> ## 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.

# Batches

> Perform batch inference with Vertex AI

With Portkey, you can perform batch inference operations with Vertex AI models. This is the most efficient way to:

* Process large volumes of data with Vertex AI models
* Test your data with different foundation models
* Perform A/B testing with different foundation models

### Upload a file for batch inference

<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 a file for batch inference
    file = portkey.files.create(
        file=open("dataset.jsonl", "rb"),
        purpose="batch"
    )

    print(file)
    ```
  </Tab>

  <Tab title="NodeJS">
    ```typescript 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
    });

    (async () => {
        // Upload a file for batch inference
        const file = await portkey.files.create({
            file: fs.createReadStream("dataset.jsonl"),
            purpose: "batch"
        });

        console.log(file);
    })();
    ```
  </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 a file for batch inference
    file = openai.files.create(
        file=open("dataset.jsonl", "rb"),
        purpose="batch"
    )

    print(file)
    ```
  </Tab>

  <Tab title="OpenAI NodeJS">
    ```typescript 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"
        })
    });

    (async () => {
        // Upload a file for batch inference
        const file = await openai.files.create({
            file: fs.createReadStream("dataset.jsonl"),
            purpose: "batch"
        });

        console.log(file);
    })();
    ```
  </Tab>

  <Tab title="REST API">
    ```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="batch"' \
     --form 'file=@dataset.jsonl' \
     'https://api.portkey.ai/v1/files'
    ```
  </Tab>
</Tabs>

### Create a batch job

<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
    )

    # Create a batch inference job
    batch_job = portkey.batches.create(
        input_file_id="<file_id>", # File ID from the upload step
        endpoint="/v1/chat/completions", # API endpoint to use
        completion_window="24h", # Time window for completion
        model="gemini-1.5-flash-001"
    )

    print(batch_job)
    ```
  </Tab>

  <Tab title="NodeJS">
    ```typescript 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
    });

    (async () => {
        // Create a batch inference job
        const batchJob = await portkey.batches.create({
            input_file_id: "<file_id>", // File ID from the upload step
            endpoint: "/v1/chat/completions", // API endpoint to use
            completion_window: "24h", // Time window for completion
            model:"gemini-1.5-flash-001"
        });

        console.log(batchJob);
    })();
    ```
  </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"
        )
    )

    # Create a batch inference job
    batch_job = openai.batches.create(
        input_file_id="<file_id>", # File ID from the upload step
        endpoint="/v1/chat/completions", # API endpoint to use
        completion_window="24h", # Time window for completion
        model="gemini-1.5-flash-001"
    )

    print(batch_job)
    ```
  </Tab>

  <Tab title="OpenAI NodeJS">
    ```typescript 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"
        })
    });

    (async () => {
        // Create a batch inference job
        const batchJob = await openai.batches.create({
            input_file_id: "<file_id>", // File ID from the upload step
            endpoint: "/v1/chat/completions", // API endpoint to use
            completion_window: "24h", // Time window for completion
            model:"gemini-1.5-flash-001"
        });

        console.log(batchJob);
    })();
    ```
  </Tab>

  <Tab title="REST API">
    ```sh theme={"system"}
    curl -X POST --header 'Content-Type: application/json' \
     --header 'x-portkey-api-key: <portkey_api_key>' \
     --header 'x-portkey-virtual-key: <vertex_virtual_key>' \
     --data \
     $'{"input_file_id": "<file_id>", "endpoint": "/v1/chat/completions", "completion_window": "24h", "model":"gemini-1.5-flash-001"}' \
    'https://api.portkey.ai/v1/batches'
    ```
  </Tab>
</Tabs>

### List batch jobs

<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
    )

    # List all batch jobs
    jobs = portkey.batches.list(
        limit=10  # Optional: Number of jobs to retrieve (default: 20)
    )

    print(jobs)
    ```
  </Tab>

  <Tab title="NodeJS">
    ```typescript 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
    });

    (async () => {
        // List all batch jobs
        const jobs = await portkey.batches.list({
            limit: 10  // Optional: Number of jobs to retrieve (default: 20)
        });

        console.log(jobs);
    })();
    ```
  </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"
        )
    )

    # List all batch jobs
    jobs = openai.batches.list(
        limit=10  # Optional: Number of jobs to retrieve (default: 20)
    )

    print(jobs)
    ```
  </Tab>

  <Tab title="OpenAI NodeJS">
    ```typescript 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"
        })
    });

    (async () => {
        // List all batch jobs
        const jobs = await openai.batches.list({
            limit: 10  // Optional: 
        });

        console.log(jobs);
    })();
    ```
  </Tab>

  <Tab title="REST API">
    ```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/batches'
    ```
  </Tab>
</Tabs>

### Get a batch job

<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
    )

    # Retrieve a specific batch job
    job = portkey.batches.retrieve(
        "job_id"  # The ID of the batch job to retrieve
    )

    print(job)
    ```
  </Tab>

  <Tab title="NodeJS">
    ```typescript 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
    });

    (async () => {
        // Retrieve a specific batch job
        const job = await portkey.batches.retrieve(
            "job_id"  // The ID of the batch job to retrieve
        );

        console.log(job);
    })();
    ```
  </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"
        )
    )

    # Retrieve a specific batch job
    job = openai.batches.retrieve(
        "job_id"  # The ID of the batch job to retrieve
    )

    print(job)
    ```
  </Tab>

  <Tab title="OpenAI NodeJS">
    ```typescript 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"
        })
    });

    (async () => {
        // Retrieve a specific batch job
        const job = await openai.batches.retrieve(
            "job_id"  // The ID of the batch job to retrieve
        );

        console.log(job);
    })();
    ```
  </Tab>

  <Tab title="REST API">
    ```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/batches/<job_id>'
    ```
  </Tab>
</Tabs>

### Get batch job output

<Tabs>
  <Tab title="REST API">
    ```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/batches/<job_id>/output'
    ```
  </Tab>
</Tabs>