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

# Dataset

> API reference for the Dataset and Conversation classes.

## Dataset

```python theme={null}
from aevyra_verdict import Dataset
```

### `Dataset.from_jsonl(path, name=None, format="auto")`

Load a dataset from a JSONL file.

```python theme={null}
dataset = Dataset.from_jsonl("data.jsonl")
dataset = Dataset.from_jsonl("data.jsonl", name="my-dataset")

# Explicit format
dataset = Dataset.from_jsonl("sharegpt_data.jsonl", format="sharegpt")
dataset = Dataset.from_jsonl("alpaca_data.jsonl", format="alpaca")
```

| Parameter | Default       | Description                                                      |
| --------- | ------------- | ---------------------------------------------------------------- |
| `path`    | —             | Path to the JSONL file.                                          |
| `name`    | filename stem | Display name for the dataset.                                    |
| `format`  | `"auto"`      | Input format: `"auto"`, `"openai"`, `"sharegpt"`, or `"alpaca"`. |

### `Dataset.from_list(items, name="inline", format="auto")`

Create a dataset from a list of dicts (same schema as JSONL lines).

```python theme={null}
dataset = Dataset.from_list([
    {"messages": [{"role": "user", "content": "Hello"}], "ideal": "Hi"},
])

# ShareGPT inline
dataset = Dataset.from_list(sharegpt_records, format="sharegpt")
```

| Parameter | Default    | Description                                                      |
| --------- | ---------- | ---------------------------------------------------------------- |
| `items`   | —          | List of records in OpenAI, ShareGPT, or Alpaca format.           |
| `name`    | `"inline"` | Display name for the dataset.                                    |
| `format`  | `"auto"`   | Input format: `"auto"`, `"openai"`, `"sharegpt"`, or `"alpaca"`. |

````

### `dataset.filter(**kwargs)`

Return a new dataset containing only conversations where metadata matches all given key-value pairs.

```python
hard = dataset.filter(difficulty="hard")
hard_reasoning = dataset.filter(difficulty="hard", category="reasoning")
````

### `dataset.summary()`

Return a dict with name, sample count, whether ideals are present, and metadata keys.

```python theme={null}
{
    "name": "data",
    "num_conversations": 50,
    "has_ideals": True,
    "metadata_keys": ["category", "difficulty"]
}
```

### `dataset.has_ideals()`

Return `True` if every conversation has an `ideal` field.

***

## Conversation

Each item in a dataset is a `Conversation`:

| Property            | Type            | Description                                           |
| ------------------- | --------------- | ----------------------------------------------------- |
| `messages`          | `list[Message]` | The conversation messages.                            |
| `ideal`             | `str \| None`   | The reference answer.                                 |
| `metadata`          | `dict`          | Arbitrary metadata.                                   |
| `prompt_messages`   | `list[dict]`    | Messages as plain dicts, ready to send to a provider. |
| `last_user_message` | `str \| None`   | The last user message in the conversation.            |