Support dense vectors based on numpy arrays

elasticsearch/dsl/__init__.py

@@ -73,6 +73,7 @@
     MatchOnlyText,
     Murmur3,
     Nested,
+    NumpyDenseVector,
     Object,
     Passthrough,
     Percolator,
@@ -189,6 +190,7 @@
     "Murmur3",
     "Nested",
     "NestedFacet",
+    "NumpyDenseVector",
     "Object",
     "Passthrough",
     "Percolator",

elasticsearch/dsl/field.py

@@ -1616,11 +1616,33 @@ def __init__(
             kwargs["multi"] = True
         super().__init__(*args, **kwargs)
 
-    def _deserialize(self, data: Any) -> Any:
-        if self._element_type == "float":
-            return float(data)
-        elif self._element_type == "byte":
-            return int(data)
+
+class NumpyDenseVector(DenseVector):
+    """A dense vector field that uses numpy arrays.
+
+    Accepts the same arguments as class ``DenseVector`` plus:
+
+    :arg dtype: The numpy data type to use for the array. If not given, numpy will select the type based on the data.
+    """
+
+    def __init__(self, *args: Any, dtype: Optional[type] = None, **kwargs: Any):
+        super().__init__(*args, **kwargs)
+        self._dtype = dtype
+
+    def deserialize(self, data: Any) -> Any:
+        if isinstance(data, list):
+            import numpy as np
+
+            return np.array(data, dtype=self._dtype)
+        return super().deserialize(data)
+
+    def clean(self, data: Any) -> Any:
+        # this method does the same as the one in the parent classes, but it
+        # avoids comparisons that do not work for numpy arrays
+        if data is not None:
+            data = self.deserialize(data)
+        if (data is None or len(data) == 0) and self._required:
+            raise ValidationException("Value required for this field.")
         return data
 
 

elasticsearch/dsl/utils.py

@@ -612,8 +612,17 @@ def to_dict(self, skip_empty: bool = True) -> Dict[str, Any]:
             if skip_empty:
                 # don't serialize empty values
                 # careful not to include numeric zeros
-                if v in ([], {}, None):
-                    continue
+                try:
+                    if v in ([], {}, None):
+                        continue
+                except ValueError:
+                    # the above fails when v is a numpy array
+                    # try using len() instead
+                    try:
+                        if len(v) == 0:
+                            continue
+                    except TypeError:
+                        pass
 
             out[k] = v
         return out

examples/quotes/backend/quotes.py

@@ -5,22 +5,30 @@
 from typing import Annotated
 
 from fastapi import FastAPI, HTTPException
-from pydantic import BaseModel, Field, ValidationError
+import numpy as np
+from pydantic import BaseModel, Field, PlainSerializer
 from sentence_transformers import SentenceTransformer
 
-from elasticsearch import NotFoundError
+from elasticsearch import NotFoundError, OrjsonSerializer
 from elasticsearch.dsl.pydantic import AsyncBaseESModel
 from elasticsearch import dsl
 
 model = SentenceTransformer("all-MiniLM-L6-v2")
-dsl.async_connections.create_connection(hosts=[os.environ['ELASTICSEARCH_URL']])
+dsl.async_connections.create_connection(hosts=[os.environ['ELASTICSEARCH_URL']], serializer=OrjsonSerializer())
 
 
 class Quote(AsyncBaseESModel):
     quote: str
     author: Annotated[str, dsl.Keyword()]
     tags: Annotated[list[str], dsl.Keyword()]
-    embedding: Annotated[list[float], dsl.DenseVector()] = Field(init=False, default=[])
+    embedding: Annotated[
+        np.ndarray,
+        PlainSerializer(lambda v: v.tolist()),
+        dsl.NumpyDenseVector(dtype=np.float32)
+    ] = Field(init=False, default_factory=lambda: np.array([], dtype=np.float32))
+
+    class Config:
+        arbitrary_types_allowed = True
 
     class Index:
         name = 'quotes'
@@ -135,7 +143,7 @@ async def search_quotes(req: SearchRequest) -> SearchResponse:
 def embed_quotes(quotes):
     embeddings = model.encode([q.quote for q in quotes])
     for q, e in zip(quotes, embeddings):
-        q.embedding = e.tolist()
+        q.embedding = e
 
 
 async def ingest_quotes():

test_elasticsearch/test_dsl/test_integration/_async/test_document.py

@@ -25,6 +25,7 @@
 from ipaddress import ip_address
 from typing import TYPE_CHECKING, Any, AsyncIterator, Dict, List, Optional, Tuple, Union
 
+import numpy as np
 import pytest
 from pytest import raises
 from pytz import timezone
@@ -47,6 +48,7 @@
     Mapping,
     MetaField,
     Nested,
+    NumpyDenseVector,
     Object,
     Q,
     RankFeatures,
@@ -865,25 +867,33 @@ class Doc(AsyncDocument):
         float_vector: List[float] = mapped_field(DenseVector())
         byte_vector: List[int] = mapped_field(DenseVector(element_type="byte"))
         bit_vector: List[int] = mapped_field(DenseVector(element_type="bit"))
+        numpy_float_vector: np.ndarray = mapped_field(NumpyDenseVector())
 
         class Index:
             name = "vectors"
 
     await Doc._index.delete(ignore_unavailable=True)
     await Doc.init()
 
+    test_float_vector = [1.0, 1.2, 2.3]
+    test_byte_vector = [12, 23, 34, 45]
+    test_bit_vector = [18, -43, -112]
+
     doc = Doc(
-        float_vector=[1.0, 1.2, 2.3],
-        byte_vector=[12, 23, 34, 45],
-        bit_vector=[18, -43, -112],
+        float_vector=test_float_vector,
+        byte_vector=test_byte_vector,
+        bit_vector=test_bit_vector,
+        numpy_float_vector=np.array(test_float_vector),
     )
     await doc.save(refresh=True)
 
     docs = await Doc.search().execute()
     assert len(docs) == 1
-    assert [round(v, 1) for v in docs[0].float_vector] == doc.float_vector
-    assert docs[0].byte_vector == doc.byte_vector
-    assert docs[0].bit_vector == doc.bit_vector
+    assert [round(v, 1) for v in docs[0].float_vector] == test_float_vector
+    assert docs[0].byte_vector == test_byte_vector
+    assert docs[0].bit_vector == test_bit_vector
+    assert type(docs[0].numpy_float_vector) is np.ndarray
+    assert [round(v, 1) for v in docs[0].numpy_float_vector] == test_float_vector
 
 
 @pytest.mark.anyio

test_elasticsearch/test_dsl/test_integration/_sync/test_document.py

@@ -25,6 +25,7 @@
 from ipaddress import ip_address
 from typing import TYPE_CHECKING, Any, Dict, Iterator, List, Optional, Tuple, Union
 
+import numpy as np
 import pytest
 from pytest import raises
 from pytz import timezone
@@ -46,6 +47,7 @@
     Mapping,
     MetaField,
     Nested,
+    NumpyDenseVector,
     Object,
     Q,
     RankFeatures,
@@ -853,25 +855,33 @@ class Doc(Document):
         float_vector: List[float] = mapped_field(DenseVector())
         byte_vector: List[int] = mapped_field(DenseVector(element_type="byte"))
         bit_vector: List[int] = mapped_field(DenseVector(element_type="bit"))
+        numpy_float_vector: np.ndarray = mapped_field(NumpyDenseVector())
 
         class Index:
             name = "vectors"
 
     Doc._index.delete(ignore_unavailable=True)
     Doc.init()
 
+    test_float_vector = [1.0, 1.2, 2.3]
+    test_byte_vector = [12, 23, 34, 45]
+    test_bit_vector = [18, -43, -112]
+
     doc = Doc(
-        float_vector=[1.0, 1.2, 2.3],
-        byte_vector=[12, 23, 34, 45],
-        bit_vector=[18, -43, -112],
+        float_vector=test_float_vector,
+        byte_vector=test_byte_vector,
+        bit_vector=test_bit_vector,
+        numpy_float_vector=np.array(test_float_vector),
     )
     doc.save(refresh=True)
 
     docs = Doc.search().execute()
     assert len(docs) == 1
-    assert [round(v, 1) for v in docs[0].float_vector] == doc.float_vector
-    assert docs[0].byte_vector == doc.byte_vector
-    assert docs[0].bit_vector == doc.bit_vector
+    assert [round(v, 1) for v in docs[0].float_vector] == test_float_vector
+    assert docs[0].byte_vector == test_byte_vector
+    assert docs[0].bit_vector == test_bit_vector
+    assert type(docs[0].numpy_float_vector) is np.ndarray
+    assert [round(v, 1) for v in docs[0].numpy_float_vector] == test_float_vector
 
 
 @pytest.mark.sync

utils/templates/field.py.tpl

@@ -418,11 +418,30 @@ class {{ k.name }}({{ k.parent }}):
             kwargs["multi"] = True
         super().__init__(*args, **kwargs)
 
-    def _deserialize(self, data: Any) -> Any:
-        if self._element_type == "float":
-            return float(data)
-        elif self._element_type == "byte":
-            return int(data)
+class NumpyDenseVector(DenseVector):
+    """A dense vector field that uses numpy arrays.
+
+    Accepts the same arguments as class ``DenseVector`` plus:
+
+    :arg dtype: The numpy data type to use for the array. If not given, numpy will select the type based on the data.
+    """
+    def __init__(self, *args: Any, dtype: Optional[type] = None, **kwargs: Any):
+        super().__init__(*args, **kwargs)
+        self._dtype = dtype
+
+    def deserialize(self, data: Any) -> Any: 
+        if isinstance(data, list):
+            import numpy as np
+            return np.array(data, dtype=self._dtype)
+        return super().deserialize(data)
+
+    def clean(self, data: Any) -> Any:
+        # this method does the same as the one in the parent classes, but it
+        # avoids comparisons that do not work for numpy arrays
+        if data is not None:
+            data = self.deserialize(data)
+        if (data is None or len(data) == 0) and self._required:
+            raise ValidationException("Value required for this field.")
         return data
         {% elif k.field == "scaled_float" %}
         if 'scaling_factor' not in kwargs: