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

# Files

> Upload files to Google Cloud Storage for Vertex AI fine-tuning and batch inference

To perform fine-tuning or batch inference with Vertex AI, you need to upload files to Google Cloud Storage.
With Obiguard, you can easily upload files to GCS and use them for fine-tuning or batch inference with Vertex AI models.

## Uploading Files

<Tabs>
  <Tab title="Python SDK">
    ```python theme={null}
    from obiguard import Obiguard

    client = Obiguard(
       obiguard_api_key="OBIGUARD_API_KEY",  # Your Obiguard API key
       virtual_key="VERTEX_VIRTUAL_KEY",   # Add your Vertex virtual key
       vertex_storage_bucket_name="your_bucket_name", # Specify the GCS bucket name
       provider_file_name="your_file_name.jsonl", # Specify the file name in GCS
       provider_model="gemini-1.5-flash-001" # Specify the model to use
    )

    upload_file_response = client.files.create(
      purpose="fine-tune", # Can be "fine-tune" or "batch"
      file=open("dataset.jsonl", "rb")
    )

    print(upload_file_response)
    ```
  </Tab>

  <Tab title="OpenAI Python SDK">
    ```python theme={null}
    from openai import OpenAI
    from obiguard import OBIGUARD_GATEWAY_URL, createHeaders

    openai = OpenAI(
        api_key='OPENAI_API_KEY',
        base_url=OBIGUARD_GATEWAY_URL,
        default_headers=createHeaders(
            virtual_key="VERTEX_VIRTUAL_KEY",
            obiguard_api_key="sk-obg******",  # Your Obiguard API key
            vertex_storage_bucket_name="your_bucket_name",
            provider_file_name="your_file_name.jsonl",
            provider_model="gemini-1.5-flash-001"
        )
    )

    upload_file_response = openai.files.create(
      purpose="fine-tune", # Can be "fine-tune" or "batch"
      file=open("dataset.jsonl", "rb")
    )

    print(upload_file_response)
    ```
  </Tab>
</Tabs>

## Get File

<Tabs>
  <Tab title="Python SDK">
    ```python theme={null}
    from obiguard import Obiguard

    client = Obiguard(
       obiguard_api_key="OBIGUARD_API_KEY",  # Your Obiguard API key
       virtual_key="VERTEX_VIRTUAL_KEY"   # Add your Vertex virtual key
    )

    file = client.files.retrieve(file_id="file_id")

    print(file)
    ```
  </Tab>

  <Tab title="OpenAI Python SDK">
    ```python theme={null}
    from openai import OpenAI
    from obiguard import OBIGUARD_GATEWAY_URL, createHeaders


    openai = OpenAI(
      api_key='OPENAI_API_KEY',
      base_url=OBIGUARD_GATEWAY_URL,
      default_headers=createHeaders(
        virtual_key="VERTEX_VIRTUAL_KEY",
        obiguard_api_key="sk-obg******",  # Your Obiguard API key
      )
    )

    file = openai.files.retrieve(file_id="file_id")

    print(file)
    ```
  </Tab>
</Tabs>

## Get File Content

<Tabs>
  <Tab title="Python SDK">
    ```python theme={null}
    from obiguard import Obiguard

    client = Obiguard(
       obiguard_api_key="OBIGUARD_API_KEY",  # Your Obiguard API key
      virtual_key="VERTEX_VIRTUAL_KEY"   # Add your Vertex virtual key
    )

    file_content = client.files.content(file_id="file_id")

    print(file_content)
    ```
  </Tab>

  <Tab title="OpenAI Python SDK">
    ```python theme={null}
    from openai import OpenAI
    from obiguard import OBIGUARD_GATEWAY_URL, createHeaders


    openai = OpenAI(
      api_key='OPENAI_API_KEY',
      base_url=OBIGUARD_GATEWAY_URL,
      default_headers=createHeaders(
        virtual_key="VERTEX_VIRTUAL_KEY",
        obiguard_api_key="sk-obg******",  # Your Obiguard API key
      )
    )

    file_content = openai.files.content(file_id="file_id")

    print(file_content)
    ```
  </Tab>
</Tabs>

Note: The `ListFiles` endpoint is not supported for Vertex AI.