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# Copyright 2019 The Feast Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Optional
import numpy as np
from feast import FeatureStore, FeatureView
from feast.online_response import OnlineResponse
class FeastVectorStore:
"""
Feast-based vector store implementation with support for text, image, and multi-modal search.
This class provides a convenient interface for vector similarity search using Feast's
vector database integrations. It supports:
- Text similarity search using text embeddings
- Image similarity search using image embeddings
- Multi-modal search combining text and image queries
"""
def __init__(self, repo_path: str, rag_view: FeatureView, features: list[str]):
"""Initialize the Feast vector store.
Args:
repo_path: Path to the Feast repo
rag_view: Feature view
features: List of feature names to retrieve
"""
self._store = None # Lazy load
self._store_repo_path = repo_path
self.rag_view = rag_view
self.features = features
@property
def store(self):
if self._store is None:
self._store = FeatureStore(repo_path=self._store_repo_path)
self._store.apply([self.rag_view])
# TODO: Add validation to ensure store type is one of supported types e.g. pgvector, elasticsearch, milvus
return self._store
def query(
self,
query_vector: Optional[np.ndarray] = None,
query_string: Optional[str] = None,
query_image_bytes: Optional[bytes] = None,
top_k: int = 10,
) -> OnlineResponse:
"""Query the Feast vector store with support for text, image, and multi-modal search.
Args:
query_vector: Optional vector to use for similarity search (text embeddings)
query_string: Optional string query for keyword/semantic search
query_image_bytes: Optional image bytes for image similarity search
top_k: Number of results to return
Returns:
An OnlineResponse
"""
query_list = query_vector.tolist() if query_vector is not None else None
distance_metric = None
for field in self.rag_view.schema:
if hasattr(field, "vector_index") and field.vector_index:
if hasattr(field, "vector_search_metric"):
distance_metric = field.vector_search_metric
break
return self.store.retrieve_online_documents_v2(
features=self.features,
query=query_list,
query_string=query_string,
query_image_bytes=query_image_bytes,
top_k=top_k,
distance_metric=distance_metric,
)