openapi.yaml

openapi: 3.0.0
info:
  title: OpenAI API
  description: The OpenAI REST API. Please see https://platform.openai.com/docs/api-reference for more details.
  version: "2.0.0"
  termsOfService: https://openai.com/policies/terms-of-use
  contact:
    name: OpenAI Support
    url: https://help.openai.com/
  license:
    name: MIT
    url: https://github.com/openai/openai-openapi/blob/master/LICENSE
servers:
  - url: https://api.openai.com/v1
tags:
  - name: OpenAI
    description: The OpenAI REST API
paths:
  # Note: When adding an endpoint, make sure you also add it in the `groups` section, in the end of this file,
  # under the appropriate group
  /chat/completions:
    post:
      operationId: createChatCompletion
      tags:
        - OpenAI
      summary: Creates a model response for the given chat conversation.
      requestBody:
        required: true
        content:
          application/json:
            schema:
              $ref: "#/components/schemas/CreateChatCompletionRequest"
      responses:
        "200":
          description: OK
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/CreateChatCompletionResponse"

      x-oaiMeta:
        name: Create chat completion
        group: chat
        returns: |
          Returns a [chat completion](/docs/api-reference/chat/object) object, or a streamed sequence of [chat completion chunk](/docs/api-reference/chat/streaming) objects if the request is streamed.
        path: create
        examples:
          - title: No Streaming
            request:
              curl: |
                curl https://api.openai.com/v1/chat/completions \
                  -H "Content-Type: application/json" \
                  -H "Authorization: Bearer $OPENAI_API_KEY" \
                  -d '{
                    "model": "VAR_model_id",
                    "messages": [
                      {
                        "role": "system",
                        "content": "You are a helpful assistant."
                      },
                      {
                        "role": "user",
                        "content": "Hello!"
                      }
                    ]
                  }'
              python: |
                import os
                import openai
                openai.api_key = os.getenv("OPENAI_API_KEY")

                completion = openai.ChatCompletion.create(
                  model="VAR_model_id",
                  messages=[
                    {"role": "system", "content": "You are a helpful assistant."},
                    {"role": "user", "content": "Hello!"}
                  ]
                )

                print(completion.choices[0].message)
              node.js: |-
                import OpenAI from "openai";

                const openai = new OpenAI();

                async function main() {
                  const completion = await openai.chat.completions.create({
                    messages: [{ role: "system", content: "You are a helpful assistant." }],
                    model: "VAR_model_id",
                  });

                  console.log(completion.choices[0]);
                }

                main();
            response: &chat_completion_example |
              {
                "id": "chatcmpl-123",
                "object": "chat.completion",
                "created": 1677652288,
                "model": "gpt-3.5-turbo-0613",
                "choices": [{
                  "index": 0,
                  "message": {
                    "role": "assistant",
                    "content": "\n\nHello there, how may I assist you today?",
                  },
                  "finish_reason": "stop"
                }],
                "usage": {
                  "prompt_tokens": 9,
                  "completion_tokens": 12,
                  "total_tokens": 21
                }
              }
          - title: Streaming
            request:
              curl: |
                curl https://api.openai.com/v1/chat/completions \
                  -H "Content-Type: application/json" \
                  -H "Authorization: Bearer $OPENAI_API_KEY" \
                  -d '{
                    "model": "VAR_model_id",
                    "messages": [
                      {
                        "role": "system",
                        "content": "You are a helpful assistant."
                      },
                      {
                        "role": "user",
                        "content": "Hello!"
                      }
                    ],
                    "stream": true
                  }'
              python: |
                import os
                import openai
                openai.api_key = os.getenv("OPENAI_API_KEY")

                completion = openai.ChatCompletion.create(
                  model="VAR_model_id",
                  messages=[
                    {"role": "system", "content": "You are a helpful assistant."},
                    {"role": "user", "content": "Hello!"}
                  ],
                  stream=True
                )

                for chunk in completion:
                  print(chunk.choices[0].delta)

              node.js: |-
                import OpenAI from "openai";

                const openai = new OpenAI();

                async function main() {
                  const completion = await openai.chat.completions.create({
                    model: "VAR_model_id",
                    messages: [
                      {"role": "system", "content": "You are a helpful assistant."},
                      {"role": "user", "content": "Hello!"}
                    ],
                    stream: true,
                  });

                  for await (const chunk of completion) {
                    console.log(chunk.choices[0].delta.content);
                  }
                }

                main();
            response: &chat_completion_chunk_example |
              {
                "id": "chatcmpl-123",
                "object": "chat.completion.chunk",
                "created": 1677652288,
                "model": "gpt-3.5-turbo",
                "choices": [{
                  "index": 0,
                  "delta": {
                    "content": "Hello",
                  },
                  "finish_reason": "stop"
                }]
              }
  /completions:
    post:
      operationId: createCompletion
      tags:
        - OpenAI
      summary: Creates a completion for the provided prompt and parameters.
      requestBody:
        required: true
        content:
          application/json:
            schema:
              $ref: "#/components/schemas/CreateCompletionRequest"
      responses:
        "200":
          description: OK
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/CreateCompletionResponse"
      x-oaiMeta:
        name: Create completion
        returns: |
          Returns a [completion](/docs/api-reference/completions/object) object, or a sequence of completion objects if the request is streamed.
        legacy: true
        examples:
          - title: No streaming
            request:
              curl: |
                curl https://api.openai.com/v1/completions \
                  -H "Content-Type: application/json" \
                  -H "Authorization: Bearer $OPENAI_API_KEY" \
                  -d '{
                    "model": "VAR_model_id",
                    "prompt": "Say this is a test",
                    "max_tokens": 7,
                    "temperature": 0
                  }'
              python: |
                import os
                import openai
                openai.api_key = os.getenv("OPENAI_API_KEY")
                openai.Completion.create(
                  model="VAR_model_id",
                  prompt="Say this is a test",
                  max_tokens=7,
                  temperature=0
                )
              node.js: |-
                import OpenAI from "openai";

                const openai = new OpenAI();

                async function main() {
                  const completion = await openai.completions.create({
                    model: "VAR_model_id",
                    prompt: "Say this is a test.",
                    max_tokens: 7,
                    temperature: 0,
                  });

                  console.log(completion);
                }
                main();
            response: |
              {
                "id": "cmpl-uqkvlQyYK7bGYrRHQ0eXlWi7",
                "object": "text_completion",
                "created": 1589478378,
                "model": "VAR_model_id",
                "choices": [
                  {
                    "text": "\n\nThis is indeed a test",
                    "index": 0,
                    "logprobs": null,
                    "finish_reason": "length"
                  }
                ],
                "usage": {
                  "prompt_tokens": 5,
                  "completion_tokens": 7,
                  "total_tokens": 12
                }
              }
          - title: Streaming
            request:
              curl: |
                curl https://api.openai.com/v1/completions \
                  -H "Content-Type: application/json" \
                  -H "Authorization: Bearer $OPENAI_API_KEY" \
                  -d '{
                    "model": "VAR_model_id",
                    "prompt": "Say this is a test",
                    "max_tokens": 7,
                    "temperature": 0,
                    "stream": true
                  }'
              python: |
                import os
                import openai
                openai.api_key = os.getenv("OPENAI_API_KEY")
                for chunk in openai.Completion.create(
                  model="VAR_model_id",
                  prompt="Say this is a test",
                  max_tokens=7,
                  temperature=0,
                  stream=True
                ):
                  print(chunk['choices'][0]['text'])
              node.js: |-
                import OpenAI from "openai";

                const openai = new OpenAI();

                async function main() {
                  const stream = await openai.completions.create({
                    model: "VAR_model_id",
                    prompt: "Say this is a test.",
                    stream: true,
                  });

                  for await (const chunk of stream) {
                    console.log(chunk.choices[0].text)
                  }
                }
                main();
            response: |
              {
                "id": "cmpl-7iA7iJjj8V2zOkCGvWF2hAkDWBQZe",
                "object": "text_completion",
                "created": 1690759702,
                "choices": [
                  {
                    "text": "This",
                    "index": 0,
                    "logprobs": null,
                    "finish_reason": null
                  }
                ],
                "model": "gpt-3.5-turbo-instruct"
              }
  /edits:
    post:
      operationId: createEdit
      deprecated: true
      tags:
        - OpenAI
      summary: Creates a new edit for the provided input, instruction, and parameters.
      requestBody:
        required: true
        content:
          application/json:
            schema:
              $ref: "#/components/schemas/CreateEditRequest"
      responses:
        "200":
          description: OK
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/CreateEditResponse"
      x-oaiMeta:
        name: Create edit
        returns: |
          Returns an [edit](/docs/api-reference/edits/object) object.
        group: edits
        examples:
          request:
            curl: |
              curl https://api.openai.com/v1/edits \
                -H "Content-Type: application/json" \
                -H "Authorization: Bearer $OPENAI_API_KEY" \
                -d '{
                  "model": "VAR_model_id",
                  "input": "What day of the wek is it?",
                  "instruction": "Fix the spelling mistakes"
                }'
            python: |
              import os
              import openai
              openai.api_key = os.getenv("OPENAI_API_KEY")
              openai.Edit.create(
                model="VAR_model_id",
                input="What day of the wek is it?",
                instruction="Fix the spelling mistakes"
              )
            node.js: |-
              import OpenAI from "openai";

              const openai = new OpenAI();

              async function main() {
                const edit = await openai.edits.create({
                  model: "VAR_model_id",
                  input: "What day of the wek is it?",
                  instruction: "Fix the spelling mistakes.",
                });

                console.log(edit);
              }

              main();
          response: &edit_example |
            {
              "object": "edit",
              "created": 1589478378,
              "choices": [
                {
                  "text": "What day of the week is it?",
                  "index": 0,
                }
              ],
              "usage": {
                "prompt_tokens": 25,
                "completion_tokens": 32,
                "total_tokens": 57
              }
            }

  /images/generations:
    post:
      operationId: createImage
      tags:
        - OpenAI
      summary: Creates an image given a prompt.
      requestBody:
        required: true
        content:
          application/json:
            schema:
              $ref: "#/components/schemas/CreateImageRequest"
      responses:
        "200":
          description: OK
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/ImagesResponse"
      x-oaiMeta:
        name: Create image
        returns: Returns a list of [image](/docs/api-reference/images/object) objects.
        examples:
          request:
            curl: |
              curl https://api.openai.com/v1/images/generations \
                -H "Content-Type: application/json" \
                -H "Authorization: Bearer $OPENAI_API_KEY" \
                -d '{
                  "prompt": "A cute baby sea otter",
                  "n": 2,
                  "size": "1024x1024"
                }'
            python: |
              import os
              import openai
              openai.api_key = os.getenv("OPENAI_API_KEY")
              openai.Image.create(
                prompt="A cute baby sea otter",
                n=2,
                size="1024x1024"
              )
            node.js: |-
              import OpenAI from "openai";

              const openai = new OpenAI();

              async function main() {
                const image = await openai.images.generate({ prompt: "A cute baby sea otter" });

                console.log(image.data);
              }
              main();
          response: |
            {
              "created": 1589478378,
              "data": [
                {
                  "url": "https://..."
                },
                {
                  "url": "https://..."
                }
              ]
            }

  /images/edits:
    post:
      operationId: createImageEdit
      tags:
        - OpenAI
      summary: Creates an edited or extended image given an original image and a prompt.
      requestBody:
        required: true
        content:
          multipart/form-data:
            schema:
              $ref: "#/components/schemas/CreateImageEditRequest"
      responses:
        "200":
          description: OK
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/ImagesResponse"
      x-oaiMeta:
        name: Create image edit
        returns: Returns a list of [image](/docs/api-reference/images/object) objects.
        examples:
          request:
            curl: |
              curl https://api.openai.com/v1/images/edits \
                -H "Authorization: Bearer $OPENAI_API_KEY" \
                -F image="@otter.png" \
                -F mask="@mask.png" \
                -F prompt="A cute baby sea otter wearing a beret" \
                -F n=2 \
                -F size="1024x1024"
            python: |
              import os
              import openai
              openai.api_key = os.getenv("OPENAI_API_KEY")
              openai.Image.create_edit(
                image=open("otter.png", "rb"),
                mask=open("mask.png", "rb"),
                prompt="A cute baby sea otter wearing a beret",
                n=2,
                size="1024x1024"
              )
            node.js: |-
              import fs from "fs";
              import OpenAI from "openai";

              const openai = new OpenAI();

              async function main() {
                const image = await openai.images.edit({
                  image: fs.createReadStream("otter.png"),
                  mask: fs.createReadStream("mask.png"),
                  prompt: "A cute baby sea otter wearing a beret",
                });

                console.log(image.data);
              }
              main();
          response: |
            {
              "created": 1589478378,
              "data": [
                {
                  "url": "https://..."
                },
                {
                  "url": "https://..."
                }
              ]
            }

  /images/variations:
    post:
      operationId: createImageVariation
      tags:
        - OpenAI
      summary: Creates a variation of a given image.
      requestBody:
        required: true
        content:
          multipart/form-data:
            schema:
              $ref: "#/components/schemas/CreateImageVariationRequest"
      responses:
        "200":
          description: OK
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/ImagesResponse"
      x-oaiMeta:
        name: Create image variation
        returns: Returns a list of [image](/docs/api-reference/images/object) objects.
        examples:
          request:
            curl: |
              curl https://api.openai.com/v1/images/variations \
                -H "Authorization: Bearer $OPENAI_API_KEY" \
                -F image="@otter.png" \
                -F n=2 \
                -F size="1024x1024"
            python: |
              import os
              import openai
              openai.api_key = os.getenv("OPENAI_API_KEY")
              openai.Image.create_variation(
                image=open("otter.png", "rb"),
                n=2,
                size="1024x1024"
              )
            node.js: |-
              import fs from "fs";
              import OpenAI from "openai";

              const openai = new OpenAI();

              async function main() {
                const image = await openai.images.createVariation({
                  image: fs.createReadStream("otter.png"),
                });

                console.log(image.data);
              }
              main();
          response: |
            {
              "created": 1589478378,
              "data": [
                {
                  "url": "https://..."
                },
                {
                  "url": "https://..."
                }
              ]
            }

  /embeddings:
    post:
      operationId: createEmbedding
      tags:
        - OpenAI
      summary: Creates an embedding vector representing the input text.
      requestBody:
        required: true
        content:
          application/json:
            schema:
              $ref: "#/components/schemas/CreateEmbeddingRequest"
      responses:
        "200":
          description: OK
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/CreateEmbeddingResponse"
      x-oaiMeta:
        name: Create embeddings
        returns: A list of [embedding](/docs/api-reference/embeddings/object) objects.
        examples:
          request:
            curl: |
              curl https://api.openai.com/v1/embeddings \
                -H "Authorization: Bearer $OPENAI_API_KEY" \
                -H "Content-Type: application/json" \
                -d '{
                  "input": "The food was delicious and the waiter...",
                  "model": "text-embedding-ada-002"
                }'
            python: |
              import os
              import openai
              openai.api_key = os.getenv("OPENAI_API_KEY")
              openai.Embedding.create(
                model="text-embedding-ada-002",
                input="The food was delicious and the waiter..."
              )
            node.js: |-
              import OpenAI from "openai";

              const openai = new OpenAI();

              async function main() {
                const embedding = await openai.embeddings.create({
                  model: "text-embedding-ada-002",
                  input: "The quick brown fox jumped over the lazy dog",
                });

                console.log(embedding);
              }

              main();
          response: |
            {
              "object": "list",
              "data": [
                {
                  "object": "embedding",
                  "embedding": [
                    0.0023064255,
                    -0.009327292,
                    .... (1536 floats total for ada-002)
                    -0.0028842222,
                  ],
                  "index": 0
                }
              ],
              "model": "text-embedding-ada-002",
              "usage": {
                "prompt_tokens": 8,
                "total_tokens": 8
              }
            }

  /audio/transcriptions:
    post:
      operationId: createTranscription
      tags:
        - OpenAI
      summary: Transcribes audio into the input language.
      requestBody:
        required: true
        content:
          multipart/form-data:
            schema:
              $ref: "#/components/schemas/CreateTranscriptionRequest"
      responses:
        "200":
          description: OK
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/CreateTranscriptionResponse"
      x-oaiMeta:
        name: Create transcription
        returns: The transcriped text.
        examples:
          request:
            curl: |
              curl https://api.openai.com/v1/audio/transcriptions \
                -H "Authorization: Bearer $OPENAI_API_KEY" \
                -H "Content-Type: multipart/form-data" \
                -F file="@/path/to/file/audio.mp3" \
                -F model="whisper-1"
            python: |
              import os
              import openai
              openai.api_key = os.getenv("OPENAI_API_KEY")
              audio_file = open("audio.mp3", "rb")
              transcript = openai.Audio.transcribe("whisper-1", audio_file)
            node: |-
              import fs from "fs";
              import OpenAI from "openai";

              const openai = new OpenAI();

              async function main() {
                const transcription = await openai.audio.transcriptions.create({
                  file: fs.createReadStream("audio.mp3"),
                  model: "whisper-1",
                });

                console.log(transcription.text);
              }
              main();
          response: |
            {
              "text": "Imagine the wildest idea that you've ever had, and you're curious about how it might scale to something that's a 100, a 1,000 times bigger. This is a place where you can get to do that."
            }

  /audio/translations:
    post:
      operationId: createTranslation
      tags:
        - OpenAI
      summary: Translates audio into English.
      requestBody:
        required: true
        content:
          multipart/form-data:
            schema:
              $ref: "#/components/schemas/CreateTranslationRequest"
      responses:
        "200":
          description: OK
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/CreateTranslationResponse"
      x-oaiMeta:
        name: Create translation
        returns: The translated text.
        examples:
          request:
            curl: |
              curl https://api.openai.com/v1/audio/translations \
                -H "Authorization: Bearer $OPENAI_API_KEY" \
                -H "Content-Type: multipart/form-data" \
                -F file="@/path/to/file/german.m4a" \
                -F model="whisper-1"
            python: |
              import os
              import openai
              openai.api_key = os.getenv("OPENAI_API_KEY")
              audio_file = open("german.m4a", "rb")
              transcript = openai.Audio.translate("whisper-1", audio_file)
            node: |
              const { Configuration, OpenAIApi } = require("openai");
              const configuration = new Configuration({
                apiKey: process.env.OPENAI_API_KEY,
              });
              const openai = new OpenAIApi(configuration);
              const resp = await openai.createTranslation(
                fs.createReadStream("audio.mp3"),
                "whisper-1"
              );
          response: |
            {
              "text": "Hello, my name is Wolfgang and I come from Germany. Where are you heading today?"
            }

  /files:
    get:
      operationId: listFiles
      tags:
        - OpenAI
      summary: Returns a list of files that belong to the user's organization.
      responses:
        "200":
          description: OK
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/ListFilesResponse"
      x-oaiMeta:
        name: List files
        returns: A list of [file](/docs/api-reference/files/object) objects.
        examples:
          request:
            curl: |
              curl https://api.openai.com/v1/files \
                -H "Authorization: Bearer $OPENAI_API_KEY"
            python: |
              import os
              import openai
              openai.api_key = os.getenv("OPENAI_API_KEY")
              openai.File.list()
            node.js: |-
              import OpenAI from "openai";

              const openai = new OpenAI();

              async function main() {
                const list = await openai.files.list();

                for await (const file of list) {
                  console.log(file);
                }
              }

              main();
          response: |
            {
              "data": [
                {
                  "id": "file-abc123",
                  "object": "file",
                  "bytes": 175,
                  "created_at": 1613677385,
                  "filename": "train.jsonl",
                  "purpose": "search"
                },
                {
                  "id": "file-abc123",
                  "object": "file",
                  "bytes": 140,
                  "created_at": 1613779121,
                  "filename": "puppy.jsonl",
                  "purpose": "search"
                }
              ],
              "object": "list"
            }
    post:
      operationId: createFile
      tags:
        - OpenAI
      summary: |
        Upload a file that contains document(s) to be used across various endpoints/features. Currently, the size of all the files uploaded by one organization can be up to 1 GB. Please contact us if you need to increase the storage limit.

      requestBody:
        required: true
        content:
          multipart/form-data:
            schema:
              $ref: "#/components/schemas/CreateFileRequest"
      responses:
        "200":
          description: OK
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/OpenAIFile"
      x-oaiMeta:
        name: Upload file
        returns: The uploaded [file](/docs/api-reference/files/object) object.
        examples:
          request:
            curl: |
              curl https://api.openai.com/v1/files \
                -H "Authorization: Bearer $OPENAI_API_KEY" \
                -F purpose="fine-tune" \
                -F file="@mydata.jsonl"
            python: |
              import os
              import openai
              openai.api_key = os.getenv("OPENAI_API_KEY")
              openai.File.create(
                file=open("mydata.jsonl", "rb"),
                purpose='fine-tune'
              )
            node.js: |-
              import fs from "fs";
              import OpenAI from "openai";

              const openai = new OpenAI();

              async function main() {
                const file = await openai.files.create({
                  file: fs.createReadStream("mydata.jsonl"),
                  purpose: "fine-tune",
                });

                console.log(file);
              }

              main();
          response: |
            {
              "id": "file-abc123",
              "object": "file",
              "bytes": 140,
              "created_at": 1613779121,
              "filename": "mydata.jsonl",
              "purpose": "fine-tune",
              "status": "uploaded" | "processed" | "pending" | "error"
            }

  /files/{file_id}:
    delete:
      operationId: deleteFile
      tags:
        - OpenAI
      summary: Delete a file.
      parameters:
        - in: path
          name: file_id
          required: true
          schema:
            type: string
          description: The ID of the file to use for this request.
      responses:
        "200":
          description: OK
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/DeleteFileResponse"
      x-oaiMeta:
        name: Delete file
        returns: Deletion status.
        examples:
          request:
            curl: |
              curl https://api.openai.com/v1/files/file-abc123 \
                -X DELETE \
                -H "Authorization: Bearer $OPENAI_API_KEY"
            python: |
              import os
              import openai
              openai.api_key = os.getenv("OPENAI_API_KEY")
              openai.File.delete("file-abc123")
            node.js: |-
              import OpenAI from "openai";

              const openai = new OpenAI();

              async function main() {
                const file = await openai.files.del("file-abc123");

                console.log(file);
              }

              main();
          response: |
            {
              "id": "file-abc123",
              "object": "file",
              "deleted": true
            }
    get:
      operationId: retrieveFile
      tags:
        - OpenAI
      summary: Returns information about a specific file.
      parameters:
        - in: path
          name: file_id
          required: true
          schema:
            type: string
          description: The ID of the file to use for this request.
      responses:
        "200":
          description: OK
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/OpenAIFile"
      x-oaiMeta:
        name: Retrieve file
        returns: The [file](/docs/api-reference/files/object) object matching the specified ID.
        examples:
          request:
            curl: |
              curl https://api.openai.com/v1/files/file-abc123 \
                -H "Authorization: Bearer $OPENAI_API_KEY"
            python: |
              import os
              import openai
              openai.api_key = os.getenv("OPENAI_API_KEY")
              openai.File.retrieve("file-abc123")
            node.js: |-
              import OpenAI from "openai";

              const openai = new OpenAI();

              async function main() {
                const file = await openai.files.retrieve("file-abc123");

                console.log(file);
              }

              main();
          response: |
            {
              "id": "file-abc123",
              "object": "file",
              "bytes": 140,
              "created_at": 1613779657,
              "filename": "mydata.jsonl",
              "purpose": "fine-tune"
            }

  /files/{file_id}/content:
    get:
      operationId: downloadFile
      tags:
        - OpenAI
      summary: Returns the contents of the specified file.
      parameters:
        - in: path
          name: file_id
          required: true
          schema:
            type: string
          description: The ID of the file to use for this request.
      responses:
        "200":
          description: OK
          content:
            application/json:
              schema:
                type: string
      x-oaiMeta:
        name: Retrieve file content
        returns: The file content.
        examples:
          request:
            curl: |
              curl https://api.openai.com/v1/files/file-abc123/content \
                -H "Authorization: Bearer $OPENAI_API_KEY" > file.jsonl
            python: |
              import os
              import openai
              openai.api_key = os.getenv("OPENAI_API_KEY")
              content = openai.File.download("file-abc123")
            node.js: |
              import OpenAI from "openai";

              const openai = new OpenAI();

              async function main() {
                const file = await openai.files.retrieveContent("file-abc123");

                console.log(file);
              }

              main();

  /fine_tuning/jobs:
    post:
      operationId: createFineTuningJob
      tags:
        - OpenAI
      summary: |
        Creates a job that fine-tunes a specified model from a given dataset.

        Response includes details of the enqueued job including job status and the name of the fine-tuned models once complete.

        [Learn more about fine-tuning](/docs/guides/fine-tuning)
      requestBody:
        required: true
        content:
          application/json:
            schema:
              $ref: "#/components/schemas/CreateFineTuningJobRequest"
      responses:
        "200":
          description: OK
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/FineTuningJob"
      x-oaiMeta:
        name: Create fine-tuning job
        returns: A [fine-tuning.job](/docs/api-reference/fine-tuning/object) object.
        examples:
          - title: No hyperparameters
            request:
              curl: |
                curl https://api.openai.com/v1/fine_tuning/jobs \
                  -H "Content-Type: application/json" \
                  -H "Authorization: Bearer $OPENAI_API_KEY" \
                  -d '{
                    "training_file": "file-abc123"
                    "model": "gpt-3.5-turbo",
                  }'
              python: |
                import os
                import openai
                openai.api_key = os.getenv("OPENAI_API_KEY")
                openai.FineTuningJob.create(training_file="file-abc123", model="gpt-3.5-turbo")
              node.js: |
                import OpenAI from "openai";

                const openai = new OpenAI();

                async function main() {
                  const fineTune = await openai.fineTuning.jobs.create({
                    training_file: "file-abc123"
                  });

                  console.log(fineTune);
                }

                main();
            response: |
              {
                "object": "fine_tuning.job",
                "id": "ft-AF1WoRqd3aJAHsqc9NY7iL8F",
                "model": "gpt-3.5-turbo-0613",
                "created_at": 1614807352,
                "fine_tuned_model": null,
                "organization_id": "org-123",
                "result_files": [],
                "status": "queued",
                "validation_file": null,
                "training_file": "file-abc123",
              }
          - title: Hyperparameters
            request:
              curl: |
                curl https://api.openai.com/v1/fine_tuning/jobs \
                  -H "Content-Type: application/json" \
                  -H "Authorization: Bearer $OPENAI_API_KEY" \
                  -d '{
                    "training_file": "file-abc123"
                    "model": "gpt-3.5-turbo",
                    "hyperparameters": {
                      "n_epochs": 2
                    }
                  }'
              python: |
                import os
                import openai
                openai.api_key = os.getenv("OPENAI_API_KEY")
                openai.FineTuningJob.create(training_file="file-abc123", model="gpt-3.5-turbo", hyperparameters={"n_epochs":2})
              node.js: |
                import OpenAI from "openai";

                const openai = new OpenAI();

                async function main() {
                  const fineTune = await openai.fineTuning.jobs.create({
                    training_file: "file-abc123",
                    model: "gpt-3.5-turbo",
                    hyperparameters: { n_epochs: 2 },
                  });

                  console.log(fineTune);
                }

                main();
            response: |
              {
                "object": "fine_tuning.job",
                "id": "ft-AF1WoRqd3aJAHsqc9NY7iL8F",
                "model": "gpt-3.5-turbo-0613",
                "created_at": 1614807352,
                "fine_tuned_model": null,
                "organization_id": "org-123",
                "result_files": [],
                "status": "queued",
                "validation_file": null,
                "training_file": "file-abc123",
                "hyperparameters":{"n_epochs":2},
              }
    get:
      operationId: listPaginatedFineTuningJobs
      tags:
        - OpenAI
      summary: |
        List your organization's fine-tuning jobs
      parameters:
        - name: after
          in: query
          description: Identifier for the last job from the previous pagination request.
          required: false
          schema:
            type: string
        - name: limit
          in: query
          description: Number of fine-tuning jobs to retrieve.
          required: false
          schema:
            type: integer
            default: 20
      responses:
        "200":
          description: OK
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/ListPaginatedFineTuningJobsResponse"
      x-oaiMeta:
        name: List fine-tuning jobs
        returns: A list of paginated [fine-tuning job](/docs/api-reference/fine-tuning/object) objects.
        examples:
          request:
            curl: |
              curl https://api.openai.com/v1/fine_tuning/jobs?limit=2 \
                -H "Authorization: Bearer $OPENAI_API_KEY"
            python: |
              import os
              import openai
              openai.api_key = os.getenv("OPENAI_API_KEY")
              openai.FineTuningJob.list()
            node.js: |-
              import OpenAI from "openai";

              const openai = new OpenAI();

              async function main() {
                const list = await openai.fineTuning.jobs.list();

                for await (const fineTune of list) {
                  console.log(fineTune);
                }
              }

              main();
          response: |
            {
              "object": "list",
              "data": [
                {
                  "object": "fine_tuning.job.event",
                  "id": "ft-event-TjX0lMfOniCZX64t9PUQT5hn",
                  "created_at": 1689813489,
                  "level": "warn",
                  "message": "Fine tuning process stopping due to job cancellation",
                  "data": null,
                  "type": "message"
                },
                { ... },
                { ... }
              ], "has_more": true
            }
  /fine_tuning/jobs/{fine_tuning_job_id}:
    get:
      operationId: retrieveFineTuningJob
      tags:
        - OpenAI
      summary: |
        Get info about a fine-tuning job.

        [Learn more about fine-tuning](/docs/guides/fine-tuning)
      parameters:
        - in: path
          name: fine_tuning_job_id
          required: true
          schema:
            type: string
            example: ft-AF1WoRqd3aJAHsqc9NY7iL8F
          description: |
            The ID of the fine-tuning job.
      responses:
        "200":
          description: OK
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/FineTuningJob"
      x-oaiMeta:
        name: Retrieve fine-tuning job
        returns: The [fine-tuning](/docs/api-reference/fine-tunes/object) object with the given ID.
        examples:
          request:
            curl: |
              curl https://api.openai.com/v1/fine_tuning/jobs/ft-AF1WoRqd3aJAHsqc9NY7iL8F \
                -H "Authorization: Bearer $OPENAI_API_KEY"
            python: |
              import os
              import openai
              openai.api_key = os.getenv("OPENAI_API_KEY")
              openai.FineTuningJob.retrieve("ft-anaKUAgnnBkNGB3QcSr4pImR")
            node.js: |
              import OpenAI from "openai";

              const openai = new OpenAI();

              async function main() {
                const fineTune = await openai.fineTuning.jobs.retrieve("ft-AF1WoRqd3aJAHsqc9NY7iL8F");
                
                console.log(fineTune);
              }

              main();
          response: &fine_tuning_example |
            {
              "object": "fine_tuning.job",
              "id": "ft-zRdUkP4QeZqeYjDcQL0wwam1",
              "model": "davinci-002",
              "created_at": 1692661014,
              "finished_at": 1692661190,
              "fine_tuned_model": "ft:davinci-002:my-org:custom_suffix:7q8mpxmy",
              "organization_id": "org-123",
              "result_files": [
                  "file-abc123"
              ],
              "status": "succeeded",
              "validation_file": null,
              "training_file": "file-abc123",
              "hyperparameters": {
                  "n_epochs": 4,
              },
              "trained_tokens": 5768
            }

  /fine_tuning/jobs/{fine_tuning_job_id}/events:
    get:
      operationId: listFineTuningEvents
      tags:
        - OpenAI
      summary: |
        Get status updates for a fine-tuning job.
      parameters:
        - in: path
          name: fine_tuning_job_id
          required: true
          schema:
            type: string
            example: ft-AF1WoRqd3aJAHsqc9NY7iL8F
          description: |
            The ID of the fine-tuning job to get events for.
        - name: after
          in: query
          description: Identifier for the last event from the previous pagination request.
          required: false
          schema:
            type: string
        - name: limit
          in: query
          description: Number of events to retrieve.
          required: false
          schema:
            type: integer
            default: 20
      responses:
        "200":
          description: OK
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/ListFineTuningJobEventsResponse"
      x-oaiMeta:
        name: List fine-tuning events
        returns: A list of fine-tuning event objects.
        examples:
          request:
            curl: |
              curl https://api.openai.com/v1/fine_tuning/jobs/ft-AF1WoRqd3aJAHsqc9NY7iL8F/events \
                -H "Authorization: Bearer $OPENAI_API_KEY"
            python: |
              import os
              import openai
              openai.api_key = os.getenv("OPENAI_API_KEY")
              openai.FineTuningJob.list_events(id="ft-w9WJrnTe9vcVopaTy9LrlGQv", limit=2)
            node.js: |-
              import OpenAI from "openai";

              const openai = new OpenAI();

              async function main() {
                const list = await openai.fineTuning.list_events(id="ft-w9WJrnTe9vcVopaTy9LrlGQv", limit=2);

                for await (const fineTune of list) {
                  console.log(fineTune);
                }
              }

              main();
          response: |
            {
              "object": "list",
              "data": [
                {
                  "object": "fine_tuning.job.event",
                  "id": "ft-event-ddTJfwuMVpfLXseO0Am0Gqjm",
                  "created_at": 1692407401,
                  "level": "info",
                  "message": "Fine tuning job successfully completed",
                  "data": null,
                  "type": "message"
                },
                {
                  "object": "fine_tuning.job.event",
                  "id": "ft-event-tyiGuB72evQncpH87xe505Sv",
                  "created_at": 1692407400,
                  "level": "info",
                  "message": "New fine-tuned model created: ft:gpt-3.5-turbo:openai::7p4lURel",
                  "data": null,
                  "type": "message"
                }
              ],
              "has_more": true
            }

  /fine_tuning/jobs/{fine_tuning_job_id}/cancel:
    post:
      operationId: cancelFineTuningJob
      tags:
        - OpenAI
      summary: |
        Immediately cancel a fine-tune job.
      parameters:
        - in: path
          name: fine_tuning_job_id
          required: true
          schema:
            type: string
            example: ft-AF1WoRqd3aJAHsqc9NY7iL8F
          description: |
            The ID of the fine-tuning job to cancel.
      responses:
        "200":
          description: OK
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/FineTuningJob"
      x-oaiMeta:
        name: Cancel fine-tuning
        returns: The cancelled [fine-tuning](/docs/api-reference/fine-tuning/object) object.
        examples:
          request:
            curl: |
              curl -X POST https://api.openai.com/v1/fine_tuning/jobs/ft-AF1WoRqd3aJAHsqc9NY7iL8F/cancel \
                -H "Authorization: Bearer $OPENAI_API_KEY"
            python: |
              import os
              import openai
              openai.api_key = os.getenv("OPENAI_API_KEY")
              openai.FineTuningJob.cancel("ft-anaKUAgnnBkNGB3QcSr4pImR")
            node.js: |-
              import OpenAI from "openai";

              const openai = new OpenAI();

              async function main() {
                const fineTune = await openai.fineTuning.jobs.cancel("ft-AF1WoRqd3aJAHsqc9NY7iL8F");

                console.log(fineTune);
              }
              main();
          response: |
            {
              "object": "fine_tuning.job",
              "id": "ft-gleYLJhWh1YFufiy29AahVpj",
              "model": "gpt-3.5-turbo-0613",
              "created_at": 1689376978,
              "fine_tuned_model": null,
              "organization_id": "org-123",
              "result_files": [],
              "hyperparameters": {
                "n_epochs":  "auto"
              },
              "status": "cancelled",
              "validation_file": "file-abc123",
              "training_file": "file-abc123"
            }

  /fine-tunes:
    post:
      operationId: createFineTune
      deprecated: true
      tags:
        - OpenAI
      summary: |
        Creates a job that fine-tunes a specified model from a given dataset.

        Response includes details of the enqueued job including job status and the name of the fine-tuned models once complete.

        [Learn more about fine-tuning](/docs/guides/legacy-fine-tuning)
      requestBody:
        required: true
        content:
          application/json:
            schema:
              $ref: "#/components/schemas/CreateFineTuneRequest"
      responses:
        "200":
          description: OK
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/FineTune"
      x-oaiMeta:
        name: Create fine-tune
        returns: A [fine-tune](/docs/api-reference/fine-tunes/object) object.
        examples:
          request:
            curl: |
              curl https://api.openai.com/v1/fine-tunes \
                -H "Content-Type: application/json" \
                -H "Authorization: Bearer $OPENAI_API_KEY" \
                -d '{
                  "training_file": "file-abc123"
                }'
            python: |
              import os
              import openai
              openai.api_key = os.getenv("OPENAI_API_KEY")
              openai.FineTune.create(training_file="file-abc123")
            node.js: |
              import OpenAI from "openai";

              const openai = new OpenAI();

              async function main() {
                const fineTune = await openai.fineTunes.create({
                  training_file: "file-abc123"
                });

                console.log(fineTune);
              }

              main();
          response: |
            {
              "id": "ft-AF1WoRqd3aJAHsqc9NY7iL8F",
              "object": "fine-tune",
              "model": "curie",
              "created_at": 1614807352,
              "events": [
                {
                  "object": "fine-tune-event",
                  "created_at": 1614807352,
                  "level": "info",
                  "message": "Job enqueued. Waiting for jobs ahead to complete. Queue number: 0."
                }
              ],
              "fine_tuned_model": null,
              "hyperparams": {
                "batch_size": 4,
                "learning_rate_multiplier": 0.1,
                "n_epochs": 4,
                "prompt_loss_weight": 0.1,
              },
              "organization_id": "org-123",
              "result_files": [],
              "status": "pending",
              "validation_files": [],
              "training_files": [
                {
                  "id": "file-abc123",
                  "object": "file",
                  "bytes": 1547276,
                  "created_at": 1610062281,
                  "filename": "my-data-train.jsonl",
                  "purpose": "fine-tune-train"
                }
              ],
              "updated_at": 1614807352,
            }
    get:
      operationId: listFineTunes
      deprecated: true
      tags:
        - OpenAI
      summary: |
        List your organization's fine-tuning jobs
      responses:
        "200":
          description: OK
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/ListFineTunesResponse"
      x-oaiMeta:
        name: List fine-tunes
        returns: A list of [fine-tune](/docs/api-reference/fine-tunes/object) objects.
        examples:
          request:
            curl: |
              curl https://api.openai.com/v1/fine-tunes \
                -H "Authorization: Bearer $OPENAI_API_KEY"
            python: |
              import os
              import openai
              openai.api_key = os.getenv("OPENAI_API_KEY")
              openai.FineTune.list()
            node.js: |-
              import OpenAI from "openai";

              const openai = new OpenAI();

              async function main() {
                const list = await openai.fineTunes.list();

                for await (const fineTune of list) {
                  console.log(fineTune);
                }
              }

              main();
          response: |
            {
              "object": "list",
              "data": [
                {
                  "id": "ft-AF1WoRqd3aJAHsqc9NY7iL8F",
                  "object": "fine-tune",
                  "model": "curie",
                  "created_at": 1614807352,
                  "fine_tuned_model": null,
                  "hyperparams": { ... },
                  "organization_id": "org-123",
                  "result_files": [],
                  "status": "pending",
                  "validation_files": [],
                  "training_files": [ { ... } ],
                  "updated_at": 1614807352,
                },
                { ... },
                { ... }
              ]
            }

  /fine-tunes/{fine_tune_id}:
    get:
      operationId: retrieveFineTune
      deprecated: true
      tags:
        - OpenAI
      summary: |
        Gets info about the fine-tune job.

        [Learn more about fine-tuning](/docs/guides/legacy-fine-tuning)
      parameters:
        - in: path
          name: fine_tune_id
          required: true
          schema:
            type: string
            example: ft-AF1WoRqd3aJAHsqc9NY7iL8F
          description: |
            The ID of the fine-tune job
      responses:
        "200":
          description: OK
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/FineTune"
      x-oaiMeta:
        name: Retrieve fine-tune
        returns: The [fine-tune](/docs/api-reference/fine-tunes/object) object with the given ID.
        examples:
          request:
            curl: |
              curl https://api.openai.com/v1/fine-tunes/ft-AF1WoRqd3aJAHsqc9NY7iL8F \
                -H "Authorization: Bearer $OPENAI_API_KEY"
            python: |
              import os
              import openai
              openai.api_key = os.getenv("OPENAI_API_KEY")
              openai.FineTune.retrieve(id="ft-AF1WoRqd3aJAHsqc9NY7iL8F")
            node.js: |-
              import OpenAI from "openai";

              const openai = new OpenAI();

              async function main() {
                const fineTune = await openai.fineTunes.retrieve("ft-AF1WoRqd3aJAHsqc9NY7iL8F");

                console.log(fineTune);
              }

              main();
          response: &fine_tune_example |
            {
              "id": "ft-AF1WoRqd3aJAHsqc9NY7iL8F",
              "object": "fine-tune",
              "model": "curie",
              "created_at": 1614807352,
              "events": [
                {
                  "object": "fine-tune-event",
                  "created_at": 1614807352,
                  "level": "info",
                  "message": "Job enqueued. Waiting for jobs ahead to complete. Queue number: 0."
                },
                {
                  "object": "fine-tune-event",
                  "created_at": 1614807356,
                  "level": "info",
                  "message": "Job started."
                },
                {
                  "object": "fine-tune-event",
                  "created_at": 1614807861,
                  "level": "info",
                  "message": "Uploaded snapshot: curie:ft-acmeco-2021-03-03-21-44-20."
                },
                {
                  "object": "fine-tune-event",
                  "created_at": 1614807864,
                  "level": "info",
                  "message": "Uploaded result files: file-abc123."
                },
                {
                  "object": "fine-tune-event",
                  "created_at": 1614807864,
                  "level": "info",
                  "message": "Job succeeded."
                }
              ],
              "fine_tuned_model": "curie:ft-acmeco-2021-03-03-21-44-20",
              "hyperparams": {
                "batch_size": 4,
                "learning_rate_multiplier": 0.1,
                "n_epochs": 4,
                "prompt_loss_weight": 0.1,
              },
              "organization_id": "org-123",
              "result_files": [
                {
                  "id": "file-abc123",
                  "object": "file",
                  "bytes": 81509,
                  "created_at": 1614807863,
                  "filename": "compiled_results.csv",
                  "purpose": "fine-tune-results"
                }
              ],
              "status": "succeeded",
              "validation_files": [],
              "training_files": [
                {
                  "id": "file-abc123",
                  "object": "file",
                  "bytes": 1547276,
                  "created_at": 1610062281,
                  "filename": "my-data-train.jsonl",
                  "purpose": "fine-tune-train"
                }
              ],
              "updated_at": 1614807865,
            }

  /fine-tunes/{fine_tune_id}/cancel:
    post:
      operationId: cancelFineTune
      deprecated: true
      tags:
        - OpenAI
      summary: |
        Immediately cancel a fine-tune job.
      parameters:
        - in: path
          name: fine_tune_id
          required: true
          schema:
            type: string
            example: ft-AF1WoRqd3aJAHsqc9NY7iL8F
          description: |
            The ID of the fine-tune job to cancel
      responses:
        "200":
          description: OK
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/FineTune"
      x-oaiMeta:
        name: Cancel fine-tune
        returns: The cancelled [fine-tune](/docs/api-reference/fine-tunes/object) object.
        examples:
          request:
            curl: |
              curl https://api.openai.com/v1/fine-tunes/ft-AF1WoRqd3aJAHsqc9NY7iL8F/cancel \
                -H "Authorization: Bearer $OPENAI_API_KEY"
            python: |
              import os
              import openai
              openai.api_key = os.getenv("OPENAI_API_KEY")
              openai.FineTune.cancel(id="ft-AF1WoRqd3aJAHsqc9NY7iL8F")
            node.js: |-
              import OpenAI from "openai";

              const openai = new OpenAI();

              async function main() {
                const fineTune = await openai.fineTunes.cancel("ft-AF1WoRqd3aJAHsqc9NY7iL8F");

                console.log(fineTune);
              }
              main();
          response: |
            {
              "id": "ft-xhrpBbvVUzYGo8oUO1FY4nI7",
              "object": "fine-tune",
              "model": "curie",
              "created_at": 1614807770,
              "events": [ { ... } ],
              "fine_tuned_model": null,
              "hyperparams": { ... },
              "organization_id": "org-123",
              "result_files": [],
              "status": "cancelled",
              "validation_files": [],
              "training_files": [
                {
                  "id": "file-abc123",
                  "object": "file",
                  "bytes": 1547276,
                  "created_at": 1610062281,
                  "filename": "my-data-train.jsonl",
                  "purpose": "fine-tune-train"
                }
              ],
              "updated_at": 1614807789,
            }

  /fine-tunes/{fine_tune_id}/events:
    get:
      operationId: listFineTuneEvents
      deprecated: true
      tags:
        - OpenAI
      summary: |
        Get fine-grained status updates for a fine-tune job.
      parameters:
        - in: path
          name: fine_tune_id
          required: true
          schema:
            type: string
            example: ft-AF1WoRqd3aJAHsqc9NY7iL8F
          description: |
            The ID of the fine-tune job to get events for.
        - in: query
          name: stream
          required: false
          schema:
            type: boolean
            default: false
          description: |
            Whether to stream events for the fine-tune job. If set to true,
            events will be sent as data-only
            [server-sent events](https://developer.mozilla.org/en-US/docs/Web/API/Server-sent_events/Using_server-sent_events#Event_stream_format)
            as they become available. The stream will terminate with a
            `data: [DONE]` message when the job is finished (succeeded, cancelled,
            or failed).

            If set to false, only events generated so far will be returned.
      responses:
        "200":
          description: OK
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/ListFineTuneEventsResponse"
      x-oaiMeta:
        name: List fine-tune events
        returns: A list of fine-tune event objects.
        examples:
          request:
            curl: |
              curl https://api.openai.com/v1/fine-tunes/ft-AF1WoRqd3aJAHsqc9NY7iL8F/events \
                -H "Authorization: Bearer $OPENAI_API_KEY"
            python: |
              import os
              import openai
              openai.api_key = os.getenv("OPENAI_API_KEY")
              openai.FineTune.list_events(id="ft-AF1WoRqd3aJAHsqc9NY7iL8F")
            node.js: |-
              import OpenAI from "openai";

              const openai = new OpenAI();

              async function main() {
                const fineTune = await openai.fineTunes.listEvents("ft-AF1WoRqd3aJAHsqc9NY7iL8F");

                console.log(fineTune);
              }
              main();
          response: |
            {
              "object": "list",
              "data": [
                {
                  "object": "fine-tune-event",
                  "created_at": 1614807352,
                  "level": "info",
                  "message": "Job enqueued. Waiting for jobs ahead to complete. Queue number: 0."
                },
                {
                  "object": "fine-tune-event",
                  "created_at": 1614807356,
                  "level": "info",
                  "message": "Job started."
                },
                {
                  "object": "fine-tune-event",
                  "created_at": 1614807861,
                  "level": "info",
                  "message": "Uploaded snapshot: curie:ft-acmeco-2021-03-03-21-44-20."
                },
                {
                  "object": "fine-tune-event",
                  "created_at": 1614807864,
                  "level": "info",
                  "message": "Uploaded result files: file-abc123"
                },
                {
                  "object": "fine-tune-event",
                  "created_at": 1614807864,
                  "level": "info",
                  "message": "Job succeeded."
                }
              ]
            }

  /models:
    get:
      operationId: listModels
      tags:
        - OpenAI
      summary: Lists the currently available models, and provides basic information about each one such as the owner and availability.
      responses:
        "200":
          description: OK
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/ListModelsResponse"
      x-oaiMeta:
        name: List models
        returns: A list of [model](/docs/api-reference/models/object) objects.
        examples:
          request:
            curl: |
              curl https://api.openai.com/v1/models \
                -H "Authorization: Bearer $OPENAI_API_KEY"
            python: |
              import os
              import openai
              openai.api_key = os.getenv("OPENAI_API_KEY")
              openai.Model.list()
            node.js: |-
              import OpenAI from "openai";

              const openai = new OpenAI();

              async function main() {
                const list = await openai.models.list();

                for await (const model of list) {
                  console.log(model);
                }
              }
              main();
          response: |
            {
              "object": "list",
              "data": [
                {
                  "id": "model-id-0",
                  "object": "model",
                  "created": 1686935002,
                  "owned_by": "organization-owner"
                },
                {
                  "id": "model-id-1",
                  "object": "model",
                  "created": 1686935002,
                  "owned_by": "organization-owner",
                },
                {
                  "id": "model-id-2",
                  "object": "model",
                  "created": 1686935002,
                  "owned_by": "openai"
                },
              ],
              "object": "list"
            }

  /models/{model}:
    get:
      operationId: retrieveModel
      tags:
        - OpenAI
      summary: Retrieves a model instance, providing basic information about the model such as the owner and permissioning.
      parameters:
        - in: path
          name: model
          required: true
          schema:
            type: string
            # ideally this will be an actual ID, so this will always work from browser
            example: gpt-3.5-turbo
          description: The ID of the model to use for this request
      responses:
        "200":
          description: OK
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/Model"
      x-oaiMeta:
        name: Retrieve model
        returns: The [model](/docs/api-reference/models/object) object matching the specified ID.
        examples:
          request:
            curl: |
              curl https://api.openai.com/v1/models/VAR_model_id \
                -H "Authorization: Bearer $OPENAI_API_KEY"
            python: |
              import os
              import openai
              openai.api_key = os.getenv("OPENAI_API_KEY")
              openai.Model.retrieve("VAR_model_id")
            node.js: |-
              import OpenAI from "openai";

              const openai = new OpenAI();

              async function main() {
                const model = await openai.models.retrieve("gpt-3.5-turbo");

                console.log(model);
              }

              main();
          response: &retrieve_model_response |
            {
              "id": "VAR_model_id",
              "object": "model",
              "created": 1686935002,
              "owned_by": "openai"
            }
    delete:
      operationId: deleteModel
      tags:
        - OpenAI
      summary: Delete a fine-tuned model. You must have the Owner role in your organization to delete a model.
      parameters:
        - in: path
          name: model
          required: true
          schema:
            type: string
            example: ft:gpt-3.5-turbo:acemeco:suffix:abc123
          description: The model to delete
      responses:
        "200":
          description: OK
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/DeleteModelResponse"
      x-oaiMeta:
        name: Delete fine-tune model
        returns: Deletion status.
        examples:
          request:
            curl: |
              curl https://api.openai.com/v1/models/ft:gpt-3.5-turbo:acemeco:suffix:abc123 \
                -X DELETE \
                -H "Authorization: Bearer $OPENAI_API_KEY"
            python: |
              import os
              import openai
              openai.api_key = os.getenv("OPENAI_API_KEY")
              openai.Model.delete("ft:gpt-3.5-turbo:acemeco:suffix:abc123")
            node.js: |-
              import OpenAI from "openai";

              const openai = new OpenAI();

              async function main() {
                const model = await openai.models.del("ft:gpt-3.5-turbo:acemeco:suffix:abc123");

                console.log(model);
              }
              main();
          response: |
            {
              "id": "ft:gpt-3.5-turbo:acemeco:suffix:abc123",
              "object": "model",
              "deleted": true
            }

  /moderations:
    post:
      operationId: createModeration
      tags:
        - OpenAI
      summary: Classifies if text violates OpenAI's Content Policy
      requestBody:
        required: true
        content:
          application/json:
            schema:
              $ref: "#/components/schemas/CreateModerationRequest"
      responses:
        "200":
          description: OK
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/CreateModerationResponse"
      x-oaiMeta:
        name: Create moderation
        returns: A [moderation](/docs/api-reference/moderations/object) object.
        examples:
          request:
            curl: |
              curl https://api.openai.com/v1/moderations \
                -H "Content-Type: application/json" \
                -H "Authorization: Bearer $OPENAI_API_KEY" \
                -d '{
                  "input": "I want to kill them."
                }'
            python: |
              import os
              import openai
              openai.api_key = os.getenv("OPENAI_API_KEY")
              openai.Moderation.create(
                input="I want to kill them.",
              )
            node.js: |
              import OpenAI from "openai";

              const openai = new OpenAI();

              async function main() {
                const moderation = await openai.moderations.create({ input: "I want to kill them." });

                console.log(moderation);
              }
              main();
          response: &moderation_example |
            {
              "id": "modr-XXXXX",
              "model": "text-moderation-005",
              "results": [
                {
                  "flagged": true,
                  "categories": {
                    "sexual": false,
                    "hate": false,
                    "harassment": false,
                    "self-harm": false,
                    "sexual/minors": false,
                    "hate/threatening": false,
                    "violence/graphic": false,
                    "self-harm/intent": false,
                    "self-harm/instructions": false,
                    "harassment/threatening": true,
                    "violence": true,
                  },
                  "category_scores": {
                    "sexual": 1.2282071e-06,
                    "hate": 0.010696256,
                    "harassment": 0.29842457,
                    "self-harm": 1.5236925e-08,
                    "sexual/minors": 5.7246268e-08,
                    "hate/threatening": 0.0060676364,
                    "violence/graphic": 4.435014e-06,
                    "self-harm/intent": 8.098441e-10,
                    "self-harm/instructions": 2.8498655e-11,
                    "harassment/threatening": 0.63055265,
                    "violence": 0.99011886,
                  }
                }
              ]
            }

components:

  securitySchemes:
    ApiKeyAuth:
      type: http
      scheme: 'bearer'

  schemas:
    Error:
      type: object
      properties:
        type:
          type: string
          nullable: false
        message:
          type: string
          nullable: false
        param:
          type: string
          nullable: true
        code:
          type: string
          nullable: true
      required:
        - type
        - message
        - param
        - code

    ErrorResponse:
      type: object
      properties:
        error:
          $ref: "#/components/schemas/Error"
      required:
        - error

    ListModelsResponse:
      type: object
      properties:
        object:
          type: string
        data:
          type: array
          items:
            $ref: "#/components/schemas/Model"
      required:
        - object
        - data

    DeleteModelResponse:
      type: object
      properties:
        id:
          type: string
        object:
          type: string
        deleted:
          type: boolean
      required:
        - id
        - object
        - deleted

    CreateCompletionRequest:
      type: object
      properties:
        model:
          description: &model_description |
            ID of the model to use. You can use the [List models](/docs/api-reference/models/list) API to see all of your available models, or see our [Model overview](/docs/models/overview) for descriptions of them.
          anyOf:
            - type: string
            - type: string
              enum:
                [
                  "babbage-002",
                  "davinci-002",
                  "gpt-3.5-turbo-instruct",
                  "text-davinci-003",
                  "text-davinci-002",
                  "text-davinci-001",
                  "code-davinci-002",
                  "text-curie-001",
                  "text-babbage-001",
                  "text-ada-001",
                ]
          x-oaiTypeLabel: string
        prompt:
          description: &completions_prompt_description |
            The prompt(s) to generate completions for, encoded as a string, array of strings, array of tokens, or array of token arrays.

            Note that <|endoftext|> is the document separator that the model sees during training, so if a prompt is not specified the model will generate as if from the beginning of a new document.
          default: "<|endoftext|>"
          nullable: true
          oneOf:
            - type: string
              default: ""
              example: "This is a test."
            - type: array
              items:
                type: string
                default: ""
                example: "This is a test."
            - type: array
              minItems: 1
              items:
                type: integer
              example: "[1212, 318, 257, 1332, 13]"
            - type: array
              minItems: 1
              items:
                type: array
                minItems: 1
                items:
                  type: integer
              example: "[[1212, 318, 257, 1332, 13]]"
        suffix:
          description: The suffix that comes after a completion of inserted text.
          default: null
          nullable: true
          type: string
          example: "test."
        max_tokens:
          type: integer
          minimum: 0
          default: 16
          example: 16
          nullable: true
          description: &completions_max_tokens_description |
            The maximum number of [tokens](/tokenizer) to generate in the completion.

            The token count of your prompt plus `max_tokens` cannot exceed the model's context length. [Example Python code](https://github.com/openai/openai-cookbook/blob/main/examples/How_to_count_tokens_with_tiktoken.ipynb) for counting tokens.
        temperature:
          type: number
          minimum: 0
          maximum: 2
          default: 1
          example: 1
          nullable: true
          description: &completions_temperature_description |
            What sampling temperature to use, between 0 and 2. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic.

            We generally recommend altering this or `top_p` but not both.
        top_p:
          type: number
          minimum: 0
          maximum: 1
          default: 1
          example: 1
          nullable: true
          description: &completions_top_p_description |
            An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10% probability mass are considered.

            We generally recommend altering this or `temperature` but not both.
        n:
          type: integer
          minimum: 1
          maximum: 128
          default: 1
          example: 1
          nullable: true
          description: &completions_completions_description |
            How many completions to generate for each prompt.

            **Note:** Because this parameter generates many completions, it can quickly consume your token quota. Use carefully and ensure that you have reasonable settings for `max_tokens` and `stop`.
        stream:
          description: >
            Whether to stream back partial progress. If set, tokens will be sent as data-only [server-sent events](https://developer.mozilla.org/en-US/docs/Web/API/Server-sent_events/Using_server-sent_events#Event_stream_format)
            as they become available, with the stream terminated by a `data: [DONE]` message. [Example Python code](https://github.com/openai/openai-cookbook/blob/main/examples/How_to_stream_completions.ipynb).
          type: boolean
          nullable: true
          default: false
        logprobs: &completions_logprobs_configuration
          type: integer
          minimum: 0
          maximum: 5
          default: null
          nullable: true
          description: &completions_logprobs_description |
            Include the log probabilities on the `logprobs` most likely tokens, as well the chosen tokens. For example, if `logprobs` is 5, the API will return a list of the 5 most likely tokens. The API will always return the `logprob` of the sampled token, so there may be up to `logprobs+1` elements in the response.

            The maximum value for `logprobs` is 5.
        echo:
          type: boolean
          default: false
          nullable: true
          description: &completions_echo_description >
            Echo back the prompt in addition to the completion
        stop:
          description: &completions_stop_description >
            Up to 4 sequences where the API will stop generating further tokens. The returned text will not contain the stop sequence.
          default: null
          nullable: true
          oneOf:
            - type: string
              default: <|endoftext|>
              example: "\n"
              nullable: true
            - type: array
              minItems: 1
              maxItems: 4
              items:
                type: string
                example: '["\n"]'
        presence_penalty:
          type: number
          default: 0
          minimum: -2
          maximum: 2
          nullable: true
          description: &completions_presence_penalty_description |
            Number between -2.0 and 2.0. Positive values penalize new tokens based on whether they appear in the text so far, increasing the model's likelihood to talk about new topics.

            [See more information about frequency and presence penalties.](/docs/guides/gpt/parameter-details)
        frequency_penalty:
          type: number
          default: 0
          minimum: -2
          maximum: 2
          nullable: true
          description: &completions_frequency_penalty_description |
            Number between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model's likelihood to repeat the same line verbatim.

            [See more information about frequency and presence penalties.](/docs/guides/gpt/parameter-details)
        best_of:
          type: integer
          default: 1
          minimum: 0
          maximum: 20
          nullable: true
          description: &completions_best_of_description |
            Generates `best_of` completions server-side and returns the "best" (the one with the highest log probability per token). Results cannot be streamed.

            When used with `n`, `best_of` controls the number of candidate completions and `n` specifies how many to return – `best_of` must be greater than `n`.

            **Note:** Because this parameter generates many completions, it can quickly consume your token quota. Use carefully and ensure that you have reasonable settings for `max_tokens` and `stop`.
        logit_bias: &completions_logit_bias
          type: object
          x-oaiTypeLabel: map
          default: null
          nullable: true
          additionalProperties:
            type: integer
          description: &completions_logit_bias_description |
            Modify the likelihood of specified tokens appearing in the completion.

            Accepts a json object that maps tokens (specified by their token ID in the GPT tokenizer) to an associated bias value from -100 to 100. You can use this [tokenizer tool](/tokenizer?view=bpe) (which works for both GPT-2 and GPT-3) to convert text to token IDs. Mathematically, the bias is added to the logits generated by the model prior to sampling. The exact effect will vary per model, but values between -1 and 1 should decrease or increase likelihood of selection; values like -100 or 100 should result in a ban or exclusive selection of the relevant token.

            As an example, you can pass `{"50256": -100}` to prevent the <|endoftext|> token from being generated.
        user: &end_user_param_configuration
          type: string
          example: user-1234
          description: |
            A unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse. [Learn more](/docs/guides/safety-best-practices/end-user-ids).
      required:
        - model
        - prompt

    CreateCompletionResponse:
      type: object
      description: |
        Represents a completion response from the API. Note: both the streamed and non-streamed response objects share the same shape (unlike the chat endpoint).
      properties:
        id:
          type: string
          description: A unique identifier for the completion.
        object:
          type: string
          description: The object type, which is always "text_completion"
        created:
          type: integer
          description: The Unix timestamp (in seconds) of when the completion was created.
        model:
          type: string
          description: The model used for completion.
        choices:
          type: array
          description: The list of completion choices the model generated for the input prompt.
          items:
            type: object
            required:
              - text
              - index
              - logprobs
              - finish_reason
            properties:
              text:
                type: string
              index:
                type: integer
              logprobs:
                type: object
                nullable: true
                properties:
                  tokens:
                    type: array
                    items:
                      type: string
                  token_logprobs:
                    type: array
                    items:
                      type: number
                  top_logprobs:
                    type: array
                    items:
                      type: object
                      additionalProperties:
                        type: integer
                  text_offset:
                    type: array
                    items:
                      type: integer
              finish_reason:
                type: string
                description: &completion_finish_reason_description |
                  The reason the model stopped generating tokens. This will be `stop` if the model hit a natural stop point or a provided stop sequence,
                  `length` if the maximum number of tokens specified in the request was reached,
                  or `content_filter` if content was omitted due to a flag from our content filters.
                enum: ["stop", "length", "content_filter"]
        usage:
          $ref: "#/components/schemas/CompletionUsage"
      required:
        - id
        - object
        - created
        - model
        - choices
      x-oaiMeta:
        name: The completion object
        legacy: true
        example: |
          {
            "id": "cmpl-uqkvlQyYK7bGYrRHQ0eXlWi7",
            "object": "text_completion",
            "created": 1589478378,
            "model": "gpt-3.5-turbo",
            "choices": [
              {
                "text": "\n\nThis is indeed a test",
                "index": 0,
                "logprobs": null,
                "finish_reason": "length"
              }
            ],
            "usage": {
              "prompt_tokens": 5,
              "completion_tokens": 7,
              "total_tokens": 12
            }
          }

    ChatCompletionRequestMessage:
      type: object
      properties:
        role:
          type: string
          enum: ["system", "user", "assistant", "function"]
          description: The role of the messages author. One of `system`, `user`, `assistant`, or `function`.
        content:
          type: string
          nullable: true
          description: The contents of the message. `content` is required for all messages, and may be null for assistant messages with function calls.
        name:
          type: string
          description: The name of the author of this message. `name` is required if role is `function`, and it should be the name of the function whose response is in the `content`. May contain a-z, A-Z, 0-9, and underscores, with a maximum length of 64 characters.
        function_call:
          type: object
          description: The name and arguments of a function that should be called, as generated by the model.
          properties:
            name:
              type: string
              description: The name of the function to call.
            arguments:
              type: string
              description: The arguments to call the function with, as generated by the model in JSON format. Note that the model does not always generate valid JSON, and may hallucinate parameters not defined by your function schema. Validate the arguments in your code before calling your function.
          required:
            - name
            - arguments
      required:
        - role
        - content

    ChatCompletionFunctionParameters:
      type: object
      description: "The parameters the functions accepts, described as a JSON Schema object. See the [guide](/docs/guides/gpt/function-calling) for examples, and the [JSON Schema reference](https://json-schema.org/understanding-json-schema/) for documentation about the format.\n\nTo describe a function that accepts no parameters, provide the value `{\"type\": \"object\", \"properties\": {}}`."
      additionalProperties: true

    ChatCompletionFunctions:
      type: object
      properties:
        name:
          type: string
          description: The name of the function to be called. Must be a-z, A-Z, 0-9, or contain underscores and dashes, with a maximum length of 64.
        description:
          type: string
          description: A description of what the function does, used by the model to choose when and how to call the function.
        parameters:
          $ref: "#/components/schemas/ChatCompletionFunctionParameters"
      required:
        - name
        - parameters

    ChatCompletionFunctionCallOption:
      type: object
      properties:
        name:
          type: string
          description: The name of the function to call.
      required:
        - name

    ChatCompletionResponseMessage:
      type: object
      description: A chat completion message generated by the model.
      properties:
        role:
          type: string
          enum: ["system", "user", "assistant", "function"]
          description: The role of the author of this message.
        content:
          type: string
          description: The contents of the message.
          nullable: true
        function_call:
          type: object
          description: The name and arguments of a function that should be called, as generated by the model.
          properties:
            name:
              type: string
              description: The name of the function to call.
            arguments:
              type: string
              description: The arguments to call the function with, as generated by the model in JSON format. Note that the model does not always generate valid JSON, and may hallucinate parameters not defined by your function schema. Validate the arguments in your code before calling your function.
          required:
            - name
            - arguments
      required:
        - role
        - content

    ChatCompletionStreamResponseDelta:
      type: object
      description: A chat completion delta generated by streamed model responses.
      properties:
        role:
          type: string
          enum: ["system", "user", "assistant", "function"]
          description: The role of the author of this message.
        content:
          type: string
          description: The contents of the chunk message.
          nullable: true
        function_call:
          type: object
          description: The name and arguments of a function that should be called, as generated by the model.
          properties:
            name:
              type: string
              description: The name of the function to call.
            arguments:
              type: string
              description: The arguments to call the function with, as generated by the model in JSON format. Note that the model does not always generate valid JSON, and may hallucinate parameters not defined by your function schema. Validate the arguments in your code before calling your function.

    CreateChatCompletionRequest:
      type: object
      properties:
        model:
          description: ID of the model to use. See the [model endpoint compatibility](/docs/models/model-endpoint-compatibility) table for details on which models work with the Chat API.
          example: "gpt-3.5-turbo"
          anyOf:
            - type: string
            - type: string
              enum:
                [
                  "gpt-4",
                  "gpt-4-0314",
                  "gpt-4-0613",
                  "gpt-4-32k",
                  "gpt-4-32k-0314",
                  "gpt-4-32k-0613",
                  "gpt-3.5-turbo",
                  "gpt-3.5-turbo-16k",
                  "gpt-3.5-turbo-0301",
                  "gpt-3.5-turbo-0613",
                  "gpt-3.5-turbo-16k-0613",
                ]
          x-oaiTypeLabel: string
        messages:
          description: A list of messages comprising the conversation so far. [Example Python code](https://github.com/openai/openai-cookbook/blob/main/examples/How_to_format_inputs_to_ChatGPT_models.ipynb).
          type: array
          minItems: 1
          items:
            $ref: "#/components/schemas/ChatCompletionRequestMessage"
        functions:
          description: A list of functions the model may generate JSON inputs for.
          type: array
          minItems: 1
          maxItems: 128
          items:
            $ref: "#/components/schemas/ChatCompletionFunctions"
        function_call:
          description: "Controls how the model responds to function calls. `none` means the model does not call a function, and responds to the end-user. `auto` means the model can pick between an end-user or calling a function.  Specifying a particular function via `{\"name\": \"my_function\"}` forces the model to call that function. `none` is the default when no functions are present. `auto` is the default if functions are present."
          oneOf:
            - type: string
              enum: [none, auto]
            - $ref: "#/components/schemas/ChatCompletionFunctionCallOption"
        temperature:
          type: number
          minimum: 0
          maximum: 2
          default: 1
          example: 1
          nullable: true
          description: *completions_temperature_description
        top_p:
          type: number
          minimum: 0
          maximum: 1
          default: 1
          example: 1
          nullable: true
          description: *completions_top_p_description
        n:
          type: integer
          minimum: 1
          maximum: 128
          default: 1
          example: 1
          nullable: true
          description: How many chat completion choices to generate for each input message.
        stream:
          description: >
            If set, partial message deltas will be sent, like in ChatGPT. Tokens will be sent as data-only [server-sent events](https://developer.mozilla.org/en-US/docs/Web/API/Server-sent_events/Using_server-sent_events#Event_stream_format)
            as they become available, with the stream terminated by a `data: [DONE]` message. [Example Python code](https://github.com/openai/openai-cookbook/blob/main/examples/How_to_stream_completions.ipynb).
          type: boolean
          nullable: true
          default: false
        stop:
          description: |
            Up to 4 sequences where the API will stop generating further tokens.
          default: null
          oneOf:
            - type: string
              nullable: true
            - type: array
              minItems: 1
              maxItems: 4
              items:
                type: string
        max_tokens:
          description: |
            The maximum number of [tokens](/tokenizer) to generate in the chat completion.

            The total length of input tokens and generated tokens is limited by the model's context length. [Example Python code](https://github.com/openai/openai-cookbook/blob/main/examples/How_to_count_tokens_with_tiktoken.ipynb) for counting tokens.
          default: inf
          type: integer
          nullable: true
        presence_penalty:
          type: number
          default: 0
          minimum: -2
          maximum: 2
          nullable: true
          description: *completions_presence_penalty_description
        frequency_penalty:
          type: number
          default: 0
          minimum: -2
          maximum: 2
          nullable: true
          description: *completions_frequency_penalty_description
        logit_bias:
          type: object
          x-oaiTypeLabel: map
          default: null
          nullable: true
          additionalProperties:
            type: integer
          description: |
            Modify the likelihood of specified tokens appearing in the completion.

            Accepts a json object that maps tokens (specified by their token ID in the tokenizer) to an associated bias value from -100 to 100. Mathematically, the bias is added to the logits generated by the model prior to sampling. The exact effect will vary per model, but values between -1 and 1 should decrease or increase likelihood of selection; values like -100 or 100 should result in a ban or exclusive selection of the relevant token.
        user: *end_user_param_configuration
      required:
        - model
        - messages

    CreateChatCompletionResponse:
      type: object
      description: Represents a chat completion response returned by model, based on the provided input.
      properties:
        id:
          type: string
          description: A unique identifier for the chat completion.
        object:
          type: string
          description: The object type, which is always `chat.completion`.
        created:
          type: integer
          description: The Unix timestamp (in seconds) of when the chat completion was created.
        model:
          type: string
          description: The model used for the chat completion.
        choices:
          type: array
          description: A list of chat completion choices. Can be more than one if `n` is greater than 1.
          items:
            type: object
            required:
              - index
              - message
              - finish_reason
            properties:
              index:
                type: integer
                description: The index of the choice in the list of choices.
              message:
                $ref: "#/components/schemas/ChatCompletionResponseMessage"
              finish_reason:
                type: string
                description: &chat_completion_finish_reason_description |
                  The reason the model stopped generating tokens. This will be `stop` if the model hit a natural stop point or a provided stop sequence,
                  `length` if the maximum number of tokens specified in the request was reached,
                  `content_filter` if content was omitted due to a flag from our content filters,
                  or `function_call` if the model called a function.
                enum: ["stop", "length", "function_call", "content_filter"]
        usage:
          $ref: "#/components/schemas/CompletionUsage"
      required:
        - id
        - object
        - created
        - model
        - choices
      x-oaiMeta:
        name: The chat completion object
        group: chat
        example: *chat_completion_example

    ListPaginatedFineTuningJobsResponse:
      type: object
      properties:
        object:
          type: string
        data:
          type: array
          items:
            $ref: "#/components/schemas/FineTuningJob"
        has_more:
          type: boolean
      required:
        - object
        - data
        - has_more

    CreateChatCompletionStreamResponse:
      type: object
      description: Represents a streamed chunk of a chat completion response returned by model, based on the provided input.
      properties:
        id:
          type: string
          description: A unique identifier for the chat completion chunk.
        object:
          type: string
          description: The object type, which is always `chat.completion.chunk`.
        created:
          type: integer
          description: The Unix timestamp (in seconds) of when the chat completion chunk was created.
        model:
          type: string
          description: The model to generate the completion.
        choices:
          type: array
          description: A list of chat completion choices. Can be more than one if `n` is greater than 1.
          items:
            type: object
            required:
              - index
              - delta
              - finish_reason
            properties:
              index:
                type: integer
                description: The index of the choice in the list of choices.
              delta:
                $ref: "#/components/schemas/ChatCompletionStreamResponseDelta"
              finish_reason:
                type: string
                description: *chat_completion_finish_reason_description
                enum: ["stop", "length", "function_call"]
                nullable: true
      required:
        - id
        - object
        - created
        - model
        - choices
      x-oaiMeta:
        name: The chat completion chunk object
        group: chat
        example: *chat_completion_chunk_example

    CreateEditRequest:
      type: object
      properties:
        model:
          description: ID of the model to use. You can use the `text-davinci-edit-001` or `code-davinci-edit-001` model with this endpoint.
          example: "text-davinci-edit-001"
          anyOf:
            - type: string
            - type: string
              enum: ["text-davinci-edit-001", "code-davinci-edit-001"]
          x-oaiTypeLabel: string
        input:
          description: The input text to use as a starting point for the edit.
          type: string
          default: ""
          nullable: true
          example: "What day of the wek is it?"
        instruction:
          description: The instruction that tells the model how to edit the prompt.
          type: string
          example: "Fix the spelling mistakes."
        n:
          type: integer
          minimum: 1
          maximum: 20
          default: 1
          example: 1
          nullable: true
          description: How many edits to generate for the input and instruction.
        temperature:
          type: number
          minimum: 0
          maximum: 2
          default: 1
          example: 1
          nullable: true
          description: *completions_temperature_description
        top_p:
          type: number
          minimum: 0
          maximum: 1
          default: 1
          example: 1
          nullable: true
          description: *completions_top_p_description
      required:
        - model
        - instruction

    CreateEditResponse:
      type: object
      title: Edit
      deprecated: true
      properties:
        object:
          type: string
          description: The object type, which is always `edit`.
        created:
          type: integer
          description: The Unix timestamp (in seconds) of when the edit was created.
        choices:
          type: array
          description: A list of edit choices. Can be more than one if `n` is greater than 1.
          items:
            type: object
            required:
              - text
              - index
              - finish_reason
            properties:
              text:
                type: string
                description: The edited result.
              index:
                type: integer
                description: The index of the choice in the list of choices.
              finish_reason:
                type: string
                description: *completion_finish_reason_description
                enum: ["stop", "length"]
        usage:
          $ref: "#/components/schemas/CompletionUsage"
      required:
        - object
        - created
        - choices
        - usage
      x-oaiMeta:
        name: The edit object
        example: *edit_example

    CreateImageRequest:
      type: object
      properties:
        prompt:
          description: A text description of the desired image(s). The maximum length is 1000 characters.
          type: string
          example: "A cute baby sea otter"
        n: &images_n
          type: integer
          minimum: 1
          maximum: 10
          default: 1
          example: 1
          nullable: true
          description: The number of images to generate. Must be between 1 and 10.
        size: &images_size
          type: string
          enum: ["256x256", "512x512", "1024x1024"]
          default: "1024x1024"
          example: "1024x1024"
          nullable: true
          description: The size of the generated images. Must be one of `256x256`, `512x512`, or `1024x1024`.
        response_format: &images_response_format
          type: string
          enum: ["url", "b64_json"]
          default: "url"
          example: "url"
          nullable: true
          description: The format in which the generated images are returned. Must be one of `url` or `b64_json`.
        user: *end_user_param_configuration
      required:
        - prompt

    ImagesResponse:
      properties:
        created:
          type: integer
        data:
          type: array
          items:
            $ref: "#/components/schemas/Image"
      required:
        - created
        - data

    Image:
      type: object
      description: Represents the url or the content of an image generated by the OpenAI API.
      properties:
        url:
          type: string
          description: The URL of the generated image, if `response_format` is `url` (default).
        b64_json:
          type: string
          description: The base64-encoded JSON of the generated image, if `response_format` is `b64_json`.
      x-oaiMeta:
        name: The image object
        example: |
          {
            "url": "..."
          }

    CreateImageEditRequest:
      type: object
      properties:
        image:
          description: The image to edit. Must be a valid PNG file, less than 4MB, and square. If mask is not provided, image must have transparency, which will be used as the mask.
          type: string
          format: binary
        mask:
          description: An additional image whose fully transparent areas (e.g. where alpha is zero) indicate where `image` should be edited. Must be a valid PNG file, less than 4MB, and have the same dimensions as `image`.
          type: string
          format: binary
        prompt:
          description: A text description of the desired image(s). The maximum length is 1000 characters.
          type: string
          example: "A cute baby sea otter wearing a beret"
        n: *images_n
        size: *images_size
        response_format: *images_response_format
        user: *end_user_param_configuration
      required:
        - prompt
        - image

    CreateImageVariationRequest:
      type: object
      properties:
        image:
          description: The image to use as the basis for the variation(s). Must be a valid PNG file, less than 4MB, and square.
          type: string
          format: binary
        n: *images_n
        size: *images_size
        response_format: *images_response_format
        user: *end_user_param_configuration
      required:
        - image

    CreateModerationRequest:
      type: object
      properties:
        input:
          description: The input text to classify
          oneOf:
            - type: string
              default: ""
              example: "I want to kill them."
            - type: array
              items:
                type: string
                default: ""
                example: "I want to kill them."
        model:
          description: |
            Two content moderations models are available: `text-moderation-stable` and `text-moderation-latest`.

            The default is `text-moderation-latest` which will be automatically upgraded over time. This ensures you are always using our most accurate model. If you use `text-moderation-stable`, we will provide advanced notice before updating the model. Accuracy of `text-moderation-stable` may be slightly lower than for `text-moderation-latest`.
          nullable: false
          default: "text-moderation-latest"
          example: "text-moderation-stable"
          anyOf:
            - type: string
            - type: string
              enum: ["text-moderation-latest", "text-moderation-stable"]
          x-oaiTypeLabel: string
      required:
        - input

    CreateModerationResponse:
      type: object
      description: Represents policy compliance report by OpenAI's content moderation model against a given input.
      properties:
        id:
          type: string
          description: The unique identifier for the moderation request.
        model:
          type: string
          description: The model used to generate the moderation results.
        results:
          type: array
          description: A list of moderation objects.
          items:
            type: object
            properties:
              flagged:
                type: boolean
                description: Whether the content violates [OpenAI's usage policies](/policies/usage-policies).
              categories:
                type: object
                description: A list of the categories, and whether they are flagged or not.
                properties:
                  hate:
                    type: boolean
                    description: Content that expresses, incites, or promotes hate based on race, gender, ethnicity, religion, nationality, sexual orientation, disability status, or caste. Hateful content aimed at non-protected groups (e.g., chess players) is harrassment.
                  hate/threatening:
                    type: boolean
                    description: Hateful content that also includes violence or serious harm towards the targeted group based on race, gender, ethnicity, religion, nationality, sexual orientation, disability status, or caste.
                  harassment:
                    type: boolean
                    description: Content that expresses, incites, or promotes harassing language towards any target.
                  harassment/threatening:
                    type: boolean
                    description: Harassment content that also includes violence or serious harm towards any target.
                  self-harm:
                    type: boolean
                    description: Content that promotes, encourages, or depicts acts of self-harm, such as suicide, cutting, and eating disorders.
                  self-harm/intent:
                    type: boolean
                    description: Content where the speaker expresses that they are engaging or intend to engage in acts of self-harm, such as suicide, cutting, and eating disorders.
                  self-harm/instructions:
                    type: boolean
                    description: Content that encourages performing acts of self-harm, such as suicide, cutting, and eating disorders, or that gives instructions or advice on how to commit such acts.
                  sexual:
                    type: boolean
                    description: Content meant to arouse sexual excitement, such as the description of sexual activity, or that promotes sexual services (excluding sex education and wellness).
                  sexual/minors:
                    type: boolean
                    description: Sexual content that includes an individual who is under 18 years old.
                  violence:
                    type: boolean
                    description: Content that depicts death, violence, or physical injury.
                  violence/graphic:
                    type: boolean
                    description: Content that depicts death, violence, or physical injury in graphic detail.
                required:
                  - hate
                  - hate/threatening
                  - harassment
                  - harassment/threatening
                  - self-harm
                  - self-harm/intent
                  - self-harm/instructions
                  - sexual
                  - sexual/minors
                  - violence
                  - violence/graphic
              category_scores:
                type: object
                description: A list of the categories along with their scores as predicted by model.
                properties:
                  hate:
                    type: number
                    description: The score for the category 'hate'.
                  hate/threatening:
                    type: number
                    description: The score for the category 'hate/threatening'.
                  harassment:
                    type: number
                    description: The score for the category 'harassment'.
                  harassment/threatening:
                    type: number
                    description: The score for the category 'harassment/threatening'.
                  self-harm:
                    type: number
                    description: The score for the category 'self-harm'.
                  self-harm/intent:
                    type: number
                    description: The score for the category 'self-harm/intent'.
                  self-harm/instructions:
                    type: number
                    description: The score for the category 'self-harm/instructions'.
                  sexual:
                    type: number
                    description: The score for the category 'sexual'.
                  sexual/minors:
                    type: number
                    description: The score for the category 'sexual/minors'.
                  violence:
                    type: number
                    description: The score for the category 'violence'.
                  violence/graphic:
                    type: number
                    description: The score for the category 'violence/graphic'.
                required:
                  - hate
                  - hate/threatening
                  - harassment
                  - harassment/threatening
                  - self-harm
                  - self-harm/intent
                  - self-harm/instructions
                  - sexual
                  - sexual/minors
                  - violence
                  - violence/graphic
            required:
              - flagged
              - categories
              - category_scores
      required:
        - id
        - model
        - results
      x-oaiMeta:
        name: The moderation object
        example: *moderation_example

    ListFilesResponse:
      type: object
      properties:
        object:
          type: string
        data:
          type: array
          items:
            $ref: "#/components/schemas/OpenAIFile"
      required:
        - object
        - data

    CreateFileRequest:
      type: object
      additionalProperties: false
      properties:
        file:
          description: |
            Name of the [JSON Lines](https://jsonlines.readthedocs.io/en/latest/) file to be uploaded.

            If the `purpose` is set to "fine-tune", the file will be used for fine-tuning.
          type: string
          format: binary
        purpose:
          description: |
            The intended purpose of the uploaded documents.

            Use "fine-tune" for [fine-tuning](/docs/api-reference/fine-tuning). This allows us to validate the format of the uploaded file.
          type: string
      required:
        - file
        - purpose

    DeleteFileResponse:
      type: object
      properties:
        id:
          type: string
        object:
          type: string
        deleted:
          type: boolean
      required:
        - id
        - object
        - deleted

    CreateFineTuningJobRequest:
      type: object
      properties:
        training_file:
          description: |
            The ID of an uploaded file that contains training data.

            See [upload file](/docs/api-reference/files/upload) for how to upload a file.

            Your dataset must be formatted as a JSONL file. Additionally, you must upload your file with the purpose `fine-tune`.

            See the [fine-tuning guide](/docs/guides/fine-tuning) for more details.
          type: string
          example: "file-abc123"
        validation_file:
          description: |
            The ID of an uploaded file that contains validation data.

            If you provide this file, the data is used to generate validation
            metrics periodically during fine-tuning. These metrics can be viewed in
            the fine-tuning results file.
            The same data should not be present in both train and validation files.

            Your dataset must be formatted as a JSONL file. You must upload your file with the purpose `fine-tune`.

            See the [fine-tuning guide](/docs/guides/fine-tuning) for more details.
          type: string
          nullable: true
          example: "file-abc123"
        model:
          description: |
            The name of the model to fine-tune. You can select one of the
            [supported models](/docs/guides/fine-tuning/what-models-can-be-fine-tuned).
          example: "gpt-3.5-turbo"
          anyOf:
            - type: string
            - type: string
              enum: ["babbage-002", "davinci-002", "gpt-3.5-turbo"]
          x-oaiTypeLabel: string
        hyperparameters:
          type: object
          description: The hyperparameters used for the fine-tuning job.
          properties:
            n_epochs:
              description: |
                The number of epochs to train the model for. An epoch refers to one
                full cycle through the training dataset.
              oneOf:
                - type: string
                  enum: [auto]
                - type: integer
                  minimum: 1
                  maximum: 50
              default: auto
        suffix:
          description: |
            A string of up to 18 characters that will be added to your fine-tuned model name.

            For example, a `suffix` of "custom-model-name" would produce a model name like `ft:gpt-3.5-turbo:openai:custom-model-name:7p4lURel`.
          type: string
          minLength: 1
          maxLength: 40
          default: null
          nullable: true
      required:
        - training_file
        - model

    ListFineTuningJobEventsResponse:
      type: object
      properties:
        object:
          type: string
        data:
          type: array
          items:
            $ref: "#/components/schemas/FineTuningJobEvent"
      required:
        - object
        - data

    CreateFineTuneRequest:
      type: object
      properties:
        training_file:
          description: |
            The ID of an uploaded file that contains training data.

            See [upload file](/docs/api-reference/files/upload) for how to upload a file.

            Your dataset must be formatted as a JSONL file, where each training
            example is a JSON object with the keys "prompt" and "completion".
            Additionally, you must upload your file with the purpose `fine-tune`.

            See the [fine-tuning guide](/docs/guides/legacy-fine-tuning/creating-training-data) for more details.
          type: string
          example: "file-abc123"
        validation_file:
          description: |
            The ID of an uploaded file that contains validation data.

            If you provide this file, the data is used to generate validation
            metrics periodically during fine-tuning. These metrics can be viewed in
            the [fine-tuning results file](/docs/guides/legacy-fine-tuning/analyzing-your-fine-tuned-model).
            Your train and validation data should be mutually exclusive.

            Your dataset must be formatted as a JSONL file, where each validation
            example is a JSON object with the keys "prompt" and "completion".
            Additionally, you must upload your file with the purpose `fine-tune`.

            See the [fine-tuning guide](/docs/guides/legacy-fine-tuning/creating-training-data) for more details.
          type: string
          nullable: true
          example: "file-abc123"
        model:
          description: |
            The name of the base model to fine-tune. You can select one of "ada",
            "babbage", "curie", "davinci", or a fine-tuned model created after 2022-04-21 and before 2023-08-22.
            To learn more about these models, see the
            [Models](/docs/models) documentation.
          default: "curie"
          example: "curie"
          nullable: true
          anyOf:
            - type: string
            - type: string
              enum: ["ada", "babbage", "curie", "davinci"]
          x-oaiTypeLabel: string
        n_epochs:
          description: |
            The number of epochs to train the model for. An epoch refers to one
            full cycle through the training dataset.
          default: 4
          type: integer
          nullable: true
        batch_size:
          description: |
            The batch size to use for training. The batch size is the number of
            training examples used to train a single forward and backward pass.

            By default, the batch size will be dynamically configured to be
            ~0.2% of the number of examples in the training set, capped at 256 -
            in general, we've found that larger batch sizes tend to work better
            for larger datasets.
          default: null
          type: integer
          nullable: true
        learning_rate_multiplier:
          description: |
            The learning rate multiplier to use for training.
            The fine-tuning learning rate is the original learning rate used for
            pretraining multiplied by this value.

            By default, the learning rate multiplier is the 0.05, 0.1, or 0.2
            depending on final `batch_size` (larger learning rates tend to
            perform better with larger batch sizes). We recommend experimenting
            with values in the range 0.02 to 0.2 to see what produces the best
            results.
          default: null
          type: number
          nullable: true
        prompt_loss_weight:
          description: |
            The weight to use for loss on the prompt tokens. This controls how
            much the model tries to learn to generate the prompt (as compared
            to the completion which always has a weight of 1.0), and can add
            a stabilizing effect to training when completions are short.

            If prompts are extremely long (relative to completions), it may make
            sense to reduce this weight so as to avoid over-prioritizing
            learning the prompt.
          default: 0.01
          type: number
          nullable: true
        compute_classification_metrics:
          description: |
            If set, we calculate classification-specific metrics such as accuracy
            and F-1 score using the validation set at the end of every epoch.
            These metrics can be viewed in the [results file](/docs/guides/legacy-fine-tuning/analyzing-your-fine-tuned-model).

            In order to compute classification metrics, you must provide a
            `validation_file`. Additionally, you must
            specify `classification_n_classes` for multiclass classification or
            `classification_positive_class` for binary classification.
          type: boolean
          default: false
          nullable: true
        classification_n_classes:
          description: |
            The number of classes in a classification task.

            This parameter is required for multiclass classification.
          type: integer
          default: null
          nullable: true
        classification_positive_class:
          description: |
            The positive class in binary classification.

            This parameter is needed to generate precision, recall, and F1
            metrics when doing binary classification.
          type: string
          default: null
          nullable: true
        classification_betas:
          description: |
            If this is provided, we calculate F-beta scores at the specified
            beta values. The F-beta score is a generalization of F-1 score.
            This is only used for binary classification.

            With a beta of 1 (i.e. the F-1 score), precision and recall are
            given the same weight. A larger beta score puts more weight on
            recall and less on precision. A smaller beta score puts more weight
            on precision and less on recall.
          type: array
          items:
            type: number
          example: [0.6, 1, 1.5, 2]
          default: null
          nullable: true
        suffix:
          description: |
            A string of up to 40 characters that will be added to your fine-tuned model name.

            For example, a `suffix` of "custom-model-name" would produce a model name like `ada:ft-your-org:custom-model-name-2022-02-15-04-21-04`.
          type: string
          minLength: 1
          maxLength: 40
          default: null
          nullable: true
      required:
        - training_file

    ListFineTunesResponse:
      type: object
      properties:
        object:
          type: string
        data:
          type: array
          items:
            $ref: "#/components/schemas/FineTune"
      required:
        - object
        - data

    ListFineTuneEventsResponse:
      type: object
      properties:
        object:
          type: string
        data:
          type: array
          items:
            $ref: "#/components/schemas/FineTuneEvent"
      required:
        - object
        - data

    CreateEmbeddingRequest:
      type: object
      additionalProperties: false
      properties:
        model:
          description: *model_description
          example: "text-embedding-ada-002"
          anyOf:
            - type: string
            - type: string
              enum: ["text-embedding-ada-002"]
          x-oaiTypeLabel: string
        input:
          description: |
            Input text to embed, encoded as a string or array of tokens. To embed multiple inputs in a single request, pass an array of strings or array of token arrays. Each input must not exceed the max input tokens for the model (8191 tokens for `text-embedding-ada-002`) and cannot be an empty string. [Example Python code](https://github.com/openai/openai-cookbook/blob/main/examples/How_to_count_tokens_with_tiktoken.ipynb) for counting tokens.
          example: "The quick brown fox jumped over the lazy dog"
          oneOf:
            - type: string
              default: ""
              example: "This is a test."
            - type: array
              items:
                type: string
                default: ""
                example: "This is a test."
            - type: array
              minItems: 1
              items:
                type: integer
              example: "[1212, 318, 257, 1332, 13]"
            - type: array
              minItems: 1
              items:
                type: array
                minItems: 1
                items:
                  type: integer
              example: "[[1212, 318, 257, 1332, 13]]"
        user: *end_user_param_configuration
      required:
        - model
        - input

    CreateEmbeddingResponse:
      type: object
      properties:
        object:
          type: string
          description: The object type, which is always "embedding".
        model:
          type: string
          description: The name of the model used to generate the embedding.
        data:
          type: array
          description: The list of embeddings generated by the model.
          items:
            $ref: "#/components/schemas/Embedding"
        usage:
          type: object
          description: The usage information for the request.
          properties:
            prompt_tokens:
              type: integer
              description: The number of tokens used by the prompt.
            total_tokens:
              type: integer
              description: The total number of tokens used by the request.
          required:
            - prompt_tokens
            - total_tokens
      required:
        - object
        - model
        - data
        - usage

    CreateTranscriptionRequest:
      type: object
      additionalProperties: false
      properties:
        file:
          description: |
            The audio file object (not file name) to transcribe, in one of these formats: flac, mp3, mp4, mpeg, mpga, m4a, ogg, wav, or webm.
          type: string
          x-oaiTypeLabel: file
          format: binary
        model:
          description: |
            ID of the model to use. Only `whisper-1` is currently available.
          example: whisper-1
          anyOf:
            - type: string
            - type: string
              enum: ["whisper-1"]
          x-oaiTypeLabel: string
        prompt:
          description: |
            An optional text to guide the model's style or continue a previous audio segment. The [prompt](/docs/guides/speech-to-text/prompting) should match the audio language.
          type: string
        response_format:
          description: |
            The format of the transcript output, in one of these options: json, text, srt, verbose_json, or vtt.
          type: string
          enum:
            - json
            - text
            - srt
            - verbose_json
            - vtt
          default: json
        temperature:
          description: |
            The sampling temperature, between 0 and 1. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic. If set to 0, the model will use [log probability](https://en.wikipedia.org/wiki/Log_probability) to automatically increase the temperature until certain thresholds are hit.
          type: number
          default: 0
        language:
          description: |
            The language of the input audio. Supplying the input language in [ISO-639-1](https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes) format will improve accuracy and latency.
          type: string
      required:
        - file
        - model

    # Note: This does not currently support the non-default response format types.
    CreateTranscriptionResponse:
      type: object
      properties:
        text:
          type: string
      required:
        - text

    CreateTranslationRequest:
      type: object
      additionalProperties: false
      properties:
        file:
          description: |
            The audio file object (not file name) translate, in one of these formats: flac, mp3, mp4, mpeg, mpga, m4a, ogg, wav, or webm.
          type: string
          x-oaiTypeLabel: file
          format: binary
        model:
          description: |
            ID of the model to use. Only `whisper-1` is currently available.
          example: whisper-1
          anyOf:
            - type: string
            - type: string
              enum: ["whisper-1"]
          x-oaiTypeLabel: string
        prompt:
          description: |
            An optional text to guide the model's style or continue a previous audio segment. The [prompt](/docs/guides/speech-to-text/prompting) should be in English.
          type: string
        response_format:
          description: |
            The format of the transcript output, in one of these options: json, text, srt, verbose_json, or vtt.
          type: string
          default: json
        temperature:
          description: |
            The sampling temperature, between 0 and 1. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic. If set to 0, the model will use [log probability](https://en.wikipedia.org/wiki/Log_probability) to automatically increase the temperature until certain thresholds are hit.
          type: number
          default: 0
      required:
        - file
        - model

    # Note: This does not currently support the non-default response format types.
    CreateTranslationResponse:
      type: object
      properties:
        text:
          type: string
      required:
        - text

    Model:
      title: Model
      description: Describes an OpenAI model offering that can be used with the API.
      properties:
        id:
          type: string
          description: The model identifier, which can be referenced in the API endpoints.
        object:
          type: string
          description: The object type, which is always "model".
        created:
          type: integer
          description: The Unix timestamp (in seconds) when the model was created.
        owned_by:
          type: string
          description: The organization that owns the model.
      required:
        - id
        - object
        - created
        - owned_by
      x-oaiMeta:
        name: The model object
        example: *retrieve_model_response

    OpenAIFile:
      title: OpenAIFile
      description: |
        The `File` object represents a document that has been uploaded to OpenAI.
      properties:
        id:
          type: string
          description: The file identifier, which can be referenced in the API endpoints.
        object:
          type: string
          description: The object type, which is always "file".
        bytes:
          type: integer
          description: The size of the file in bytes.
        created_at:
          type: integer
          description: The Unix timestamp (in seconds) for when the file was created.
        filename:
          type: string
          description: The name of the file.
        purpose:
          type: string
          description: The intended purpose of the file. Currently, only "fine-tune" is supported.
        status:
          type: string
          description: The current status of the file, which can be either `uploaded`, `processed`, `pending`, `error`, `deleting` or `deleted`.
        status_details:
          type: string
          nullable: true
          description: |
            Additional details about the status of the file. If the file is in the `error` state, this will include a message describing the error.
      required:
        - id
        - object
        - bytes
        - created_at
        - filename
        - purpose
        - format
      x-oaiMeta:
        name: The file object
        example: |
          {
            "id": "file-abc123",
            "object": "file",
            "bytes": 120000,
            "created_at": 1677610602,
            "filename": "my_file.jsonl",
            "purpose": "fine-tune",
            "status": "uploaded",
            "status_details": null
          }
    Embedding:
      type: object
      description: |
        Represents an embedding vector returned by embedding endpoint.
      properties:
        index:
          type: integer
          description: The index of the embedding in the list of embeddings.
        object:
          type: string
          description: The object type, which is always "embedding".
        embedding:
          type: array
          description: |
            The embedding vector, which is a list of floats. The length of vector depends on the model as listed in the [embedding guide](/docs/guides/embeddings).
          items:
            type: number
      required:
        - index
        - object
        - embedding
      x-oaiMeta:
        name: The embedding object
        example: |
          {
            "object": "embedding",
            "embedding": [
              0.0023064255,
              -0.009327292,
              .... (1536 floats total for ada-002)
              -0.0028842222,
            ],
            "index": 0
          }

    FineTuningJob:
      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.
        object:
          type: string
          description: The object type, which is always "fine_tuning.job".
        created_at:
          type: integer
          description: The Unix timestamp (in seconds) for when the fine-tuning job was created.
        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.
        model:
          type: string
          description: The base model that is being fine-tuned.
        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.
        organization_id:
          type: string
          description: The organization that owns the fine-tuning job.
        status:
          type: string
          description: The current status of the fine-tuning job, which can be either `validating_files`, `queued`, `running`, `succeeded`, `failed`, or `cancelled`.
        hyperparameters:
          type: object
          description: The hyperparameters used for the fine-tuning job. See the [fine-tuning guide](/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
        training_file:
          type: string
          description: The file ID used for training. You can retrieve the training data with the [Files API](/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](/docs/api-reference/files/retrieve-contents).
        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](/docs/api-reference/files/retrieve-contents).
          items:
            type: string
            example: file-abc123
        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.
        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:
            message:
              type: string
              description: A human-readable error message.
            code:
              type: string
              description: A machine-readable error code.
            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:
            - message
            - code
            - param
      required:
        - id
        - object
        - created_at
        - finished_at
        - model
        - fine_tuned_model
        - organization_id
        - status
        - hyperparameters
        - training_file
        - validation_file
        - result_files
        - trained_tokens
        - error
      x-oaiMeta:
        name: The fine-tuning job object
        example: *fine_tuning_example

    FineTuningEvent:
      title: FineTuningEvent
      properties:
        object:
          type: string
        created_at:
          type: integer
        level:
          type: string
        message:
          type: string
        data:
          oneOf:
              - type: string
                default: none
                enum: [none, string]
        type:
          oneOf:
              - type: string
                default: none
                enum: ["message", "metrics"]
      required:
        - object
        - created_at
        - level
        - message
      x-oiMeta:
        name: The fine-tuning event object
        example: |
          {
            "object": "fine_tuning.job.event",
            "created_at": "1689376978",
            "level": "info" | "warn" | "error",
            "message": "<human-readable message>",
            "data": null | JSON,
            "type": "message"| "metrics"
          }

    FineTune:
      title: FineTune
      deprecated: true
      description: |
        The `FineTune` object represents a legacy fine-tune job that has been created through the API.
      properties:
        id:
          type: string
          description: The object identifier, which can be referenced in the API endpoints.
        object:
          type: string
          description: The object type, which is always "fine-tune".
        created_at:
          type: integer
          description: The Unix timestamp (in seconds) for when the fine-tuning job was created.
        updated_at:
          type: integer
          description: The Unix timestamp (in seconds) for when the fine-tuning job was last updated.
        model:
          type: string
          description: The base model that is being fine-tuned.
        fine_tuned_model:
          type: string
          nullable: true
          description: The name of the fine-tuned model that is being created.
        organization_id:
          type: string
          description: The organization that owns the fine-tuning job.
        status:
          type: string
          description: The current status of the fine-tuning job, which can be either `created`, `running`, `succeeded`, `failed`, or `cancelled`.
        hyperparams:
          type: object
          description: The hyperparameters used for the fine-tuning job. See the [fine-tuning guide](/docs/guides/legacy-fine-tuning/hyperparameters) for more details.
          properties:
            n_epochs:
              type: integer
              description: |
                The number of epochs to train the model for. An epoch refers to one
                full cycle through the training dataset.
            batch_size:
              type: integer
              description: |
                The batch size to use for training. The batch size is the number of
                training examples used to train a single forward and backward pass.
            prompt_loss_weight:
              type: number
              description: |
                The weight to use for loss on the prompt tokens.
            learning_rate_multiplier:
              type: number
              description: |
                The learning rate multiplier to use for training.
            compute_classification_metrics:
              type: boolean
              description: |
                The classification metrics to compute using the validation dataset at the end of every epoch.
            classification_positive_class:
              type: string
              description: |
                The positive class to use for computing classification metrics.
            classification_n_classes:
              type: integer
              description: |
                The number of classes to use for computing classification metrics.
          required:
            - n_epochs
            - batch_size
            - prompt_loss_weight
            - learning_rate_multiplier
        training_files:
          type: array
          description: The list of files used for training.
          items:
            $ref: "#/components/schemas/OpenAIFile"
        validation_files:
          type: array
          description: The list of files used for validation.
          items:
            $ref: "#/components/schemas/OpenAIFile"
        result_files:
          type: array
          description: The compiled results files for the fine-tuning job.
          items:
            $ref: "#/components/schemas/OpenAIFile"
        events:
          type: array
          description: The list of events that have been observed in the lifecycle of the FineTune job.
          items:
            $ref: "#/components/schemas/FineTuneEvent"
      required:
        - id
        - object
        - created_at
        - updated_at
        - model
        - fine_tuned_model
        - organization_id
        - status
        - hyperparams
        - training_files
        - validation_files
        - result_files
      x-oaiMeta:
        name: The fine-tune object
        example: *fine_tune_example

    FineTuningJobEvent:
      title: FineTuningJobEvent
      properties:
        id:
          type: string
        object:
          type: string
        created_at:
          type: integer
        level:
          type: string
          enum: ["info", "warn", "error"]
        message:
          type: string
      required:
        - id
        - object
        - created_at
        - level
        - message
      x-oiMeta:
        name: The fine-tuning job event object
        example: |
          {
            "object": "event",
            "id": "ftevent-abc123"
            "created_at": 1677610602,
            "level": "info",
            "message": "Created fine-tuning job"
          }

    FineTuneEvent:
      title: FineTuneEvent
      properties:
        object:
          type: string
        created_at:
          type: integer
        level:
          type: string
        message:
          type: string
      required:
        - object
        - created_at
        - level
        - message
      x-oiMeta:
        name: The fine-tune event object
        example: |
          {
            "object": "event",
            "created_at": 1677610602,
            "level": "info",
            "message": "Created fine-tune job"
          }
    CompletionUsage:
      type: object
      description: Usage statistics for the completion request.
      properties:
        prompt_tokens:
          type: integer
          description: Number of tokens in the prompt.
        completion_tokens:
          type: integer
          description: Number of tokens in the generated completion.
        total_tokens:
          type: integer
          description: Total number of tokens used in the request (prompt + completion).
      required:
        - prompt_tokens
        - completion_tokens
        - total_tokens

security:
  - ApiKeyAuth: []

x-oaiMeta:
  groups:
    # > General Notes
    # The `groups` section is used to generate the API reference pages and navigation, in the same
    # order listed below. Additionally, each `group` can have a list of `sections`, each of which
    # will become a navigation subroute and subsection under the group. Each section has:
    #  - `type`: Currently, either an `endpoint` or `object`, depending on how the section needs to
    #            be rendered
    #  - `key`: The reference key that can be used to lookup the section definition
    #  - `path`: The path (url) of the section, which is used to generate the navigation link.
    #
    # > The `object` sections maps to a schema component and the following fields are read for rendering
    # - `x-oaiMeta.name`: The name of the object, which will become the section title
    # - `x-oaiMeta.example`: The example object, which will be used to generate the example sample (always JSON)
    # - `description`: The description of the object, which will be used to generate the section description
    #
    # > The `endpoint` section maps to an operation path and the following fields are read for rendering:
    # - `x-oaiMeta.name`: The name of the endpoint, which will become the section title
    # - `x-oaiMeta.examples`: The endpoint examples, which can be an object (meaning a single variation, most
    #                         endpoints, or an array of objects, meaning multiple variations, e.g. the
    #                         chat completion and completion endpoints, with streamed and non-streamed examples.
    # - `x-oaiMeta.returns`: text describing what the endpoint returns.
    # - `summary`: The summary of the endpoint, which will be used to generate the section description
    - id: audio
      title: Audio
      description: |
        Learn how to turn audio into text.

        Related guide: [Speech to text](/docs/guides/speech-to-text)
      sections:
        - type: endpoint
          key: createTranscription
          path: createTranscription
        - type: endpoint
          key: createTranslation
          path: createTranslation
    - id: chat
      title: Chat
      description: |
        Given a list of messages comprising a conversation, the model will return a response.

        Related guide: [Chat completions](/docs/guides/gpt)
      sections:
        - type: object
          key: CreateChatCompletionResponse
          path: object
        - type: object
          key: CreateChatCompletionStreamResponse
          path: streaming
        - type: endpoint
          key: createChatCompletion
          path: create
    - id: completions
      title: Completions
      legacy: true
      description: |
        Given a prompt, the model will return one or more predicted completions, and can also return the probabilities of alternative tokens at each position. We recommend most users use our Chat completions API. [Learn more](/docs/deprecations/2023-07-06-gpt-and-embeddings)

        Related guide: [Legacy Completions](/docs/guides/gpt/completions-api)
      sections:
        - type: object
          key: CreateCompletionResponse
          path: object
        - type: endpoint
          key: createCompletion
          path: create
    - 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](/docs/guides/embeddings)
      sections:
        - type: object
          key: Embedding
          path: object
        - type: endpoint
          key: createEmbedding
          path: create
    - 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](/docs/guides/fine-tuning)
      sections:
        - type: object
          path: object
          key: FineTuningJob
        - type: endpoint
          key: createFineTuningJob
          path: create
        - type: endpoint
          key: listPaginatedFineTuningJobs
        - type: endpoint
          key: retrieveFineTuningJob
          path: retrieve
        - type: endpoint
          key: cancelFineTuningJob
          path: cancel
        - type: endpoint
          key: listFineTuningEvents
          path: list-events
    - id: files
      title: Files
      description: |
        Files are used to upload documents that can be used with features like [fine-tuning](/docs/api-reference/fine-tuning).
      sections:
        - type: object
          key: OpenAIFile
          path: object
        - type: endpoint
          key: listFiles
          path: list
        - type: endpoint
          key: createFile
          path: create
        - type: endpoint
          key: deleteFile
          path: delete
        - type: endpoint
          key: retrieveFile
          path: retrieve
        - type: endpoint
          key: downloadFile
          path: retrieve-contents
    - id: images
      title: Images
      description: |
        Given a prompt and/or an input image, the model will generate a new image.

        Related guide: [Image generation](/docs/guides/images)
      sections:
        - type: object
          key: Image
          path: object
        - type: endpoint
          key: createImage
          path: create
        - type: endpoint
          key: createImageEdit
          path: createEdit
        - type: endpoint
          key: createImageVariation
          path: createVariation
    - id: models
      title: Models
      description: |
        List and describe the various models available in the API. You can refer to the [Models](/docs/models) documentation to understand what models are available and the differences between them.
      sections:
        - type: object
          key: Model
          path: object
        - type: endpoint
          key: listModels
          path: list
        - type: endpoint
          key: retrieveModel
          path: retrieve
        - type: endpoint
          key: deleteModel
          path: delete
    - id: moderations
      title: Moderations
      description: |
        Given a input text, outputs if the model classifies it as violating OpenAI's content policy.

        Related guide: [Moderations](/docs/guides/moderation)
      sections:
        - type: object
          key: CreateModerationResponse
          path: object
        - type: endpoint
          key: createModeration
          path: create
    - id: fine-tunes
      title: Fine-tunes
      deprecated: true
      description: |
        Manage legacy fine-tuning jobs to tailor a model to your specific training data.

        We recommend transitioning to the updating [fine-tuning API](/docs/guides/fine-tuning)
      sections:
        - type: object
          path: object
          key: FineTune
        - type: endpoint
          key: createFineTune
          path: create
        - type: endpoint
          key: listFineTunes
          path: list
        - type: endpoint
          key: retrieveFineTune
          path: retrieve
        - type: endpoint
          key: cancelFineTune
          path: cancel
        - type: endpoint
          key: listFineTuneEvents
          path: list-events
    - id: edits
      title: Edits
      deprecated: true
      description: |
        Given a prompt and an instruction, the model will return an edited version of the prompt.
      sections:
        - type: object
          key: CreateEditResponse
          path: object
        - type: endpoint
          key: createEdit
          path: create