# Display the first few rows of the updated dataframe
dataset_df[["company", "ticker", "combined_attributes"]].head()
```

<div>
  <table border="1" class="dataframe fern-table">
    <thead>
      <tr>
        <th />

        <th>
          company
        </th>

        <th>
          ticker
        </th>

        <th>
          combined_attributes
        </th>
      </tr>
    </thead>

    <tbody>
      <tr>
        <th>
          0
        </th>

        <td>
          CyberDefense Dynamics
        </td>

        <td>
          CDDY
        </td>

        <td>
          CyberDefense Dynamics Information Technology 2...
        </td>
      </tr>

      <tr>
        <th>
          1
        </th>

        <td>
          CloudCompute Pro
        </td>

        <td>
          CCPR
        </td>

        <td>
          CloudCompute Pro Information Technology 2023 C...
        </td>
      </tr>

      <tr>
        <th>
          2
        </th>

        <td>
          VirtualReality Systems
        </td>

        <td>
          VRSY
        </td>

        <td>
          VirtualReality Systems Information Technology ...
        </td>
      </tr>

      <tr>
        <th>
          3
        </th>

        <td>
          BioTech Innovations
        </td>

        <td>
          BTCI
        </td>

        <td>
          BioTech Innovations Information Technology 202...
        </td>
      </tr>

      <tr>
        <th>
          4
        </th>

        <td>
          QuantumComputing Inc
        </td>

        <td>
          QCMP
        </td>

        <td>
          QuantumComputing Inc Information Technology 20...
        </td>
      </tr>
    </tbody>
  </table>
</div>

## Step 3: Embedding Generation with Cohere

```python
from tqdm import tqdm


def get_embedding(
    text: str, input_type: str = "search_document"
) -> list[float]:
    if not text.strip():
        print("Attempted to get embedding for empty text.")
        return []

    model = "embed-v4.0"
    response = co.embed(
        texts=[text],
        model=model,
        input_type=input_type,  # Used for embeddings of search queries run against a vector DB to find relevant documents
        embedding_types=["float"],
    )

    return response.embeddings.float[0]