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feast-ci-bot merged 1 commit into from
Sep 22, 2022
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chore: Fix header levels in feature retrieval page #3225

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8 changes: 4 additions & 4 deletions docs/getting-started/concepts/feature-retrieval.md
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Original file line number Diff line number Diff line change
Expand Up @@ -180,15 +180,15 @@ Feast accepts either:
- [feature services](feature-retrieval.md#feature-services), which group features needed for a model version
- [feature references](feature-retrieval.md#feature-references)

### Example: querying a feature service (recommended)
#### Example: querying a feature service (recommended)
```python
training_df = store.get_historical_features(
entity_df=entity_df,
features=store.get_feature_service("model_v1"),
).to_df()
```

### Example: querying a list of feature references
#### Example: querying a list of feature references
```python
training_df = store.get_historical_features(
entity_df=entity_df,
Expand All @@ -204,7 +204,7 @@ Feast accepts either a **Pandas dataframe** as the entity dataframe (including e

Both approaches must specify the full **entity key** needed as well as the **timestamps**. Feast then joins features onto this dataframe.

### Example: entity dataframe for generating training data
#### Example: entity dataframe for generating training data
```python
entity_df = pd.DataFrame.from_dict(
{
Expand All @@ -228,7 +228,7 @@ training_df = store.get_historical_features(
).to_df()
```

### Example: entity SQL query for generating training data
#### Example: entity SQL query for generating training data
You can also pass a SQL string to generate the above dataframe. This is useful for getting all entities in a timeframe from some data source.

```python
Expand Down