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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Signed-off-by: doc-johnson <hustler@mail.ru>
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Pull request overview
Adds a new feast init template (recommendation) that scaffolds a small product-recommendation demo using vector similarity search backed by a local SQLite online store (with vector search enabled). This extends Feast’s CLI templates with a concrete end-to-end example for vector retrieval.
Changes:
- Adds a new
sdk/python/feast/templates/recommendation/template with bootstrap data generation (embeddings), feature definitions, local SQLite config, and an end-to-end demo script. - Registers the
recommendationtemplate in the CLI--templatechoices. - Adds a unit test to validate the presence of key template artifacts and vector-search-related configuration.
Reviewed changes
Copilot reviewed 6 out of 8 changed files in this pull request and generated 1 comment.
Show a summary per file
| File | Description |
|---|---|
| sdk/python/tests/unit/local_feast_tests/test_init.py | Adds assertions that the recommendation template exists and is configured for SQLite vector search. |
| sdk/python/feast/templates/recommendation/bootstrap.py | Bootstraps a small product catalog and writes precomputed embeddings to parquet; fills template placeholders. |
| sdk/python/feast/templates/recommendation/feature_repo/feature_definitions.py | Defines the entity/FeatureView with a vector-indexed embedding field and a FeatureService. |
| sdk/python/feast/templates/recommendation/feature_repo/feature_store.yaml | Configures a local SQLite online store with vector_enabled: true. |
| sdk/python/feast/templates/recommendation/feature_repo/test_workflow.py | End-to-end demo script: apply, materialize, vector similarity query via retrieve_online_documents_v2, teardown. |
| sdk/python/feast/templates/recommendation/init.py | Adds package marker for the new template directory. |
| sdk/python/feast/templates/recommendation/feature_repo/init.py | Adds package marker for the template feature repo. |
| sdk/python/feast/cli/cli.py | Adds recommendation to the accepted feast init --template values. |
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| features=[ | ||
| "product_embeddings:embedding", | ||
| "product_embeddings:product_name", | ||
| "product_embeddings:category", | ||
| "product_embeddings:price", |
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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