forked from Archives/langchain
3124 lines
105 KiB
YAML
3124 lines
105 KiB
YAML
|
openapi: 3.0.0
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|
info:
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title: OpenAI API
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description: APIs for sampling from and fine-tuning language models
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version: '1.1.0'
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servers:
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- url: https://api.openai.com/v1
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tags:
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- name: OpenAI
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description: The OpenAI REST API
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paths:
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/engines:
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get:
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operationId: listEngines
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deprecated: true
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tags:
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- OpenAI
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summary: Lists the currently available (non-finetuned) models, and provides basic information about each one such as the owner and availability.
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responses:
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"200":
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description: OK
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content:
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application/json:
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schema:
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$ref: '#/components/schemas/ListEnginesResponse'
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x-oaiMeta:
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name: List engines
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group: engines
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path: list
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examples:
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curl: |
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curl https://api.openai.com/v1/engines \
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-H 'Authorization: Bearer YOUR_API_KEY'
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python: |
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import os
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import openai
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openai.api_key = os.getenv("OPENAI_API_KEY")
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openai.Engine.list()
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node.js: |
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const { Configuration, OpenAIApi } = require("openai");
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const configuration = new Configuration({
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apiKey: process.env.OPENAI_API_KEY,
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});
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const openai = new OpenAIApi(configuration);
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const response = await openai.listEngines();
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|
response: |
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{
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|
"data": [
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|
{
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|
"id": "engine-id-0",
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"object": "engine",
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|
"owner": "organization-owner",
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|
"ready": true
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|
},
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|
{
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|
"id": "engine-id-2",
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|
"object": "engine",
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|
"owner": "organization-owner",
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|
"ready": true
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|
},
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|
{
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|
"id": "engine-id-3",
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|
"object": "engine",
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|
"owner": "openai",
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"ready": false
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},
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],
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"object": "list"
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}
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/engines/{engine_id}:
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get:
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operationId: retrieveEngine
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deprecated: true
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tags:
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- OpenAI
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summary: Retrieves a model instance, providing basic information about it such as the owner and availability.
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parameters:
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- in: path
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name: engine_id
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required: true
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schema:
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type: string
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# ideally this will be an actual ID, so this will always work from browser
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|
example:
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davinci
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description: &engine_id_description >
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The ID of the engine to use for this request
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|
responses:
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|
"200":
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|
description: OK
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|
content:
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|
application/json:
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schema:
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$ref: '#/components/schemas/Engine'
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|
x-oaiMeta:
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|
name: Retrieve engine
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group: engines
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|
path: retrieve
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|
examples:
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|
curl: |
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curl https://api.openai.com/v1/engines/VAR_model_id \
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-H 'Authorization: Bearer YOUR_API_KEY'
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|
python: |
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|
import os
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|
import openai
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openai.api_key = os.getenv("OPENAI_API_KEY")
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openai.Engine.retrieve("VAR_model_id")
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|
node.js: |
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const { Configuration, OpenAIApi } = require("openai");
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|
const configuration = new Configuration({
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|
apiKey: process.env.OPENAI_API_KEY,
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});
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const openai = new OpenAIApi(configuration);
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const response = await openai.retrieveEngine("VAR_model_id");
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|
response: |
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{
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"id": "VAR_model_id",
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|
"object": "engine",
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"owner": "openai",
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"ready": true
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}
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/completions:
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post:
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operationId: createCompletion
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|
tags:
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|
- OpenAI
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|
summary: Creates a completion for the provided prompt and parameters
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|
requestBody:
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|
required: true
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|
content:
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|
application/json:
|
|||
|
schema:
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|
$ref: '#/components/schemas/CreateCompletionRequest'
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|
responses:
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|
"200":
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|
description: OK
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|
content:
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|
application/json:
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|
schema:
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|
$ref: '#/components/schemas/CreateCompletionResponse'
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|
x-oaiMeta:
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|
name: Create completion
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group: completions
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|
path: create
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|
examples:
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|
curl: |
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curl https://api.openai.com/v1/completions \
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-H 'Content-Type: application/json' \
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-H 'Authorization: Bearer YOUR_API_KEY' \
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|
-d '{
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"model": "VAR_model_id",
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"prompt": "Say this is a test",
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"max_tokens": 7,
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"temperature": 0
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}'
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|
python: |
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|
import os
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|
import openai
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|
openai.api_key = os.getenv("OPENAI_API_KEY")
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|
openai.Completion.create(
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|
model="VAR_model_id",
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|
prompt="Say this is a test",
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max_tokens=7,
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temperature=0
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)
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|
node.js: |
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|
const { Configuration, OpenAIApi } = require("openai");
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|
const configuration = new Configuration({
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|
apiKey: process.env.OPENAI_API_KEY,
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|
});
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const openai = new OpenAIApi(configuration);
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const response = await openai.createCompletion({
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|
model: "VAR_model_id",
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|
prompt: "Say this is a test",
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|
max_tokens: 7,
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|
temperature: 0,
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|
});
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|
parameters: |
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|
{
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|
"model": "VAR_model_id",
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|
"prompt": "Say this is a test",
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|
"max_tokens": 7,
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|
"temperature": 0,
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|
"top_p": 1,
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"n": 1,
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"stream": false,
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"logprobs": null,
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"stop": "\n"
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}
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|
response: |
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|
{
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|||
|
"id": "cmpl-uqkvlQyYK7bGYrRHQ0eXlWi7",
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|||
|
"object": "text_completion",
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|||
|
"created": 1589478378,
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|
"model": "VAR_model_id",
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|
"choices": [
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|
{
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|
"text": "\n\nThis is indeed a test",
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|
"index": 0,
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|||
|
"logprobs": null,
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|||
|
"finish_reason": "length"
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|||
|
}
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|||
|
],
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|||
|
"usage": {
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|||
|
"prompt_tokens": 5,
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|||
|
"completion_tokens": 7,
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|
"total_tokens": 12
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|
}
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}
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|
/edits:
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|||
|
post:
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|||
|
operationId: createEdit
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|||
|
tags:
|
|||
|
- OpenAI
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|
summary: Creates a new edit for the provided input, instruction, and parameters
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|||
|
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
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|||
|
group: edits
|
|||
|
path: create
|
|||
|
examples:
|
|||
|
curl: |
|
|||
|
curl https://api.openai.com/v1/edits \
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|||
|
-H 'Content-Type: application/json' \
|
|||
|
-H 'Authorization: Bearer YOUR_API_KEY' \
|
|||
|
-d '{
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|
"model": "VAR_model_id",
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|
"input": "What day of the wek is it?",
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|||
|
"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).
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- id: fine-tunes
|
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title: Fine-tunes
|
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description: |
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Manage fine-tuning jobs to tailor a model to your specific training data.
|
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Related guide: [Fine-tune models](/docs/guides/fine-tuning)
|
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- id: moderations
|
|||
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title: Moderations
|
|||
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description: |
|
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Given a input text, outputs if the model classifies it as violating OpenAI's content policy.
|
|||
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Related guide: [Moderations](/docs/guides/moderation)
|
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- id: searches
|
|||
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title: Searches
|
|||
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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).
|
|||
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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).
|