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GeezerVison is showing github.com — feast/sdk/python/feast/vector_store.py at master · iamcodingcat/feast Expand file tree/ Copy pathvector_store.pyMore file actions 87 lines (75 loc) · 3.22 KB / Copy pathvector_store.pyFile metadata and controls87 lines (75 loc) · 3.22 KB 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 # 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, ) |
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