# Run the pipeline to transform the text
nodes = pipeline.run(nodes=nodes)
```

```txt title="Output"
/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning:
The secret `HF_TOKEN` does not exist in your Colab secrets.
To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.
You will be able to reuse this secret in all of your notebooks.
Please note that authentication is recommended but still optional to access public models or datasets.
    warnings.warn(



tokenizer_config.json:   0%|          | 0.00/7.92k [00:00<?, ?B/s]



tokenization_cohere_fast.py:   0%|          | 0.00/43.7k [00:00<?, ?B/s]



configuration_cohere.py:   0%|          | 0.00/7.37k [00:00<?, ?B/s]


A new version of the following files was downloaded from https://huggingface.co/CohereForAI/c4ai-command-r-v01:
- configuration_cohere.py
. Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.
A new version of the following files was downloaded from https://huggingface.co/CohereForAI/c4ai-command-r-v01:
- tokenization_cohere_fast.py
- configuration_cohere.py
. Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.



tokenizer.json:   0%|          | 0.00/12.8M [00:00<?, ?B/s]



special_tokens_map.json:   0%|          | 0.00/429 [00:00<?, ?B/s]


Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
```

## **Step 2: Load document into a LlamaIndex vector store**

Loading the document into a LlamaIndex vector store will allow us to use the Cohere embedding model and rerank model to retrieve the relevant parts of the form to pass into Command.

```python PYTHON
from llama_index.core import Settings, VectorStoreIndex

from llama_index.postprocessor.cohere_rerank import CohereRerank

from llama_index.embeddings.cohere import CohereEmbedding