langchain/docs/modules/agents/agent_toolkits/openai_openapi.yml

openapi: 3.0.0
info:
  title: OpenAI API
  description: APIs for sampling from and fine-tuning language models
  version: '1.1.0'
servers:
  - url: https://api.openai.com/v1
tags:
- name: OpenAI
  description: The OpenAI REST API
paths:
  /engines:
    get:
      operationId: listEngines
      deprecated: true
      tags:
      - OpenAI
      summary: Lists the currently available (non-finetuned) 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/ListEnginesResponse'
      x-oaiMeta:
        name: List engines
        group: engines
        path: list
        examples:
          curl: |
            curl https://api.openai.com/v1/engines \
              -H 'Authorization: Bearer YOUR_API_KEY'            
          python: |
            import os
            import openai
            openai.api_key = os.getenv("OPENAI_API_KEY")
            openai.Engine.list()            
          node.js: |
            const { Configuration, OpenAIApi } = require("openai");
            const configuration = new Configuration({
              apiKey: process.env.OPENAI_API_KEY,
            });
            const openai = new OpenAIApi(configuration);
            const response = await openai.listEngines();            
        response: |
          {
            "data": [
              {
                "id": "engine-id-0",
                "object": "engine",
                "owner": "organization-owner",
                "ready": true
              },
              {
                "id": "engine-id-2",
                "object": "engine",
                "owner": "organization-owner",
                "ready": true
              },
              {
                "id": "engine-id-3",
                "object": "engine",
                "owner": "openai",
                "ready": false
              },
            ],
            "object": "list"
          }          
  /engines/{engine_id}:
    get:
      operationId: retrieveEngine
      deprecated: true
      tags:
      - OpenAI
      summary: Retrieves a model instance, providing basic information about it such as the owner and availability.
      parameters:
        - in: path
          name: engine_id
          required: true
          schema:
            type: string
            # ideally this will be an actual ID, so this will always work from browser
            example:
              davinci
          description: &engine_id_description >
                        The ID of the engine to use for this request
      responses:
        "200":
          description: OK
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/Engine'
      x-oaiMeta:
        name: Retrieve engine
        group: engines
        path: retrieve
        examples:
          curl: |
            curl https://api.openai.com/v1/engines/VAR_model_id \
              -H 'Authorization: Bearer YOUR_API_KEY'            
          python: |
            import os
            import openai
            openai.api_key = os.getenv("OPENAI_API_KEY")
            openai.Engine.retrieve("VAR_model_id")            
          node.js: |
            const { Configuration, OpenAIApi } = require("openai");
            const configuration = new Configuration({
              apiKey: process.env.OPENAI_API_KEY,
            });
            const openai = new OpenAIApi(configuration);
            const response = await openai.retrieveEngine("VAR_model_id");            
        response: |
          {
            "id": "VAR_model_id",
            "object": "engine",
            "owner": "openai",
            "ready": true
          }          
  /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
        group: completions
        path: create
        examples:
          curl: |
            curl https://api.openai.com/v1/completions \
              -H 'Content-Type: application/json' \
              -H 'Authorization: Bearer YOUR_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: |
            const { Configuration, OpenAIApi } = require("openai");
            const configuration = new Configuration({
              apiKey: process.env.OPENAI_API_KEY,
            });
            const openai = new OpenAIApi(configuration);
            const response = await openai.createCompletion({
              model: "VAR_model_id",
              prompt: "Say this is a test",
              max_tokens: 7,
              temperature: 0,
            });            
        parameters: |
          {
            "model": "VAR_model_id",
            "prompt": "Say this is a test",
            "max_tokens": 7,
            "temperature": 0,
            "top_p": 1,
            "n": 1,
            "stream": false,
            "logprobs": null,
            "stop": "\n"
          }          
        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
            }
          }          
  /edits:
    post:
      operationId: createEdit
      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
        group: edits
        path: create
        examples:
          curl: |
            curl https://api.openai.com/v1/edits \
              -H 'Content-Type: application/json' \
              -H 'Authorization: Bearer YOUR_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: |
            const { Configuration, OpenAIApi } = require("openai");
            const configuration = new Configuration({
              apiKey: process.env.OPENAI_API_KEY,
            });
            const openai = new OpenAIApi(configuration);
            const response = await openai.createEdit({
              model: "VAR_model_id",
              input: "What day of the wek is it?",
              instruction: "Fix the spelling mistakes",
            });            
        parameters: |
          {
            "model": "VAR_model_id",
            "input": "What day of the wek is it?",
            "instruction": "Fix the spelling mistakes",
          }          
        response: |
          {
            "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
        group: images
        path: create
        examples:
          curl: |
            curl https://api.openai.com/v1/images/generations \
              -H 'Content-Type: application/json' \
              -H 'Authorization: Bearer YOUR_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: |
            const { Configuration, OpenAIApi } = require("openai");
            const configuration = new Configuration({
              apiKey: process.env.OPENAI_API_KEY,
            });
            const openai = new OpenAIApi(configuration);
            const response = await openai.createImage({
              prompt: "A cute baby sea otter",
              n: 2,
              size: "1024x1024",
            });            
        parameters: |
          {
            "prompt": "A cute baby sea otter",
            "n": 2,
            "size": "1024x1024"
          }          
        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
        group: images
        path: create-edit
        examples:
          curl: |
            curl https://api.openai.com/v1/images/edits \
              -H 'Authorization: Bearer YOUR_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: |
            const { Configuration, OpenAIApi } = require("openai");
            const configuration = new Configuration({
              apiKey: process.env.OPENAI_API_KEY,
            });
            const openai = new OpenAIApi(configuration);
            const response = await openai.createImageEdit(
              fs.createReadStream("otter.png"),
              fs.createReadStream("mask.png"),
              "A cute baby sea otter wearing a beret",
              2,
              "1024x1024"
            );            
        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
        group: images
        path: create-variation
        examples:
          curl: |
            curl https://api.openai.com/v1/images/variations \
              -H 'Authorization: Bearer YOUR_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: |
            const { Configuration, OpenAIApi } = require("openai");
            const configuration = new Configuration({
              apiKey: process.env.OPENAI_API_KEY,
            });
            const openai = new OpenAIApi(configuration);
            const response = await openai.createImageVariation(
              fs.createReadStream("otter.png"),
              2,
              "1024x1024"
            );            
        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
        group: embeddings
        path: create
        examples:
          curl: |
            curl https://api.openai.com/v1/embeddings \
              -X POST \
              -H "Authorization: Bearer YOUR_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: |
            const { Configuration, OpenAIApi } = require("openai");
            const configuration = new Configuration({
              apiKey: process.env.OPENAI_API_KEY,
            });
            const openai = new OpenAIApi(configuration);
            const response = await openai.createEmbedding({
              model: "text-embedding-ada-002",
              input: "The food was delicious and the waiter...",
            });            
        parameters: |
          {
            "model": "text-embedding-ada-002",
            "input": "The food was delicious and the waiter..."
          }          
        response: |
          {
            "object": "list",
            "data": [
              {
                "object": "embedding",
                "embedding": [
                  0.0023064255,
                  -0.009327292,
                  .... (1056 floats total for ada)
                  -0.0028842222,
                ],
                "index": 0
              }
            ],
            "model": "text-embedding-ada-002",
            "usage": {
              "prompt_tokens": 8,
              "total_tokens": 8
            }
          }          
  /engines/{engine_id}/search:
    post:
      operationId: createSearch
      deprecated: true
      tags:
      - OpenAI
      summary: |
        The search endpoint computes similarity scores between provided query and documents. Documents can be passed directly to the API if there are no more than 200 of them.
        To go beyond the 200 document limit, documents can be processed offline and then used for efficient retrieval at query time. When `file` is set, the search endpoint searches over all the documents in the given file and returns up to the `max_rerank` number of documents. These documents will be returned along with their search scores.
        The similarity score is a positive score that usually ranges from 0 to 300 (but can sometimes go higher), where a score above 200 usually means the document is semantically similar to the query.        
      parameters:
        - in: path
          name: engine_id
          required: true
          schema:
            type: string
            example: davinci
          description: The ID of the engine to use for this request.  You can select one of `ada`, `babbage`, `curie`, or `davinci`.
      requestBody:
        required: true
        content:
          application/json:
            schema:
              $ref: '#/components/schemas/CreateSearchRequest'
      responses:
        "200":
          description: OK
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/CreateSearchResponse'
      x-oaiMeta:
        name: Create search
        group: searches
        path: create
        examples:
          curl: |
            curl https://api.openai.com/v1/engines/davinci/search \
              -H "Content-Type: application/json" \
              -H 'Authorization: Bearer YOUR_API_KEY' \
              -d '{
              "documents": ["White House", "hospital", "school"],
              "query": "the president"
            }'            
          python: |
            import os
            import openai
            openai.api_key = os.getenv("OPENAI_API_KEY")
            openai.Engine("davinci").search(
              documents=["White House", "hospital", "school"],
              query="the president"
            )            
          node.js: |
            const { Configuration, OpenAIApi } = require("openai");
            const configuration = new Configuration({
              apiKey: process.env.OPENAI_API_KEY,
            });
            const openai = new OpenAIApi(configuration);
            const response = await openai.createSearch("davinci", {
              documents: ["White House", "hospital", "school"],
              query: "the president",
            });            
        parameters: |
          {
            "documents": [
              "White House",
              "hospital",
              "school"
            ],
            "query": "the president"
          }          
        response: |
          {
            "data": [
              {
                "document": 0,
                "object": "search_result",
                "score": 215.412
              },
              {
                "document": 1,
                "object": "search_result",
                "score": 40.316
              },
              {
                "document": 2,
                "object": "search_result",
                "score":  55.226
              }
            ],
            "object": "list"
          }          
  /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
        group: files
        path: list
        examples:
          curl: |
            curl https://api.openai.com/v1/files \
              -H 'Authorization: Bearer YOUR_API_KEY'            
          python: |
            import os
            import openai
            openai.api_key = os.getenv("OPENAI_API_KEY")
            openai.File.list()            
          node.js: |
            const { Configuration, OpenAIApi } = require("openai");
            const configuration = new Configuration({
              apiKey: process.env.OPENAI_API_KEY,
            });
            const openai = new OpenAIApi(configuration);
            const response = await openai.listFiles();            
        response: |
          {
            "data": [
              {
                "id": "file-ccdDZrC3iZVNiQVeEA6Z66wf",
                "object": "file",
                "bytes": 175,
                "created_at": 1613677385,
                "filename": "train.jsonl",
                "purpose": "search"
              },
              {
                "id": "file-XjGxS3KTG0uNmNOK362iJua3",
                "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
        group: files
        path: upload
        examples:
          curl: |
            curl https://api.openai.com/v1/files \
              -H "Authorization: Bearer YOUR_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: |
            const fs = require("fs");
            const { Configuration, OpenAIApi } = require("openai");
            const configuration = new Configuration({
              apiKey: process.env.OPENAI_API_KEY,
            });
            const openai = new OpenAIApi(configuration);
            const response = await openai.createFile(
              fs.createReadStream("mydata.jsonl"),
              "fine-tune"
            );            
        response: |
          {
            "id": "file-XjGxS3KTG0uNmNOK362iJua3",
            "object": "file",
            "bytes": 140,
            "created_at": 1613779121,
            "filename": "mydata.jsonl",
            "purpose": "fine-tune"
          }          
  /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
        group: files
        path: delete
        examples:
          curl: |
            curl https://api.openai.com/v1/files/file-XjGxS3KTG0uNmNOK362iJua3 \
              -X DELETE \
              -H 'Authorization: Bearer YOUR_API_KEY'            
          python: |
            import os
            import openai
            openai.api_key = os.getenv("OPENAI_API_KEY")
            openai.File.delete("file-XjGxS3KTG0uNmNOK362iJua3")            
          node.js: |
            const { Configuration, OpenAIApi } = require("openai");
            const configuration = new Configuration({
              apiKey: process.env.OPENAI_API_KEY,
            });
            const openai = new OpenAIApi(configuration);
            const response = await openai.deleteFile("file-XjGxS3KTG0uNmNOK362iJua3");            
        response: |
          {
            "id": "file-XjGxS3KTG0uNmNOK362iJua3",
            "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
        group: files
        path: retrieve
        examples:
          curl: |
            curl https://api.openai.com/v1/files/file-XjGxS3KTG0uNmNOK362iJua3 \
              -H 'Authorization: Bearer YOUR_API_KEY'            
          python: |
            import os
            import openai
            openai.api_key = os.getenv("OPENAI_API_KEY")
            openai.File.retrieve("file-XjGxS3KTG0uNmNOK362iJua3")            
          node.js: |
            const { Configuration, OpenAIApi } = require("openai");
            const configuration = new Configuration({
              apiKey: process.env.OPENAI_API_KEY,
            });
            const openai = new OpenAIApi(configuration);
            const response = await openai.retrieveFile("file-XjGxS3KTG0uNmNOK362iJua3");            
        response: |
          {
            "id": "file-XjGxS3KTG0uNmNOK362iJua3",
            "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
        group: files
        path: retrieve-content
        examples:
          curl: |
            curl https://api.openai.com/v1/files/file-XjGxS3KTG0uNmNOK362iJua3/content \
              -H 'Authorization: Bearer YOUR_API_KEY' > file.jsonl            
          python: |
            import os
            import openai
            openai.api_key = os.getenv("OPENAI_API_KEY")
            content = openai.File.download("file-XjGxS3KTG0uNmNOK362iJua3")            
          node.js: |
            const { Configuration, OpenAIApi } = require("openai");
            const configuration = new Configuration({
              apiKey: process.env.OPENAI_API_KEY,
            });
            const openai = new OpenAIApi(configuration);
            const response = await openai.downloadFile("file-XjGxS3KTG0uNmNOK362iJua3");            
  /answers:
    post:
      operationId: createAnswer
      deprecated: true
      tags:
      - OpenAI
      summary: |
        Answers the specified question using the provided documents and examples.
        The endpoint first [searches](/docs/api-reference/searches) over provided documents or files to find relevant context. The relevant context is combined with the provided examples and question to create the prompt for [completion](/docs/api-reference/completions).        
      requestBody:
        required: true
        content:
          application/json:
            schema:
              $ref: '#/components/schemas/CreateAnswerRequest'
      responses:
        "200":
          description: OK
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/CreateAnswerResponse'
      x-oaiMeta:
        name: Create answer
        group: answers
        path: create
        examples:
          curl: |
            curl https://api.openai.com/v1/answers \
              -X POST \
              -H "Authorization: Bearer YOUR_API_KEY" \
              -H 'Content-Type: application/json' \
              -d '{
                "documents": ["Puppy A is happy.", "Puppy B is sad."],
                "question": "which puppy is happy?",
                "search_model": "ada",
                "model": "curie",
                "examples_context": "In 2017, U.S. life expectancy was 78.6 years.",
                "examples": [["What is human life expectancy in the United States?","78 years."]],
                "max_tokens": 5,
                "stop": ["\n", "<|endoftext|>"]
              }'            
          python: |
            import os
            import openai
            openai.api_key = os.getenv("OPENAI_API_KEY")
            openai.Answer.create(
              search_model="ada",
              model="curie",
              question="which puppy is happy?",
              documents=["Puppy A is happy.", "Puppy B is sad."],
              examples_context="In 2017, U.S. life expectancy was 78.6 years.",
              examples=[["What is human life expectancy in the United States?","78 years."]],
              max_tokens=5,
              stop=["\n", "<|endoftext|>"],
            )            
          node.js: |
            const { Configuration, OpenAIApi } = require("openai");
            const configuration = new Configuration({
              apiKey: process.env.OPENAI_API_KEY,
            });
            const openai = new OpenAIApi(configuration);
            const response = await openai.createAnswer({
              search_model: "ada",
              model: "curie",
              question: "which puppy is happy?",
              documents: ["Puppy A is happy.", "Puppy B is sad."],
              examples_context: "In 2017, U.S. life expectancy was 78.6 years.",
              examples: [["What is human life expectancy in the United States?","78 years."]],
              max_tokens: 5,
              stop: ["\n", "<|endoftext|>"],
            });            
        parameters: |
          {
            "documents": ["Puppy A is happy.", "Puppy B is sad."],
            "question": "which puppy is happy?",
            "search_model": "ada",
            "model": "curie",
            "examples_context": "In 2017, U.S. life expectancy was 78.6 years.",
            "examples": [["What is human life expectancy in the United States?","78 years."]],
            "max_tokens": 5,
            "stop": ["\n", "<|endoftext|>"]
          }          
        response: |
          {
            "answers": [
              "puppy A."
            ],
            "completion": "cmpl-2euVa1kmKUuLpSX600M41125Mo9NI",
            "model": "curie:2020-05-03",
            "object": "answer",
            "search_model": "ada",
            "selected_documents": [
              {
                "document": 0,
                "text": "Puppy A is happy. "
              },
              {
                "document": 1,
                "text": "Puppy B is sad. "
              }
            ]
          }          
  /classifications:
    post:
      operationId: createClassification
      deprecated: true
      tags:
      - OpenAI
      summary: |
        Classifies the specified `query` using provided examples.
        The endpoint first [searches](/docs/api-reference/searches) over the labeled examples
        to select the ones most relevant for the particular query. Then, the relevant examples
        are combined with the query to construct a prompt to produce the final label via the
        [completions](/docs/api-reference/completions) endpoint.
        Labeled examples can be provided via an uploaded `file`, or explicitly listed in the
        request using the `examples` parameter for quick tests and small scale use cases.        
      requestBody:
        required: true
        content:
          application/json:
            schema:
              $ref: '#/components/schemas/CreateClassificationRequest'
      responses:
        "200":
          description: OK
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/CreateClassificationResponse'
      x-oaiMeta:
        name: Create classification
        group: classifications
        path: create
        examples:
          curl: |
            curl https://api.openai.com/v1/classifications \
              -X POST \
              -H "Authorization: Bearer YOUR_API_KEY" \
              -H 'Content-Type: application/json' \
              -d '{
                "examples": [
                  ["A happy moment", "Positive"],
                  ["I am sad.", "Negative"],
                  ["I am feeling awesome", "Positive"]],
                "query": "It is a raining day :(",
                "search_model": "ada",
                "model": "curie",
                "labels":["Positive", "Negative", "Neutral"]
              }'            
          python: |
            import os
            import openai
            openai.api_key = os.getenv("OPENAI_API_KEY")
            openai.Classification.create(
              search_model="ada",
              model="curie",
              examples=[
                ["A happy moment", "Positive"],
                ["I am sad.", "Negative"],
                ["I am feeling awesome", "Positive"]
              ],
              query="It is a raining day :(",
              labels=["Positive", "Negative", "Neutral"],
            )            
          node.js: |
            const { Configuration, OpenAIApi } = require("openai");
            const configuration = new Configuration({
              apiKey: process.env.OPENAI_API_KEY,
            });
            const openai = new OpenAIApi(configuration);
            const response = await openai.createClassification({
              search_model: "ada",
              model: "curie",
              examples: [
                ["A happy moment", "Positive"],
                ["I am sad.", "Negative"],
                ["I am feeling awesome", "Positive"]
              ],
              query:"It is a raining day :(",
              labels: ["Positive", "Negative", "Neutral"],
            });            
        parameters: |
          {
            "examples": [
              ["A happy moment", "Positive"],
              ["I am sad.", "Negative"],
              ["I am feeling awesome", "Positive"]
            ],
            "labels": ["Positive", "Negative", "Neutral"],
            "query": "It is a raining day :(",
            "search_model": "ada",
            "model": "curie"
          }          
        response: |
          {
            "completion": "cmpl-2euN7lUVZ0d4RKbQqRV79IiiE6M1f",
            "label": "Negative",
            "model": "curie:2020-05-03",
            "object": "classification",
            "search_model": "ada",
            "selected_examples": [
              {
                "document": 1,
                "label": "Negative",
                "text": "I am sad."
              },
              {
                "document": 0,
                "label": "Positive",
                "text": "A happy moment"
              },
              {
                "document": 2,
                "label": "Positive",
                "text": "I am feeling awesome"
              }
            ]
          }          
  /fine-tunes:
    post:
      operationId: createFineTune
      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/CreateFineTuneRequest'
      responses:
        "200":
          description: OK
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/FineTune'
      x-oaiMeta:
        name: Create fine-tune
        group: fine-tunes
        path: create
        beta: true
        examples:
          curl: |
            curl https://api.openai.com/v1/fine-tunes \
              -X POST \
              -H "Content-Type: application/json" \
              -H "Authorization: Bearer YOUR_API_KEY" \
              -d '{
              "training_file": "file-XGinujblHPwGLSztz8cPS8XY"
            }'            
          python: |
            import os
            import openai
            openai.api_key = os.getenv("OPENAI_API_KEY")
            openai.FineTune.create(training_file="file-XGinujblHPwGLSztz8cPS8XY")            
          node.js: |
            const { Configuration, OpenAIApi } = require("openai");
            const configuration = new Configuration({
              apiKey: process.env.OPENAI_API_KEY,
            });
            const openai = new OpenAIApi(configuration);
            const response = await openai.createFineTune({
              training_file: "file-XGinujblHPwGLSztz8cPS8XY",
            });            
        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-...",
            "result_files": [],
            "status": "pending",
            "validation_files": [],
            "training_files": [
              {
                "id": "file-XGinujblHPwGLSztz8cPS8XY",
                "object": "file",
                "bytes": 1547276,
                "created_at": 1610062281,
                "filename": "my-data-train.jsonl",
                "purpose": "fine-tune-train"
              }
            ],
            "updated_at": 1614807352,
          }          
    get:
      operationId: listFineTunes
      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
        group: fine-tunes
        path: list
        beta: true
        examples:
          curl: |
            curl https://api.openai.com/v1/fine-tunes \
              -H 'Authorization: Bearer YOUR_API_KEY'            
          python: |
            import os
            import openai
            openai.api_key = os.getenv("OPENAI_API_KEY")
            openai.FineTune.list()            
          node.js: |
            const { Configuration, OpenAIApi } = require("openai");
            const configuration = new Configuration({
              apiKey: process.env.OPENAI_API_KEY,
            });
            const openai = new OpenAIApi(configuration);
            const response = await openai.listFineTunes();            
        response: |
          {
            "object": "list",
            "data": [
              {
                "id": "ft-AF1WoRqd3aJAHsqc9NY7iL8F",
                "object": "fine-tune",
                "model": "curie",
                "created_at": 1614807352,
                "fine_tuned_model": null,
                "hyperparams": { ... },
                "organization_id": "org-...",
                "result_files": [],
                "status": "pending",
                "validation_files": [],
                "training_files": [ { ... } ],
                "updated_at": 1614807352,
              },
              { ... },
              { ... }
            ]
          }          
  /fine-tunes/{fine_tune_id}:
    get:
      operationId: retrieveFineTune
      tags:
      - OpenAI
      summary: |
        Gets info about the fine-tune job.
        [Learn more about Fine-tuning](/docs/guides/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
        group: fine-tunes
        path: retrieve
        beta: true
        examples:
          curl: |
            curl https://api.openai.com/v1/fine-tunes/ft-AF1WoRqd3aJAHsqc9NY7iL8F \
              -H "Authorization: Bearer YOUR_API_KEY"            
          python: |
            import os
            import openai
            openai.api_key = os.getenv("OPENAI_API_KEY")
            openai.FineTune.retrieve(id="ft-AF1WoRqd3aJAHsqc9NY7iL8F")            
          node.js: |
            const { Configuration, OpenAIApi } = require("openai");
            const configuration = new Configuration({
              apiKey: process.env.OPENAI_API_KEY,
            });
            const openai = new OpenAIApi(configuration);
            const response = await openai.retrieveFineTune("ft-AF1WoRqd3aJAHsqc9NY7iL8F");            
        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."
              },
              {
                "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-QQm6ZpqdNwAaVC3aSz5sWwLT."
              },
              {
                "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-...",
            "result_files": [
              {
                "id": "file-QQm6ZpqdNwAaVC3aSz5sWwLT",
                "object": "file",
                "bytes": 81509,
                "created_at": 1614807863,
                "filename": "compiled_results.csv",
                "purpose": "fine-tune-results"
              }
            ],
            "status": "succeeded",
            "validation_files": [],
            "training_files": [
              {
                "id": "file-XGinujblHPwGLSztz8cPS8XY",
                "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
      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
        group: fine-tunes
        path: cancel
        beta: true
        examples:
          curl: |
            curl https://api.openai.com/v1/fine-tunes/ft-AF1WoRqd3aJAHsqc9NY7iL8F/cancel \
              -X POST \
              -H "Authorization: Bearer YOUR_API_KEY"            
          python: |
            import os
            import openai
            openai.api_key = os.getenv("OPENAI_API_KEY")
            openai.FineTune.cancel(id="ft-AF1WoRqd3aJAHsqc9NY7iL8F")            
          node.js: |
            const { Configuration, OpenAIApi } = require("openai");
            const configuration = new Configuration({
              apiKey: process.env.OPENAI_API_KEY,
            });
            const openai = new OpenAIApi(configuration);
            const response = await openai.cancelFineTune("ft-AF1WoRqd3aJAHsqc9NY7iL8F");            
        response: |
          {
            "id": "ft-xhrpBbvVUzYGo8oUO1FY4nI7",
            "object": "fine-tune",
            "model": "curie",
            "created_at": 1614807770,
            "events": [ { ... } ],
            "fine_tuned_model": null,
            "hyperparams": { ... },
            "organization_id": "org-...",
            "result_files": [],
            "status": "cancelled",
            "validation_files": [],
            "training_files": [
              {
                "id": "file-XGinujblHPwGLSztz8cPS8XY",
                "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
      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
        group: fine-tunes
        path: events
        beta: true
        examples:
          curl: |
            curl https://api.openai.com/v1/fine-tunes/ft-AF1WoRqd3aJAHsqc9NY7iL8F/events \
              -H "Authorization: Bearer YOUR_API_KEY"            
          python: |
            import os
            import openai
            openai.api_key = os.getenv("OPENAI_API_KEY")
            openai.FineTune.list_events(id="ft-AF1WoRqd3aJAHsqc9NY7iL8F")            
          node.js: |
            const { Configuration, OpenAIApi } = require("openai");
            const configuration = new Configuration({
              apiKey: process.env.OPENAI_API_KEY,
            });
            const openai = new OpenAIApi(configuration);
            const response = await openai.listFineTuneEvents("ft-AF1WoRqd3aJAHsqc9NY7iL8F");            
        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-QQm6ZpqdNwAaVC3aSz5sWwLT."
              },
              {
                "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
        group: models
        path: list
        examples:
          curl: |
            curl https://api.openai.com/v1/models \
              -H 'Authorization: Bearer YOUR_API_KEY'            
          python: |
            import os
            import openai
            openai.api_key = os.getenv("OPENAI_API_KEY")
            openai.Model.list()            
          node.js: |
            const { Configuration, OpenAIApi } = require("openai");
            const configuration = new Configuration({
              apiKey: process.env.OPENAI_API_KEY,
            });
            const openai = new OpenAIApi(configuration);
            const response = await openai.listModels();            
        response: |
          {
            "data": [
              {
                "id": "model-id-0",
                "object": "model",
                "owned_by": "organization-owner",
                "permission": [...]
              },
              {
                "id": "model-id-1",
                "object": "model",
                "owned_by": "organization-owner",
                "permission": [...]
              },
              {
                "id": "model-id-2",
                "object": "model",
                "owned_by": "openai",
                "permission": [...]
              },
            ],
            "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:
              text-davinci-001
          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
        group: models
        path: retrieve
        examples:
          curl: |
            curl https://api.openai.com/v1/models/VAR_model_id \
              -H 'Authorization: Bearer YOUR_API_KEY'            
          python: |
            import os
            import openai
            openai.api_key = os.getenv("OPENAI_API_KEY")
            openai.Model.retrieve("VAR_model_id")            
          node.js: |
            const { Configuration, OpenAIApi } = require("openai");
            const configuration = new Configuration({
              apiKey: process.env.OPENAI_API_KEY,
            });
            const openai = new OpenAIApi(configuration);
            const response = await openai.retrieveModel("VAR_model_id");            
        response: |
          {
            "id": "VAR_model_id",
            "object": "model",
            "owned_by": "openai",
            "permission": [...]
          }          
    delete:
      operationId: deleteModel
      tags:
      - OpenAI
      summary: Delete a fine-tuned model. You must have the Owner role in your organization.
      parameters:
        - in: path
          name: model
          required: true
          schema:
            type: string
            example: curie:ft-acmeco-2021-03-03-21-44-20
          description: The model to delete
      responses:
        "200":
          description: OK
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/DeleteModelResponse'
      x-oaiMeta:
        name: Delete fine-tune model
        group: fine-tunes
        path: delete-model
        beta: true
        examples:
          curl: |
            curl https://api.openai.com/v1/models/curie:ft-acmeco-2021-03-03-21-44-20 \
              -X DELETE \
              -H "Authorization: Bearer YOUR_API_KEY"            
          python: |
            import os
            import openai
            openai.api_key = os.getenv("OPENAI_API_KEY")
            openai.Model.delete("curie:ft-acmeco-2021-03-03-21-44-20")            
          node.js: |
            const { Configuration, OpenAIApi } = require("openai");
            const configuration = new Configuration({
              apiKey: process.env.OPENAI_API_KEY,
            });
            const openai = new OpenAIApi(configuration);
            const response = await openai.deleteModel('curie:ft-acmeco-2021-03-03-21-44-20');            
        response: |
          {
            "id": "curie:ft-acmeco-2021-03-03-21-44-20",
            "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
        group: moderations
        path: create
        examples:
          curl: |
            curl https://api.openai.com/v1/moderations \
              -H 'Content-Type: application/json' \
              -H 'Authorization: Bearer YOUR_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: |
            const { Configuration, OpenAIApi } = require("openai");
            const configuration = new Configuration({
              apiKey: process.env.OPENAI_API_KEY,
            });
            const openai = new OpenAIApi(configuration);
            const response = await openai.createModeration({
              input: "I want to kill them.",
            });            
        parameters: |
          {
            "input": "I want to kill them."
          }          
        response: |
          {
            "id": "modr-5MWoLO",
            "model": "text-moderation-001",
            "results": [
              {
                "categories": {
                  "hate": false,
                  "hate/threatening": true,
                  "self-harm": false,
                  "sexual": false,
                  "sexual/minors": false,
                  "violence": true,
                  "violence/graphic": false
                },
                "category_scores": {
                  "hate": 0.22714105248451233,
                  "hate/threatening": 0.4132447838783264,
                  "self-harm": 0.005232391878962517,
                  "sexual": 0.01407341007143259,
                  "sexual/minors": 0.0038522258400917053,
                  "violence": 0.9223177433013916,
                  "violence/graphic": 0.036865197122097015
                },
                "flagged": true
              }
            ]
          }          
components:
  schemas:
    ListEnginesResponse:
      type: object
      properties:
        object:
          type: string
        data:
          type: array
          items:
            $ref: '#/components/schemas/Engine'
      required:
        - object
        - data

    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: &model_configuration
          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.
          type: 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. Most models have a context length of 2048 tokens (except for the newest models, which support 4096).            
        temperature:
          type: number
          minimum: 0
          maximum: 2
          default: 1
          example: 1
          nullable: true
          description: &completions_temperature_description |
            What [sampling temperature](https://towardsdatascience.com/how-to-sample-from-language-models-682bceb97277) to use. Higher values means the model will take more risks. Try 0.9 for more creative applications, and 0 (argmax sampling) for ones with a well-defined answer.

            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.            
          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. If you need more than this, please contact us through our [Help center](https://help.openai.com) and describe your use case.            
        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/api-reference/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/api-reference/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
          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

    CreateCompletionResponse:
      type: object
      properties:
        id:
          type: string
        object:
          type: string
        created:
          type: integer
        model:
          type: string
        choices:
          type: array
          items:
            type: object
            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
                  text_offset:
                    type: array
                    items:
                      type: integer
              finish_reason:
                type: string
        usage:
          type: object
          properties:
            prompt_tokens:
              type: integer
            completion_tokens:
              type: integer
            total_tokens:
              type: integer
          required: 
            - prompt_tokens
            - completion_tokens
            - total_tokens
      required: 
        - id
        - object
        - created
        - model
        - choices

    CreateEditRequest:
      type: object
      properties:
        model: *model_configuration
        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
      properties:
        object:
          type: string
        created:
          type: integer
        choices:
          type: array
          items:
            type: object
            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
                  text_offset:
                    type: array
                    items:
                      type: integer
              finish_reason:
                type: string
        usage:
          type: object
          properties:
            prompt_tokens:
              type: integer
            completion_tokens:
              type: integer
            total_tokens:
              type: integer
          required: 
            - prompt_tokens
            - completion_tokens
            - total_tokens
      required: 
        - object
        - created
        - choices
        - usage

    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:
            type: object
            properties:
              url:
                type: string
              b64_json:
                type: string
      required:
        - created
        - data

    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`.            
          type: string
          nullable: false
          default: "text-moderation-latest"
          example: "text-moderation-stable"
      required:
        - input

    CreateModerationResponse:
      type: object
      properties:
        id:
          type: string
        model:
          type: string
        results:
          type: array
          items:
            type: object
            properties:
              flagged:
                type: boolean
              categories:
                type: object
                properties:
                  hate:
                    type: boolean
                  hate/threatening:
                    type: boolean
                  self-harm:
                    type: boolean
                  sexual:
                    type: boolean
                  sexual/minors:
                    type: boolean
                  violence:
                    type: boolean
                  violence/graphic:
                    type: boolean
                required: 
                  - hate
                  - hate/threatening
                  - self-harm
                  - sexual
                  - sexual/minors
                  - violence
                  - violence/graphic
              category_scores:
                type: object
                properties:
                  hate:
                    type: number
                  hate/threatening:
                    type: number
                  self-harm:
                    type: number
                  sexual:
                    type: number
                  sexual/minors:
                    type: number
                  violence:
                    type: number
                  violence/graphic:
                    type: number
                required: 
                  - hate
                  - hate/threatening
                  - self-harm
                  - sexual
                  - sexual/minors
                  - violence
                  - violence/graphic
            required: 
              - flagged
              - categories
              - category_scores
      required: 
        - id
        - model
        - results

    CreateSearchRequest:
      type: object
      properties:
        query:
          description: Query to search against the documents.
          type: string
          example: "the president"
          minLength: 1
        documents:
          description: |
            Up to 200 documents to search over, provided as a list of strings.
            The maximum document length (in tokens) is 2034 minus the number of tokens in the query.
            You should specify either `documents` or a `file`, but not both.            
          type: array
          minItems: 1
          maxItems: 200
          items:
            type: string
          nullable: true
          example: "['White House', 'hospital', 'school']"
        file:
          description: |
            The ID of an uploaded file that contains documents to search over.
            You should specify either `documents` or a `file`, but not both.            
          type: string
          nullable: true
        max_rerank:
          description: |
            The maximum number of documents to be re-ranked and returned by search.
            This flag only takes effect when `file` is set.            
          type: integer
          minimum: 1
          default: 200
          nullable: true
        return_metadata: &return_metadata_configuration
          description: |
            A special boolean flag for showing metadata. If set to `true`, each document entry in the returned JSON will contain a "metadata" field.
            This flag only takes effect when `file` is set.            
          type: boolean
          default: false
          nullable: true
        user: *end_user_param_configuration
      required:
        - query

    CreateSearchResponse:
      type: object
      properties:
        object:
          type: string
        model:
          type: string
        data:
          type: array
          items:
            type: object
            properties:
              object:
                type: string
              document:
                type: integer
              score:
                type: number

    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", each line is a JSON record with "prompt" and "completion" fields representing your [training examples](/docs/guides/fine-tuning/prepare-training-data).            
          type: string
          format: binary
        purpose:
          description: |
            The intended purpose of the uploaded documents.
            Use "fine-tune" for [Fine-tuning](/docs/api-reference/fine-tunes). 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

    CreateAnswerRequest:
      type: object
      additionalProperties: false
      properties:
        model:
          description: ID of the model to use for completion. You can select one of `ada`, `babbage`, `curie`, or `davinci`.
          type: string
        question:
          description: Question to get answered.
          type: string
          minLength: 1
          example: "What is the capital of Japan?"
        examples:
          description: List of (question, answer) pairs that will help steer the model towards the tone and answer format you'd like. We recommend adding 2 to 3 examples.
          type: array
          minItems: 1
          maxItems: 200
          items:
            type: array
            minItems: 2
            maxItems: 2
            items:
              type: string
              minLength: 1
          example: "[['What is the capital of Canada?', 'Ottawa'], ['Which province is Ottawa in?', 'Ontario']]"
        examples_context:
          description: A text snippet containing the contextual information used to generate the answers for the `examples` you provide.
          type: string
          example: "Ottawa, Canada's capital, is located in the east of southern Ontario, near the city of Montréal and the U.S. border."
        documents:
          description: |
            List of documents from which the answer for the input `question` should be derived. If this is an empty list, the question will be answered based on the question-answer examples.
            You should specify either `documents` or a `file`, but not both.            
          type: array
          maxItems: 200
          items:
            type: string
          example: "['Japan is an island country in East Asia, located in the northwest Pacific Ocean.', 'Tokyo is the capital and most populous prefecture of Japan.']"
          nullable: true
        file:
          description: |
            The ID of an uploaded file that contains documents to search over. See [upload file](/docs/api-reference/files/upload) for how to upload a file of the desired format and purpose.
            You should specify either `documents` or a `file`, but not both.            
          type: string
          nullable: true
        search_model: &search_model_configuration
          description: ID of the model to use for [Search](/docs/api-reference/searches/create). You can select one of `ada`, `babbage`, `curie`, or `davinci`.
          type: string
          default: ada
          nullable: true
        max_rerank:
          description: The maximum number of documents to be ranked by [Search](/docs/api-reference/searches/create) when using `file`. Setting it to a higher value leads to improved accuracy but with increased latency and cost.
          type: integer
          default: 200
          nullable: true
        temperature:
          description: What [sampling temperature](https://towardsdatascience.com/how-to-sample-from-language-models-682bceb97277) to use. Higher values mean the model will take more risks and value 0 (argmax sampling) works better for scenarios with a well-defined answer.
          type: number
          default: 0
          nullable: true
        logprobs: &context_completions_logprobs_configuration
          type: integer
          minimum: 0
          maximum: 5
          default: null
          nullable: true
          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. If you need more than this, please contact us through our [Help center](https://help.openai.com) and describe your use case.
            When `logprobs` is set, `completion` will be automatically added into `expand` to get the logprobs.            
        max_tokens:
          description: The maximum number of tokens allowed for the generated answer
          type: integer
          default: 16
          nullable: true
        stop:
          description: *completions_stop_description
          default: null
          oneOf:
            - type: string
              default: <|endoftext|>
              example: "\n"
            - type: array
              minItems: 1
              maxItems: 4
              items:
                type: string
                example: '["\n"]'
          nullable: true
        n:
          description: How many answers to generate for each question.
          type: integer
          minimum: 1
          maximum: 10
          default: 1
          nullable: true
        logit_bias: *completions_logit_bias
        return_metadata: *return_metadata_configuration
        return_prompt: &return_prompt_configuration
          description: If set to `true`, the returned JSON will include a "prompt" field containing the final prompt that was used to request a completion. This is mainly useful for debugging purposes.
          type: boolean
          default: false
          nullable: true
        expand: &expand_configuration
          description: If an object name is in the list, we provide the full information of the object; otherwise, we only provide the object ID. Currently we support `completion` and `file` objects for expansion.
          type: array
          items: {}
          nullable: true
          default: []
        user: *end_user_param_configuration
      required:
        - model
        - question
        - examples
        - examples_context

    CreateAnswerResponse:
      type: object
      properties:
        object:
          type: string
        model:
          type: string
        search_model:
          type: string
        completion:
          type: string
        answers:
          type: array
          items:
            type: string
        selected_documents:
          type: array
          items:
            type: object
            properties:
              document:
                type: integer
              text:
                type: string

    CreateClassificationRequest:
      type: object
      additionalProperties: false
      properties:
        model: *model_configuration
        query:
          description: Query to be classified.
          type: string
          minLength: 1
          example: "The plot is not very attractive."
        examples:
          description: |
            A list of examples with labels, in the following format:
            `[["The movie is so interesting.", "Positive"], ["It is quite boring.", "Negative"], ...]`
            All the label strings will be normalized to be capitalized.
            You should specify either `examples` or `file`, but not both.            
          type: array
          minItems: 2
          maxItems: 200
          items:
            type: array
            minItems: 2
            maxItems: 2
            items:
              type: string
              minLength: 1
          example: "[['Do not see this film.', 'Negative'], ['Smart, provocative and blisteringly funny.', 'Positive']]"
          nullable: true
        file:
          description: |
            The ID of the uploaded file that contains training examples. See [upload file](/docs/api-reference/files/upload) for how to upload a file of the desired format and purpose.
            You should specify either `examples` or `file`, but not both.            
          type: string
          nullable: true
        labels:
          description: The set of categories being classified. If not specified, candidate labels will be automatically collected from the examples you provide. All the label strings will be normalized to be capitalized.
          type: array
          minItems: 2
          maxItems: 200
          default: null
          items:
            type: string
          example: ["Positive", "Negative"]
          nullable: true
        search_model: *search_model_configuration
        temperature:
          description:
            What sampling `temperature` to use. Higher values mean the model will take more risks. Try 0.9 for more creative applications, and 0 (argmax sampling) for ones with a well-defined answer.
          type: number
          minimum: 0
          maximum: 2
          default: 0
          nullable: true
          example: 0
        logprobs: *context_completions_logprobs_configuration
        max_examples:
          description: The maximum number of examples to be ranked by [Search](/docs/api-reference/searches/create) when using `file`. Setting it to a higher value leads to improved accuracy but with increased latency and cost.
          type: integer
          default: 200
          nullable: true
        logit_bias: *completions_logit_bias
        return_prompt: *return_prompt_configuration
        return_metadata: *return_metadata_configuration
        expand: *expand_configuration
        user: *end_user_param_configuration
      required:
        - model
        - query

    CreateClassificationResponse:
      type: object
      properties:
        object:
          type: string
        model:
          type: string
        search_model:
          type: string
        completion:
          type: string
        label:
          type: string
        selected_examples:
          type: array
          items:
            type: object
            properties:
              document:
                type: integer
              text:
                type: string
              label:
                type: string

    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/fine-tuning/creating-training-data) for more details.            
          type: string
          example: "file-ajSREls59WBbvgSzJSVWxMCB"
        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/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/fine-tuning/creating-training-data) for more details.            
          type: string
          nullable: true
          example: "file-XjSREls59WBbvgSzJSVWxMCa"
        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.
            To learn more about these models, see the
            [Models](https://beta.openai.com/docs/models) documentation.            
          default: "curie"
          type: string
          nullable: true
        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/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: *model_configuration
        input:
          description: |
                        Input text to get embeddings for, encoded as a string or array of tokens. To get embeddings for multiple inputs in a single request, pass an array of strings or array of token arrays. Each input must not exceed 8192 tokens in length.
          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
        model:
          type: string
        data:
          type: array
          items:
            type: object
            properties:
              index:
                type: integer
              object:
                type: string
              embedding:
                type: array
                items:
                  type: number
            required: 
              - index
              - object
              - embedding
        usage:
          type: object
          properties:
            prompt_tokens:
              type: integer
            total_tokens:
              type: integer
          required: 
            - prompt_tokens
            - total_tokens
      required: 
        - object
        - model
        - data
        - usage

    Engine:
      title: Engine
      properties:
        id:
          type: string
        object:
          type: string
        created:
          type: integer
          nullable: true
        ready:
          type: boolean
      required: 
        - id
        - object
        - created
        - ready

    Model:
      title: Model
      properties:
        id:
          type: string
        object:
          type: string
        created:
          type: integer
        owned_by:
          type: string
      required: 
        - id
        - object
        - created
        - owned_by

    OpenAIFile:
      title: OpenAIFile
      properties:
        id:
          type: string
        object:
          type: string
        bytes:
          type: integer
        created_at:
          type: integer
        filename:
          type: string
        purpose:
          type: string
        status:
          type: string
        status_details:
          type: object
          nullable: true
      required: 
        - id
        - object
        - bytes
        - created_at
        - filename
        - purpose

    FineTune:
      title: FineTune
      properties:
        id:
          type: string
        object:
          type: string
        created_at:
          type: integer
        updated_at:
          type: integer
        model:
          type: string
        fine_tuned_model:
          type: string
          nullable: true
        organization_id:
          type: string
        status:
          type: string
        hyperparams:
          type: object
        training_files:
          type: array
          items:
            $ref: '#/components/schemas/OpenAIFile'
        validation_files:
          type: array
          items:
            $ref: '#/components/schemas/OpenAIFile'
        result_files:
          type: array
          items:
            $ref: '#/components/schemas/OpenAIFile'
        events:
          type: array
          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

    FineTuneEvent:
      title: FineTuneEvent
      properties:
        object:
          type: string
        created_at:
          type: integer
        level:
          type: string
        message:
          type: string
      required: 
        - object
        - created_at
        - level
        - message

x-oaiMeta:
  groups:
    - 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.
    - id: completions
      title: Completions
      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.
    - id: edits
      title: Edits
      description: |
                Given a prompt and an instruction, the model will return an edited version of the prompt.
    - 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)        
    - 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)        
    - id: files
      title: Files
      description: |
                Files are used to upload documents that can be used with features like [Fine-tuning](/docs/api-reference/fine-tunes).
    - id: fine-tunes
      title: Fine-tunes
      description: |
        Manage fine-tuning jobs to tailor a model to your specific training data.
        Related guide: [Fine-tune models](/docs/guides/fine-tuning)        
    - 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)        
    - id: searches
      title: Searches
      warning:
        title: This endpoint is deprecated and will be removed on December 3rd, 2022
        message: We’ve developed new methods with better performance. [Learn more](https://help.openai.com/en/articles/6272952-search-transition-guide).
      description: |
        Given a query and a set of documents or labels, the model ranks each document based on its semantic similarity to the provided query.
        Related guide: [Search](/docs/guides/search)        
    - id: classifications
      title: Classifications
      warning:
        title: This endpoint is deprecated and will be removed on December 3rd, 2022
        message: We’ve developed new methods with better performance. [Learn more](https://help.openai.com/en/articles/6272941-classifications-transition-guide).
      description: |
        Given a query and a set of labeled examples, the model will predict the most likely label for the query. Useful as a drop-in replacement for any ML classification or text-to-label task.
        Related guide: [Classification](/docs/guides/classifications)        
    - id: answers
      title: Answers
      warning:
        title: This endpoint is deprecated and will be removed on December 3rd, 2022
        message: We’ve developed new methods with better performance. [Learn more](https://help.openai.com/en/articles/6233728-answers-transition-guide).
      description: |
        Given a question, a set of documents, and some examples, the API generates an answer to the question based on the information in the set of documents. This is useful for question-answering applications on sources of truth, like company documentation or a knowledge base.
        Related guide: [Question answering](/docs/guides/answers)        
    - id: engines
      title: Engines
      description: These endpoints describe and provide access to the various engines available in the API.
      warning:
        title: The Engines endpoints are deprecated.
        message: Please use their replacement, [Models](/docs/api-reference/models), instead. [Learn more](https://help.openai.com/TODO).