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

# Quickstart

<Steps>
  <Step title="Install the SDK">
    Install the OuterProduct SDK from PyPI.

    ```shellscript theme={null}
    pip install outerproduct
    ```

    Or with the uv package manager:

    ```shellscript theme={null}
    uv add outerproduct
    ```
  </Step>

  <Step title="Initialize and open a workspace">
    Call `op.init()` once to authenticate, then open a `Workspace`. The workspace is your entry point for unified data queries, model training, and reasoning.

    ```python theme={null}
    import outerproduct as op

    op.init()  # reads OUTERPRODUCT_API_KEY from the environment to authenticate
    ws = op.Workspace()
    ```
  </Step>

  <Step title="Register a source table">
    Register a source table using the appropriate `register_*` function. For example,`register_csv` registers a csv file to the workspace.  (See the Overview for an example of registering multiple sources.)

    ```python theme={null}
    ws.register_csv("customers.csv", table_name="customers")
    ```
  </Step>

  <Step title="Build a training set and fit a reasoning model">
    Query over the registered sources, then train a `ReasoningModel`. (See the Overview for an example of training over multiple models, setting hyper-parameter configurations, and more.)

    ```python theme={null}
    train = ws.table("customers")
    model = op.reasoning.fit(train, task=op.Binclass(label_column="churn")).wait()
    ```
  </Step>

  <Step title="Run inference with native reasoning">
    Reasoning models come with additional reasoning functions.  For example, get predictions with real-time drivers. (See Overview for additional examples of reasoning like running scenarios.)

    ```python theme={null}
    recent = ws.table("customers").filter(op.col("signup_year") == 2025)
    predictions = model.predict(recent)
    reasoning = model.explain(recent)

    print(reasoning.feature_names)
    print(reasoning.drivers.shape)  
    ```
  </Step>
</Steps>

## Putting it all together

```python theme={null}
import outerproduct as op

op.init()  # reads OUTERPRODUCT_API_KEY from the environment
ws = op.Workspace()

# Register a CSV as a source table you can query.
ws.register_csv("customers.csv", table_name="customers")

# Build the training set as a query, then fit a ReasoningModel.
train = ws.table("customers")
model = op.reasoning.fit(train, task=op.Binclass(label_column="churn")).wait()

# Score recent customers with a query; get predictions and reasoning.
recent = ws.table("customers").filter(op.col("signup_year") == 2025)
predictions = model.predict(recent)
reasoning = model.explain(recent)

print(reasoning.feature_names)
print(reasoning.drivers.shape)
```