> ## Documentation Index
> Fetch the complete documentation index at: https://docs.wyvern.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Model Training

**Context:**
Feast has some really useful documentation around how [point-in-time](https://docs.feast.dev/getting-started/concepts/point-in-time-joins) joins work.

For model training, you want to get all the offline features and real-time features that are used in production.

WyvernAPI provides the `get_historical_features` function to retrieve a number of features that correspond to a specific entity (or set of entities) at the time a user request happened. It not only covers the batch features that correspond to the entity (or entity set), which is what feast's get\_historical\_features does, but also covers the realt time features that are logged by Wyvern.

For example, let’s say we had a user request like this:

```json theme={null}
{
    "request_id": "example_request_id".
    "api_source": "/api/v1/product-search-ranking"
    "candidates": [{"product_id": "p_1"}, {"product_id": "p_2"}],
		"user": {"user_id": "u_1"},
		"query": {"query": "chocolate"}
}
```

The request data in a notebook may look like this, and all of this information would be supplied to `get_historical_features`:

**Input Dataframe (`entities`):**

| timestamp           | request              | product | brand | user | query     | was\_clicked | was\_ordered |
| ------------------- | -------------------- | ------- | ----- | ---- | --------- | ------------ | ------------ |
| 2023-07-07T22:01:00 | example\_request\_id | p\_1    | b\_1  | u\_1 | chocolate | 0            | 0            |
| 2023-07-07T22:01:00 | example\_request\_id | p\_2    | b\_1  | u\_1 | chocolate | 1            | 0            |

The goal of the `get_historical_features` call is to retrieve all of the features that were available to the machine learning model at the time of the above request. Specifically for this example input, it retrieves all requested `product`, `user`, `query`, and combination features, as they were at July 7th, 2023, 10:01pm

Besides this input dataframe, the list of features has to be passed to the `get_historical_features` call as well.