feast-user-guide

# Feast User Guide ## Quick Start A Feast project requires: 1. A `feature_store.yaml` config file 2. Python files defining entities, data sources, feature views, and feature services 3. Running `feast apply` to register definitions ```bash feast init my_project cd my_project feast apply ``` ## Core Concepts ### Entity An entity is a collection of semantically related features (e.g., a customer, a driver). Entities have join keys used to look up features. ```python from feast import Entity from feast.value_type import ValueType driver = Entity( name="driver_id", description="Driver identifier", value_type=ValueType.INT64, ) ``` ### Data Sources Data sources describe where raw feature data lives. ```python from feast import FileSource, BigQuerySource, KafkaSource, PushSource, RequestSource from feast.data_format import ParquetFormat # Batch source (file) driver_stats_source = FileSource( name="driver_stats_source", path="data/driver_stats.parquet", timestamp_field="event_timestamp", created_timestamp_column="created", ) # Request source (for on-demand features) input_request = RequestSource( name="vals_to_add", schema=[Field(name="val_to_add", dtype=Float64)], ) ``` ### FeatureView Maps features from a data source to entities with a schema, TTL, and online/offline settings. ```python from feast import FeatureView, Field from feast.types import Float32, Int64, String from datetime import timedelta driver_hourly_stats = FeatureView( name="driver_hourly_stats", entities=[driver], ttl=timedelta(days=365), schema=[ Field(name="conv_rate", dtype=Float32), Field(name="acc_rate", dtype=Float32), Field(name="avg_daily_trips", dtype=Int64), ], online=True, source=driver_stats_source, ) ``` ### OnDemandFeatureView Computes features at request time from other feature views and/or request data. ```python from feast import on_demand_feature_view import pandas as pd @on_demand_feature_view( sources=[driver_hourly_stats, input_request], schema=[Field(name="conv_rate_plus_val", dtype=Float64)], mode="pandas", ) def transformed_conv_rate(inputs: pd.DataFrame) -> pd.DataFrame: df = pd.DataFrame() df["conv_rate_plus_val"] = inputs["conv_rate"] + inputs["val_to_add"] return df ``` ### FeatureService Groups features from multiple views for retrieval. ```python from feast import FeatureService driver_fs = FeatureService( name="driver_ranking", features=[driver_hourly_stats, transformed_conv_rate], ) ``` ## Feature Retrieval ### Online (low-latency) ```python from feast import FeatureStore store = FeatureStore(repo_path=".") features = store.get_online_features( features=[ "driver_hourly_stats:conv_rate", "driver_hourly_stats:acc_rate", ], entity_rows=[{"driver_id": 1001}, {"driver_id": 1002}], ).to_dict() ``` ### Historical (training data with point-in-time joins) ```python entity_df = pd.DataFrame({ "driver_id": [1001, 1002], "event_timestamp": [datetime(2023, 1, 1), datetime(2023, 1, 2)], }) training_df = store.get_historical_features( entity_df=entity_df, features=["driver_hourly_stats:conv_rate", "driver_hourly_stats:acc_rate"], ).to_df() ``` Or use a FeatureService: ```python training_df = store.get_historical_features( entity_df=entity_df, features=driver_fs, ).to_df() ``` ## Materialization Load features from offline store into online store: ```bash # Full materialization over a time range feast materialize 2023-01-01T00:00:00 2023-12-31T23:59:59 # Incremental (from last materialized timestamp) feast materialize-incremental $(date -u +"%Y-%m-%dT%H:%M:%S") ``` Python API: ```python from datetime import datetime store.materialize(start_date=datetime(2023, 1, 1), end_date=datetime(2023, 12, 31)) store.materialize_incremental(end_date=datetime.utcnow()) ``` ## CLI Commands | Command | Purpose | |---------|---------| | `feast init [DIR]` | Create new feature repository | | `feast apply` | Register/update feature definitions | | `feast plan` | Preview changes without applying | | `feast materialize START END` | Materialize features to online store | | `feast materialize-incremental END` | Incremental materialization | | `feast entities list` | List registered entities | | `feast feature-views list` | List feature views | | `feast feature-services list` | List feature services | | `feast on-demand-feature-views list` | List on-demand feature views | | `feast teardown` | Remove infrastructure resources | | `feast version` | Show SDK version | Options: `--chdir` / `-c` (run in different directory), `--feature-store-yaml` / `-f` (override config path). ## Vector Search / RAG Define a feature view with vector fields for similarity search: ```python from feast.types import Array, Float32 wiki_passages = FeatureView( name="wiki_passages", entities=[passage_entity], schema=[ Field(name="passage_text", dtype=String), Field( name="embedding", dtype=Array(Float32), vector_index=True, vector_length=384, vector_search_metric="COSINE", ), ], source=passages_source, online=True, ) ``` Retrieve similar documents: ```python results = store.retrieve_online_documents( feature="wiki_passages:embedding", query=query_embedding, top_k=5, ) ``` ## feature_store.yaml Minimal Config ```yaml project: my_project registry: data/registry.db provider: local online_store: type: sqlite path: data/online_store.db ``` ## Common Imports ```python from feast import ( Entity, FeatureView, OnDemandFeatureView, FeatureService, Field, FileSource, RequestSource, FeatureStore, ) from feast.on_demand_feature_view import on_demand_feature_view from feast.types import Float32, Float64, Int64, String, Bool, Array from feast.value_type import ValueType from datetime import timedelta ``` ## Detailed References - **Feature definitions** (all types, parameters, patterns): See [references/feature-definitions.md](references/feature-definitions.md) - **Configuration** (feature_store.yaml, all store types, auth): See [references/configuration.md](references/configuration.md) - **Retrieval & RAG** (online/offline retrieval, vector search, RAG retriever): See [references/retrieval-and-rag.md](references/retrieval-and-rag.md)