docarray · JoanFM · Jul 31, 2023 · Jul 26, 2023 · Jul 26, 2023 · Jul 27, 2023
diff --git a/docarray/index/backends/hnswlib.py b/docarray/index/backends/hnswlib.py
@@ -2,7 +2,7 @@
 import hashlib
 import os
 import sqlite3
-from collections import defaultdict
+from collections import OrderedDict, defaultdict
 from dataclasses import dataclass, field
 from pathlib import Path
 from typing import (
@@ -32,7 +32,9 @@
     _raise_not_composable,
     _raise_not_supported,
 )
-from docarray.index.backends.helper import _collect_query_args
+from docarray.index.backends.helper import (
+    _collect_query_args,
+)
 from docarray.proto import DocProto
 from docarray.typing.tensor.abstract_tensor import AbstractTensor
 from docarray.typing.tensor.ndarray import NdArray
@@ -63,7 +65,6 @@
     HNSWLIB_PY_VEC_TYPES.append(tf.Tensor)
     HNSWLIB_PY_VEC_TYPES.append(TensorFlowTensor)
 
-
 TSchema = TypeVar('TSchema', bound=BaseDoc)
 T = TypeVar('T', bound='HnswDocumentIndex')
 
@@ -107,7 +108,11 @@ def __init__(self, db_config=None, **kwargs):
             if col.config
         }
         self._hnsw_indices = {}
+        sub_docs_exist = False
+        cosine_metric_index_exist = False
         for col_name, col in self._column_infos.items():
+            if '__' in col_name:
+                sub_docs_exist = True
             if safe_issubclass(col.docarray_type, AnyDocArray):
                 continue
             if not col.config:
@@ -127,7 +132,12 @@ def __init__(self, db_config=None, **kwargs):
             else:
                 self._hnsw_indices[col_name] = self._create_index(col_name, col)
                 self._logger.info(f'Created a new index for column `{col_name}`')
+            if self._hnsw_indices[col_name].space == 'cosine':
+                cosine_metric_index_exist = True
 
+        self._apply_optim_no_embedding_in_sqlite = (
+            not sub_docs_exist and not cosine_metric_index_exist
+        )  # optimization consisting in not serializing embeddings to SQLite because they are expensive to send and they can be reconstructed from the HNSW index itself.
         # SQLite setup
         self._sqlite_db_path = os.path.join(self._work_dir, 'docs_sqlite.db')
         self._logger.debug(f'DB path set to {self._sqlite_db_path}')
@@ -276,9 +286,7 @@ def index(self, docs: Union[BaseDoc, Sequence[BaseDoc]], **kwargs):
         docs_validated = self._validate_docs(docs)
         self._update_subindex_data(docs_validated)
         data_by_columns = self._get_col_value_dict(docs_validated)
-
         self._index(data_by_columns, docs_validated, **kwargs)
-
         self._send_docs_to_sqlite(docs_validated)
         self._sqlite_conn.commit()
         self._num_docs = 0  # recompute again when needed
@@ -332,7 +340,19 @@ def _filter(
         limit: int,
     ) -> DocList:
         rows = self._execute_filter(filter_query=filter_query, limit=limit)
-        return DocList[self.out_schema](self._doc_from_bytes(blob) for _, blob in rows)  # type: ignore[name-defined]
+        hashed_ids = [doc_id for doc_id, _ in rows]
+        embeddings: OrderedDict[str, list] = OrderedDict()
+        for col_name, index in self._hnsw_indices.items():
+            embeddings[col_name] = index.get_items(hashed_ids)
+
+        docs = DocList.__class_getitem__(cast(Type[BaseDoc], self.out_schema))()
+        for i, row in enumerate(rows):
+            reconstruct_embeddings = {}
+            for col_name in embeddings.keys():
+                reconstruct_embeddings[col_name] = embeddings[col_name][i]
+            docs.append(self._doc_from_bytes(row[1], reconstruct_embeddings))
+
+        return docs
 
     def _filter_batched(
         self,
@@ -501,12 +521,24 @@ def _get_docs_sqlite_unsorted(self, univ_ids: Sequence[int], out: bool = True):
             assert isinstance(id_, int) or is_np_int(id_)
         sql_id_list = '(' + ', '.join(str(id_) for id_ in univ_ids) + ')'
         self._sqlite_cursor.execute(
-            'SELECT data FROM docs WHERE doc_id IN %s' % sql_id_list,
+            'SELECT doc_id, data FROM docs WHERE doc_id IN %s' % sql_id_list,
         )
-        rows = self._sqlite_cursor.fetchall()
+        rows = (
+            self._sqlite_cursor.fetchall()
+        )  # doc_ids do not come back in the same order
+        embeddings: OrderedDict[str, list] = OrderedDict()
+        for col_name, index in self._hnsw_indices.items():
+            embeddings[col_name] = index.get_items([row[0] for row in rows])
+
         schema = self.out_schema if out else self._schema
-        docs_cls = DocList.__class_getitem__(cast(Type[BaseDoc], schema))
-        return docs_cls([self._doc_from_bytes(row[0], out) for row in rows])
+        docs = DocList.__class_getitem__(cast(Type[BaseDoc], schema))()
+        for i, (_, data_bytes) in enumerate(rows):
+            reconstruct_embeddings = {}
+            for col_name in embeddings.keys():
+                reconstruct_embeddings[col_name] = embeddings[col_name][i]
+            docs.append(self._doc_from_bytes(data_bytes, reconstruct_embeddings, out))
+
+        return docs
 
     def _get_docs_sqlite_doc_id(
         self, doc_ids: Sequence[str], out: bool = True
@@ -541,12 +573,32 @@ def _get_num_docs_sqlite(self) -> int:
 
     # serialization helpers
     def _doc_to_bytes(self, doc: BaseDoc) -> bytes:
-        return doc.to_protobuf().SerializeToString()
-
-    def _doc_from_bytes(self, data: bytes, out: bool = True) -> BaseDoc:
+        pb = doc.to_protobuf()
+        if self._apply_optim_no_embedding_in_sqlite:
+            for col_name in self._hnsw_indices.keys():
+                pb.data[col_name].Clear()
+                pb.data[col_name].Clear()
+        return pb.SerializeToString()
+
+    def _doc_from_bytes(
+        self, data: bytes, reconstruct_embeddings: Dict, out: bool = True
+    ) -> BaseDoc:
         schema = self.out_schema if out else self._schema
         schema_cls = cast(Type[BaseDoc], schema)
-        return schema_cls.from_protobuf(DocProto.FromString(data))
+        pb = DocProto.FromString(
+            data
+        )  # I cannot reconstruct directly the DA object because it may fail at validation because embedding may not be Optional
+        if self._apply_optim_no_embedding_in_sqlite:
+            for k, v in reconstruct_embeddings.items():
+                node_proto = (
+                    schema_cls._get_field_type(k)
+                    ._docarray_from_ndarray(np.array(v))
+                    ._to_node_protobuf()
+                )
+                pb.data[k].MergeFrom(node_proto)
+
+        doc = schema_cls.from_protobuf(pb)
+        return doc
 
     def _get_root_doc_id(self, id: str, root: str, sub: str) -> str:
         """Get the root_id given the id of a subindex Document and the root and subindex name for hnswlib.
@@ -608,25 +660,24 @@ def _search_and_filter(
             return _FindResultBatched(documents=[], scores=[])  # type: ignore
 
         # Set limit as the minimum of the provided limit and the total number of documents
-        limit = min(limit, self.num_docs())
+        limit = limit
 
         # Ensure the search field is in the HNSW indices
         if search_field not in self._hnsw_indices:
             raise ValueError(
                 f'Search field {search_field} is not present in the HNSW indices'
             )
 
-        index = self._hnsw_indices[search_field]
-
         def accept_hashed_ids(id):
             """Accepts IDs that are in hashed_ids."""
             return id in hashed_ids  # type: ignore[operator]
 
-        # Choose the appropriate filter function based on whether hashed_ids was provided
         extra_kwargs = {'filter': accept_hashed_ids} if hashed_ids else {}
 
         # If hashed_ids is provided, k is the minimum of limit and the length of hashed_ids; else it is limit
         k = min(limit, len(hashed_ids)) if hashed_ids else limit
+        index = self._hnsw_indices[search_field]
+
         try:
             labels, distances = index.knn_query(queries, k=k, **extra_kwargs)
         except RuntimeError:
@@ -639,7 +690,6 @@ def accept_hashed_ids(id):
             )
             for ids_per_query in labels
         ]
-
         return _FindResultBatched(documents=result_das, scores=distances)
 
     @classmethod

diff --git a/tests/index/elastic/v7/docker-compose.yml b/tests/index/elastic/v7/docker-compose.yml
@@ -8,9 +8,3 @@ services:
       - ES_JAVA_OPTS=-Xmx1024m
     ports:
       - "9200:9200"
-    networks:
-      - elastic
-
-networks:
-  elastic:
-    name: elastic
diff --git a/tests/index/elastic/v8/docker-compose.yml b/tests/index/elastic/v8/docker-compose.yml
@@ -8,9 +8,3 @@ services:
       - ES_JAVA_OPTS=-Xmx1024m
     ports:
       - "9200:9200"
-    networks:
-      - elastic
-
-networks:
-  elastic:
-    name: elastic
diff --git a/tests/index/hnswlib/test_filter.py b/tests/index/hnswlib/test_filter.py
@@ -69,10 +69,14 @@ def test_build_query_invalid_query():
         HnswDocumentIndex._build_filter_query(query, param_values)
 
 
-def test_filter_eq(doc_index):
-    docs = doc_index.filter({'text': {'$eq': 'text 1'}})
-    assert len(docs) == 1
-    assert docs[0].text == 'text 1'
+def test_filter_eq(doc_index, docs):
+    filter_result = doc_index.filter({'text': {'$eq': 'text 1'}})
+    assert len(filter_result) == 1
+    assert filter_result[0].text == 'text 1'
+    assert filter_result[0].text == docs[1].text
+    assert filter_result[0].price == docs[1].price
+    assert filter_result[0].id == docs[1].id
+    assert np.allclose(filter_result[0].tensor, docs[1].tensor)
 
 
 def test_filter_neq(doc_index):

diff --git a/tests/index/hnswlib/test_index_get_del.py b/tests/index/hnswlib/test_index_get_del.py
@@ -211,7 +211,7 @@ def test_get_single(ten_simple_docs, ten_flat_docs, ten_nested_docs, tmp_path):
     for d in ten_simple_docs:
         id_ = d.id
         assert index[id_].id == id_
-        assert np.all(index[id_].tens == d.tens)
+        assert np.allclose(index[id_].tens, d.tens)
 
     # flat
     index = HnswDocumentIndex[FlatDoc](work_dir=str(flat_path))
@@ -221,8 +221,8 @@ def test_get_single(ten_simple_docs, ten_flat_docs, ten_nested_docs, tmp_path):
     for d in ten_flat_docs:
         id_ = d.id
         assert index[id_].id == id_
-        assert np.all(index[id_].tens_one == d.tens_one)
-        assert np.all(index[id_].tens_two == d.tens_two)
+        assert np.allclose(index[id_].tens_one, d.tens_one)
+        assert np.allclose(index[id_].tens_two, d.tens_two)
 
     # nested
     index = HnswDocumentIndex[NestedDoc](work_dir=str(nested_path))
@@ -233,7 +233,7 @@ def test_get_single(ten_simple_docs, ten_flat_docs, ten_nested_docs, tmp_path):
         id_ = d.id
         assert index[id_].id == id_
         assert index[id_].d.id == d.d.id
-        assert np.all(index[id_].d.tens == d.d.tens)
+        assert np.allclose(index[id_].d.tens, d.d.tens)
 
 
 def test_get_multiple(ten_simple_docs, ten_flat_docs, ten_nested_docs, tmp_path):
@@ -252,7 +252,7 @@ def test_get_multiple(ten_simple_docs, ten_flat_docs, ten_nested_docs, tmp_path)
     retrieved_docs = index[ids_to_get]
     for id_, d_in, d_out in zip(ids_to_get, docs_to_get, retrieved_docs):
         assert d_out.id == id_
-        assert np.all(d_out.tens == d_in.tens)
+        assert np.allclose(d_out.tens, d_in.tens)
 
     # flat
     index = HnswDocumentIndex[FlatDoc](work_dir=str(flat_path))
@@ -264,8 +264,8 @@ def test_get_multiple(ten_simple_docs, ten_flat_docs, ten_nested_docs, tmp_path)
     retrieved_docs = index[ids_to_get]
     for id_, d_in, d_out in zip(ids_to_get, docs_to_get, retrieved_docs):
         assert d_out.id == id_
-        assert np.all(d_out.tens_one == d_in.tens_one)
-        assert np.all(d_out.tens_two == d_in.tens_two)
+        assert np.allclose(d_out.tens_one, d_in.tens_one)
+        assert np.allclose(d_out.tens_two, d_in.tens_two)
 
     # nested
     index = HnswDocumentIndex[NestedDoc](work_dir=str(nested_path))
@@ -278,7 +278,7 @@ def test_get_multiple(ten_simple_docs, ten_flat_docs, ten_nested_docs, tmp_path)
     for id_, d_in, d_out in zip(ids_to_get, docs_to_get, retrieved_docs):
         assert d_out.id == id_
         assert d_out.d.id == d_in.d.id
-        assert np.all(d_out.d.tens == d_in.d.tens)
+        assert np.allclose(d_out.d.tens, d_in.d.tens)
 
 
 def test_get_key_error(ten_simple_docs, ten_flat_docs, ten_nested_docs, tmp_path):
@@ -303,7 +303,7 @@ def test_del_single(ten_simple_docs, ten_flat_docs, ten_nested_docs, tmp_path):
                 index[id_]
         else:
             assert index[id_].id == id_
-            assert np.all(index[id_].tens == d.tens)
+            assert np.allclose(index[id_].tens, d.tens)
     # delete again
     del index[ten_simple_docs[3].id]
     assert index.num_docs() == 8
@@ -314,7 +314,7 @@ def test_del_single(ten_simple_docs, ten_flat_docs, ten_nested_docs, tmp_path):
                 index[id_]
         else:
             assert index[id_].id == id_
-            assert np.all(index[id_].tens == d.tens)
+            assert np.allclose(index[id_].tens, d.tens)
 
 
 def test_del_multiple(ten_simple_docs, ten_flat_docs, ten_nested_docs, tmp_path):
@@ -333,7 +333,7 @@ def test_del_multiple(ten_simple_docs, ten_flat_docs, ten_nested_docs, tmp_path)
                 index[doc.id]
         else:
             assert index[doc.id].id == doc.id
-            assert np.all(index[doc.id].tens == doc.tens)
+            assert np.allclose(index[doc.id].tens, doc.tens)
 
 
 def test_del_key_error(ten_simple_docs, ten_flat_docs, ten_nested_docs, tmp_path):
@@ -410,5 +410,5 @@ class TextSimpleDoc(SimpleDoc):
     for doc in res.documents:
         if doc.id == docs[0].id:
             found = True
-            assert (doc.tens == new_tensor).all()
+            assert np.allclose(doc.tens, new_tensor)
     assert found
diff --git a/tests/index/hnswlib/test_persist_data.py b/tests/index/hnswlib/test_persist_data.py
@@ -22,7 +22,7 @@ def test_persist_and_restore(tmp_path):
     query = SimpleDoc(tens=np.random.random((10,)))
 
     # create index
-    index = HnswDocumentIndex[SimpleDoc](work_dir=str(tmp_path))
+    _ = HnswDocumentIndex[SimpleDoc](work_dir=str(tmp_path))
 
     # load existing index file
     index = HnswDocumentIndex[SimpleDoc](work_dir=str(tmp_path))
@@ -38,7 +38,7 @@ def test_persist_and_restore(tmp_path):
     find_results_after = index.find(query, search_field='tens', limit=5)
     for doc_before, doc_after in zip(find_results_before[0], find_results_after[0]):
         assert doc_before.id == doc_after.id
-        assert (doc_before.tens == doc_after.tens).all()
+        assert np.allclose(doc_before.tens, doc_after.tens)
 
     # add new data
     index.index([SimpleDoc(tens=np.random.random((10,))) for _ in range(5)])
@@ -70,7 +70,7 @@ def test_persist_and_restore_nested(tmp_path):
     find_results_after = index.find(query, search_field='d__tens', limit=5)
     for doc_before, doc_after in zip(find_results_before[0], find_results_after[0]):
         assert doc_before.id == doc_after.id
-        assert (doc_before.tens == doc_after.tens).all()
+        assert np.allclose(doc_before.tens, doc_after.tens)
 
     # delete and restore
     index.index(