> ## Documentation Index
> Fetch the complete documentation index at: https://portkey-docs-feat-rerank-documentation.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Files

> Upload files to S3 for Bedrock batch inference

To perform batch inference with Bedrock, you need to upload files to S3.
This process can be cumbersome and duplicative in nature because you need to transform your data into model specific formats.

With Portkey, you can upload the file in [OpenAI format](https://platform.openai.com/docs/guides/batch#1-preparing-your-batch-file) and portkey will handle transforming the file into the format required by Bedrock on the fly!

This is the most efficient way to

* Test your data with different foundation models
* Perform A/B testing with different foundation models
* Perform batch inference with different foundation 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="VIRTUAL_KEY",   # Add your provider's virtual key
        provider="bedrock",
        aws_region="YOUR_AWS_REGION",
        aws_s3_bucket="YOUR_AWS_S3_BUCKET",
        aws_s3_object_key="YOUR_AWS_S3_OBJECT_KEY",
        aws_bedrock_model="YOUR_AWS_BEDROCK_MODEL",
        amz_server_side_encryption: "ENCRYPTION_TYPE", # [optional] default is aws:kms
        amz_server_side_encryption_aws_kms_key_id: "KMS_KEY_ID" # [optional] use this only if you want to use a KMS key to encrypt the file at rest
    )

    upload_file_response = portkey.files.create(
      purpose="batch",
      file=open("file.pdf", "rb")
    )

    print(upload_file_response)
    ```
  </Tab>

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

    // Initialize the Portkey client
    const portkey = new Portkey({
        apiKey: "PORTKEY_API_KEY",  // Replace with your Portkey API key
        virtualKey: "VIRTUAL_KEY",   // Add your provider's virtual key
        provider: "bedrock",
        awsRegion: "YOUR_AWS_REGION",
        awsS3Bucket: "YOUR_AWS_S3_BUCKET",
        awsS3ObjectKey: "YOUR_AWS_S3_OBJECT_KEY",
        awsBedrockModel: "YOUR_AWS_BEDROCK_MODEL",
        amzServerSideEncryption: "ENCRYPTION_TYPE", // [optional] default is aws:kms
        amzServerSideEncryptionAwsKmsKeyId: "KMS_KEY_ID" // [optional] use this only if you want to use a KMS key to encrypt the file at rest
    });

    const uploadFile = async () => {
      const file = await portkey.files.create({
        purpose: "batch",
        file: fs.createReadStream("file.pdf")
      });

      console.log(file);
    }

    await uploadFile();

    ```
  </Tab>

  <Tab title="REST">
    ```sh theme={"system"}
    # you can also use a virtual key here
    curl --location 'https://api.portkey.ai/v1/files' \
    --header 'x-portkey-api-key: <portkey_api_key>' \
    --header 'x-portkey-provider: bedrock' \
    --header 'Content-Type: application/json' \
    --header 'x-portkey-aws-access-key-id: {YOUR_AWS_ACCESS_KEY_ID}' \
    --header 'x-portkey-aws-secret-access-key: {YOUR_AWS_SECRET_ACCESS_KEY}' \
    --header 'x-portkey-aws-region: {YOUR_AWS_REGION}' \
    --header 'x-portkey-aws-s3-bucket: {YOUR_AWS_S3_BUCKET}' \
    --header 'x-portkey-aws-s3-object-key: {YOUR_AWS_S3_OBJECT_KEY}' \
    --header 'x-portkey-aws-bedrock-model: {YOUR_AWS_BEDROCK_MODEL}' \
    --header 'x-portkey-amz-server-side-encryption: {ENCRYPTION_TYPE}' \
    --header 'x-portkey-amz-server-side-encryption-aws-kms-key-id: {KMS_KEY_ID}' \
    --form 'file=@"{YOUR_FILE_PATH}"',
    --form 'purpose="batch"'
    ```
  </Tab>

  <Tab title="OpenAI NodeJS">
    ```js theme={"system"}
    import OpenAI from 'openai'; // We're using the v4 SDK
    import { PORTKEY_GATEWAY_URL, createHeaders } from 'portkey-ai'

    const openai = new OpenAI({
      apiKey: 'OPENAI_API_KEY', // defaults to process.env["OPENAI_API_KEY"],
      baseURL: PORTKEY_GATEWAY_URL,
      defaultHeaders: createHeaders({
        provider: "openai",
        apiKey: "PORTKEY_API_KEY", // defaults to process.env["PORTKEY_API_KEY"]
        awsRegion: "YOUR_AWS_REGION",
        awsS3Bucket: "YOUR_AWS_S3_BUCKET",
        awsS3ObjectKey: "YOUR_AWS_S3_OBJECT_KEY",
        awsBedrockModel: "YOUR_AWS_BEDROCK_MODEL",
        amzServerSideEncryption: "ENCRYPTION_TYPE", // [optional] default is aws:kms
        amzServerSideEncryptionAwsKmsKeyId: "KMS_KEY_ID" // [optional] use this only if you want to use a KMS key to encrypt the file at rest
      })
    });

    const uploadFile = async () => {
      const file = await openai.files.create({
        purpose: "batch",
        file: fs.createReadStream("file.pdf")
      });

      console.log(file);
    }

    await 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(
            provider="openai",
            api_key="PORTKEY_API_KEY",
            aws_region="YOUR_AWS_REGION",
            aws_s3_bucket="YOUR_AWS_S3_BUCKET",
            aws_s3_object_key="YOUR_AWS_S3_OBJECT_KEY",
            aws_bedrock_model="YOUR_AWS_BEDROCK_MODEL",
            amz_server_side_encryption: "ENCRYPTION_TYPE", # [optional] default is aws:kms
            amz_server_side_encryption_aws_kms_key_id: "KMS_KEY_ID" # [optional] use this only if you want to use a KMS key to encrypt the file at rest
        )
    )

    upload_file_response = openai.files.create(
      purpose="batch",
      file=open("file.pdf", "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="VIRTUAL_KEY",   # Add your provider's virtual key
        aws_region="YOUR_AWS_REGION",
    )

    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 = new Portkey({
        apiKey: "PORTKEY_API_KEY",  // Replace with your Portkey API key
        virtualKey: "VIRTUAL_KEY",   // Add your provider's virtual key
        awsRegion="YOUR_AWS_REGION",
    });

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

      console.log(file);
    }

    await getFile();

    ```
  </Tab>

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

  <Tab title="OpenAI NodeJS">
    ```js theme={"system"}
    import OpenAI from 'openai'; // We're using the v4 SDK
    import { PORTKEY_GATEWAY_URL, createHeaders } from 'portkey-ai'

    const openai = new OpenAI({
      apiKey: 'OPENAI_API_KEY', // defaults to process.env["OPENAI_API_KEY"],
      baseURL: PORTKEY_GATEWAY_URL,
      defaultHeaders: createHeaders({
        provider: "openai",
        apiKey: "PORTKEY_API_KEY", // defaults to process.env["PORTKEY_API_KEY"]
        awsRegion="YOUR_AWS_REGION",
      })
    });

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

      console.log(file);
    }

    await 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(
            provider="openai",
            api_key="PORTKEY_API_KEY",
            aws_region="YOUR_AWS_REGION",
        )
    )

    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="VIRTUAL_KEY",   # Add your provider's virtual key
        aws_region="YOUR_AWS_REGION",
    )

    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 = new Portkey({
        apiKey: "PORTKEY_API_KEY",  // Replace with your Portkey API key
        virtualKey: "VIRTUAL_KEY",   // Add your provider's virtual key
        awsRegion="YOUR_AWS_REGION",
    });

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

      console.log(file_content);
    }

    await getFileContent();

    ```
  </Tab>

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

  <Tab title="OpenAI NodeJS">
    ```js theme={"system"}
    import OpenAI from 'openai'; // We're using the v4 SDK
    import { PORTKEY_GATEWAY_URL, createHeaders } from 'portkey-ai'

    const openai = new OpenAI({
      apiKey: 'OPENAI_API_KEY', // defaults to process.env["OPENAI_API_KEY"],
      baseURL: PORTKEY_GATEWAY_URL,
      defaultHeaders: createHeaders({
        provider: "openai",
        apiKey: "PORTKEY_API_KEY", // defaults to process.env["PORTKEY_API_KEY"]
        awsRegion="YOUR_AWS_REGION",
      })
    });

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

      console.log(file_content);
    }

    await 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(
            provider="openai",
            api_key="PORTKEY_API_KEY",
            aws_region="YOUR_AWS_REGION",
        )
    )

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

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

<Note>
  The following endpoints are **NOT** supported for Bedrock for security reasons:

  * `GET /v1/files`
  * `DELETE /v1/files/{file_id}`
</Note>