feat: Added Agent skills for AI Agents

Agent-Skill/SKILL.md

@@ -0,0 +1,248 @@
+---
+name: feast-user-guide
+description: Guide for working with Feast (Feature Store) — defining features, configuring feature_store.yaml, retrieving features online/offline, using the CLI, and building RAG retrieval pipelines. Use when the user asks about creating entities, feature views, on-demand feature views, stream feature views, feature services, data sources, feature_store.yaml configuration, feast apply/materialize commands, online or historical feature retrieval, or vector-based document retrieval with Feast.
+---
+
+# 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)