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258 | class ObjectBoxVectorStore(BasePydanticVectorStore):
"""ObjectBox vector store.
In this vector store, embeddings are stored within a ObjectBox `Box` (collection).
During query time, the index uses ObjectBox to query for the top-K most similar nodes.
Args:
embedding_dimensions (int): Number of elements in the embedding to be stored
distance_type (objectbox.model.properties.VectorDistanceType):
Distance metric to be used for vector search
db_directory (str): File path where ObjectBox database files will be stored
clear_db (bool): Whether to delete any existing database on `db_directory`
do_log (bool): enable/disable logging
Examples:
`pip install llama-index-vector-stores-objectbox`
```python
from llama_index.vector_stores.objectbox import ObjectBoxVectorStore
from objectbox import VectorDistanceType
vector_store = ObjectBoxVectorStore(
embedding_dim,
distance_type=VectorDistanceType.COSINE,
db_directory="obx_data",
clear_db=False,
do_log=True
)
```
"""
stores_text: bool = True
embedding_dimensions: int
distance_type: VectorDistanceType = VectorDistanceType.EUCLIDEAN
db_directory: Optional[str] = None
clear_db: Optional[bool] = False
do_log: Optional[bool] = False
_store: Store = PrivateAttr()
_entity_class: Entity = PrivateAttr()
_box: Box = PrivateAttr()
def __init__(
self,
embedding_dimensions: int,
distance_type: VectorDistanceType = VectorDistanceType.EUCLIDEAN,
db_directory: Optional[str] = None,
clear_db: Optional[bool] = False,
do_log: Optional[bool] = False,
**data: Any,
):
super().__init__(
embedding_dimensions=embedding_dimensions,
distance_type=distance_type,
db_directory=db_directory,
clear_db=clear_db,
do_log=do_log,
**data,
)
self._entity_class = self._create_entity_class()
self._store = self._create_box_store()
self._box = self._store.box(self._entity_class)
@property
def client(self) -> Any:
return self._box
def add(self, nodes: List[BaseNode], **kwargs: Any) -> List[str]:
ids: list[str] = []
start = time.perf_counter()
with self._store.write_tx():
for node in nodes:
if node.embedding is None:
_logger.info("A node with no embedding was found ")
continue
self._box.put(
self._entity_class(
node_id=node.node_id,
doc_id=node.ref_doc_id if node.ref_doc_id is not None else "",
text=node.get_content(metadata_mode=MetadataMode.NONE),
metadata=node.metadata,
embeddings=node.embedding,
)
)
ids.append(node.node_id)
if self.do_log:
end = time.perf_counter()
_logger.info(
f"ObjectBox stored {len(ids)} nodes in {end - start} seconds"
)
return ids
def delete(self, ref_doc_id: str, **delete_kwargs: Any) -> None:
self._box.query(self._entity_class.doc_id.equals(ref_doc_id)).build().remove()
def delete_nodes(
self,
node_ids: Optional[List[str]] = None,
filters: Optional[MetadataFilters] = None,
**delete_kwargs: Any,
) -> None:
if filters is not None:
raise NotImplementedError(
"ObjectBox does not yet support delete_nodes() with metadata filters - contact us if you need this feature"
)
if node_ids is not None:
query_obj = self._box.query(
self._entity_class.node_id.equals("node_id").alias("node_id")
).build()
for node_id in node_ids:
query_obj.set_parameter_alias_string("node_id", node_id)
query_obj.remove()
def get_nodes(
self,
node_ids: Optional[List[str]] = None,
filters: Optional[MetadataFilters] = None,
) -> List[BaseNode]:
if filters is not None:
raise NotImplementedError(
"ObjectBox does not yet support get_nodes() with metadata filters - contact us if you need this feature"
)
if node_ids is not None:
retrieved_nodes: list[BaseNode] = []
with self._store.read_tx():
query_obj = self._box.query(
self._entity_class.node_id.equals("node_id").alias("node_id")
).build()
for node_id in node_ids:
try:
query_obj.set_parameter_alias_string("node_id", node_id)
entities = query_obj.find()
if len(entities) == 0:
_logger.info(f"No entity with id = {node_id} was found")
continue
retrieved_nodes.append(
TextNode(
text=entities[0].text,
id_=entities[0].node_id,
metadata=entities[0].metadata,
)
)
except ValueError:
raise ValueError(f"Invalid node id: {node_id}")
return retrieved_nodes
else:
raise ValueError("node_ids cannot be None")
def query(self, query: VectorStoreQuery, **kwargs: Any) -> VectorStoreQueryResult:
if query.filters is not None:
raise NotImplementedError(
"ObjectBox does not yet support query() with metadata filters - contact us if you need this feature"
)
query_embedding = query.query_embedding
n_results = query.similarity_top_k
nodes: list[TextNode] = []
similarities: list[float] = []
ids: list[str] = []
start = time.perf_counter()
query: Query = self._box.query(
self._entity_class.embeddings.nearest_neighbor(query_embedding, n_results)
).build()
results: list[tuple[Entity, float]] = query.find_with_scores()
end = time.perf_counter()
if self.do_log:
_logger.info(
f"ObjectBox retrieved {len(results)} vectors in {end - start} seconds"
)
for entity, score in results:
node = TextNode(
text=entity.text, id_=entity.node_id, metadata=entity.metadata
)
ids.append(entity.node_id)
nodes.append(node)
similarities.append(score)
return VectorStoreQueryResult(nodes=nodes, similarities=similarities, ids=ids)
def count(self) -> int:
return self._box.count()
def clear(self) -> None:
self._box.remove_all()
def close(self):
self._store.close()
def _create_entity_class(self) -> Entity:
"""Dynamically define an Entity class according to the parameters."""
@Entity()
class VectorEntity:
id = Id()
node_id = String()
doc_id = String()
text = String()
metadata = Property(dict, type=PropertyType.flex)
embeddings = Float32Vector(
index=HnswIndex(
dimensions=self.embedding_dimensions,
distance_type=self.distance_type,
)
)
return VectorEntity
def _create_box_store(self) -> Store:
"""Registering the VectorEntity model and setting up objectbox database."""
db_path = DIRECTORY if self.db_directory is None else self.db_directory
if self.clear_db and os.path.exists(db_path):
shutil.rmtree(db_path)
model = Model()
model.entity(self._entity_class)
return Store(model=model, directory=db_path)
|
