From 83078d3712d20244e0d8c7bfd32041d2eb559a7e Mon Sep 17 00:00:00 2001 From: kennethmhc Date: Fri, 31 May 2024 11:02:40 +0200 Subject: [PATCH] [FSTORE-1418] Add embeding_index to fg creation guide (#388) * add similarity search reference * Update docs/user_guides/fs/vector_similarity_search.md Co-authored-by: Ralf --------- Co-authored-by: Ralf --- docs/user_guides/fs/feature_group/create.md | 2 +- docs/user_guides/fs/vector_similarity_search.md | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/user_guides/fs/feature_group/create.md b/docs/user_guides/fs/feature_group/create.md index 41f06355c..e5a86fd9c 100644 --- a/docs/user_guides/fs/feature_group/create.md +++ b/docs/user_guides/fs/feature_group/create.md @@ -36,7 +36,7 @@ The first step to create a feature group is to create the API metadata object re ) ``` -The full method documentation is available [here](https://docs.hopsworks.ai/feature-store-api/{{{ hopsworks_version }}}/generated/api/feature_group_api/#featuregroup). `name` is the only mandatory parameter of the `create_feature_group` and represents the name of the feature group. +The full method documentation is available [here](https://docs.hopsworks.ai/feature-store-api/{{{ hopsworks_version }}}/generated/api/feature_group_api/#featuregroup). If you need to create a feature group with vector similarity search supported, refer to [this guide](../vector_similarity_search.md#extending-feature-groups-with-similarity-search). `name` is the only mandatory parameter of the `create_feature_group` and represents the name of the feature group. In the example above we created the first version of a feature group named *weather*, we provide a description to make it searchable to the other project members, as well as making the feature group available online. diff --git a/docs/user_guides/fs/vector_similarity_search.md b/docs/user_guides/fs/vector_similarity_search.md index 48303a03c..406f74212 100644 --- a/docs/user_guides/fs/vector_similarity_search.md +++ b/docs/user_guides/fs/vector_similarity_search.md @@ -17,7 +17,7 @@ from hsfs import embedding emb = embedding.EmbeddingIndex(index_name="news_fg") ``` -Then, add one or more embedding features to the index. Name and dimension of the embedding features are required for identifying which features should be indexed for k-nearest neighbor (KNN) search. In this example, we get the dimension of the embedding by taking the length of the value of the `embedding_heading` column in the first row of the dataframe `df`. Optionally, you can specify the similarity function among `l2_norm`, `cosine`, and `dot_product`. +Then, add one or more embedding features to the index. Name and dimension of the embedding features are required for identifying which features should be indexed for k-nearest neighbor (KNN) search. In this example, we get the dimension of the embedding by taking the length of the value of the `embedding_heading` column in the first row of the dataframe `df`. Optionally, you can specify the similarity function among `l2_norm`, `cosine`, and `dot_product`. Refer to [add_embedding](https://docs.hopsworks.ai/feature-store-api/{{{ hopsworks_version }}}/generated/api/embedding_index_api/#add_embedding) for the full list of arguments. ```aidl # Add embedding feature to the index emb.add_embedding("embedding_heading", len(df["embedding_heading"][0]))