:

spacy/util.py

@@ -118,6 +118,7 @@ class registry(thinc.registry):
     augmenters = catalogue.create("spacy", "augmenters", entry_points=True)
     loggers = catalogue.create("spacy", "loggers", entry_points=True)
     scorers = catalogue.create("spacy", "scorers", entry_points=True)
+    vectors = catalogue.create("spacy", "vectors", entry_points=True)
     # These are factories registered via third-party packages and the
     # spacy_factories entry point. This registry only exists so we can easily
     # load them via the entry points. The "true" factories are added via the

spacy/vectors.pyx

@@ -1,3 +1,6 @@
+# cython: infer_types=True, profile=True, binding=True
+from typing import Callable
+
 from cython.operator cimport dereference as deref
 from libc.stdint cimport uint32_t, uint64_t
 from libcpp.set cimport set as cppset
@@ -5,7 +8,8 @@ from murmurhash.mrmr cimport hash128_x64
 
 import warnings
 from enum import Enum
-from typing import cast
+from pathlib import Path
+from typing import TYPE_CHECKING, Union, cast
 
 import numpy
 import srsly
@@ -21,6 +25,9 @@ from .attrs import IDS
 from .errors import Errors, Warnings
 from .strings import get_string_id
 
+if TYPE_CHECKING:
+    from .vocab import Vocab  # noqa: F401  # no-cython-lint
+
 
 def unpickle_vectors(bytes_data):
     return Vectors().from_bytes(bytes_data)
@@ -35,7 +42,71 @@ class Mode(str, Enum):
         return list(cls.__members__.keys())
 
 
-cdef class Vectors:
+cdef class BaseVectors:
+    def __init__(self, *, strings=None):
+        # Make sure abstract BaseVectors is not instantiated.
+        if self.__class__ == BaseVectors:
+            raise TypeError(
+                Errors.E1046.format(cls_name=self.__class__.__name__)
+            )
+
+    def __getitem__(self, key):
+        raise NotImplementedError
+
+    def __contains__(self, key):
+        raise NotImplementedError
+
+    def is_full(self):
+        raise NotImplementedError
+
+    def get_batch(self, keys):
+        raise NotImplementedError
+
+    @property
+    def shape(self):
+        raise NotImplementedError
+
+    def __len__(self):
+        raise NotImplementedError
+
+    @property
+    def vectors_length(self):
+        raise NotImplementedError
+
+    @property
+    def size(self):
+        raise NotImplementedError
+
+    def add(self, key, *, vector=None):
+        raise NotImplementedError
+
+    def to_ops(self, ops: Ops):
+        pass
+
+    # add dummy methods for to_bytes, from_bytes, to_disk and from_disk to
+    # allow serialization
+    def to_bytes(self, **kwargs):
+        return b""
+
+    def from_bytes(self, data: bytes, **kwargs):
+        return self
+
+    def to_disk(self, path: Union[str, Path], **kwargs):
+        return None
+
+    def from_disk(self, path: Union[str, Path], **kwargs):
+        return self
+
+
+@util.registry.vectors("spacy.Vectors.v1")
+def create_mode_vectors() -> Callable[["Vocab"], BaseVectors]:
+    def vectors_factory(vocab: "Vocab") -> BaseVectors:
+        return Vectors(strings=vocab.strings)
+
+    return vectors_factory
+
+
+cdef class Vectors(BaseVectors):
     """Store, save and load word vectors.
 
     Vectors data is kept in the vectors.data attribute, which should be an

spacy/vocab.pyx

@@ -94,6 +94,7 @@ cdef class Vocab:
             return self._vectors
 
         def __set__(self, vectors):
+            if hasattr(vectors, "strings"):
                 for s in vectors.strings:
                     self.strings.add(s)
             self._vectors = vectors
@@ -193,7 +194,7 @@ cdef class Vocab:
         lex = <LexemeC*>mem.alloc(1, sizeof(LexemeC))
         lex.orth = self.strings.add(string)
         lex.length = len(string)
-        if self.vectors is not None:
+        if self.vectors is not None and hasattr(self.vectors, "key2row"):
             lex.id = self.vectors.key2row.get(lex.orth, OOV_RANK)
         else:
             lex.id = OOV_RANK
@@ -289,12 +290,17 @@ cdef class Vocab:
 
     @property
     def vectors_length(self):
+        if hasattr(self.vectors, "shape"):
             return self.vectors.shape[1]
+        else:
+            return -1
 
     def reset_vectors(self, *, width=None, shape=None):
         """Drop the current vector table. Because all vectors must be the same
         width, you have to call this to change the size of the vectors.
         """
+        if not isinstance(self.vectors, Vectors):
+            raise ValueError(Errors.E849.format(method="reset_vectors", vectors_type=type(self.vectors)))
         if width is not None and shape is not None:
             raise ValueError(Errors.E065.format(width=width, shape=shape))
         elif shape is not None:
@@ -304,6 +310,8 @@ cdef class Vocab:
             self.vectors = Vectors(strings=self.strings, shape=(self.vectors.shape[0], width))
 
     def deduplicate_vectors(self):
+        if not isinstance(self.vectors, Vectors):
+            raise ValueError(Errors.E849.format(method="deduplicate_vectors", vectors_type=type(self.vectors)))
         if self.vectors.mode != VectorsMode.default:
             raise ValueError(Errors.E858.format(
                 mode=self.vectors.mode,
@@ -357,6 +365,8 @@ cdef class Vocab:
 
         DOCS: https://spacy.io/api/vocab#prune_vectors
         """
+        if not isinstance(self.vectors, Vectors):
+            raise ValueError(Errors.E849.format(method="prune_vectors", vectors_type=type(self.vectors)))
         if self.vectors.mode != VectorsMode.default:
             raise ValueError(Errors.E858.format(
                 mode=self.vectors.mode,

website/docs/api/basevectors.mdx

@@ -0,0 +1,143 @@
+---
+title: BaseVectors
+teaser: Abstract class for word vectors
+tag: class
+source: spacy/vectors.pyx
+version: 3.7
+---
+
+`BaseVectors` is an abstract class to support the development of custom vectors
+implementations.
+
+For use in training with [`StaticVectors`](/api/architectures#staticvectors),
+`get_batch` must be implemented. For improved performance, use efficient
+batching in `get_batch` and implement `to_ops` to copy the vector data to the
+current device. See an example custom implementation for
+[BPEmb subword embeddings](/usage/embeddings-transformers#custom-vectors).
+
+## BaseVectors.\_\_init\_\_ {id="init",tag="method"}
+
+Create a new vector store.
+
+| Name           | Description                                                                                                           |
+| -------------- | --------------------------------------------------------------------------------------------------------------------- |
+| _keyword-only_ |                                                                                                                       |
+| `strings`      | The string store. A new string store is created if one is not provided. Defaults to `None`. ~~Optional[StringStore]~~ |
+
+## BaseVectors.\_\_getitem\_\_ {id="getitem",tag="method"}
+
+Get a vector by key. If the key is not found in the table, a `KeyError` should
+be raised.
+
+| Name        | Description                                                      |
+| ----------- | ---------------------------------------------------------------- |
+| `key`       | The key to get the vector for. ~~Union[int, str]~~               |
+| **RETURNS** | The vector for the key. ~~numpy.ndarray[ndim=1, dtype=float32]~~ |
+
+## BaseVectors.\_\_len\_\_ {id="len",tag="method"}
+
+Return the number of vectors in the table.
+
+| Name        | Description                                 |
+| ----------- | ------------------------------------------- |
+| **RETURNS** | The number of vectors in the table. ~~int~~ |
+
+## BaseVectors.\_\_contains\_\_ {id="contains",tag="method"}
+
+Check whether there is a vector entry for the given key.
+
+| Name        | Description                                  |
+| ----------- | -------------------------------------------- |
+| `key`       | The key to check. ~~int~~                    |
+| **RETURNS** | Whether the key has a vector entry. ~~bool~~ |
+
+## BaseVectors.add {id="add",tag="method"}
+
+Add a key to the table, if possible. If no keys can be added, return `-1`.
+
+| Name        | Description                                                                         |
+| ----------- | ----------------------------------------------------------------------------------- |
+| `key`       | The key to add. ~~Union[str, int]~~                                                 |
+| **RETURNS** | The row the vector was added to, or `-1` if the operation is not supported. ~~int~~ |
+
+## BaseVectors.shape {id="shape",tag="property"}
+
+Get `(rows, dims)` tuples of number of rows and number of dimensions in the
+vector table.
+
+| Name        | Description                                |
+| ----------- | ------------------------------------------ |
+| **RETURNS** | A `(rows, dims)` pair. ~~Tuple[int, int]~~ |
+
+## BaseVectors.size {id="size",tag="property"}
+
+The vector size, i.e. `rows * dims`.
+
+| Name        | Description              |
+| ----------- | ------------------------ |
+| **RETURNS** | The vector size. ~~int~~ |
+
+## BaseVectors.is_full {id="is_full",tag="property"}
+
+Whether the vectors table is full and no slots are available for new keys.
+
+| Name        | Description                                 |
+| ----------- | ------------------------------------------- |
+| **RETURNS** | Whether the vectors table is full. ~~bool~~ |
+
+## BaseVectors.get_batch {id="get_batch",tag="method",version="3.2"}
+
+Get the vectors for the provided keys efficiently as a batch. Required to use
+the vectors with [`StaticVectors`](/api/architectures#StaticVectors) for
+training.
+
+| Name   | Description                             |
+| ------ | --------------------------------------- |
+| `keys` | The keys. ~~Iterable[Union[int, str]]~~ |
+
+## BaseVectors.to_ops {id="to_ops",tag="method"}
+
+Dummy method. Implement this to change the embedding matrix to use different
+Thinc ops.
+
+| Name  | Description                                              |
+| ----- | -------------------------------------------------------- |
+| `ops` | The Thinc ops to switch the embedding matrix to. ~~Ops~~ |
+
+## BaseVectors.to_disk {id="to_disk",tag="method"}
+
+Dummy method to allow serialization. Implement to save vector data with the
+pipeline.
+
+| Name   | Description                                                                                                                                |
+| ------ | ------------------------------------------------------------------------------------------------------------------------------------------ |
+| `path` | A path to a directory, which will be created if it doesn't exist. Paths may be either strings or `Path`-like objects. ~~Union[str, Path]~~ |
+
+## BaseVectors.from_disk {id="from_disk",tag="method"}
+
+Dummy method to allow serialization. Implement to load vector data from a saved
+pipeline.
+
+| Name        | Description                                                                                     |
+| ----------- | ----------------------------------------------------------------------------------------------- |
+| `path`      | A path to a directory. Paths may be either strings or `Path`-like objects. ~~Union[str, Path]~~ |
+| **RETURNS** | The modified vectors object. ~~BaseVectors~~                                                    |
+
+## BaseVectors.to_bytes {id="to_bytes",tag="method"}
+
+Dummy method to allow serialization. Implement to serialize vector data to a
+binary string.
+
+| Name        | Description                                          |
+| ----------- | ---------------------------------------------------- |
+| **RETURNS** | The serialized form of the vectors object. ~~bytes~~ |
+
+## BaseVectors.from_bytes {id="from_bytes",tag="method"}
+
+Dummy method to allow serialization. Implement to load vector data from a binary
+string.
+
+| Name        | Description                         |
+| ----------- | ----------------------------------- |
+| `data`      | The data to load from. ~~bytes~~    |
+| **RETURNS** | The vectors object. ~~BaseVectors~~ |

website/docs/api/vectors.mdx

@@ -297,10 +297,9 @@ The vector size, i.e. `rows * dims`.
 
 ## Vectors.is_full {id="is_full",tag="property"}
 
-Whether the vectors table is full and has no slots are available for new keys.
+Whether the vectors table is full and no slots are available for new keys. If a
-If a table is full, it can be resized using
+table is full, it can be resized using [`Vectors.resize`](/api/vectors#resize).
-[`Vectors.resize`](/api/vectors#resize). In `floret` mode, the table is always
+In `floret` mode, the table is always full and cannot be resized.
-full and cannot be resized.
 
 > #### Example
 >
@@ -441,7 +440,7 @@ Load state from a binary string.
 > #### Example
 >
 > ```python
-> fron spacy.vectors import Vectors
+> from spacy.vectors import Vectors
 > vectors_bytes = vectors.to_bytes()
 > new_vectors = Vectors(StringStore())
 > new_vectors.from_bytes(vectors_bytes)

website/docs/usage/embeddings-transformers.mdx

@@ -632,6 +632,165 @@ def MyCustomVectors(
     )
 ```
 
+#### Creating a custom vectors implementation {id="custom-vectors",version="3.7"}
+
+You can specify a custom registered vectors class under `[nlp.vectors]` in order
+to use static vectors in formats other than the ones supported by
+[`Vectors`](/api/vectors). Extend the abstract [`BaseVectors`](/api/basevectors)
+class to implement your custom vectors.
+
+As an example, the following `BPEmbVectors` class implements support for
+[BPEmb subword embeddings](https://bpemb.h-its.org/):
+
+```python
+# requires: pip install bpemb
+import warnings
+from pathlib import Path
+from typing import Callable, Optional, cast
+
+from bpemb import BPEmb
+from thinc.api import Ops, get_current_ops
+from thinc.backends import get_array_ops
+from thinc.types import Floats2d
+
+from spacy.strings import StringStore
+from spacy.util import registry
+from spacy.vectors import BaseVectors
+from spacy.vocab import Vocab
+
+
+class BPEmbVectors(BaseVectors):
+    def __init__(
+        self,
+        *,
+        strings: Optional[StringStore] = None,
+        lang: Optional[str] = None,
+        vs: Optional[int] = None,
+        dim: Optional[int] = None,
+        cache_dir: Optional[Path] = None,
+        encode_extra_options: Optional[str] = None,
+        model_file: Optional[Path] = None,
+        emb_file: Optional[Path] = None,
+    ):
+        kwargs = {}
+        if lang is not None:
+            kwargs["lang"] = lang
+        if vs is not None:
+            kwargs["vs"] = vs
+        if dim is not None:
+            kwargs["dim"] = dim
+        if cache_dir is not None:
+            kwargs["cache_dir"] = cache_dir
+        if encode_extra_options is not None:
+            kwargs["encode_extra_options"] = encode_extra_options
+        if model_file is not None:
+            kwargs["model_file"] = model_file
+        if emb_file is not None:
+            kwargs["emb_file"] = emb_file
+        self.bpemb = BPEmb(**kwargs)
+        self.strings = strings
+        self.name = repr(self.bpemb)
+        self.n_keys = -1
+        self.mode = "BPEmb"
+        self.to_ops(get_current_ops())
+
+    def __contains__(self, key):
+        return True
+
+    def is_full(self):
+        return True
+
+    def add(self, key, *, vector=None, row=None):
+        warnings.warn(
+            (
+                "Skipping BPEmbVectors.add: the bpemb vector table cannot be "
+                "modified. Vectors are calculated from bytepieces."
+            )
+        )
+        return -1
+
+    def __getitem__(self, key):
+        return self.get_batch([key])[0]
+
+    def get_batch(self, keys):
+        keys = [self.strings.as_string(key) for key in keys]
+        bp_ids = self.bpemb.encode_ids(keys)
+        ops = get_array_ops(self.bpemb.emb.vectors)
+        indices = ops.asarray(ops.xp.hstack(bp_ids), dtype="int32")
+        lengths = ops.asarray([len(x) for x in bp_ids], dtype="int32")
+        vecs = ops.reduce_mean(cast(Floats2d, self.bpemb.emb.vectors[indices]), lengths)
+        return vecs
+
+    @property
+    def shape(self):
+        return self.bpemb.vectors.shape
+
+    def __len__(self):
+        return self.shape[0]
+
+    @property
+    def vectors_length(self):
+        return self.shape[1]
+
+    @property
+    def size(self):
+        return self.bpemb.vectors.size
+
+    def to_ops(self, ops: Ops):
+        self.bpemb.emb.vectors = ops.asarray(self.bpemb.emb.vectors)
+
+
+@registry.vectors("BPEmbVectors.v1")
+def create_bpemb_vectors(
+    lang: Optional[str] = "multi",
+    vs: Optional[int] = None,
+    dim: Optional[int] = None,
+    cache_dir: Optional[Path] = None,
+    encode_extra_options: Optional[str] = None,
+    model_file: Optional[Path] = None,
+    emb_file: Optional[Path] = None,
+) -> Callable[[Vocab], BPEmbVectors]:
+    def bpemb_vectors_factory(vocab: Vocab) -> BPEmbVectors:
+        return BPEmbVectors(
+            strings=vocab.strings,
+            lang=lang,
+            vs=vs,
+            dim=dim,
+            cache_dir=cache_dir,
+            encode_extra_options=encode_extra_options,
+            model_file=model_file,
+            emb_file=emb_file,
+        )
+
+    return bpemb_vectors_factory
+```
+
+<Infobox variant="warning">
+
+Note that the serialization methods are not implemented, so the embeddings are
+loaded from your local cache or downloaded by `BPEmb` each time the pipeline is
+loaded.
+
+</Infobox>
+
+To use this in your pipeline, specify this registered function under
+`[nlp.vectors]` in your config:
+
+```ini
+[nlp.vectors]
+@vectors = "BPEmbVectors.v1"
+lang = "en"
+```
+
+Or specify it when creating a blank pipeline:
+
+```python
+nlp = spacy.blank("en", config={"nlp.vectors": {"@vectors": "BPEmbVectors.v1", "lang": "en"}})
+```
+
+Remember to include this code with `--code` when using
+[`spacy train`](/api/cli#train) and [`spacy package`](/api/cli#package).
+
 ## Pretraining {id="pretraining"}
 
 The [`spacy pretrain`](/api/cli#pretrain) command lets you initialize your

website/meta/sidebars.json

@@ -131,6 +131,7 @@
                 "label": "Other",
                 "items": [
                     { "text": "Attributes", "url": "/api/attributes" },
+                    { "text": "BaseVectors", "url": "/api/basevectors" },
                     { "text": "Corpus", "url": "/api/corpus" },
                     { "text": "InMemoryLookupKB", "url": "/api/inmemorylookupkb" },
                     { "text": "KnowledgeBase", "url": "/api/kb" },