Portkey Finetune API

openapi: 3.0.0
info:
  title: Portkey Analytics > Graphs Finetune API
  description: The Portkey REST API. Please see https://portkey.ai/docs/api-reference for more details.
  version: 2.0.0
  termsOfService: https://portkey.ai/terms
  contact:
    name: Portkey Developer Forum
    url: https://portkey.wiki/community
  license:
    name: MIT
    url: https://github.com/Portkey-AI/portkey-openapi/blob/master/LICENSE
servers:
- url: https://api.portkey.ai/v1
  description: Portkey API Public Endpoint
security:
- Portkey-Key: []
tags:
- name: Finetune
paths:
  /fine_tuning/jobs:
    servers:
    - url: https://api.portkey.ai/v1
      description: Portkey API Public Endpoint
    - url: SELF_HOSTED_GATEWAY_URL
      description: Self-Hosted Gateway URL
    post:
      operationId: createFineTuningJob
      summary: Create a Finetune Job
      description: Finetune a provider model
      parameters: []
      responses:
        '200':
          description: The request has succeeded.
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/FineTuningJob'
      tags:
      - Finetune
      requestBody:
        required: true
        content:
          application/json:
            schema:
              anyOf:
              - $ref: '#/components/schemas/OpenAIFinetuneJob'
              - $ref: '#/components/schemas/BedrockFinetuneJob'
              - $ref: '#/components/schemas/PortkeyFinetuneJob'
      security:
      - Portkey-Key: []
        Virtual-Key: []
      - Portkey-Key: []
        Provider-Auth: []
        Provider-Name: []
      - Portkey-Key: []
        Config: []
      - Portkey-Key: []
        Provider-Auth: []
        Provider-Name: []
        Custom-Host: []
      x-code-samples:
      - lang: curl
        label: Default
        source: "curl https://api.portkey.ai/v1/fine_tuning/jobs \\\n  -H \"Content-Type: application/json\" \\\n  -H \"x-portkey-api-key: $PORTKEY_API_KEY\" \\\n  -H \"x-portkey-virtual-key: $PORTKEY_PROVIDER_VIRTUAL_KEY\" \\\n  -d '{\n    \"training_file\": \"file-BK7bzQj3FfZFXr7DbL6xJwfo\",\n    \"model\": \"gpt-3.5-turbo\"\n  }'\n"
      - lang: python
        label: Default
        source: "from portkey_ai import Portkey\n\nclient = Portkey(\n  api_key = \"PORTKEY_API_KEY\",\n  virtual_key = \"PROVIDER_VIRTUAL_KEY\"\n)\n\nclient.fine_tuning.jobs.create(\n  training_file=\"file-abc123\",\n  model=\"gpt-3.5-turbo\"\n)\n"
      - lang: javascript
        label: Default
        source: "import Portkey from 'portkey-ai';\n\nconst client = new Portkey({\n  apiKey: 'PORTKEY_API_KEY',\n  virtualKey: 'PROVIDER_VIRTUAL_KEY'\n});\n\nasync function main() {\n  const fineTune = await client.fineTuning.jobs.create({\n    training_file: \"file-abc123\"\n  });\n\n  console.log(fineTune);\n}\n\nmain();\n"
      - lang: curl
        label: Self-hosted
        source: "curl https://SELF_HOSTED_GATEWAY_URL/fine_tuning/jobs \\\n  -H \"Content-Type: application/json\" \\\n  -H \"x-portkey-api-key: $PORTKEY_API_KEY\" \\\n  -H \"x-portkey-virtual-key: $PORTKEY_PROVIDER_VIRTUAL_KEY\" \\\n  -d '{\n    \"training_file\": \"file-BK7bzQj3FfZFXr7DbL6xJwfo\",\n    \"model\": \"gpt-3.5-turbo\"\n  }'\n"
      - lang: python
        label: Self-hosted
        source: "from portkey_ai import Portkey\n\nclient = Portkey(\n  api_key = \"PORTKEY_API_KEY\",\n  base_url = \"SELF_HOSTED_GATEWAY_URL\",\n  virtual_key = \"PROVIDER_VIRTUAL_KEY\"\n)\n\nclient.fine_tuning.jobs.create(\n  training_file=\"file-abc123\",\n  model=\"gpt-3.5-turbo\"\n)\n"
      - lang: javascript
        label: Self-hosted
        source: "import Portkey from 'portkey-ai';\n\nconst client = new Portkey({\n  apiKey: 'PORTKEY_API_KEY',\n  baseUrl: 'SELF_HOSTED_GATEWAY_URL',\n  virtualKey: 'PROVIDER_VIRTUAL_KEY'\n});\n\nasync function main() {\n  const fineTune = await client.fineTuning.jobs.create({\n    training_file: \"file-abc123\"\n  });\n\n  console.log(fineTune);\n}\n\nmain();\n"
components:
  schemas:
    FineTuningJob:
      type: object
      title: FineTuningJob
      description: 'The `fine_tuning.job` object represents a fine-tuning job that has been created through the API.

        '
      properties:
        id:
          type: string
          description: The object identifier, which can be referenced in the API endpoints.
        created_at:
          type: integer
          description: The Unix timestamp (in seconds) for when the fine-tuning job was created.
        error:
          type: object
          nullable: true
          description: For fine-tuning jobs that have `failed`, this will contain more information on the cause of the failure.
          properties:
            code:
              type: string
              description: A machine-readable error code.
            message:
              type: string
              description: A human-readable error message.
            param:
              type: string
              description: The parameter that was invalid, usually `training_file` or `validation_file`. This field will be null if the failure was not parameter-specific.
              nullable: true
          required:
          - code
          - message
          - param
        fine_tuned_model:
          type: string
          nullable: true
          description: The name of the fine-tuned model that is being created. The value will be null if the fine-tuning job is still running.
        finished_at:
          type: integer
          nullable: true
          description: The Unix timestamp (in seconds) for when the fine-tuning job was finished. The value will be null if the fine-tuning job is still running.
        hyperparameters:
          type: object
          description: The hyperparameters used for the fine-tuning job. See the [fine-tuning guide](https://platform.openai.com/docs/guides/fine-tuning) for more details.
          properties:
            n_epochs:
              oneOf:
              - type: string
                enum:
                - auto
              - type: integer
                minimum: 1
                maximum: 50
              default: auto
              description: 'The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.

                "auto" decides the optimal number of epochs based on the size of the dataset. If setting the number manually, we support any number between 1 and 50 epochs.'
          required:
          - n_epochs
        model:
          type: string
          description: The base model that is being fine-tuned.
        object:
          type: string
          description: The object type, which is always "fine_tuning.job".
          enum:
          - fine_tuning.job
        organization_id:
          type: string
          description: The organization that owns the fine-tuning job.
        result_files:
          type: array
          description: The compiled results file ID(s) for the fine-tuning job. You can retrieve the results with the [Files API](https://platform.openai.com/docs/api-reference/files/retrieve-contents).
          items:
            type: string
            example: file-abc123
        status:
          type: string
          description: The current status of the fine-tuning job, which can be either `validating_files`, `queued`, `running`, `succeeded`, `failed`, or `cancelled`.
          enum:
          - validating_files
          - queued
          - running
          - succeeded
          - failed
          - cancelled
        trained_tokens:
          type: integer
          nullable: true
          description: The total number of billable tokens processed by this fine-tuning job. The value will be null if the fine-tuning job is still running.
        training_file:
          type: string
          description: The file ID used for training. You can retrieve the training data with the [Files API](https://platform.openai.com/docs/api-reference/files/retrieve-contents).
        validation_file:
          type: string
          nullable: true
          description: The file ID used for validation. You can retrieve the validation results with the [Files API](https://platform.openai.com/docs/api-reference/files/retrieve-contents).
        integrations:
          type: array
          nullable: true
          description: A list of integrations to enable for this fine-tuning job.
          maxItems: 5
          items:
            oneOf:
            - $ref: '#/components/schemas/FineTuningIntegration'
            x-oaiExpandable: true
        seed:
          type: integer
          description: The seed used for the fine-tuning job.
        estimated_finish:
          type: integer
          nullable: true
          description: The Unix timestamp (in seconds) for when the fine-tuning job is estimated to finish. The value will be null if the fine-tuning job is not running.
      required:
      - created_at
      - error
      - finished_at
      - fine_tuned_model
      - hyperparameters
      - id
      - model
      - object
      - organization_id
      - result_files
      - status
      - trained_tokens
      - training_file
      - validation_file
      - seed
    BedrockFinetuneJob:
      type: object
      description: Gateway supported body params for bedrock fine-tuning.
      title: Bedrock Params
      properties:
        job_name:
          type: string
          description: Job name for the bedrock finetune job
        role_arn:
          type: string
          description: Role ARN for the bedrock finetune job
        output_file:
          type: string
          description: Finetune job's output s3 location, will be constructed based on `training_file` if not provided
      allOf:
      - $ref: '#/components/schemas/OpenAIFinetuneJob'
    OpenAIFinetuneJob:
      type: object
      description: Gateway supported body params for OpenAI, Azure OpenAI and VertexAI.
      title: OpenAI Params
      required:
      - model
      - training_file
      - suffix
      - method
      properties:
        model:
          type: string
          description: The base model to finetune
        training_file:
          type: string
          description: The training file to use for the finetune job
        validation_file:
          type: string
          description: The validation file to use for the finetune job
        suffix:
          type: string
          description: The suffix to append to the fine-tuned model name
        method:
          type: object
          properties:
            type:
              type: string
              enum:
              - supervised
              - dpo
            supervised:
              type: object
              properties:
                hyperparameters:
                  type: object
                  properties:
                    n_epochs:
                      type: integer
                      format: int32
                    learning_rate_multiplier:
                      type: number
                      format: float
                    batch_size:
                      type: integer
                      format: int32
                  required:
                  - n_epochs
                  - learning_rate_multiplier
                  - batch_size
              required:
              - hyperparameters
            dpo:
              type: object
              properties:
                hyperparameters:
                  type: object
                  properties:
                    n_epochs:
                      type: integer
                      format: int32
                    learning_rate_multiplier:
                      type: number
                      format: float
                    batch_size:
                      type: integer
                      format: int32
                  required:
                  - n_epochs
                  - learning_rate_multiplier
                  - batch_size
              required:
              - hyperparameters
          required:
          - type
          description: Hyperparameters for the finetune job
    PortkeyOptions:
      type: object
      required:
      - x-portkey-virtual-key
      properties:
        x-portkey-virtual-key:
          type: string
          description: The virtual key to communicate with the provider
        x-portkey-aws-s3-bucket:
          type: string
          description: The AWS S3 bucket to use for file upload during finetune
        x-portkey-vertex-storage-bucket-name:
          type: string
          description: Google Storage bucket to use for file upload during finetune
      example:
        x-portkey-virtual-key: vkey-1234567890
        x-portkey-aws-s3-bucket: my-bucket
        x-portkey-vertex-storage-bucket-name: my-bucket
      description: Options to be passed to the provider, supports all options supported by the provider from gateway.
    BedrockParams:
      type: object
      properties:
        job_name:
          type: string
          description: Job name for the bedrock finetune job
        role_arn:
          type: string
          description: Role ARN for the bedrock finetune job
        output_file:
          type: string
          description: Finetune job's output s3 location, will be constructed based on `training_file` if not provided
    PortkeyFinetuneJob:
      type: object
      properties:
        job_name:
          type: string
          description: Job name for the bedrock finetune job
        role_arn:
          type: string
          description: Role ARN for the bedrock finetune job
        output_file:
          type: string
          description: Finetune job's output s3 location, will be constructed based on `training_file` if not provided
        portkey_options:
          allOf:
          - $ref: '#/components/schemas/PortkeyOptions'
          description: Portkey Gateway Provider specific headers to be passed to the provider, if portkey is used as a provider
        provider_options:
          allOf:
          - $ref: '#/components/schemas/BedrockParams'
          description: Provider specific options to be passed to the provider, optional can be passed directly as well. Can be skipped if same keys are passed at top the level.
      allOf:
      - $ref: '#/components/schemas/OpenAIFinetuneJob'
      description: Gateway supported body params for portkey managed fine-tuning.
      title: Portkey Params
    FineTuningIntegration:
      type: object
      title: Fine-Tuning Job Integration
      required:
      - type
      - wandb
      properties:
        type:
          type: string
          description: The type of the integration being enabled for the fine-tuning job
          enum:
          - wandb
        wandb:
          type: object
          description: 'The settings for your integration with Weights and Biases. This payload specifies the project that

            metrics will be sent to. Optionally, you can set an explicit display name for your run, add tags

            to your run, and set a default entity (team, username, etc) to be associated with your run.

            '
          required:
          - project
          properties:
            project:
              description: 'The name of the project that the new run will be created under.

                '
              type: string
              example: my-wandb-project
            name:
              description: 'A display name to set for the run. If not set, we will use the Job ID as the name.

                '
              nullable: true
              type: string
            entity:
              description: 'The entity to use for the run. This allows you to set the team or username of the WandB user that you would

                like associated with the run. If not set, the default entity for the registered WandB API key is used.

                '
              nullable: true
              type: string
            tags:
              description: 'A list of tags to be attached to the newly created run. These tags are passed through directly to WandB. Some

                default tags are generated by OpenAI: "openai/finetune", "openai/{base-model}", "openai/{ftjob-abcdef}".

                '
              type: array
              items:
                type: string
                example: custom-tag
  securitySchemes:
    Portkey-Key:
      type: apiKey
      in: header
      name: x-portkey-api-key
    Virtual-Key:
      type: apiKey
      in: header
      name: x-portkey-virtual-key
    Provider-Auth:
      type: http
      scheme: bearer
    Provider-Name:
      type: apiKey
      in: header
      name: x-portkey-provider
    Config:
      type: apiKey
      in: header
      name: x-portkey-config
    Custom-Host:
      type: apiKey
      in: header
      name: x-portkey-custom-host
x-server-groups:
  ControlPlaneServers:
  - url: https://api.portkey.ai/v1
    description: Portkey API Public Endpoint
  - url: SELF_HOSTED_CONTROL_PLANE_URL
    description: Self-Hosted Control Plane URL
  DataPlaneServers:
  - url: https://api.portkey.ai/v1
    description: Portkey API Public Endpoint
  - url: SELF_HOSTED_GATEWAY_URL
    description: Self-Hosted Gateway URL
  PublicServers:
  - url: https://api.portkey.ai
    description: Portkey Public API (no auth required)
x-mint:
  mcp:
    enabled: true
    name: Portkey MCP
    description: Official MCP Server for Portkey Docs & APIs
x-code-samples:
  navigationGroups:
  - id: endpoints
    title: Endpoints
  - id: assistants
    title: Assistants
  - id: legacy
    title: Legacy
  groups:
  - id: audio
    title: Audio
    description: 'Learn how to turn audio into text or text into audio.


      Related guide: [Speech to text](https://platform.openai.com/docs/guides/speech-to-text)

      '
    navigationGroup: endpoints
    sections:
    - type: endpoint
      key: createSpeech
      path: createSpeech
    - type: endpoint
      key: createTranscription
      path: createTranscription
    - type: endpoint
      key: createTranslation
      path: createTranslation
    - type: object
      key: CreateTranscriptionResponseJson
      path: json-object
    - type: object
      key: CreateTranscriptionResponseVerboseJson
      path: verbose-json-object
  - id: chat
    title: Chat
    description: 'Given a list of messages comprising a conversation, the model will return a response.


      Related guide: [Chat Completions](https://platform.openai.com/docs/guides/text-generation)

      '
    navigationGroup: endpoints
    sections:
    - type: endpoint
      key: createChatCompletion
      path: create
    - type: object
      key: CreateChatCompletionResponse
      path: object
    - type: object
      key: CreateChatCompletionStreamResponse
      path: streaming
  - id: realtime
    title: Realtime
    description: 'WebSocket proxy for provider Realtime APIs (`GET` upgrade). Use `wss://` with the same `/v1` data-plane base as other gateway routes.


      Related guide: [OpenAI Realtime API](https://platform.openai.com/docs/guides/realtime)

      '
    navigationGroup: endpoints
    sections:
    - type: endpoint
      key: connectRealtime
      path: connect
  - id: embeddings
    title: Embeddings
    description: 'Get a vector representation of a given input that can be easily consumed by machine learning models and algorithms.


      Related guide: [Embeddings](https://platform.openai.com/docs/guides/embeddings)

      '
    navigationGroup: endpoints
    sections:
    - type: endpoint
      key: createEmbedding
      path: create
    - type: object
      key: Embedding
      path: object
  - id: rerank
    title: Rerank
    description: 'Rerank a list of documents based on their relevance to a query. Reranking improves search results by scoring documents based on semantic relevance rather than keyword matching.


      Supported providers: Cohere, Voyage, Jina, Pinecone, Bedrock, Azure AI.

      '
    navigationGroup: endpoints
    sections:
    - type: endpoint
      key: createRerank
      path: create
    - type: object
      key: CreateRerankResponse
      path: object
  - id: fine-tuning
    title: Fine-tuning
    description: 'Manage fine-tuning jobs to tailor a model to your specific training data.


      Related guide: [Fine-tune models](https://platform.openai.com/docs/guides/fine-tuning)

      '
    navigationGroup: endpoints
    sections:
    - type: endpoint
      key: createFineTuningJob
      path: create
    - type: endpoint
      key: listPaginatedFineTuningJobs
      path: list
    - type: endpoint
      key: listFineTuningEvents
      path: list-events
    - type: endpoint
      key: listFineTuningJobCheckpoints
      path: list-checkpoints
    - type: endpoint
      key: retrieveFineTuningJob
      path: retrieve
    - type: endpoint
      key: cancelFineTuningJob
      path: cancel
    - type: object
      key: FinetuneChatRequestInput
      path: chat-input
    - type: object
      key: FinetuneCompletionRequestInput
      path: completions-input
    - type: object
      key: FineTuningJob
      path: object
    - type: object
      key: FineTuningJobEvent
      path: event-object
    - type: object
      key: FineTuningJobCheckpoint
      path: checkpoint-object
  - id: batch
    title: Batch
    description: 'Create large batches of API requests for asynchronous processing. The Batch API returns completions within 24 hours for a 50% discount.


      Related guide: [Batch](https://platform.openai.com/docs/guides/batch)

      '
    navigationGroup: endpoints
    sections:
    - type: endpoint
      key: createBatch
      path: create
    - type: endpoint
      key: retrieveBatch
      path: retrieve
    - type: endpoint
      key: cancelBatch
      path: cancel
    - type: endpoint
      key: listBatches
      path: list
    - type: object
      key: Batch
      path: object
    - type: object
      key: BatchRequestInput
      path: request-input
    - type: object
      key: BatchRequestOutput
      path: request-output
  - id: files
    title: Files
    description: 'Files are used to upload documents that can be used with features like [Assistants](https://platform.openai.com/docs/api-reference/assistants), [Fine-tuning](https://platform.openai.com/docs/api-reference/fine-tuning), and [Batch API](https://platform.openai.com/docs/guides/batch).

      '
    navigationGroup: endpoints
    sections:
    - type: endpoint
      key: createFile
      path: create
    - type: endpoint
      key: listFiles
      path: list
    - type: endpoint
      key: retrieveFile
      path: retrieve
    - type: endpoint
      key: deleteFile
      path: delete
    - type: endpoint
      key: downloadFile
      path: retrieve-contents
    - type: object
      key: OpenAIFile
      path: object
  - id: images
    title: Images
    description: 'Given a prompt and/or an input image, the model will generate a new image.


      Related guide: [Image generation](https://platform.openai.com/docs/guides/images)

      '
    navigationGroup: endpoints
    sections:
    - type: endpoint
      key: createImage
      path: create
    - type: endpoint
      key: createImageEdit
      path: createEdit
    - type: endpoint
      key: createImageVariation
      path: createVariation
    - type: object
      key: Image
      path: object
  - id: models
    title: Models
    description: 'List and describe the various models available in the API. You can refer to the [Models](https://platform.openai.com/docs/models) documentation to understand what models are available and the differences between them.

      '
    navigationGroup: endpoints
    sections:
    - type: endpoint
      key: listModels
      path: list
    - type: endpoint
      key: retrieveModel
      path: retrieve
    - type: endpoint
      key: deleteModel
      path: delete
    - type: object
      key: Model
      path: object
  - id: moderations
    title: Moderations
    description: 'Given some input text, outputs if the model classifies it as potentially harmful across several categories.


      Related guide: [Moderations](https://platform.openai.com/docs/guides/moderation)

      '
    navigationGroup: endpoints
    sections:
    - type: endpoint
      key: createModeration
      path: create
    - type: object
      key: CreateModerationResponse
      path: object
  - id: assistants
    title: Assistants
    beta: true
    description: 'Build assistants that can call models and use tools to perform tasks.


      [Get started with the Assistants API](https://platform.openai.com/docs/assistants)

      '
    navigationGroup: assistants
    sections:
    - type: endpoint
      key: createAssistant
      path: createAssistant
    - type: endpoint
      key: listAssistants
      path: listAssistants
    - type: endpoint
      key: getAssistant
      path: getAssistant
    - type: endpoint
      key: modifyAssistant
      path: modifyAssistant
    - type: endpoint
      key: deleteAssistant
      path: deleteAssistant
    - type: object
      key: AssistantObject
      path: object
  - id: threads
    title: Threads
    beta: true
    description: 'Create threads that assistants can interact with.


      Related guide: [Assistants](https://platform.openai.com/docs/assistants/overview)

      '
    navigationGroup: assistants
    sections:
    - type: endpoint
      key: createThread
      path: createThread
    - type: endpoint
      key: getThread
      path: getThread
    - type: endpoint
      key: modifyThread
      path: modifyThread
    - type: endpoint
      key: deleteThread
      path: deleteThread
    - type: object
      key: ThreadObject
      path: object
  - id: messages
    title: Messages
    beta: true
    description: 'Create messages within threads


      Related guide: [Assistants](https://platform.openai.com/docs/assistants/overview)

      '
    navigationGroup: assistants
    sections:
    - type: endpoint
      key: createMessage
      path: createMessage
    - type: endpoint
      key: listMessages
      path: listMessages
    - type: endpoint
      key: getMessage
      path: getMessage
    - type: endpoint
      key: modifyMessage
      path: modifyMessage
    - type: endpoint
      key: deleteMessage
      path: deleteMessage
    - type: object
      key: MessageObject
      path: object
  - id: runs
    title: Runs
    beta: true
    description: 'Represents an execution run on a thread.


      Related guide: [Assistants](https://platform.openai.com/docs/assistants/overview)

      '
    navigationGroup: assistants
    sections:
    - type: endpoint
      key: createRun
      path: createRun
    - type: endpoint
      key: createThreadAndRun
      path: createThreadAndRun
    - type: endpoint
      key: listRuns
      path: listRuns
    - type: endpoint
      key: getRun
      path: getRun
    - type: endpoint
      key: modifyRun
      path: modifyRun
    - type: endpoint
      key: submitToolOuputsToRun
      path: submitToolOutputs
    - type: endpoint
      key: cancelRun
      path: cancelRun
    - type: object
      key: RunObject
      path: object
  - id: run-steps
    title: Run Steps
    beta: true
    description: 'Represents the steps (model and tool calls) taken during the run.


      Related guide: [Assistants](https://platform.openai.com/docs/assistants/overview)

      '
    navigationGroup: assistants
    sections:
    - type: endpoint
      key: listRunSteps
      path: listRunSteps
    - type: endpoint
      key: getRunStep
      path: getRunStep
    - type: object
      key: RunStepObject
      path: step-object
  - id: vector-stores
    title: Vector Stores
    beta: true
    description: 'Vector stores are used to store files for use by the `file_search` tool.


      Related guide: [File Search](https://platform.openai.com/docs/assistants/tools/file-search)

      '
    navigationGroup: assistants
    sections:
    - type: endpoint
      key: createVectorStore
      path: create
    - type: endpoint
      key: listVectorStores
      path: list
    - type: endpoint
      key: getVectorStore
      path: retrieve
    - type: endpoint
      key: modifyVectorStore
      path: modify
    - type: endpoint
      key: deleteVectorStore
      path: delete
    - type: object
      key: VectorStoreObject
      path: object
  - id: vector-stores-files
    title: Vector Store Files
    beta: true
    description: 'Vector store files represent files inside a vector store.


      Related guide: [File Search](https://platform.openai.com/docs/assistants/tools/file-search)

      '
    navigationGroup: assistants
    sections:
    - type: endpoint
      key: createVectorStoreFile
      path: createFile
    - type: endpoint
      key: listVectorStoreFiles
      path: listFiles
    - type: endpoint
      key: getVectorStoreFile
      path: getFile
    - type: endpoint
      key: deleteVectorStoreFile
      path: deleteFile
    - type: object
      key: VectorStoreFileObject
      path: file-object
  - id: vector-stores-file-batches
    title: Vector Store File Batches
    beta: true
    description: 'Vector store file batches represent operations to add multiple files to a vector store.


      Related guide: [File Search](https://platform.openai.com/docs/assistants/tools/file-search)

      '
    navigationGroup: assistants
    sections:
    - type: endpoint
      key: createVectorStoreFileBatch
      path: createBatch
    - type: endpoint
      key: getVectorStoreFileBatch
      path: getBatch
    - type: endpoint
      key: cancelVectorStoreFileBatch
      path: cancelBatch
    - type: endpoint
      key: listFilesInVectorStoreBatch
      path: listBatchFiles
    - type: object
      key: VectorStoreFileBatchObject
      path: batch-object
  - id: assistants-streaming
    title: Streaming
    beta: true
    description: 'Stream the result of executing a Run or resuming a Run after submitting tool outputs.


      You can stream events from the [Create Thread and Run](https://platform.openai.com/docs/api-reference/runs/createThreadAndRun),

      [Create Run](https://platform.openai.com/docs/api-reference/runs/createRun), and [Submit Tool Outputs](https://platform.openai.com/docs/api-reference/runs/submitToolOutputs)

      endpoints by passing `"stream": true`. The response will be a [Server-Sent events](https://html.spec.whatwg.org/multipage/server-sent-events.html#server-sent-events) stream.


      Our Node and Python SDKs provide helpful utilities to make streaming easy. Reference the

      [Assistants API quickstart](https://platform.openai.com/docs/assistants/overview) to learn more.

      '
    navigationGroup: assistants
    sections:
    - type: object
      key: MessageDeltaObject
      path: message-delta-object
    - type: object
      key: RunStepDeltaObject
      path: run-step-delta-object
    - type: object
      key: AssistantStreamEvent
      path: events
  - id: completions
    title: Completions
    legacy: true
    navigationGroup: legacy
    description: 'Given a prompt, the model will return one or more predicted completions along with the probabilities of alternative tokens at each position. Most developer should use our [Chat Completions API](https://platform.openai.com/docs/guides/text-generation/text-generation-models) to leverage our best and newest models.

      '
    sections:
    - type: endpoint
      key: createCompletion
      path: create
    - type: object
      key: CreateCompletionResponse
      path: object