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feat: Faiss and In memory store (feast-dev#4464)
* add faiss & in memory online store Signed-off-by: cmuhao <sduxuhao@gmail.com> * add faiss & in memory online store Signed-off-by: cmuhao <sduxuhao@gmail.com> * add faiss & in memory online store Signed-off-by: cmuhao <sduxuhao@gmail.com> * add faiss & in memory online store Signed-off-by: cmuhao <sduxuhao@gmail.com> * add faiss & in memory online store Signed-off-by: cmuhao <sduxuhao@gmail.com> * add faiss & in memory online store Signed-off-by: cmuhao <sduxuhao@gmail.com> * add faiss & in memory online store Signed-off-by: cmuhao <sduxuhao@gmail.com> * add faiss & in memory online store Signed-off-by: cmuhao <sduxuhao@gmail.com> * add faiss & in memory online store Signed-off-by: cmuhao <sduxuhao@gmail.com> * add faiss & in memory online store Signed-off-by: cmuhao <sduxuhao@gmail.com> * add faiss & in memory online store Signed-off-by: cmuhao <sduxuhao@gmail.com> * add faiss & in memory online store Signed-off-by: cmuhao <sduxuhao@gmail.com> * add dependency Signed-off-by: cmuhao <sduxuhao@gmail.com> * update package name Signed-off-by: cmuhao <sduxuhao@gmail.com> --------- Signed-off-by: cmuhao <sduxuhao@gmail.com>
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‎sdk/python/feast/infra/online_stores/contrib/faiss_online_store.py

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Original file line numberDiff line numberDiff line change
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import logging
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from datetime import datetime
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from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple
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import faiss
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import numpy as np
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from google.protobuf.timestamp_pb2 import Timestamp
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from feast import Entity, FeatureView, RepoConfig
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from feast.infra.key_encoding_utils import serialize_entity_key
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from feast.infra.online_stores.online_store import OnlineStore
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from feast.protos.feast.types.EntityKey_pb2 import EntityKey
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from feast.protos.feast.types.Value_pb2 import Value
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from feast.repo_config import FeastConfigBaseModel
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class FaissOnlineStoreConfig(FeastConfigBaseModel):
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dimension: int
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index_path: str
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index_type: str = "IVFFlat"
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nlist: int = 100
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class InMemoryStore:
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def __init__(self):
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self.feature_names: List[str] = []
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self.entity_keys: Dict[str, int] = {}
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def update(self, feature_names: List[str], entity_keys: Dict[str, int]):
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self.feature_names = feature_names
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self.entity_keys = entity_keys
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def delete(self, entity_keys: List[str]):
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for entity_key in entity_keys:
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if entity_key in self.entity_keys:
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del self.entity_keys[entity_key]
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def read(self, entity_keys: List[str]) -> List[Optional[int]]:
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return [self.entity_keys.get(entity_key) for entity_key in entity_keys]
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def teardown(self):
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self.feature_names = []
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self.entity_keys = {}
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class FaissOnlineStore(OnlineStore):
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_index: Optional[faiss.IndexIVFFlat] = None
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_in_memory_store: InMemoryStore = InMemoryStore()
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_config: Optional[FaissOnlineStoreConfig] = None
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_logger: logging.Logger = logging.getLogger(__name__)
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def _get_index(self, config: RepoConfig) -> faiss.IndexIVFFlat:
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if self._index is None or self._config is None:
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raise ValueError("Index is not initialized")
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return self._index
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def update(
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self,
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config: RepoConfig,
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tables_to_delete: Sequence[FeatureView],
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tables_to_keep: Sequence[FeatureView],
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entities_to_delete: Sequence[Entity],
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entities_to_keep: Sequence[Entity],
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partial: bool,
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):
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feature_views = tables_to_keep
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if not feature_views:
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return
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feature_names = [f.name for f in feature_views[0].features]
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dimension = len(feature_names)
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self._config = FaissOnlineStoreConfig(**config.online_store.dict())
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if self._index is None or not partial:
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quantizer = faiss.IndexFlatL2(dimension)
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self._index = faiss.IndexIVFFlat(quantizer, dimension, self._config.nlist)
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self._index.train(
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np.random.rand(self._config.nlist * 100, dimension).astype(np.float32)
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)
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self._in_memory_store = InMemoryStore()
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self._in_memory_store.update(feature_names, {})
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def teardown(
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self,
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config: RepoConfig,
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tables: Sequence[FeatureView],
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entities: Sequence[Entity],
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):
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self._index = None
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self._in_memory_store.teardown()
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def online_read(
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self,
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config: RepoConfig,
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table: FeatureView,
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entity_keys: List[EntityKey],
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requested_features: Optional[List[str]] = None,
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) -> List[Tuple[Optional[datetime], Optional[Dict[str, Value]]]]:
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if self._index is None:
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return [(None, None)] * len(entity_keys)
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results: List[Tuple[Optional[datetime], Optional[Dict[str, Any]]]] = []
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for entity_key in entity_keys:
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serialized_key = serialize_entity_key(
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entity_key, config.entity_key_serialization_version
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).hex()
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idx = self._in_memory_store.entity_keys.get(serialized_key, -1)
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if idx == -1:
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results.append((None, None))
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else:
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feature_vector = self._index.reconstruct(int(idx))
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feature_dict = {
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name: Value(double_val=value)
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for name, value in zip(
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self._in_memory_store.feature_names, feature_vector
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)
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}
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results.append((None, feature_dict))
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return results
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def online_write_batch(
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self,
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config: RepoConfig,
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table: FeatureView,
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data: List[Tuple[EntityKey, Dict[str, Value], datetime, Optional[datetime]]],
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progress: Optional[Callable[[int], Any]],
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) -> None:
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if self._index is None:
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self._logger.warning("Index is not initialized. Skipping write operation.")
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return
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feature_vectors = []
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serialized_keys = []
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for entity_key, feature_dict, _, _ in data:
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serialized_key = serialize_entity_key(
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entity_key, config.entity_key_serialization_version
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).hex()
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feature_vector = np.array(
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[
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feature_dict[name].double_val
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for name in self._in_memory_store.feature_names
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],
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dtype=np.float32,
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)
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feature_vectors.append(feature_vector)
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serialized_keys.append(serialized_key)
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feature_vectors_array = np.array(feature_vectors)
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existing_indices = [
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self._in_memory_store.entity_keys.get(sk, -1) for sk in serialized_keys
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]
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mask = np.array(existing_indices) != -1
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if np.any(mask):
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self._index.remove_ids(
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np.array([idx for idx in existing_indices if idx != -1])
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)
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new_indices = np.arange(
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self._index.ntotal, self._index.ntotal + len(feature_vectors_array)
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)
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self._index.add(feature_vectors_array)
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for sk, idx in zip(serialized_keys, new_indices):
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self._in_memory_store.entity_keys[sk] = idx
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if progress:
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progress(len(data))
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def retrieve_online_documents(
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self,
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config: RepoConfig,
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table: FeatureView,
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requested_feature: str,
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embedding: List[float],
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top_k: int,
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distance_metric: Optional[str] = None,
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) -> List[
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Tuple[
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Optional[datetime],
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Optional[Value],
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Optional[Value],
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Optional[Value],
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]
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]:
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if self._index is None:
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self._logger.warning("Index is not initialized. Returning empty result.")
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return []
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query_vector = np.array(embedding, dtype=np.float32).reshape(1, -1)
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distances, indices = self._index.search(query_vector, top_k)
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results: List[
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Tuple[
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Optional[datetime],
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Optional[Value],
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Optional[Value],
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Optional[Value],
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]
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] = []
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for i, idx in enumerate(indices[0]):
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if idx == -1:
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continue
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feature_vector = self._index.reconstruct(int(idx))
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timestamp = Timestamp()
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timestamp.GetCurrentTime()
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feature_value = Value(string_val=",".join(map(str, feature_vector)))
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vector_value = Value(string_val=",".join(map(str, feature_vector)))
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distance_value = Value(float_val=distances[0][i])
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results.append(
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(
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timestamp.ToDatetime(),
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feature_value,
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vector_value,
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distance_value,
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)
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)
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return results
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async def online_read_async(
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self,
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config: RepoConfig,
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table: FeatureView,
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entity_keys: List[EntityKey],
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requested_features: Optional[List[str]] = None,
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) -> List[Tuple[Optional[datetime], Optional[Dict[str, Value]]]]:
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# Implement async read if needed
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raise NotImplementedError("Async read is not implemented for FaissOnlineStore")

‎setup.py

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MSSQL_REQUIRED = ["ibis-framework[mssql]>=9.0.0,<10"]
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FAISS_REQUIRED = ["faiss-cpu>=1.7.0,<2"]
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CI_REQUIRED = (
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[
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"build",
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+ SQLITE_VEC_REQUIRED
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+ SINGLESTORE_REQUIRED
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+ OPENTELEMETRY
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+ FAISS_REQUIRED
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)
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DOCS_REQUIRED = CI_REQUIRED
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"sqlite_vec": SQLITE_VEC_REQUIRED,
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"singlestore": SINGLESTORE_REQUIRED,
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"opentelemetry": OPENTELEMETRY,
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"faiss": FAISS_REQUIRED,
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},
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include_package_data=True,
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license="Apache",

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