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feat: Enable other distance metrics for Vector DB and Update docs #4170

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Merged
franciscojavierarceo merged 8 commits into from
May 7, 2024
Merged

feat: Enable other distance metrics for Vector DB and Update docs #4170

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0032969
updated PGVector docs
franciscojavierarceo May 2, 2024
e242a93
adding distance metric to arguments and defaulting to L2
franciscojavierarceo May 2, 2024
6dd845d
linter
franciscojavierarceo May 2, 2024
d192c88
testing other distance metric
franciscojavierarceo May 2, 2024
3b9b3da
updated default
franciscojavierarceo May 2, 2024
cf840c3
linter
franciscojavierarceo May 2, 2024
ee3448f
fixed some copy
franciscojavierarceo May 2, 2024
1d88b01
updated
franciscojavierarceo May 3, 2024
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fixed some copy 
Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>
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franciscojavierarceo committed May 2, 2024
commit ee3448fb2f6465d5fcfb0a185ca6f352536a3bb9
2 changes: 1 addition & 1 deletion sdk/python/feast/infra/online_stores/online_store.py
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Expand Up @@ -158,7 +158,7 @@ def retrieve_online_documents(
table: The feature view whose feature values should be read.
requested_feature: The name of the feature whose embeddings should be used for retrieval.
embedding: The embeddings to use for retrieval.
top_k: The number of nearest neighbors to retrieve.
top_k: The number of documents to retrieve.

Returns:
object: A list of top k closest documents to the specified embedding. Each item in the list is a tuple
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4 changes: 2 additions & 2 deletions sdk/python/feast/infra/provider.py
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Expand Up @@ -313,14 +313,14 @@ def retrieve_online_documents(
]
]:
"""
Searches for the top-k nearest neighbors of the given document in the online document store.
Searches for the top-k most similar documents in the online document store.

Args:
config: The config for the current feature store.
table: The feature view whose embeddings should be searched.
requested_feature: the requested document feature name.
query: The query embedding to search for.
top_k: The number of nearest neighbors to return.
top_k: The number of documents to return.

Returns:
A list of dictionaries, where each dictionary contains the document feature.
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