feat(cli): Add recommendation template for feast init#6078
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Add a new "recommendation" template demonstrating product recommendations using vector similarity search with pre-computed embeddings (all-MiniLM-L6-v2) and SQLite online store with vector_enabled. Signed-off-by: doc-johnson <hustler@mail.ru>
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Covers point 3 from #5478, a recommendation-engine template for
feast init.The template is a small product-recommendation setup built around vector similarity search. It ships 12 sample products across four categories (Electronics, Books, Sports, Home), pre-computes their embeddings with
all-MiniLM-L6-v2(384 dims) at bootstrap time, and stores them in a SQLite online store with vector search enabled.test_workflow.pyruns the whole thing end to end: apply, materialize, a top-k similarity query throughretrieve_online_documents_v2, then teardown.What is in it:
bootstrap.pybuilds the product catalog and writes the embeddings out to parquetfeature_definitions.pydefines the entity, a FeatureView with avector_indexfield, and a FeatureServicefeature_store.yamlis a plain local SQLite config withvector_enabled: truetest_workflow.pyis the runnable democli.pyregistersrecommendationin thefeast inittemplate choicesWhat I checked locally:
feast init test_rec --template recommendationscaffolds a working projectfeast applyregisters the entity, view and service, and a second run reports no changespython test_workflow.pygoes through the full cycle without errorslocaltemplate still behaves the sameruff checkandruff formatare clean