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feat: Updating retrieve online documents v2 to work for other fields for sq… (#5082)
* Updating retrieve online documents v2 to work for other fields for sqlite Signed-off-by: Francisco Javier Arceo <farceo@redhat.com> * updating tests...not working entirely yet but close Signed-off-by: Francisco Javier Arceo <farceo@redhat.com> * bug fix for addition of new features Signed-off-by: Francisco Javier Arceo <farceo@redhat.com> * updated to implement full text search Signed-off-by: Francisco Javier Arceo <farceo@redhat.com> --------- Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>
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‎sdk/python/feast/feature_server.py‎

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@@ -75,6 +75,7 @@ class GetOnlineFeaturesRequest(BaseModel):
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features: Optional[List[str]] = None
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full_feature_names: bool = False
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query_embedding: Optional[List[float]] = None
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query_string: Optional[str] = None
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def _get_features(request: GetOnlineFeaturesRequest, store: "feast.FeatureStore"):
@@ -195,6 +196,7 @@ async def retrieve_online_documents(
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entity_rows=request.entities,
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full_feature_names=request.full_feature_names,
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query=request.query_embedding,
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query_string=request.query_string,
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)
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response = await run_in_threadpool(

‎sdk/python/feast/feature_store.py‎

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@@ -1867,6 +1867,7 @@ def retrieve_online_documents_v2(
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top_k: int,
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features: List[str],
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distance_metric: Optional[str] = "L2",
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query_string: Optional[str] = None,
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) -> OnlineResponse:
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"""
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Retrieves the top k closest document features. Note, embeddings are a subset of features.
@@ -1878,6 +1879,7 @@ def retrieve_online_documents_v2(
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query: The query to retrieve the closest document features for.
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top_k: The number of closest document features to retrieve.
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distance_metric: The distance metric to use for retrieval.
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query_string: The query string to retrieve the closest document features using keyword search (bm25).
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"""
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if isinstance(query, str):
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raise ValueError(
@@ -1919,6 +1921,7 @@ def retrieve_online_documents_v2(
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query,
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top_k,
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distance_metric,
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query_string,
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)
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def _retrieve_from_online_store(
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query: List[float],
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top_k: int,
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distance_metric: Optional[str],
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query_string: Optional[str],
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) -> OnlineResponse:
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"""
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Search and return document features from the online document store.
@@ -2003,6 +2007,7 @@ def _retrieve_from_online_store_v2(
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query=query,
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top_k=top_k,
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distance_metric=distance_metric,
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query_string=query_string,
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)
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entity_key_dict: Dict[str, List[ValueProto]] = {}

‎sdk/python/feast/feature_view.py‎

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@@ -192,6 +192,10 @@ def __init__(
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else:
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features.append(field)
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assert len([f for f in features if f.vector_index]) < 2, (
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f"Only one vector feature is allowed per feature view. Please update {self.name}."
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)
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# TODO(felixwang9817): Add more robust validation of features.
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cols = [field.name for field in schema]
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for col in cols:

‎sdk/python/feast/infra/online_stores/milvus_online_store/milvus.py‎

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@@ -463,6 +463,7 @@ def retrieve_online_documents_v2(
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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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query_string: Optional[str] = None,
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) -> List[
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Tuple[
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Optional[datetime],

‎sdk/python/feast/infra/online_stores/online_store.py‎

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@@ -439,6 +439,7 @@ def retrieve_online_documents_v2(
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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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query_string: Optional[str] = None,
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) -> List[
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Tuple[
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Optional[datetime],
@@ -456,6 +457,7 @@ def retrieve_online_documents_v2(
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requested_features: The list of features whose embeddings should be used for retrieval.
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embedding: The embeddings to use for retrieval.
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top_k: The number of documents to retrieve.
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query_string: The query string to search for using keyword search (bm25) (optional)
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Returns:
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object: A list of top k closest documents to the specified embedding. Each item in the list is a tuple

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