> ## Documentation Index
> Fetch the complete documentation index at: https://docs.portkey.ai/docs/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 Portkey, 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">
    ```python theme={"system"}
    from portkey_ai import Portkey

    # Initialize the Portkey client
    portkey = Portkey(
        api_key="PORTKEY_API_KEY",  # Replace with your Portkey 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 = portkey.files.create(
      purpose="fine-tune", # Can be "fine-tune" or "batch"
      file=open("dataset.jsonl", "rb")
    )

    print(upload_file_response)
    ```
  </Tab>

  <Tab title="NodeJS">
    ```js theme={"system"}
    import { Portkey } from 'portkey-ai';
    import * as fs from 'fs';

    // Initialize the Portkey client
    const portkey = Portkey({
        apiKey: "PORTKEY_API_KEY",  // Replace with your Portkey API key
        virtualKey: "VERTEX_VIRTUAL_KEY",   // Add your Vertex virtual key
        vertexStorageBucketName: "your_bucket_name", // Specify the GCS bucket name
        providerFileName: "your_file_name.jsonl", // Specify the file name in GCS
        providerModel: "gemini-1.5-flash-001" // Specify the model to use
    });

    const uploadFile = async () => {
      const file = await portkey.files.create({
        purpose: "fine-tune", // Can be "fine-tune" or "batch"
        file: fs.createReadStream("dataset.jsonl")
      });

      console.log(file);
    }

    uploadFile();
    ```
  </Tab>

  <Tab title="REST">
    ```sh theme={"system"}
    curl -X POST --header 'x-portkey-api-key: <portkey_api_key>' \
     --header 'x-portkey-virtual-key: <vertex_virtual_key>' \
     --header 'x-portkey-vertex-storage-bucket-name: <bucket_name>' \
     --header 'x-portkey-provider-file-name: <file_name>.jsonl' \
     --header 'x-portkey-provider-model: <model_name>' \
     --form 'purpose="fine-tune"' \
     --form 'file=@dataset.jsonl' \
     'https://api.portkey.ai/v1/files'
    ```
  </Tab>

  <Tab title="OpenAI NodeJS">
    ```js theme={"system"}
    import OpenAI from 'openai';
    import { PORTKEY_GATEWAY_URL, createHeaders } from 'portkey-ai';
    import * as fs from 'fs';

    const openai = new OpenAI({
      apiKey: 'OPENAI_API_KEY',
      baseURL: PORTKEY_GATEWAY_URL,
      defaultHeaders: createHeaders({
        virtualKey: "VERTEX_VIRTUAL_KEY",
        apiKey: "PORTKEY_API_KEY",
        vertexStorageBucketName: "your_bucket_name",
        providerFileName: "your_file_name.jsonl",
        providerModel: "gemini-1.5-flash-001"
      })
    });

    const uploadFile = async () => {
      const file = await openai.files.create({
        purpose: "fine-tune", // Can be "fine-tune" or "batch"
        file: fs.createReadStream("dataset.jsonl")
      });

      console.log(file);
    }

    uploadFile();
    ```
  </Tab>

  <Tab title="OpenAI Python">
    ```python theme={"system"}
    from openai import OpenAI
    from portkey_ai import PORTKEY_GATEWAY_URL, createHeaders

    openai = OpenAI(
        api_key='OPENAI_API_KEY',
        base_url=PORTKEY_GATEWAY_URL,
        default_headers=createHeaders(
            virtual_key="VERTEX_VIRTUAL_KEY",
            api_key="PORTKEY_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">
    ```python theme={"system"}
    from portkey_ai import Portkey

    # Initialize the Portkey client
    portkey = Portkey(
        api_key="PORTKEY_API_KEY",  # Replace with your Portkey API key
        virtual_key="VERTEX_VIRTUAL_KEY"   # Add your Vertex virtual key
    )

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

    print(file)
    ```
  </Tab>

  <Tab title="NodeJS">
    ```js theme={"system"}
    import { Portkey } from 'portkey-ai';

    // Initialize the Portkey client
    const portkey = Portkey({
        apiKey: "PORTKEY_API_KEY",  // Replace with your Portkey API key
        virtualKey: "VERTEX_VIRTUAL_KEY"   // Add your Vertex virtual key
    });

    const getFile = async () => {
      const file = await portkey.files.retrieve("file_id");

      console.log(file);
    }

    getFile();
    ```
  </Tab>

  <Tab title="REST">
    ```sh theme={"system"}
    curl -X GET --header 'x-portkey-api-key: <portkey_api_key>' \
    --header 'x-portkey-virtual-key: <vertex_virtual_key>' \
    'https://api.portkey.ai/v1/files/<file_id>'
    ```
  </Tab>

  <Tab title="OpenAI NodeJS">
    ```js theme={"system"}
    import OpenAI from 'openai';
    import { PORTKEY_GATEWAY_URL, createHeaders } from 'portkey-ai';

    const openai = new OpenAI({
      apiKey: 'OPENAI_API_KEY',
      baseURL: PORTKEY_GATEWAY_URL,
      defaultHeaders: createHeaders({
        virtualKey: "VERTEX_VIRTUAL_KEY",
        apiKey: "PORTKEY_API_KEY"
      })
    });

    const getFile = async () => {
      const file = await openai.files.retrieve("file_id");

      console.log(file);
    }

    getFile();
    ```
  </Tab>

  <Tab title="OpenAI Python">
    ```python theme={"system"}
    from openai import OpenAI
    from portkey_ai import PORTKEY_GATEWAY_URL, createHeaders

    openai = OpenAI(
        api_key='OPENAI_API_KEY',
        base_url=PORTKEY_GATEWAY_URL,
        default_headers=createHeaders(
            virtual_key="VERTEX_VIRTUAL_KEY",
            api_key="PORTKEY_API_KEY"
        )
    )

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

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

## Get File Content

<Tabs>
  <Tab title="Python">
    ```python theme={"system"}
    from portkey_ai import Portkey

    # Initialize the Portkey client
    portkey = Portkey(
        api_key="PORTKEY_API_KEY",  # Replace with your Portkey API key
        virtual_key="VERTEX_VIRTUAL_KEY"   # Add your Vertex virtual key
    )

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

    print(file_content)
    ```
  </Tab>

  <Tab title="NodeJS">
    ```js theme={"system"}
    import { Portkey } from 'portkey-ai';

    // Initialize the Portkey client
    const portkey = Portkey({
        apiKey: "PORTKEY_API_KEY",  // Replace with your Portkey API key
        virtualKey: "VERTEX_VIRTUAL_KEY"   // Add your Vertex virtual key
    });

    const getFileContent = async () => {
      const fileContent = await portkey.files.content("file_id");

      console.log(fileContent);
    }

    getFileContent();
    ```
  </Tab>

  <Tab title="REST">
    ```sh theme={"system"}
    curl -X GET --header 'x-portkey-api-key: <portkey_api_key>' \
    --header 'x-portkey-virtual-key: <vertex_virtual_key>' \
    'https://api.portkey.ai/v1/files/<file_id>/content'
    ```
  </Tab>

  <Tab title="OpenAI NodeJS">
    ```js theme={"system"}
    import OpenAI from 'openai';
    import { PORTKEY_GATEWAY_URL, createHeaders } from 'portkey-ai';

    const openai = new OpenAI({
      apiKey: 'OPENAI_API_KEY',
      baseURL: PORTKEY_GATEWAY_URL,
      defaultHeaders: createHeaders({
        virtualKey: "VERTEX_VIRTUAL_KEY",
        apiKey: "PORTKEY_API_KEY"
      })
    });

    const getFileContent = async () => {
      const fileContent = await openai.files.content("file_id");

      console.log(fileContent);
    }

    getFileContent();
    ```
  </Tab>

  <Tab title="OpenAI Python">
    ```python theme={"system"}
    from openai import OpenAI
    from portkey_ai import PORTKEY_GATEWAY_URL, createHeaders

    openai = OpenAI(
        api_key='OPENAI_API_KEY',
        base_url=PORTKEY_GATEWAY_URL,
        default_headers=createHeaders(
            virtual_key="VERTEX_VIRTUAL_KEY",
            api_key="PORTKEY_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.