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openapi.yaml
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openapi: 3.0.0
info:
title: OpenAI API
description: The OpenAI REST API. Please see
https://platform.openai.com/docs/api-reference for more details.
version: 2.3.0
termsOfService: https://openai.com/policies/terms-of-use
contact:
name: OpenAI Support
url: https://help.openai.com/
license:
name: MIT
url: https://github.com/openai/openai-openapi/blob/master/LICENSE
servers:
- url: https://api.openai.com/v1
tags:
- name: Assistants
description: Build Assistants that can call models and use tools.
- name: Audio
description: Turn audio into text or text into audio.
- name: Chat
description: Given a list of messages comprising a conversation, the model will
return a response.
- name: 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.
- name: Embeddings
description: Get a vector representation of a given input that can be easily
consumed by machine learning models and algorithms.
- name: Fine-tuning
description: Manage fine-tuning jobs to tailor a model to your specific training data.
- name: Batch
description: Create large batches of API requests to run asynchronously.
- name: Files
description: Files are used to upload documents that can be used with features
like Assistants and Fine-tuning.
- name: Uploads
description: Use Uploads to upload large files in multiple parts.
- name: Images
description: Given a prompt and/or an input image, the model will generate a new image.
- name: Models
description: List and describe the various models available in the API.
- name: Moderations
description: Given text and/or image inputs, classifies if those inputs are
potentially harmful.
- name: Audit Logs
description: List user actions and configuration changes within this organization.
paths:
/assistants:
get:
operationId: listAssistants
tags:
- Assistants
summary: Returns a list of assistants.
parameters:
- name: limit
in: query
description: >
A limit on the number of objects to be returned. Limit can range
between 1 and 100, and the default is 20.
required: false
schema:
type: integer
default: 20
- name: order
in: query
description: >
Sort order by the `created_at` timestamp of the objects. `asc` for
ascending order and `desc` for descending order.
schema:
type: string
default: desc
enum:
- asc
- desc
- name: after
in: query
description: >
A cursor for use in pagination. `after` is an object ID that defines
your place in the list. For instance, if you make a list request and
receive 100 objects, ending with obj_foo, your subsequent call can
include after=obj_foo in order to fetch the next page of the list.
schema:
type: string
- name: before
in: query
description: >
A cursor for use in pagination. `before` is an object ID that
defines your place in the list. For instance, if you make a list
request and receive 100 objects, starting with obj_foo, your
subsequent call can include before=obj_foo in order to fetch the
previous page of the list.
schema:
type: string
responses:
"200":
description: OK
content:
application/json:
schema:
$ref: "#/components/schemas/ListAssistantsResponse"
x-oaiMeta:
name: List assistants
group: assistants
beta: true
returns: A list of [assistant](/docs/api-reference/assistants/object) objects.
examples:
request:
curl: |
curl "https://api.openai.com/v1/assistants?order=desc&limit=20" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $OPENAI_API_KEY" \
-H "OpenAI-Beta: assistants=v2"
python: |
from openai import OpenAI
client = OpenAI()
my_assistants = client.beta.assistants.list(
order="desc",
limit="20",
)
print(my_assistants.data)
node.js: |-
import OpenAI from "openai";
const openai = new OpenAI();
async function main() {
const myAssistants = await openai.beta.assistants.list({
order: "desc",
limit: "20",
});
console.log(myAssistants.data);
}
main();
response: >
{
"object": "list",
"data": [
{
"id": "asst_abc123",
"object": "assistant",
"created_at": 1698982736,
"name": "Coding Tutor",
"description": null,
"model": "gpt-4o",
"instructions": "You are a helpful assistant designed to make me better at coding!",
"tools": [],
"tool_resources": {},
"metadata": {},
"top_p": 1.0,
"temperature": 1.0,
"response_format": "auto"
},
{
"id": "asst_abc456",
"object": "assistant",
"created_at": 1698982718,
"name": "My Assistant",
"description": null,
"model": "gpt-4o",
"instructions": "You are a helpful assistant designed to make me better at coding!",
"tools": [],
"tool_resources": {},
"metadata": {},
"top_p": 1.0,
"temperature": 1.0,
"response_format": "auto"
},
{
"id": "asst_abc789",
"object": "assistant",
"created_at": 1698982643,
"name": null,
"description": null,
"model": "gpt-4o",
"instructions": null,
"tools": [],
"tool_resources": {},
"metadata": {},
"top_p": 1.0,
"temperature": 1.0,
"response_format": "auto"
}
],
"first_id": "asst_abc123",
"last_id": "asst_abc789",
"has_more": false
}
post:
operationId: createAssistant
tags:
- Assistants
summary: Create an assistant with a model and instructions.
requestBody:
required: true
content:
application/json:
schema:
$ref: "#/components/schemas/CreateAssistantRequest"
responses:
"200":
description: OK
content:
application/json:
schema:
$ref: "#/components/schemas/AssistantObject"
x-oaiMeta:
name: Create assistant
group: assistants
beta: true
returns: An [assistant](/docs/api-reference/assistants/object) object.
examples:
- title: Code Interpreter
request:
curl: >
curl "https://api.openai.com/v1/assistants" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $OPENAI_API_KEY" \
-H "OpenAI-Beta: assistants=v2" \
-d '{
"instructions": "You are a personal math tutor. When asked a question, write and run Python code to answer the question.",
"name": "Math Tutor",
"tools": [{"type": "code_interpreter"}],
"model": "gpt-4o"
}'
python: >
from openai import OpenAI
client = OpenAI()
my_assistant = client.beta.assistants.create(
instructions="You are a personal math tutor. When asked a question, write and run Python code to answer the question.",
name="Math Tutor",
tools=[{"type": "code_interpreter"}],
model="gpt-4o",
)
print(my_assistant)
node.js: >-
import OpenAI from "openai";
const openai = new OpenAI();
async function main() {
const myAssistant = await openai.beta.assistants.create({
instructions:
"You are a personal math tutor. When asked a question, write and run Python code to answer the question.",
name: "Math Tutor",
tools: [{ type: "code_interpreter" }],
model: "gpt-4o",
});
console.log(myAssistant);
}
main();
response: >
{
"id": "asst_abc123",
"object": "assistant",
"created_at": 1698984975,
"name": "Math Tutor",
"description": null,
"model": "gpt-4o",
"instructions": "You are a personal math tutor. When asked a question, write and run Python code to answer the question.",
"tools": [
{
"type": "code_interpreter"
}
],
"metadata": {},
"top_p": 1.0,
"temperature": 1.0,
"response_format": "auto"
}
- title: Files
request:
curl: >
curl https://api.openai.com/v1/assistants \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $OPENAI_API_KEY" \
-H "OpenAI-Beta: assistants=v2" \
-d '{
"instructions": "You are an HR bot, and you have access to files to answer employee questions about company policies.",
"tools": [{"type": "file_search"}],
"tool_resources": {"file_search": {"vector_store_ids": ["vs_123"]}},
"model": "gpt-4o"
}'
python: >
from openai import OpenAI
client = OpenAI()
my_assistant = client.beta.assistants.create(
instructions="You are an HR bot, and you have access to files to answer employee questions about company policies.",
name="HR Helper",
tools=[{"type": "file_search"}],
tool_resources={"file_search": {"vector_store_ids": ["vs_123"]}},
model="gpt-4o"
)
print(my_assistant)
node.js: >-
import OpenAI from "openai";
const openai = new OpenAI();
async function main() {
const myAssistant = await openai.beta.assistants.create({
instructions:
"You are an HR bot, and you have access to files to answer employee questions about company policies.",
name: "HR Helper",
tools: [{ type: "file_search" }],
tool_resources: {
file_search: {
vector_store_ids: ["vs_123"]
}
},
model: "gpt-4o"
});
console.log(myAssistant);
}
main();
response: >
{
"id": "asst_abc123",
"object": "assistant",
"created_at": 1699009403,
"name": "HR Helper",
"description": null,
"model": "gpt-4o",
"instructions": "You are an HR bot, and you have access to files to answer employee questions about company policies.",
"tools": [
{
"type": "file_search"
}
],
"tool_resources": {
"file_search": {
"vector_store_ids": ["vs_123"]
}
},
"metadata": {},
"top_p": 1.0,
"temperature": 1.0,
"response_format": "auto"
}
/assistants/{assistant_id}:
get:
operationId: getAssistant
tags:
- Assistants
summary: Retrieves an assistant.
parameters:
- in: path
name: assistant_id
required: true
schema:
type: string
description: The ID of the assistant to retrieve.
responses:
"200":
description: OK
content:
application/json:
schema:
$ref: "#/components/schemas/AssistantObject"
x-oaiMeta:
name: Retrieve assistant
group: assistants
beta: true
returns: The [assistant](/docs/api-reference/assistants/object) object matching
the specified ID.
examples:
request:
curl: |
curl https://api.openai.com/v1/assistants/asst_abc123 \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $OPENAI_API_KEY" \
-H "OpenAI-Beta: assistants=v2"
python: |
from openai import OpenAI
client = OpenAI()
my_assistant = client.beta.assistants.retrieve("asst_abc123")
print(my_assistant)
node.js: |-
import OpenAI from "openai";
const openai = new OpenAI();
async function main() {
const myAssistant = await openai.beta.assistants.retrieve(
"asst_abc123"
);
console.log(myAssistant);
}
main();
response: >
{
"id": "asst_abc123",
"object": "assistant",
"created_at": 1699009709,
"name": "HR Helper",
"description": null,
"model": "gpt-4o",
"instructions": "You are an HR bot, and you have access to files to answer employee questions about company policies.",
"tools": [
{
"type": "file_search"
}
],
"metadata": {},
"top_p": 1.0,
"temperature": 1.0,
"response_format": "auto"
}
post:
operationId: modifyAssistant
tags:
- Assistants
summary: Modifies an assistant.
parameters:
- in: path
name: assistant_id
required: true
schema:
type: string
description: The ID of the assistant to modify.
requestBody:
required: true
content:
application/json:
schema:
$ref: "#/components/schemas/ModifyAssistantRequest"
responses:
"200":
description: OK
content:
application/json:
schema:
$ref: "#/components/schemas/AssistantObject"
x-oaiMeta:
name: Modify assistant
group: assistants
beta: true
returns: The modified [assistant](/docs/api-reference/assistants/object) object.
examples:
request:
curl: >
curl https://api.openai.com/v1/assistants/asst_abc123 \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $OPENAI_API_KEY" \
-H "OpenAI-Beta: assistants=v2" \
-d '{
"instructions": "You are an HR bot, and you have access to files to answer employee questions about company policies. Always response with info from either of the files.",
"tools": [{"type": "file_search"}],
"model": "gpt-4o"
}'
python: >
from openai import OpenAI
client = OpenAI()
my_updated_assistant = client.beta.assistants.update(
"asst_abc123",
instructions="You are an HR bot, and you have access to files to answer employee questions about company policies. Always response with info from either of the files.",
name="HR Helper",
tools=[{"type": "file_search"}],
model="gpt-4o"
)
print(my_updated_assistant)
node.js: >-
import OpenAI from "openai";
const openai = new OpenAI();
async function main() {
const myUpdatedAssistant = await openai.beta.assistants.update(
"asst_abc123",
{
instructions:
"You are an HR bot, and you have access to files to answer employee questions about company policies. Always response with info from either of the files.",
name: "HR Helper",
tools: [{ type: "file_search" }],
model: "gpt-4o"
}
);
console.log(myUpdatedAssistant);
}
main();
response: >
{
"id": "asst_123",
"object": "assistant",
"created_at": 1699009709,
"name": "HR Helper",
"description": null,
"model": "gpt-4o",
"instructions": "You are an HR bot, and you have access to files to answer employee questions about company policies. Always response with info from either of the files.",
"tools": [
{
"type": "file_search"
}
],
"tool_resources": {
"file_search": {
"vector_store_ids": []
}
},
"metadata": {},
"top_p": 1.0,
"temperature": 1.0,
"response_format": "auto"
}
delete:
operationId: deleteAssistant
tags:
- Assistants
summary: Delete an assistant.
parameters:
- in: path
name: assistant_id
required: true
schema:
type: string
description: The ID of the assistant to delete.
responses:
"200":
description: OK
content:
application/json:
schema:
$ref: "#/components/schemas/DeleteAssistantResponse"
x-oaiMeta:
name: Delete assistant
group: assistants
beta: true
returns: Deletion status
examples:
request:
curl: |
curl https://api.openai.com/v1/assistants/asst_abc123 \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $OPENAI_API_KEY" \
-H "OpenAI-Beta: assistants=v2" \
-X DELETE
python: |
from openai import OpenAI
client = OpenAI()
response = client.beta.assistants.delete("asst_abc123")
print(response)
node.js: >-
import OpenAI from "openai";
const openai = new OpenAI();
async function main() {
const response = await openai.beta.assistants.del("asst_abc123");
console.log(response);
}
main();
response: |
{
"id": "asst_abc123",
"object": "assistant.deleted",
"deleted": true
}
/audio/speech:
post:
operationId: createSpeech
tags:
- Audio
summary: Generates audio from the input text.
requestBody:
required: true
content:
application/json:
schema:
$ref: "#/components/schemas/CreateSpeechRequest"
responses:
"200":
description: OK
headers:
Transfer-Encoding:
schema:
type: string
description: chunked
content:
application/octet-stream:
schema:
type: string
format: binary
x-oaiMeta:
name: Create speech
group: audio
returns: The audio file content.
examples:
request:
curl: |
curl https://api.openai.com/v1/audio/speech \
-H "Authorization: Bearer $OPENAI_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "tts-1",
"input": "The quick brown fox jumped over the lazy dog.",
"voice": "alloy"
}' \
--output speech.mp3
python: |
from pathlib import Path
import openai
speech_file_path = Path(__file__).parent / "speech.mp3"
response = openai.audio.speech.create(
model="tts-1",
voice="alloy",
input="The quick brown fox jumped over the lazy dog."
)
response.stream_to_file(speech_file_path)
node: >
import fs from "fs";
import path from "path";
import OpenAI from "openai";
const openai = new OpenAI();
const speechFile = path.resolve("./speech.mp3");
async function main() {
const mp3 = await openai.audio.speech.create({
model: "tts-1",
voice: "alloy",
input: "Today is a wonderful day to build something people love!",
});
console.log(speechFile);
const buffer = Buffer.from(await mp3.arrayBuffer());
await fs.promises.writeFile(speechFile, buffer);
}
main();
/audio/transcriptions:
post:
operationId: createTranscription
tags:
- Audio
summary: Transcribes audio into the input language.
requestBody:
required: true
content:
multipart/form-data:
schema:
$ref: "#/components/schemas/CreateTranscriptionRequest"
responses:
"200":
description: OK
content:
application/json:
schema:
oneOf:
- $ref: "#/components/schemas/CreateTranscriptionResponseJson"
- $ref: "#/components/schemas/CreateTranscriptionResponseVerboseJson"
x-oaiMeta:
name: Create transcription
group: audio
returns: The [transcription object](/docs/api-reference/audio/json-object) or a
[verbose transcription
object](/docs/api-reference/audio/verbose-json-object).
examples:
- title: Default
request:
curl: |
curl https://api.openai.com/v1/audio/transcriptions \
-H "Authorization: Bearer $OPENAI_API_KEY" \
-H "Content-Type: multipart/form-data" \
-F file="@/path/to/file/audio.mp3" \
-F model="whisper-1"
python: |
from openai import OpenAI
client = OpenAI()
audio_file = open("speech.mp3", "rb")
transcript = client.audio.transcriptions.create(
model="whisper-1",
file=audio_file
)
node: >
import fs from "fs";
import OpenAI from "openai";
const openai = new OpenAI();
async function main() {
const transcription = await openai.audio.transcriptions.create({
file: fs.createReadStream("audio.mp3"),
model: "whisper-1",
});
console.log(transcription.text);
}
main();
response: >
{
"text": "Imagine the wildest idea that you've ever had, and you're curious about how it might scale to something that's a 100, a 1,000 times bigger. This is a place where you can get to do that."
}
- title: Word timestamps
request:
curl: |
curl https://api.openai.com/v1/audio/transcriptions \
-H "Authorization: Bearer $OPENAI_API_KEY" \
-H "Content-Type: multipart/form-data" \
-F file="@/path/to/file/audio.mp3" \
-F "timestamp_granularities[]=word" \
-F model="whisper-1" \
-F response_format="verbose_json"
python: |
from openai import OpenAI
client = OpenAI()
audio_file = open("speech.mp3", "rb")
transcript = client.audio.transcriptions.create(
file=audio_file,
model="whisper-1",
response_format="verbose_json",
timestamp_granularities=["word"]
)
print(transcript.words)
node: >
import fs from "fs";
import OpenAI from "openai";
const openai = new OpenAI();
async function main() {
const transcription = await openai.audio.transcriptions.create({
file: fs.createReadStream("audio.mp3"),
model: "whisper-1",
response_format: "verbose_json",
timestamp_granularities: ["word"]
});
console.log(transcription.text);
}
main();
response: >
{
"task": "transcribe",
"language": "english",
"duration": 8.470000267028809,
"text": "The beach was a popular spot on a hot summer day. People were swimming in the ocean, building sandcastles, and playing beach volleyball.",
"words": [
{
"word": "The",
"start": 0.0,
"end": 0.23999999463558197
},
...
{
"word": "volleyball",
"start": 7.400000095367432,
"end": 7.900000095367432
}
]
}
- title: Segment timestamps
request:
curl: |
curl https://api.openai.com/v1/audio/transcriptions \
-H "Authorization: Bearer $OPENAI_API_KEY" \
-H "Content-Type: multipart/form-data" \
-F file="@/path/to/file/audio.mp3" \
-F "timestamp_granularities[]=segment" \
-F model="whisper-1" \
-F response_format="verbose_json"
python: |
from openai import OpenAI
client = OpenAI()
audio_file = open("speech.mp3", "rb")
transcript = client.audio.transcriptions.create(
file=audio_file,
model="whisper-1",
response_format="verbose_json",
timestamp_granularities=["segment"]
)
print(transcript.words)
node: >
import fs from "fs";
import OpenAI from "openai";
const openai = new OpenAI();
async function main() {
const transcription = await openai.audio.transcriptions.create({
file: fs.createReadStream("audio.mp3"),
model: "whisper-1",
response_format: "verbose_json",
timestamp_granularities: ["segment"]
});
console.log(transcription.text);
}
main();
response: >
{
"task": "transcribe",
"language": "english",
"duration": 8.470000267028809,
"text": "The beach was a popular spot on a hot summer day. People were swimming in the ocean, building sandcastles, and playing beach volleyball.",
"segments": [
{
"id": 0,
"seek": 0,
"start": 0.0,
"end": 3.319999933242798,
"text": " The beach was a popular spot on a hot summer day.",
"tokens": [
50364, 440, 7534, 390, 257, 3743, 4008, 322, 257, 2368, 4266, 786, 13, 50530
],
"temperature": 0.0,
"avg_logprob": -0.2860786020755768,
"compression_ratio": 1.2363636493682861,
"no_speech_prob": 0.00985979475080967
},
...
]
}
/audio/translations:
post:
operationId: createTranslation
tags:
- Audio
summary: Translates audio into English.
requestBody:
required: true
content:
multipart/form-data:
schema:
$ref: "#/components/schemas/CreateTranslationRequest"
responses:
"200":
description: OK
content:
application/json:
schema:
oneOf:
- $ref: "#/components/schemas/CreateTranslationResponseJson"
- $ref: "#/components/schemas/CreateTranslationResponseVerboseJson"
x-oaiMeta:
name: Create translation
group: audio
returns: The translated text.
examples:
request:
curl: |
curl https://api.openai.com/v1/audio/translations \
-H "Authorization: Bearer $OPENAI_API_KEY" \
-H "Content-Type: multipart/form-data" \
-F file="@/path/to/file/german.m4a" \
-F model="whisper-1"
python: |
from openai import OpenAI
client = OpenAI()
audio_file = open("speech.mp3", "rb")
transcript = client.audio.translations.create(
model="whisper-1",
file=audio_file
)
node: |
import fs from "fs";
import OpenAI from "openai";
const openai = new OpenAI();
async function main() {
const translation = await openai.audio.translations.create({
file: fs.createReadStream("speech.mp3"),
model: "whisper-1",
});
console.log(translation.text);
}
main();
response: >
{
"text": "Hello, my name is Wolfgang and I come from Germany. Where are you heading today?"
}
/batches:
post:
summary: Creates and executes a batch from an uploaded file of requests
operationId: createBatch
tags:
- Batch
requestBody:
required: true
content:
application/json:
schema:
type: object
required:
- input_file_id
- endpoint
- completion_window
properties:
input_file_id:
type: string
description: >
The ID of an uploaded file that contains requests for the
new batch.
See [upload file](/docs/api-reference/files/create) for how
to upload a file.
Your input file must be formatted as a [JSONL
file](/docs/api-reference/batch/request-input), and must be
uploaded with the purpose `batch`. The file can contain up
to 50,000 requests, and can be up to 200 MB in size.
endpoint:
type: string
enum:
- /v1/chat/completions
- /v1/embeddings
- /v1/completions
description: The endpoint to be used for all requests in the batch. Currently
`/v1/chat/completions`, `/v1/embeddings`, and
`/v1/completions` are supported. Note that `/v1/embeddings`
batches are also restricted to a maximum of 50,000 embedding
inputs across all requests in the batch.
completion_window:
type: string
enum:
- 24h
description: The time frame within which the batch should be processed.
Currently only `24h` is supported.
metadata:
$ref: "#/components/schemas/Metadata"
responses:
"200":
description: Batch created successfully.
content:
application/json:
schema:
$ref: "#/components/schemas/Batch"
x-oaiMeta:
name: Create batch
group: batch
returns: The created [Batch](/docs/api-reference/batch/object) object.
examples:
request: