openapi: 3.0.0 info: title: OpenAI API description: The OpenAI REST API. Please see https://platform.openai.com/docs/api-reference for more details. version: "2.0.0" termsOfService: https://openai.com/policies/terms-of-use contact: name: OpenAI Support url: https://help.openai.com/ license: name: MIT url: https://github.com/openai/openai-openapi/blob/master/LICENSE servers: - url: https://api.openai.com/v1 tags: - name: Assistants description: Build Assistants that can call models and use tools. - name: Audio description: Learn how to 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: 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 a input text, outputs if the model classifies it as potentially harmful. paths: # Note: When adding an endpoint, make sure you also add it in the `groups` section, in the end of this file, # under the appropriate group /chat/completions: post: operationId: createChatCompletion tags: - Chat summary: Creates a model response for the given chat conversation. requestBody: required: true content: application/json: schema: $ref: "#/components/schemas/CreateChatCompletionRequest" responses: "200": description: OK content: application/json: schema: $ref: "#/components/schemas/CreateChatCompletionResponse" x-oaiMeta: name: Create chat completion group: chat returns: | Returns a [chat completion](/docs/api-reference/chat/object) object, or a streamed sequence of [chat completion chunk](/docs/api-reference/chat/streaming) objects if the request is streamed. path: create examples: - title: Default request: curl: | curl https://api.openai.com/v1/chat/completions \ -H "Content-Type: application/json" \ -H "Authorization: Bearer $OPENAI_API_KEY" \ -d '{ "model": "VAR_model_id", "messages": [ { "role": "system", "content": "You are a helpful assistant." }, { "role": "user", "content": "Hello!" } ] }' python: | from openai import OpenAI client = OpenAI() completion = client.chat.completions.create( model="VAR_model_id", messages=[ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "Hello!"} ] ) print(completion.choices[0].message) node.js: |- import OpenAI from "openai"; const openai = new OpenAI(); async function main() { const completion = await openai.chat.completions.create({ messages: [{ role: "system", content: "You are a helpful assistant." }], model: "VAR_model_id", }); console.log(completion.choices[0]); } main(); response: &chat_completion_example | { "id": "chatcmpl-123", "object": "chat.completion", "created": 1677652288, "model": "gpt-3.5-turbo-0125", "system_fingerprint": "fp_44709d6fcb", "choices": [{ "index": 0, "message": { "role": "assistant", "content": "\n\nHello there, how may I assist you today?", }, "logprobs": null, "finish_reason": "stop" }], "usage": { "prompt_tokens": 9, "completion_tokens": 12, "total_tokens": 21 } } - title: Image input request: curl: | curl https://api.openai.com/v1/chat/completions \ -H "Content-Type: application/json" \ -H "Authorization: Bearer $OPENAI_API_KEY" \ -d '{ "model": "gpt-4-turbo", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "What'\''s in this image?" }, { "type": "image_url", "image_url": { "url": "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg" } } ] } ], "max_tokens": 300 }' python: | from openai import OpenAI client = OpenAI() response = client.chat.completions.create( model="gpt-4-turbo", messages=[ { "role": "user", "content": [ {"type": "text", "text": "What's in this image?"}, { "type": "image_url", "image_url": "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg", }, ], } ], max_tokens=300, ) print(response.choices[0]) node.js: |- import OpenAI from "openai"; const openai = new OpenAI(); async function main() { const response = await openai.chat.completions.create({ model: "gpt-4-turbo", messages: [ { role: "user", content: [ { type: "text", text: "What's in this image?" }, { type: "image_url", image_url: "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg", }, ], }, ], }); console.log(response.choices[0]); } main(); response: &chat_completion_image_example | { "id": "chatcmpl-123", "object": "chat.completion", "created": 1677652288, "model": "gpt-3.5-turbo-0125", "system_fingerprint": "fp_44709d6fcb", "choices": [{ "index": 0, "message": { "role": "assistant", "content": "\n\nThis image shows a wooden boardwalk extending through a lush green marshland.", }, "logprobs": null, "finish_reason": "stop" }], "usage": { "prompt_tokens": 9, "completion_tokens": 12, "total_tokens": 21 } } - title: Streaming request: curl: | curl https://api.openai.com/v1/chat/completions \ -H "Content-Type: application/json" \ -H "Authorization: Bearer $OPENAI_API_KEY" \ -d '{ "model": "VAR_model_id", "messages": [ { "role": "system", "content": "You are a helpful assistant." }, { "role": "user", "content": "Hello!" } ], "stream": true }' python: | from openai import OpenAI client = OpenAI() completion = client.chat.completions.create( model="VAR_model_id", messages=[ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "Hello!"} ], stream=True ) for chunk in completion: print(chunk.choices[0].delta) node.js: |- import OpenAI from "openai"; const openai = new OpenAI(); async function main() { const completion = await openai.chat.completions.create({ model: "VAR_model_id", messages: [ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "Hello!"} ], stream: true, }); for await (const chunk of completion) { console.log(chunk.choices[0].delta.content); } } main(); response: &chat_completion_chunk_example | {"id":"chatcmpl-123","object":"chat.completion.chunk","created":1694268190,"model":"gpt-3.5-turbo-0125", "system_fingerprint": "fp_44709d6fcb", "choices":[{"index":0,"delta":{"role":"assistant","content":""},"logprobs":null,"finish_reason":null}]} {"id":"chatcmpl-123","object":"chat.completion.chunk","created":1694268190,"model":"gpt-3.5-turbo-0125", "system_fingerprint": "fp_44709d6fcb", "choices":[{"index":0,"delta":{"content":"Hello"},"logprobs":null,"finish_reason":null}]} .... {"id":"chatcmpl-123","object":"chat.completion.chunk","created":1694268190,"model":"gpt-3.5-turbo-0125", "system_fingerprint": "fp_44709d6fcb", "choices":[{"index":0,"delta":{},"logprobs":null,"finish_reason":"stop"}]} - title: Functions request: curl: | curl https://api.openai.com/v1/chat/completions \ -H "Content-Type: application/json" \ -H "Authorization: Bearer $OPENAI_API_KEY" \ -d '{ "model": "gpt-4-turbo", "messages": [ { "role": "user", "content": "What'\''s the weather like in Boston today?" } ], "tools": [ { "type": "function", "function": { "name": "get_current_weather", "description": "Get the current weather in a given location", "parameters": { "type": "object", "properties": { "location": { "type": "string", "description": "The city and state, e.g. San Francisco, CA" }, "unit": { "type": "string", "enum": ["celsius", "fahrenheit"] } }, "required": ["location"] } } } ], "tool_choice": "auto" }' python: | from openai import OpenAI client = OpenAI() tools = [ { "type": "function", "function": { "name": "get_current_weather", "description": "Get the current weather in a given location", "parameters": { "type": "object", "properties": { "location": { "type": "string", "description": "The city and state, e.g. San Francisco, CA", }, "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}, }, "required": ["location"], }, } } ] messages = [{"role": "user", "content": "What's the weather like in Boston today?"}] completion = client.chat.completions.create( model="VAR_model_id", messages=messages, tools=tools, tool_choice="auto" ) print(completion) node.js: |- import OpenAI from "openai"; const openai = new OpenAI(); async function main() { const messages = [{"role": "user", "content": "What's the weather like in Boston today?"}]; const tools = [ { "type": "function", "function": { "name": "get_current_weather", "description": "Get the current weather in a given location", "parameters": { "type": "object", "properties": { "location": { "type": "string", "description": "The city and state, e.g. San Francisco, CA", }, "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}, }, "required": ["location"], }, } } ]; const response = await openai.chat.completions.create({ model: "gpt-4-turbo", messages: messages, tools: tools, tool_choice: "auto", }); console.log(response); } main(); response: &chat_completion_function_example | { "id": "chatcmpl-abc123", "object": "chat.completion", "created": 1699896916, "model": "gpt-3.5-turbo-0125", "choices": [ { "index": 0, "message": { "role": "assistant", "content": null, "tool_calls": [ { "id": "call_abc123", "type": "function", "function": { "name": "get_current_weather", "arguments": "{\n\"location\": \"Boston, MA\"\n}" } } ] }, "logprobs": null, "finish_reason": "tool_calls" } ], "usage": { "prompt_tokens": 82, "completion_tokens": 17, "total_tokens": 99 } } - title: Logprobs request: curl: | curl https://api.openai.com/v1/chat/completions \ -H "Content-Type: application/json" \ -H "Authorization: Bearer $OPENAI_API_KEY" \ -d '{ "model": "VAR_model_id", "messages": [ { "role": "user", "content": "Hello!" } ], "logprobs": true, "top_logprobs": 2 }' python: | from openai import OpenAI client = OpenAI() completion = client.chat.completions.create( model="VAR_model_id", messages=[ {"role": "user", "content": "Hello!"} ], logprobs=True, top_logprobs=2 ) print(completion.choices[0].message) print(completion.choices[0].logprobs) node.js: |- import OpenAI from "openai"; const openai = new OpenAI(); async function main() { const completion = await openai.chat.completions.create({ messages: [{ role: "user", content: "Hello!" }], model: "VAR_model_id", logprobs: true, top_logprobs: 2, }); console.log(completion.choices[0]); } main(); response: | { "id": "chatcmpl-123", "object": "chat.completion", "created": 1702685778, "model": "gpt-3.5-turbo-0125", "choices": [ { "index": 0, "message": { "role": "assistant", "content": "Hello! How can I assist you today?" }, "logprobs": { "content": [ { "token": "Hello", "logprob": -0.31725305, "bytes": [72, 101, 108, 108, 111], "top_logprobs": [ { "token": "Hello", "logprob": -0.31725305, "bytes": [72, 101, 108, 108, 111] }, { "token": "Hi", "logprob": -1.3190403, "bytes": [72, 105] } ] }, { "token": "!", "logprob": -0.02380986, "bytes": [ 33 ], "top_logprobs": [ { "token": "!", "logprob": -0.02380986, "bytes": [33] }, { "token": " there", "logprob": -3.787621, "bytes": [32, 116, 104, 101, 114, 101] } ] }, { "token": " How", "logprob": -0.000054669687, "bytes": [32, 72, 111, 119], "top_logprobs": [ { "token": " How", "logprob": -0.000054669687, "bytes": [32, 72, 111, 119] }, { "token": "<|end|>", "logprob": -10.953937, "bytes": null } ] }, { "token": " can", "logprob": -0.015801601, "bytes": [32, 99, 97, 110], "top_logprobs": [ { "token": " can", "logprob": -0.015801601, "bytes": [32, 99, 97, 110] }, { "token": " may", "logprob": -4.161023, "bytes": [32, 109, 97, 121] } ] }, { "token": " I", "logprob": -3.7697225e-6, "bytes": [ 32, 73 ], "top_logprobs": [ { "token": " I", "logprob": -3.7697225e-6, "bytes": [32, 73] }, { "token": " assist", "logprob": -13.596657, "bytes": [32, 97, 115, 115, 105, 115, 116] } ] }, { "token": " assist", "logprob": -0.04571125, "bytes": [32, 97, 115, 115, 105, 115, 116], "top_logprobs": [ { "token": " assist", "logprob": -0.04571125, "bytes": [32, 97, 115, 115, 105, 115, 116] }, { "token": " help", "logprob": -3.1089056, "bytes": [32, 104, 101, 108, 112] } ] }, { "token": " you", "logprob": -5.4385737e-6, "bytes": [32, 121, 111, 117], "top_logprobs": [ { "token": " you", "logprob": -5.4385737e-6, "bytes": [32, 121, 111, 117] }, { "token": " today", "logprob": -12.807695, "bytes": [32, 116, 111, 100, 97, 121] } ] }, { "token": " today", "logprob": -0.0040071653, "bytes": [32, 116, 111, 100, 97, 121], "top_logprobs": [ { "token": " today", "logprob": -0.0040071653, "bytes": [32, 116, 111, 100, 97, 121] }, { "token": "?", "logprob": -5.5247097, "bytes": [63] } ] }, { "token": "?", "logprob": -0.0008108172, "bytes": [63], "top_logprobs": [ { "token": "?", "logprob": -0.0008108172, "bytes": [63] }, { "token": "?\n", "logprob": -7.184561, "bytes": [63, 10] } ] } ] }, "finish_reason": "stop" } ], "usage": { "prompt_tokens": 9, "completion_tokens": 9, "total_tokens": 18 }, "system_fingerprint": null } /completions: post: operationId: createCompletion tags: - Completions summary: Creates a completion for the provided prompt and parameters. requestBody: required: true content: application/json: schema: $ref: "#/components/schemas/CreateCompletionRequest" responses: "200": description: OK content: application/json: schema: $ref: "#/components/schemas/CreateCompletionResponse" x-oaiMeta: name: Create completion group: completions returns: | Returns a [completion](/docs/api-reference/completions/object) object, or a sequence of completion objects if the request is streamed. legacy: true examples: - title: No streaming request: curl: | curl https://api.openai.com/v1/completions \ -H "Content-Type: application/json" \ -H "Authorization: Bearer $OPENAI_API_KEY" \ -d '{ "model": "VAR_model_id", "prompt": "Say this is a test", "max_tokens": 7, "temperature": 0 }' python: | from openai import OpenAI client = OpenAI() client.completions.create( model="VAR_model_id", prompt="Say this is a test", max_tokens=7, temperature=0 ) node.js: |- import OpenAI from "openai"; const openai = new OpenAI(); async function main() { const completion = await openai.completions.create({ model: "VAR_model_id", prompt: "Say this is a test.", max_tokens: 7, temperature: 0, }); console.log(completion); } main(); response: | { "id": "cmpl-uqkvlQyYK7bGYrRHQ0eXlWi7", "object": "text_completion", "created": 1589478378, "model": "VAR_model_id", "system_fingerprint": "fp_44709d6fcb", "choices": [ { "text": "\n\nThis is indeed a test", "index": 0, "logprobs": null, "finish_reason": "length" } ], "usage": { "prompt_tokens": 5, "completion_tokens": 7, "total_tokens": 12 } } - title: Streaming request: curl: | curl https://api.openai.com/v1/completions \ -H "Content-Type: application/json" \ -H "Authorization: Bearer $OPENAI_API_KEY" \ -d '{ "model": "VAR_model_id", "prompt": "Say this is a test", "max_tokens": 7, "temperature": 0, "stream": true }' python: | from openai import OpenAI client = OpenAI() for chunk in client.completions.create( model="VAR_model_id", prompt="Say this is a test", max_tokens=7, temperature=0, stream=True ): print(chunk.choices[0].text) node.js: |- import OpenAI from "openai"; const openai = new OpenAI(); async function main() { const stream = await openai.completions.create({ model: "VAR_model_id", prompt: "Say this is a test.", stream: true, }); for await (const chunk of stream) { console.log(chunk.choices[0].text) } } main(); response: | { "id": "cmpl-7iA7iJjj8V2zOkCGvWF2hAkDWBQZe", "object": "text_completion", "created": 1690759702, "choices": [ { "text": "This", "index": 0, "logprobs": null, "finish_reason": null } ], "model": "gpt-3.5-turbo-instruct" "system_fingerprint": "fp_44709d6fcb", } /images/generations: post: operationId: createImage tags: - Images 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 returns: Returns a list of [image](/docs/api-reference/images/object) objects. examples: request: curl: | curl https://api.openai.com/v1/images/generations \ -H "Content-Type: application/json" \ -H "Authorization: Bearer $OPENAI_API_KEY" \ -d '{ "model": "dall-e-3", "prompt": "A cute baby sea otter", "n": 1, "size": "1024x1024" }' python: | from openai import OpenAI client = OpenAI() client.images.generate( model="dall-e-3", prompt="A cute baby sea otter", n=1, size="1024x1024" ) node.js: |- import OpenAI from "openai"; const openai = new OpenAI(); async function main() { const image = await openai.images.generate({ model: "dall-e-3", prompt: "A cute baby sea otter" }); console.log(image.data); } main(); response: | { "created": 1589478378, "data": [ { "url": "https://..." }, { "url": "https://..." } ] } /images/edits: post: operationId: createImageEdit tags: - Images 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 returns: Returns a list of [image](/docs/api-reference/images/object) objects. examples: request: curl: | curl https://api.openai.com/v1/images/edits \ -H "Authorization: Bearer $OPENAI_API_KEY" \ -F image="@otter.png" \ -F mask="@mask.png" \ -F prompt="A cute baby sea otter wearing a beret" \ -F n=2 \ -F size="1024x1024" python: | from openai import OpenAI client = OpenAI() client.images.edit( image=open("otter.png", "rb"), mask=open("mask.png", "rb"), prompt="A cute baby sea otter wearing a beret", n=2, size="1024x1024" ) node.js: |- import fs from "fs"; import OpenAI from "openai"; const openai = new OpenAI(); async function main() { const image = await openai.images.edit({ image: fs.createReadStream("otter.png"), mask: fs.createReadStream("mask.png"), prompt: "A cute baby sea otter wearing a beret", }); console.log(image.data); } main(); response: | { "created": 1589478378, "data": [ { "url": "https://..." }, { "url": "https://..." } ] } /images/variations: post: operationId: createImageVariation tags: - Images 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 returns: Returns a list of [image](/docs/api-reference/images/object) objects. examples: request: curl: | curl https://api.openai.com/v1/images/variations \ -H "Authorization: Bearer $OPENAI_API_KEY" \ -F image="@otter.png" \ -F n=2 \ -F size="1024x1024" python: | from openai import OpenAI client = OpenAI() response = client.images.create_variation( image=open("image_edit_original.png", "rb"), n=2, size="1024x1024" ) node.js: |- import fs from "fs"; import OpenAI from "openai"; const openai = new OpenAI(); async function main() { const image = await openai.images.createVariation({ image: fs.createReadStream("otter.png"), }); console.log(image.data); } main(); response: | { "created": 1589478378, "data": [ { "url": "https://..." }, { "url": "https://..." } ] } /embeddings: post: operationId: createEmbedding tags: - Embeddings 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 returns: A list of [embedding](/docs/api-reference/embeddings/object) objects. examples: request: curl: | curl https://api.openai.com/v1/embeddings \ -H "Authorization: Bearer $OPENAI_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "input": "The food was delicious and the waiter...", "model": "text-embedding-ada-002", "encoding_format": "float" }' python: | from openai import OpenAI client = OpenAI() client.embeddings.create( model="text-embedding-ada-002", input="The food was delicious and the waiter...", encoding_format="float" ) node.js: |- import OpenAI from "openai"; const openai = new OpenAI(); async function main() { const embedding = await openai.embeddings.create({ model: "text-embedding-ada-002", input: "The quick brown fox jumped over the lazy dog", encoding_format: "float", }); console.log(embedding); } main(); response: | { "object": "list", "data": [ { "object": "embedding", "embedding": [ 0.0023064255, -0.009327292, .... (1536 floats total for ada-002) -0.0028842222, ], "index": 0 } ], "model": "text-embedding-ada-002", "usage": { "prompt_tokens": 8, "total_tokens": 8 } } /audio/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: &basic_transcription_response_example | { "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: &verbose_transcription_response_example | { "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?" } /files: get: operationId: listFiles tags: - Files summary: Returns a list of files that belong to the user's organization. parameters: - in: query name: purpose required: false schema: type: string description: Only return files with the given purpose. responses: "200": description: OK content: application/json: schema: $ref: "#/components/schemas/ListFilesResponse" x-oaiMeta: name: List files group: files returns: A list of [File](/docs/api-reference/files/object) objects. examples: request: curl: | curl https://api.openai.com/v1/files \ -H "Authorization: Bearer $OPENAI_API_KEY" python: | from openai import OpenAI client = OpenAI() client.files.list() node.js: |- import OpenAI from "openai"; const openai = new OpenAI(); async function main() { const list = await openai.files.list(); for await (const file of list) { console.log(file); } } main(); response: | { "data": [ { "id": "file-abc123", "object": "file", "bytes": 175, "created_at": 1613677385, "filename": "salesOverview.pdf", "purpose": "assistants", }, { "id": "file-abc123", "object": "file", "bytes": 140, "created_at": 1613779121, "filename": "puppy.jsonl", "purpose": "fine-tune", } ], "object": "list" } post: operationId: createFile tags: - Files summary: | Upload a file that can be used across various endpoints. Individual files can be up to 512 MB, and the size of all files uploaded by one organization can be up to 100 GB. The Assistants API supports files up to 2 million tokens and of specific file types. See the [Assistants Tools guide](/docs/assistants/tools) for details. The Fine-tuning API only supports `.jsonl` files. The Batch API only supports `.jsonl` files up to 100 MB in size. Please [contact us](https://help.openai.com/) if you need to increase these storage limits. 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 returns: The uploaded [File](/docs/api-reference/files/object) object. examples: request: curl: | curl https://api.openai.com/v1/files \ -H "Authorization: Bearer $OPENAI_API_KEY" \ -F purpose="fine-tune" \ -F file="@mydata.jsonl" python: | from openai import OpenAI client = OpenAI() client.files.create( file=open("mydata.jsonl", "rb"), purpose="fine-tune" ) node.js: |- import fs from "fs"; import OpenAI from "openai"; const openai = new OpenAI(); async function main() { const file = await openai.files.create({ file: fs.createReadStream("mydata.jsonl"), purpose: "fine-tune", }); console.log(file); } main(); response: | { "id": "file-abc123", "object": "file", "bytes": 120000, "created_at": 1677610602, "filename": "mydata.jsonl", "purpose": "fine-tune", } /files/{file_id}: delete: operationId: deleteFile tags: - Files 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 returns: Deletion status. examples: request: curl: | curl https://api.openai.com/v1/files/file-abc123 \ -X DELETE \ -H "Authorization: Bearer $OPENAI_API_KEY" python: | from openai import OpenAI client = OpenAI() client.files.delete("file-abc123") node.js: |- import OpenAI from "openai"; const openai = new OpenAI(); async function main() { const file = await openai.files.del("file-abc123"); console.log(file); } main(); response: | { "id": "file-abc123", "object": "file", "deleted": true } get: operationId: retrieveFile tags: - Files 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 returns: The [File](/docs/api-reference/files/object) object matching the specified ID. examples: request: curl: | curl https://api.openai.com/v1/files/file-abc123 \ -H "Authorization: Bearer $OPENAI_API_KEY" python: | from openai import OpenAI client = OpenAI() client.files.retrieve("file-abc123") node.js: |- import OpenAI from "openai"; const openai = new OpenAI(); async function main() { const file = await openai.files.retrieve("file-abc123"); console.log(file); } main(); response: | { "id": "file-abc123", "object": "file", "bytes": 120000, "created_at": 1677610602, "filename": "mydata.jsonl", "purpose": "fine-tune", } /files/{file_id}/content: get: operationId: downloadFile tags: - Files 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 returns: The file content. examples: request: curl: | curl https://api.openai.com/v1/files/file-abc123/content \ -H "Authorization: Bearer $OPENAI_API_KEY" > file.jsonl python: | from openai import OpenAI client = OpenAI() content = client.files.content("file-abc123") node.js: | import OpenAI from "openai"; const openai = new OpenAI(); async function main() { const file = await openai.files.content("file-abc123"); console.log(file); } main(); /fine_tuning/jobs: post: operationId: createFineTuningJob tags: - Fine-tuning summary: | Creates a fine-tuning job which begins the process of creating a new model from a given dataset. Response includes details of the enqueued job including job status and the name of the fine-tuned models once complete. [Learn more about fine-tuning](/docs/guides/fine-tuning) requestBody: required: true content: application/json: schema: $ref: "#/components/schemas/CreateFineTuningJobRequest" responses: "200": description: OK content: application/json: schema: $ref: "#/components/schemas/FineTuningJob" x-oaiMeta: name: Create fine-tuning job group: fine-tuning returns: A [fine-tuning.job](/docs/api-reference/fine-tuning/object) object. examples: - title: Default request: curl: | curl https://api.openai.com/v1/fine_tuning/jobs \ -H "Content-Type: application/json" \ -H "Authorization: Bearer $OPENAI_API_KEY" \ -d '{ "training_file": "file-BK7bzQj3FfZFXr7DbL6xJwfo", "model": "gpt-3.5-turbo" }' python: | from openai import OpenAI client = OpenAI() client.fine_tuning.jobs.create( training_file="file-abc123", model="gpt-3.5-turbo" ) node.js: | import OpenAI from "openai"; const openai = new OpenAI(); async function main() { const fineTune = await openai.fineTuning.jobs.create({ training_file: "file-abc123" }); console.log(fineTune); } main(); response: | { "object": "fine_tuning.job", "id": "ftjob-abc123", "model": "gpt-3.5-turbo-0125", "created_at": 1614807352, "fine_tuned_model": null, "organization_id": "org-123", "result_files": [], "status": "queued", "validation_file": null, "training_file": "file-abc123", } - title: Epochs request: curl: | curl https://api.openai.com/v1/fine_tuning/jobs \ -H "Content-Type: application/json" \ -H "Authorization: Bearer $OPENAI_API_KEY" \ -d '{ "training_file": "file-abc123", "model": "gpt-3.5-turbo", "hyperparameters": { "n_epochs": 2 } }' python: | from openai import OpenAI client = OpenAI() client.fine_tuning.jobs.create( training_file="file-abc123", model="gpt-3.5-turbo", hyperparameters={ "n_epochs":2 } ) node.js: | import OpenAI from "openai"; const openai = new OpenAI(); async function main() { const fineTune = await openai.fineTuning.jobs.create({ training_file: "file-abc123", model: "gpt-3.5-turbo", hyperparameters: { n_epochs: 2 } }); console.log(fineTune); } main(); response: | { "object": "fine_tuning.job", "id": "ftjob-abc123", "model": "gpt-3.5-turbo-0125", "created_at": 1614807352, "fine_tuned_model": null, "organization_id": "org-123", "result_files": [], "status": "queued", "validation_file": null, "training_file": "file-abc123", "hyperparameters": {"n_epochs": 2}, } - title: Validation file request: curl: | curl https://api.openai.com/v1/fine_tuning/jobs \ -H "Content-Type: application/json" \ -H "Authorization: Bearer $OPENAI_API_KEY" \ -d '{ "training_file": "file-abc123", "validation_file": "file-abc123", "model": "gpt-3.5-turbo" }' python: | from openai import OpenAI client = OpenAI() client.fine_tuning.jobs.create( training_file="file-abc123", validation_file="file-def456", model="gpt-3.5-turbo" ) node.js: | import OpenAI from "openai"; const openai = new OpenAI(); async function main() { const fineTune = await openai.fineTuning.jobs.create({ training_file: "file-abc123", validation_file: "file-abc123" }); console.log(fineTune); } main(); response: | { "object": "fine_tuning.job", "id": "ftjob-abc123", "model": "gpt-3.5-turbo-0125", "created_at": 1614807352, "fine_tuned_model": null, "organization_id": "org-123", "result_files": [], "status": "queued", "validation_file": "file-abc123", "training_file": "file-abc123", } - title: W&B Integration request: curl: | curl https://api.openai.com/v1/fine_tuning/jobs \ -H "Content-Type: application/json" \ -H "Authorization: Bearer $OPENAI_API_KEY" \ -d '{ "training_file": "file-abc123", "validation_file": "file-abc123", "model": "gpt-3.5-turbo", "integrations": [ { "type": "wandb", "wandb": { "project": "my-wandb-project", "name": "ft-run-display-name" "tags": [ "first-experiment", "v2" ] } } ] }' response: | { "object": "fine_tuning.job", "id": "ftjob-abc123", "model": "gpt-3.5-turbo-0125", "created_at": 1614807352, "fine_tuned_model": null, "organization_id": "org-123", "result_files": [], "status": "queued", "validation_file": "file-abc123", "training_file": "file-abc123", "integrations": [ { "type": "wandb", "wandb": { "project": "my-wandb-project", "entity": None, "run_id": "ftjob-abc123" } } ] } get: operationId: listPaginatedFineTuningJobs tags: - Fine-tuning summary: | List your organization's fine-tuning jobs parameters: - name: after in: query description: Identifier for the last job from the previous pagination request. required: false schema: type: string - name: limit in: query description: Number of fine-tuning jobs to retrieve. required: false schema: type: integer default: 20 responses: "200": description: OK content: application/json: schema: $ref: "#/components/schemas/ListPaginatedFineTuningJobsResponse" x-oaiMeta: name: List fine-tuning jobs group: fine-tuning returns: A list of paginated [fine-tuning job](/docs/api-reference/fine-tuning/object) objects. examples: request: curl: | curl https://api.openai.com/v1/fine_tuning/jobs?limit=2 \ -H "Authorization: Bearer $OPENAI_API_KEY" python: | from openai import OpenAI client = OpenAI() client.fine_tuning.jobs.list() node.js: |- import OpenAI from "openai"; const openai = new OpenAI(); async function main() { const list = await openai.fineTuning.jobs.list(); for await (const fineTune of list) { console.log(fineTune); } } main(); response: | { "object": "list", "data": [ { "object": "fine_tuning.job.event", "id": "ft-event-TjX0lMfOniCZX64t9PUQT5hn", "created_at": 1689813489, "level": "warn", "message": "Fine tuning process stopping due to job cancellation", "data": null, "type": "message" }, { ... }, { ... } ], "has_more": true } /fine_tuning/jobs/{fine_tuning_job_id}: get: operationId: retrieveFineTuningJob tags: - Fine-tuning summary: | Get info about a fine-tuning job. [Learn more about fine-tuning](/docs/guides/fine-tuning) parameters: - in: path name: fine_tuning_job_id required: true schema: type: string example: ft-AF1WoRqd3aJAHsqc9NY7iL8F description: | The ID of the fine-tuning job. responses: "200": description: OK content: application/json: schema: $ref: "#/components/schemas/FineTuningJob" x-oaiMeta: name: Retrieve fine-tuning job group: fine-tuning returns: The [fine-tuning](/docs/api-reference/fine-tuning/object) object with the given ID. examples: request: curl: | curl https://api.openai.com/v1/fine_tuning/jobs/ft-AF1WoRqd3aJAHsqc9NY7iL8F \ -H "Authorization: Bearer $OPENAI_API_KEY" python: | from openai import OpenAI client = OpenAI() client.fine_tuning.jobs.retrieve("ftjob-abc123") node.js: | import OpenAI from "openai"; const openai = new OpenAI(); async function main() { const fineTune = await openai.fineTuning.jobs.retrieve("ftjob-abc123"); console.log(fineTune); } main(); response: &fine_tuning_example | { "object": "fine_tuning.job", "id": "ftjob-abc123", "model": "davinci-002", "created_at": 1692661014, "finished_at": 1692661190, "fine_tuned_model": "ft:davinci-002:my-org:custom_suffix:7q8mpxmy", "organization_id": "org-123", "result_files": [ "file-abc123" ], "status": "succeeded", "validation_file": null, "training_file": "file-abc123", "hyperparameters": { "n_epochs": 4, "batch_size": 1, "learning_rate_multiplier": 1.0 }, "trained_tokens": 5768, "integrations": [], "seed": 0, "estimated_finish": 0 } /fine_tuning/jobs/{fine_tuning_job_id}/events: get: operationId: listFineTuningEvents tags: - Fine-tuning summary: | Get status updates for a fine-tuning job. parameters: - in: path name: fine_tuning_job_id required: true schema: type: string example: ft-AF1WoRqd3aJAHsqc9NY7iL8F description: | The ID of the fine-tuning job to get events for. - name: after in: query description: Identifier for the last event from the previous pagination request. required: false schema: type: string - name: limit in: query description: Number of events to retrieve. required: false schema: type: integer default: 20 responses: "200": description: OK content: application/json: schema: $ref: "#/components/schemas/ListFineTuningJobEventsResponse" x-oaiMeta: name: List fine-tuning events group: fine-tuning returns: A list of fine-tuning event objects. examples: request: curl: | curl https://api.openai.com/v1/fine_tuning/jobs/ftjob-abc123/events \ -H "Authorization: Bearer $OPENAI_API_KEY" python: | from openai import OpenAI client = OpenAI() client.fine_tuning.jobs.list_events( fine_tuning_job_id="ftjob-abc123", limit=2 ) node.js: |- import OpenAI from "openai"; const openai = new OpenAI(); async function main() { const list = await openai.fineTuning.list_events(id="ftjob-abc123", limit=2); for await (const fineTune of list) { console.log(fineTune); } } main(); response: | { "object": "list", "data": [ { "object": "fine_tuning.job.event", "id": "ft-event-ddTJfwuMVpfLXseO0Am0Gqjm", "created_at": 1692407401, "level": "info", "message": "Fine tuning job successfully completed", "data": null, "type": "message" }, { "object": "fine_tuning.job.event", "id": "ft-event-tyiGuB72evQncpH87xe505Sv", "created_at": 1692407400, "level": "info", "message": "New fine-tuned model created: ft:gpt-3.5-turbo:openai::7p4lURel", "data": null, "type": "message" } ], "has_more": true } /fine_tuning/jobs/{fine_tuning_job_id}/cancel: post: operationId: cancelFineTuningJob tags: - Fine-tuning summary: | Immediately cancel a fine-tune job. parameters: - in: path name: fine_tuning_job_id required: true schema: type: string example: ft-AF1WoRqd3aJAHsqc9NY7iL8F description: | The ID of the fine-tuning job to cancel. responses: "200": description: OK content: application/json: schema: $ref: "#/components/schemas/FineTuningJob" x-oaiMeta: name: Cancel fine-tuning group: fine-tuning returns: The cancelled [fine-tuning](/docs/api-reference/fine-tuning/object) object. examples: request: curl: | curl -X POST https://api.openai.com/v1/fine_tuning/jobs/ftjob-abc123/cancel \ -H "Authorization: Bearer $OPENAI_API_KEY" python: | from openai import OpenAI client = OpenAI() client.fine_tuning.jobs.cancel("ftjob-abc123") node.js: |- import OpenAI from "openai"; const openai = new OpenAI(); async function main() { const fineTune = await openai.fineTuning.jobs.cancel("ftjob-abc123"); console.log(fineTune); } main(); response: | { "object": "fine_tuning.job", "id": "ftjob-abc123", "model": "gpt-3.5-turbo-0125", "created_at": 1689376978, "fine_tuned_model": null, "organization_id": "org-123", "result_files": [], "hyperparameters": { "n_epochs": "auto" }, "status": "cancelled", "validation_file": "file-abc123", "training_file": "file-abc123" } /fine_tuning/jobs/{fine_tuning_job_id}/checkpoints: get: operationId: listFineTuningJobCheckpoints tags: - Fine-tuning summary: | List checkpoints for a fine-tuning job. parameters: - in: path name: fine_tuning_job_id required: true schema: type: string example: ft-AF1WoRqd3aJAHsqc9NY7iL8F description: | The ID of the fine-tuning job to get checkpoints for. - name: after in: query description: Identifier for the last checkpoint ID from the previous pagination request. required: false schema: type: string - name: limit in: query description: Number of checkpoints to retrieve. required: false schema: type: integer default: 10 responses: "200": description: OK content: application/json: schema: $ref: "#/components/schemas/ListFineTuningJobCheckpointsResponse" x-oaiMeta: name: List fine-tuning checkpoints group: fine-tuning returns: A list of fine-tuning [checkpoint objects](/docs/api-reference/fine-tuning/checkpoint-object) for a fine-tuning job. examples: request: curl: | curl https://api.openai.com/v1/fine_tuning/jobs/ftjob-abc123/checkpoints \ -H "Authorization: Bearer $OPENAI_API_KEY" response: | { "object": "list" "data": [ { "object": "fine_tuning.job.checkpoint", "id": "ftckpt_zc4Q7MP6XxulcVzj4MZdwsAB", "created_at": 1519129973, "fine_tuned_model_checkpoint": "ft:gpt-3.5-turbo-0125:my-org:custom-suffix:96olL566:ckpt-step-2000", "metrics": { "full_valid_loss": 0.134, "full_valid_mean_token_accuracy": 0.874 }, "fine_tuning_job_id": "ftjob-abc123", "step_number": 2000, }, { "object": "fine_tuning.job.checkpoint", "id": "ftckpt_enQCFmOTGj3syEpYVhBRLTSy", "created_at": 1519129833, "fine_tuned_model_checkpoint": "ft:gpt-3.5-turbo-0125:my-org:custom-suffix:7q8mpxmy:ckpt-step-1000", "metrics": { "full_valid_loss": 0.167, "full_valid_mean_token_accuracy": 0.781 }, "fine_tuning_job_id": "ftjob-abc123", "step_number": 1000, }, ], "first_id": "ftckpt_zc4Q7MP6XxulcVzj4MZdwsAB", "last_id": "ftckpt_enQCFmOTGj3syEpYVhBRLTSy", "has_more": true } /models: get: operationId: listModels tags: - Models 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 returns: A list of [model](/docs/api-reference/models/object) objects. examples: request: curl: | curl https://api.openai.com/v1/models \ -H "Authorization: Bearer $OPENAI_API_KEY" python: | from openai import OpenAI client = OpenAI() client.models.list() node.js: |- import OpenAI from "openai"; const openai = new OpenAI(); async function main() { const list = await openai.models.list(); for await (const model of list) { console.log(model); } } main(); response: | { "object": "list", "data": [ { "id": "model-id-0", "object": "model", "created": 1686935002, "owned_by": "organization-owner" }, { "id": "model-id-1", "object": "model", "created": 1686935002, "owned_by": "organization-owner", }, { "id": "model-id-2", "object": "model", "created": 1686935002, "owned_by": "openai" }, ], "object": "list" } /models/{model}: get: operationId: retrieveModel tags: - Models summary: Retrieves a model instance, providing basic information about the model such as the owner and permissioning. parameters: - in: path name: model required: true schema: type: string # ideally this will be an actual ID, so this will always work from browser example: gpt-3.5-turbo description: The ID of the model to use for this request responses: "200": description: OK content: application/json: schema: $ref: "#/components/schemas/Model" x-oaiMeta: name: Retrieve model group: models returns: The [model](/docs/api-reference/models/object) object matching the specified ID. examples: request: curl: | curl https://api.openai.com/v1/models/VAR_model_id \ -H "Authorization: Bearer $OPENAI_API_KEY" python: | from openai import OpenAI client = OpenAI() client.models.retrieve("VAR_model_id") node.js: |- import OpenAI from "openai"; const openai = new OpenAI(); async function main() { const model = await openai.models.retrieve("VAR_model_id"); console.log(model); } main(); response: &retrieve_model_response | { "id": "VAR_model_id", "object": "model", "created": 1686935002, "owned_by": "openai" } delete: operationId: deleteModel tags: - Models summary: Delete a fine-tuned model. You must have the Owner role in your organization to delete a model. parameters: - in: path name: model required: true schema: type: string example: ft:gpt-3.5-turbo:acemeco:suffix:abc123 description: The model to delete responses: "200": description: OK content: application/json: schema: $ref: "#/components/schemas/DeleteModelResponse" x-oaiMeta: name: Delete a fine-tuned model group: models returns: Deletion status. examples: request: curl: | curl https://api.openai.com/v1/models/ft:gpt-3.5-turbo:acemeco:suffix:abc123 \ -X DELETE \ -H "Authorization: Bearer $OPENAI_API_KEY" python: | from openai import OpenAI client = OpenAI() client.models.delete("ft:gpt-3.5-turbo:acemeco:suffix:abc123") node.js: |- import OpenAI from "openai"; const openai = new OpenAI(); async function main() { const model = await openai.models.del("ft:gpt-3.5-turbo:acemeco:suffix:abc123"); console.log(model); } main(); response: | { "id": "ft:gpt-3.5-turbo:acemeco:suffix:abc123", "object": "model", "deleted": true } /moderations: post: operationId: createModeration tags: - Moderations summary: Classifies if text is potentially harmful. 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 returns: A [moderation](/docs/api-reference/moderations/object) object. examples: request: curl: | curl https://api.openai.com/v1/moderations \ -H "Content-Type: application/json" \ -H "Authorization: Bearer $OPENAI_API_KEY" \ -d '{ "input": "I want to kill them." }' python: | from openai import OpenAI client = OpenAI() moderation = client.moderations.create(input="I want to kill them.") print(moderation) node.js: | import OpenAI from "openai"; const openai = new OpenAI(); async function main() { const moderation = await openai.moderations.create({ input: "I want to kill them." }); console.log(moderation); } main(); response: &moderation_example | { "id": "modr-XXXXX", "model": "text-moderation-005", "results": [ { "flagged": true, "categories": { "sexual": false, "hate": false, "harassment": false, "self-harm": false, "sexual/minors": false, "hate/threatening": false, "violence/graphic": false, "self-harm/intent": false, "self-harm/instructions": false, "harassment/threatening": true, "violence": true, }, "category_scores": { "sexual": 1.2282071e-06, "hate": 0.010696256, "harassment": 0.29842457, "self-harm": 1.5236925e-08, "sexual/minors": 5.7246268e-08, "hate/threatening": 0.0060676364, "violence/graphic": 4.435014e-06, "self-harm/intent": 8.098441e-10, "self-harm/instructions": 2.8498655e-11, "harassment/threatening": 0.63055265, "violence": 0.99011886, } } ] } /assistants: get: operationId: listAssistants tags: - Assistants summary: Returns a list of assistants. parameters: - name: limit in: query description: &pagination_limit_param_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: &pagination_order_param_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: &pagination_after_param_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: &pagination_before_param_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, ending 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: &list_assistants_example | { "object": "list", "data": [ { "id": "asst_abc123", "object": "assistant", "created_at": 1698982736, "name": "Coding Tutor", "description": null, "model": "gpt-4-turbo", "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-4-turbo", "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-4-turbo", "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-4-turbo" }' 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-4-turbo", ) 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-4-turbo", }); console.log(myAssistant); } main(); response: &create_assistants_example | { "id": "asst_abc123", "object": "assistant", "created_at": 1698984975, "name": "Math Tutor", "description": null, "model": "gpt-4-turbo", "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-4-turbo" }' 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-4-turbo" ) 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-4-turbo" }); console.log(myAssistant); } main(); response: | { "id": "asst_abc123", "object": "assistant", "created_at": 1699009403, "name": "HR Helper", "description": null, "model": "gpt-4-turbo", "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-4-turbo", "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-4-turbo" }' 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-4-turbo" ) 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-4-turbo" } ); console.log(myUpdatedAssistant); } main(); response: | { "id": "asst_123", "object": "assistant", "created_at": 1699009709, "name": "HR Helper", "description": null, "model": "gpt-4-turbo", "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 } /threads: post: operationId: createThread tags: - Assistants summary: Create a thread. requestBody: content: application/json: schema: $ref: "#/components/schemas/CreateThreadRequest" responses: "200": description: OK content: application/json: schema: $ref: "#/components/schemas/ThreadObject" x-oaiMeta: name: Create thread group: threads beta: true returns: A [thread](/docs/api-reference/threads) object. examples: - title: Empty request: curl: | curl https://api.openai.com/v1/threads \ -H "Content-Type: application/json" \ -H "Authorization: Bearer $OPENAI_API_KEY" \ -H "OpenAI-Beta: assistants=v2" \ -d '' python: | from openai import OpenAI client = OpenAI() empty_thread = client.beta.threads.create() print(empty_thread) node.js: |- import OpenAI from "openai"; const openai = new OpenAI(); async function main() { const emptyThread = await openai.beta.threads.create(); console.log(emptyThread); } main(); response: | { "id": "thread_abc123", "object": "thread", "created_at": 1699012949, "metadata": {}, "tool_resources": {} } - title: Messages request: curl: | curl https://api.openai.com/v1/threads \ -H "Content-Type: application/json" \ -H "Authorization: Bearer $OPENAI_API_KEY" \ -H "OpenAI-Beta: assistants=v2" \ -d '{ "messages": [{ "role": "user", "content": "Hello, what is AI?" }, { "role": "user", "content": "How does AI work? Explain it in simple terms." }] }' python: | from openai import OpenAI client = OpenAI() message_thread = client.beta.threads.create( messages=[ { "role": "user", "content": "Hello, what is AI?" }, { "role": "user", "content": "How does AI work? Explain it in simple terms." }, ] ) print(message_thread) node.js: |- import OpenAI from "openai"; const openai = new OpenAI(); async function main() { const messageThread = await openai.beta.threads.create({ messages: [ { role: "user", content: "Hello, what is AI?" }, { role: "user", content: "How does AI work? Explain it in simple terms.", }, ], }); console.log(messageThread); } main(); response: | { "id": "thread_abc123", "object": "thread", "created_at": 1699014083, "metadata": {}, "tool_resources": {} } /threads/{thread_id}: get: operationId: getThread tags: - Assistants summary: Retrieves a thread. parameters: - in: path name: thread_id required: true schema: type: string description: The ID of the thread to retrieve. responses: "200": description: OK content: application/json: schema: $ref: "#/components/schemas/ThreadObject" x-oaiMeta: name: Retrieve thread group: threads beta: true returns: The [thread](/docs/api-reference/threads/object) object matching the specified ID. examples: request: curl: | curl https://api.openai.com/v1/threads/thread_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_thread = client.beta.threads.retrieve("thread_abc123") print(my_thread) node.js: |- import OpenAI from "openai"; const openai = new OpenAI(); async function main() { const myThread = await openai.beta.threads.retrieve( "thread_abc123" ); console.log(myThread); } main(); response: | { "id": "thread_abc123", "object": "thread", "created_at": 1699014083, "metadata": {}, "tool_resources": { "code_interpreter": { "file_ids": [] } } } post: operationId: modifyThread tags: - Assistants summary: Modifies a thread. parameters: - in: path name: thread_id required: true schema: type: string description: The ID of the thread to modify. Only the `metadata` can be modified. requestBody: required: true content: application/json: schema: $ref: "#/components/schemas/ModifyThreadRequest" responses: "200": description: OK content: application/json: schema: $ref: "#/components/schemas/ThreadObject" x-oaiMeta: name: Modify thread group: threads beta: true returns: The modified [thread](/docs/api-reference/threads/object) object matching the specified ID. examples: request: curl: | curl https://api.openai.com/v1/threads/thread_abc123 \ -H "Content-Type: application/json" \ -H "Authorization: Bearer $OPENAI_API_KEY" \ -H "OpenAI-Beta: assistants=v2" \ -d '{ "metadata": { "modified": "true", "user": "abc123" } }' python: | from openai import OpenAI client = OpenAI() my_updated_thread = client.beta.threads.update( "thread_abc123", metadata={ "modified": "true", "user": "abc123" } ) print(my_updated_thread) node.js: |- import OpenAI from "openai"; const openai = new OpenAI(); async function main() { const updatedThread = await openai.beta.threads.update( "thread_abc123", { metadata: { modified: "true", user: "abc123" }, } ); console.log(updatedThread); } main(); response: | { "id": "thread_abc123", "object": "thread", "created_at": 1699014083, "metadata": { "modified": "true", "user": "abc123" }, "tool_resources": {} } delete: operationId: deleteThread tags: - Assistants summary: Delete a thread. parameters: - in: path name: thread_id required: true schema: type: string description: The ID of the thread to delete. responses: "200": description: OK content: application/json: schema: $ref: "#/components/schemas/DeleteThreadResponse" x-oaiMeta: name: Delete thread group: threads beta: true returns: Deletion status examples: request: curl: | curl https://api.openai.com/v1/threads/thread_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.threads.delete("thread_abc123") print(response) node.js: |- import OpenAI from "openai"; const openai = new OpenAI(); async function main() { const response = await openai.beta.threads.del("thread_abc123"); console.log(response); } main(); response: | { "id": "thread_abc123", "object": "thread.deleted", "deleted": true } /threads/{thread_id}/messages: get: operationId: listMessages tags: - Assistants summary: Returns a list of messages for a given thread. parameters: - in: path name: thread_id required: true schema: type: string description: The ID of the [thread](/docs/api-reference/threads) the messages belong to. - name: limit in: query description: *pagination_limit_param_description required: false schema: type: integer default: 20 - name: order in: query description: *pagination_order_param_description schema: type: string default: desc enum: ["asc", "desc"] - name: after in: query description: *pagination_after_param_description schema: type: string - name: before in: query description: *pagination_before_param_description schema: type: string - name: run_id in: query description: | Filter messages by the run ID that generated them. schema: type: string responses: "200": description: OK content: application/json: schema: $ref: "#/components/schemas/ListMessagesResponse" x-oaiMeta: name: List messages group: threads beta: true returns: A list of [message](/docs/api-reference/messages) objects. examples: request: curl: | curl https://api.openai.com/v1/threads/thread_abc123/messages \ -H "Content-Type: application/json" \ -H "Authorization: Bearer $OPENAI_API_KEY" \ -H "OpenAI-Beta: assistants=v2" python: | from openai import OpenAI client = OpenAI() thread_messages = client.beta.threads.messages.list("thread_abc123") print(thread_messages.data) node.js: |- import OpenAI from "openai"; const openai = new OpenAI(); async function main() { const threadMessages = await openai.beta.threads.messages.list( "thread_abc123" ); console.log(threadMessages.data); } main(); response: | { "object": "list", "data": [ { "id": "msg_abc123", "object": "thread.message", "created_at": 1699016383, "assistant_id": null, "thread_id": "thread_abc123", "run_id": null, "role": "user", "content": [ { "type": "text", "text": { "value": "How does AI work? Explain it in simple terms.", "annotations": [] } } ], "attachments": [], "metadata": {} }, { "id": "msg_abc456", "object": "thread.message", "created_at": 1699016383, "assistant_id": null, "thread_id": "thread_abc123", "run_id": null, "role": "user", "content": [ { "type": "text", "text": { "value": "Hello, what is AI?", "annotations": [] } } ], "attachments": [], "metadata": {} } ], "first_id": "msg_abc123", "last_id": "msg_abc456", "has_more": false } post: operationId: createMessage tags: - Assistants summary: Create a message. parameters: - in: path name: thread_id required: true schema: type: string description: The ID of the [thread](/docs/api-reference/threads) to create a message for. requestBody: required: true content: application/json: schema: $ref: "#/components/schemas/CreateMessageRequest" responses: "200": description: OK content: application/json: schema: $ref: "#/components/schemas/MessageObject" x-oaiMeta: name: Create message group: threads beta: true returns: A [message](/docs/api-reference/messages/object) object. examples: request: curl: | curl https://api.openai.com/v1/threads/thread_abc123/messages \ -H "Content-Type: application/json" \ -H "Authorization: Bearer $OPENAI_API_KEY" \ -H "OpenAI-Beta: assistants=v2" \ -d '{ "role": "user", "content": "How does AI work? Explain it in simple terms." }' python: | from openai import OpenAI client = OpenAI() thread_message = client.beta.threads.messages.create( "thread_abc123", role="user", content="How does AI work? Explain it in simple terms.", ) print(thread_message) node.js: |- import OpenAI from "openai"; const openai = new OpenAI(); async function main() { const threadMessages = await openai.beta.threads.messages.create( "thread_abc123", { role: "user", content: "How does AI work? Explain it in simple terms." } ); console.log(threadMessages); } main(); response: | { "id": "msg_abc123", "object": "thread.message", "created_at": 1713226573, "assistant_id": null, "thread_id": "thread_abc123", "run_id": null, "role": "user", "content": [ { "type": "text", "text": { "value": "How does AI work? Explain it in simple terms.", "annotations": [] } } ], "attachments": [], "metadata": {} } /threads/{thread_id}/messages/{message_id}: get: operationId: getMessage tags: - Assistants summary: Retrieve a message. parameters: - in: path name: thread_id required: true schema: type: string description: The ID of the [thread](/docs/api-reference/threads) to which this message belongs. - in: path name: message_id required: true schema: type: string description: The ID of the message to retrieve. responses: "200": description: OK content: application/json: schema: $ref: "#/components/schemas/MessageObject" x-oaiMeta: name: Retrieve message group: threads beta: true returns: The [message](/docs/api-reference/threads/messages/object) object matching the specified ID. examples: request: curl: | curl https://api.openai.com/v1/threads/thread_abc123/messages/msg_abc123 \ -H "Content-Type: application/json" \ -H "Authorization: Bearer $OPENAI_API_KEY" \ -H "OpenAI-Beta: assistants=v2" python: | from openai import OpenAI client = OpenAI() message = client.beta.threads.messages.retrieve( message_id="msg_abc123", thread_id="thread_abc123", ) print(message) node.js: |- import OpenAI from "openai"; const openai = new OpenAI(); async function main() { const message = await openai.beta.threads.messages.retrieve( "thread_abc123", "msg_abc123" ); console.log(message); } main(); response: | { "id": "msg_abc123", "object": "thread.message", "created_at": 1699017614, "assistant_id": null, "thread_id": "thread_abc123", "run_id": null, "role": "user", "content": [ { "type": "text", "text": { "value": "How does AI work? Explain it in simple terms.", "annotations": [] } } ], "attachments": [], "metadata": {} } post: operationId: modifyMessage tags: - Assistants summary: Modifies a message. parameters: - in: path name: thread_id required: true schema: type: string description: The ID of the thread to which this message belongs. - in: path name: message_id required: true schema: type: string description: The ID of the message to modify. requestBody: required: true content: application/json: schema: $ref: "#/components/schemas/ModifyMessageRequest" responses: "200": description: OK content: application/json: schema: $ref: "#/components/schemas/MessageObject" x-oaiMeta: name: Modify message group: threads beta: true returns: The modified [message](/docs/api-reference/threads/messages/object) object. examples: request: curl: | curl https://api.openai.com/v1/threads/thread_abc123/messages/msg_abc123 \ -H "Content-Type: application/json" \ -H "Authorization: Bearer $OPENAI_API_KEY" \ -H "OpenAI-Beta: assistants=v2" \ -d '{ "metadata": { "modified": "true", "user": "abc123" } }' python: | from openai import OpenAI client = OpenAI() message = client.beta.threads.messages.update( message_id="msg_abc12", thread_id="thread_abc123", metadata={ "modified": "true", "user": "abc123", }, ) print(message) node.js: |- import OpenAI from "openai"; const openai = new OpenAI(); async function main() { const message = await openai.beta.threads.messages.update( "thread_abc123", "msg_abc123", { metadata: { modified: "true", user: "abc123", }, } }' response: | { "id": "msg_abc123", "object": "thread.message", "created_at": 1699017614, "assistant_id": null, "thread_id": "thread_abc123", "run_id": null, "role": "user", "content": [ { "type": "text", "text": { "value": "How does AI work? Explain it in simple terms.", "annotations": [] } } ], "file_ids": [], "metadata": { "modified": "true", "user": "abc123" } } delete: operationId: deleteMessage tags: - Assistants summary: Deletes a message. parameters: - in: path name: thread_id required: true schema: type: string description: The ID of the thread to which this message belongs. - in: path name: message_id required: true schema: type: string description: The ID of the message to delete. responses: "200": description: OK content: application/json: schema: $ref: "#/components/schemas/DeleteMessageResponse" x-oaiMeta: name: Delete message group: threads beta: true returns: Deletion status examples: request: curl: | curl -X DELETE https://api.openai.com/v1/threads/thread_abc123/messages/msg_abc123 \ -H "Content-Type: application/json" \ -H "Authorization: Bearer $OPENAI_API_KEY" \ -H "OpenAI-Beta: assistants=v2" python: | from openai import OpenAI client = OpenAI() deleted_message = client.beta.threads.messages.delete( message_id="msg_abc12", thread_id="thread_abc123", ) print(deleted_message) node.js: |- import OpenAI from "openai"; const openai = new OpenAI(); async function main() { const deletedMessage = await openai.beta.threads.messages.del( "thread_abc123", "msg_abc123" ); console.log(deletedMessage); } response: | { "id": "msg_abc123", "object": "thread.message.deleted", "deleted": true } /threads/runs: post: operationId: createThreadAndRun tags: - Assistants summary: Create a thread and run it in one request. requestBody: required: true content: application/json: schema: $ref: "#/components/schemas/CreateThreadAndRunRequest" responses: "200": description: OK content: application/json: schema: $ref: "#/components/schemas/RunObject" x-oaiMeta: name: Create thread and run group: threads beta: true returns: A [run](/docs/api-reference/runs/object) object. examples: - title: Default request: curl: | curl https://api.openai.com/v1/threads/runs \ -H "Authorization: Bearer $OPENAI_API_KEY" \ -H "Content-Type: application/json" \ -H "OpenAI-Beta: assistants=v2" \ -d '{ "assistant_id": "asst_abc123", "thread": { "messages": [ {"role": "user", "content": "Explain deep learning to a 5 year old."} ] } }' python: | from openai import OpenAI client = OpenAI() run = client.beta.threads.create_and_run( assistant_id="asst_abc123", thread={ "messages": [ {"role": "user", "content": "Explain deep learning to a 5 year old."} ] } ) print(run) node.js: | import OpenAI from "openai"; const openai = new OpenAI(); async function main() { const run = await openai.beta.threads.createAndRun({ assistant_id: "asst_abc123", thread: { messages: [ { role: "user", content: "Explain deep learning to a 5 year old." }, ], }, }); console.log(run); } main(); response: | { "id": "run_abc123", "object": "thread.run", "created_at": 1699076792, "assistant_id": "asst_abc123", "thread_id": "thread_abc123", "status": "queued", "started_at": null, "expires_at": 1699077392, "cancelled_at": null, "failed_at": null, "completed_at": null, "required_action": null, "last_error": null, "model": "gpt-4-turbo", "instructions": "You are a helpful assistant.", "tools": [], "tool_resources": {}, "metadata": {}, "temperature": 1.0, "top_p": 1.0, "max_completion_tokens": null, "max_prompt_tokens": null, "truncation_strategy": { "type": "auto", "last_messages": null }, "incomplete_details": null, "usage": null, "response_format": "auto", "tool_choice": "auto" } - title: Streaming request: curl: | curl https://api.openai.com/v1/threads/runs \ -H "Authorization: Bearer $OPENAI_API_KEY" \ -H "Content-Type: application/json" \ -H "OpenAI-Beta: assistants=v2" \ -d '{ "assistant_id": "asst_123", "thread": { "messages": [ {"role": "user", "content": "Hello"} ] }, "stream": true }' python: | from openai import OpenAI client = OpenAI() stream = client.beta.threads.create_and_run( assistant_id="asst_123", thread={ "messages": [ {"role": "user", "content": "Hello"} ] }, stream=True ) for event in stream: print(event) node.js: | import OpenAI from "openai"; const openai = new OpenAI(); async function main() { const stream = await openai.beta.threads.createAndRun({ assistant_id: "asst_123", thread: { messages: [ { role: "user", content: "Hello" }, ], }, stream: true }); for await (const event of stream) { console.log(event); } } main(); response: | event: thread.created data: {"id":"thread_123","object":"thread","created_at":1710348075,"metadata":{}} event: thread.run.created data: {"id":"run_123","object":"thread.run","created_at":1710348075,"assistant_id":"asst_123","thread_id":"thread_123","status":"queued","started_at":null,"expires_at":1710348675,"cancelled_at":null,"failed_at":null,"completed_at":null,"required_action":null,"last_error":null,"model":"gpt-4-turbo","instructions":null,"tools":[],"tool_resources":{},"metadata":{},"temperature":1.0,"top_p":1.0,"max_completion_tokens":null,"max_prompt_tokens":null,"truncation_strategy":{"type":"auto","last_messages":null},"incomplete_details":null,"usage":null,"response_format":"auto","tool_choice":"auto"} event: thread.run.queued data: {"id":"run_123","object":"thread.run","created_at":1710348075,"assistant_id":"asst_123","thread_id":"thread_123","status":"queued","started_at":null,"expires_at":1710348675,"cancelled_at":null,"failed_at":null,"completed_at":null,"required_action":null,"last_error":null,"model":"gpt-4-turbo","instructions":null,"tools":[],"tool_resources":{},"metadata":{},"temperature":1.0,"top_p":1.0,"max_completion_tokens":null,"max_prompt_tokens":null,"truncation_strategy":{"type":"auto","last_messages":null},"incomplete_details":null,"usage":null,"response_format":"auto","tool_choice":"auto"} event: thread.run.in_progress data: {"id":"run_123","object":"thread.run","created_at":1710348075,"assistant_id":"asst_123","thread_id":"thread_123","status":"in_progress","started_at":null,"expires_at":1710348675,"cancelled_at":null,"failed_at":null,"completed_at":null,"required_action":null,"last_error":null,"model":"gpt-4-turbo","instructions":null,"tools":[],"tool_resources":{},"metadata":{},"temperature":1.0,"top_p":1.0,"max_completion_tokens":null,"max_prompt_tokens":null,"truncation_strategy":{"type":"auto","last_messages":null},"incomplete_details":null,"usage":null,"response_format":"auto","tool_choice":"auto"} event: thread.run.step.created data: {"id":"step_001","object":"thread.run.step","created_at":1710348076,"run_id":"run_123","assistant_id":"asst_123","thread_id":"thread_123","type":"message_creation","status":"in_progress","cancelled_at":null,"completed_at":null,"expires_at":1710348675,"failed_at":null,"last_error":null,"step_details":{"type":"message_creation","message_creation":{"message_id":"msg_001"}},"usage":null} event: thread.run.step.in_progress data: {"id":"step_001","object":"thread.run.step","created_at":1710348076,"run_id":"run_123","assistant_id":"asst_123","thread_id":"thread_123","type":"message_creation","status":"in_progress","cancelled_at":null,"completed_at":null,"expires_at":1710348675,"failed_at":null,"last_error":null,"step_details":{"type":"message_creation","message_creation":{"message_id":"msg_001"}},"usage":null} event: thread.message.created data: {"id":"msg_001","object":"thread.message","created_at":1710348076,"assistant_id":"asst_123","thread_id":"thread_123","run_id":"run_123","status":"in_progress","incomplete_details":null,"incomplete_at":null,"completed_at":null,"role":"assistant","content":[], "metadata":{}} event: thread.message.in_progress data: {"id":"msg_001","object":"thread.message","created_at":1710348076,"assistant_id":"asst_123","thread_id":"thread_123","run_id":"run_123","status":"in_progress","incomplete_details":null,"incomplete_at":null,"completed_at":null,"role":"assistant","content":[], "metadata":{}} event: thread.message.delta data: {"id":"msg_001","object":"thread.message.delta","delta":{"content":[{"index":0,"type":"text","text":{"value":"Hello","annotations":[]}}]}} ... event: thread.message.delta data: {"id":"msg_001","object":"thread.message.delta","delta":{"content":[{"index":0,"type":"text","text":{"value":" today"}}]}} event: thread.message.delta data: {"id":"msg_001","object":"thread.message.delta","delta":{"content":[{"index":0,"type":"text","text":{"value":"?"}}]}} event: thread.message.completed data: {"id":"msg_001","object":"thread.message","created_at":1710348076,"assistant_id":"asst_123","thread_id":"thread_123","run_id":"run_123","status":"completed","incomplete_details":null,"incomplete_at":null,"completed_at":1710348077,"role":"assistant","content":[{"type":"text","text":{"value":"Hello! How can I assist you today?","annotations":[]}}], "metadata":{}} event: thread.run.step.completed data: {"id":"step_001","object":"thread.run.step","created_at":1710348076,"run_id":"run_123","assistant_id":"asst_123","thread_id":"thread_123","type":"message_creation","status":"completed","cancelled_at":null,"completed_at":1710348077,"expires_at":1710348675,"failed_at":null,"last_error":null,"step_details":{"type":"message_creation","message_creation":{"message_id":"msg_001"}},"usage":{"prompt_tokens":20,"completion_tokens":11,"total_tokens":31}} event: thread.run.completed {"id":"run_123","object":"thread.run","created_at":1710348076,"assistant_id":"asst_123","thread_id":"thread_123","status":"completed","started_at":1713226836,"expires_at":null,"cancelled_at":null,"failed_at":null,"completed_at":1713226837,"required_action":null,"last_error":null,"model":"gpt-4-turbo","instructions":null,"tools":[],"metadata":{},"temperature":1.0,"top_p":1.0,"max_completion_tokens":null,"max_prompt_tokens":null,"truncation_strategy":{"type":"auto","last_messages":null},"incomplete_details":null,"usage":{"prompt_tokens":345,"completion_tokens":11,"total_tokens":356},"response_format":"auto","tool_choice":"auto"} event: done data: [DONE] - title: Streaming with Functions request: curl: | curl https://api.openai.com/v1/threads/runs \ -H "Authorization: Bearer $OPENAI_API_KEY" \ -H "Content-Type: application/json" \ -H "OpenAI-Beta: assistants=v2" \ -d '{ "assistant_id": "asst_abc123", "thread": { "messages": [ {"role": "user", "content": "What is the weather like in San Francisco?"} ] }, "tools": [ { "type": "function", "function": { "name": "get_current_weather", "description": "Get the current weather in a given location", "parameters": { "type": "object", "properties": { "location": { "type": "string", "description": "The city and state, e.g. San Francisco, CA" }, "unit": { "type": "string", "enum": ["celsius", "fahrenheit"] } }, "required": ["location"] } } } ], "stream": true }' python: | from openai import OpenAI client = OpenAI() tools = [ { "type": "function", "function": { "name": "get_current_weather", "description": "Get the current weather in a given location", "parameters": { "type": "object", "properties": { "location": { "type": "string", "description": "The city and state, e.g. San Francisco, CA", }, "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}, }, "required": ["location"], }, } } ] stream = client.beta.threads.create_and_run( thread={ "messages": [ {"role": "user", "content": "What is the weather like in San Francisco?"} ] }, assistant_id="asst_abc123", tools=tools, stream=True ) for event in stream: print(event) node.js: | import OpenAI from "openai"; const openai = new OpenAI(); const tools = [ { "type": "function", "function": { "name": "get_current_weather", "description": "Get the current weather in a given location", "parameters": { "type": "object", "properties": { "location": { "type": "string", "description": "The city and state, e.g. San Francisco, CA", }, "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}, }, "required": ["location"], }, } } ]; async function main() { const stream = await openai.beta.threads.createAndRun({ assistant_id: "asst_123", thread: { messages: [ { role: "user", content: "What is the weather like in San Francisco?" }, ], }, tools: tools, stream: true }); for await (const event of stream) { console.log(event); } } main(); response: | event: thread.created data: {"id":"thread_123","object":"thread","created_at":1710351818,"metadata":{}} event: thread.run.created data: {"id":"run_123","object":"thread.run","created_at":1710351818,"assistant_id":"asst_123","thread_id":"thread_123","status":"queued","started_at":null,"expires_at":1710352418,"cancelled_at":null,"failed_at":null,"completed_at":null,"required_action":null,"last_error":null,"model":"gpt-4-turbo","instructions":null,"tools":[{"type":"function","function":{"name":"get_current_weather","description":"Get the current weather in a given location","parameters":{"type":"object","properties":{"location":{"type":"string","description":"The city and state, e.g. San Francisco, CA"},"unit":{"type":"string","enum":["celsius","fahrenheit"]}},"required":["location"]}}}],"metadata":{},"temperature":1.0,"top_p":1.0,"max_completion_tokens":null,"max_prompt_tokens":null,"truncation_strategy":{"type":"auto","last_messages":null},"incomplete_details":null,"usage":null,"response_format":"auto","tool_choice":"auto"}} event: thread.run.queued data: {"id":"run_123","object":"thread.run","created_at":1710351818,"assistant_id":"asst_123","thread_id":"thread_123","status":"queued","started_at":null,"expires_at":1710352418,"cancelled_at":null,"failed_at":null,"completed_at":null,"required_action":null,"last_error":null,"model":"gpt-4-turbo","instructions":null,"tools":[{"type":"function","function":{"name":"get_current_weather","description":"Get the current weather in a given location","parameters":{"type":"object","properties":{"location":{"type":"string","description":"The city and state, e.g. San Francisco, CA"},"unit":{"type":"string","enum":["celsius","fahrenheit"]}},"required":["location"]}}}],"metadata":{},"temperature":1.0,"top_p":1.0,"max_completion_tokens":null,"max_prompt_tokens":null,"truncation_strategy":{"type":"auto","last_messages":null},"incomplete_details":null,"usage":null,"response_format":"auto","tool_choice":"auto"}} event: thread.run.in_progress data: {"id":"run_123","object":"thread.run","created_at":1710351818,"assistant_id":"asst_123","thread_id":"thread_123","status":"in_progress","started_at":1710351818,"expires_at":1710352418,"cancelled_at":null,"failed_at":null,"completed_at":null,"required_action":null,"last_error":null,"model":"gpt-4-turbo","instructions":null,"tools":[{"type":"function","function":{"name":"get_current_weather","description":"Get the current weather in a given location","parameters":{"type":"object","properties":{"location":{"type":"string","description":"The city and state, e.g. San Francisco, CA"},"unit":{"type":"string","enum":["celsius","fahrenheit"]}},"required":["location"]}}}],"metadata":{},"temperature":1.0,"top_p":1.0,"max_completion_tokens":null,"max_prompt_tokens":null,"truncation_strategy":{"type":"auto","last_messages":null},"incomplete_details":null,"usage":null,"response_format":"auto","tool_choice":"auto"}} event: thread.run.step.created data: {"id":"step_001","object":"thread.run.step","created_at":1710351819,"run_id":"run_123","assistant_id":"asst_123","thread_id":"thread_123","type":"tool_calls","status":"in_progress","cancelled_at":null,"completed_at":null,"expires_at":1710352418,"failed_at":null,"last_error":null,"step_details":{"type":"tool_calls","tool_calls":[]},"usage":null} event: thread.run.step.in_progress data: {"id":"step_001","object":"thread.run.step","created_at":1710351819,"run_id":"run_123","assistant_id":"asst_123","thread_id":"thread_123","type":"tool_calls","status":"in_progress","cancelled_at":null,"completed_at":null,"expires_at":1710352418,"failed_at":null,"last_error":null,"step_details":{"type":"tool_calls","tool_calls":[]},"usage":null} event: thread.run.step.delta data: {"id":"step_001","object":"thread.run.step.delta","delta":{"step_details":{"type":"tool_calls","tool_calls":[{"index":0,"id":"call_XXNp8YGaFrjrSjgqxtC8JJ1B","type":"function","function":{"name":"get_current_weather","arguments":"","output":null}}]}}} event: thread.run.step.delta data: {"id":"step_001","object":"thread.run.step.delta","delta":{"step_details":{"type":"tool_calls","tool_calls":[{"index":0,"type":"function","function":{"arguments":"{\""}}]}}} event: thread.run.step.delta data: {"id":"step_001","object":"thread.run.step.delta","delta":{"step_details":{"type":"tool_calls","tool_calls":[{"index":0,"type":"function","function":{"arguments":"location"}}]}}} ... event: thread.run.step.delta data: {"id":"step_001","object":"thread.run.step.delta","delta":{"step_details":{"type":"tool_calls","tool_calls":[{"index":0,"type":"function","function":{"arguments":"ahrenheit"}}]}}} event: thread.run.step.delta data: {"id":"step_001","object":"thread.run.step.delta","delta":{"step_details":{"type":"tool_calls","tool_calls":[{"index":0,"type":"function","function":{"arguments":"\"}"}}]}}} event: thread.run.requires_action data: {"id":"run_123","object":"thread.run","created_at":1710351818,"assistant_id":"asst_123","thread_id":"thread_123","status":"requires_action","started_at":1710351818,"expires_at":1710352418,"cancelled_at":null,"failed_at":null,"completed_at":null,"required_action":{"type":"submit_tool_outputs","submit_tool_outputs":{"tool_calls":[{"id":"call_XXNp8YGaFrjrSjgqxtC8JJ1B","type":"function","function":{"name":"get_current_weather","arguments":"{\"location\":\"San Francisco, CA\",\"unit\":\"fahrenheit\"}"}}]}},"last_error":null,"model":"gpt-4-turbo","instructions":null,"tools":[{"type":"function","function":{"name":"get_current_weather","description":"Get the current weather in a given location","parameters":{"type":"object","properties":{"location":{"type":"string","description":"The 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description: *pagination_after_param_description schema: type: string - name: before in: query description: *pagination_before_param_description schema: type: string responses: "200": description: OK content: application/json: schema: $ref: "#/components/schemas/ListRunsResponse" x-oaiMeta: name: List runs group: threads beta: true returns: A list of [run](/docs/api-reference/runs/object) objects. examples: request: curl: | curl https://api.openai.com/v1/threads/thread_abc123/runs \ -H "Authorization: Bearer $OPENAI_API_KEY" \ -H "Content-Type: application/json" \ -H "OpenAI-Beta: assistants=v2" python: | from openai import OpenAI client = OpenAI() runs = client.beta.threads.runs.list( "thread_abc123" ) print(runs) node.js: | import OpenAI from "openai"; const openai = new OpenAI(); async function main() { const runs = await openai.beta.threads.runs.list( "thread_abc123" ); console.log(runs); } main(); response: | { "object": "list", "data": [ { "id": "run_abc123", "object": "thread.run", "created_at": 1699075072, "assistant_id": "asst_abc123", "thread_id": "thread_abc123", "status": "completed", "started_at": 1699075072, "expires_at": null, "cancelled_at": null, "failed_at": null, "completed_at": 1699075073, "last_error": null, "model": "gpt-4-turbo", "instructions": null, "incomplete_details": null, "tools": [ { "type": "code_interpreter" } ], "tool_resources": { "code_interpreter": { "file_ids": [ "file-abc123", "file-abc456" ] } }, "metadata": {}, "usage": { "prompt_tokens": 123, "completion_tokens": 456, "total_tokens": 579 }, "temperature": 1.0, "top_p": 1.0, "max_prompt_tokens": 1000, "max_completion_tokens": 1000, "truncation_strategy": { "type": "auto", "last_messages": null }, "response_format": "auto", "tool_choice": "auto" }, { "id": "run_abc456", "object": "thread.run", "created_at": 1699063290, "assistant_id": "asst_abc123", "thread_id": "thread_abc123", "status": "completed", "started_at": 1699063290, "expires_at": null, "cancelled_at": null, "failed_at": null, "completed_at": 1699063291, "last_error": null, "model": "gpt-4-turbo", "instructions": null, "incomplete_details": null, "tools": [ { "type": "code_interpreter" } ], "tool_resources": { "code_interpreter": { "file_ids": [ "file-abc123", "file-abc456" ] } }, "metadata": {}, "usage": { "prompt_tokens": 123, "completion_tokens": 456, "total_tokens": 579 }, "temperature": 1.0, "top_p": 1.0, "max_prompt_tokens": 1000, "max_completion_tokens": 1000, "truncation_strategy": { "type": "auto", "last_messages": null }, "response_format": "auto", "tool_choice": "auto" } ], "first_id": "run_abc123", "last_id": "run_abc456", "has_more": false } post: operationId: createRun tags: - Assistants summary: Create a run. parameters: - in: path name: thread_id required: true schema: type: string description: The ID of the thread to run. requestBody: required: true content: application/json: schema: $ref: "#/components/schemas/CreateRunRequest" responses: "200": description: OK content: application/json: schema: $ref: "#/components/schemas/RunObject" x-oaiMeta: name: Create run group: threads beta: true returns: A [run](/docs/api-reference/runs/object) object. examples: - title: Default request: curl: | curl https://api.openai.com/v1/threads/thread_abc123/runs \ -H "Authorization: Bearer $OPENAI_API_KEY" \ -H "Content-Type: application/json" \ -H "OpenAI-Beta: assistants=v2" \ -d '{ "assistant_id": "asst_abc123" }' python: | from openai import OpenAI client = OpenAI() run = client.beta.threads.runs.create( thread_id="thread_abc123", assistant_id="asst_abc123" ) print(run) node.js: | import OpenAI from "openai"; const openai = new OpenAI(); async function main() { const run = await openai.beta.threads.runs.create( "thread_abc123", { assistant_id: "asst_abc123" } ); console.log(run); } main(); response: &run_object_example | { "id": "run_abc123", "object": "thread.run", "created_at": 1699063290, "assistant_id": "asst_abc123", "thread_id": "thread_abc123", "status": "queued", "started_at": 1699063290, "expires_at": null, "cancelled_at": null, "failed_at": null, "completed_at": 1699063291, "last_error": null, "model": "gpt-4-turbo", "instructions": null, "incomplete_details": null, "tools": [ { "type": "code_interpreter" } ], "metadata": {}, "usage": null, "temperature": 1.0, "top_p": 1.0, "max_prompt_tokens": 1000, "max_completion_tokens": 1000, "truncation_strategy": { "type": "auto", "last_messages": null }, "response_format": "auto", "tool_choice": "auto" } - title: Streaming request: curl: | curl https://api.openai.com/v1/threads/thread_123/runs \ -H "Authorization: Bearer $OPENAI_API_KEY" \ -H "Content-Type: application/json" \ -H "OpenAI-Beta: assistants=v2" \ -d '{ "assistant_id": "asst_123", "stream": true }' python: | from openai import OpenAI client = OpenAI() stream = client.beta.threads.runs.create( thread_id="thread_123", assistant_id="asst_123", stream=True ) for event in stream: print(event) node.js: | import OpenAI from "openai"; const openai = new OpenAI(); async function main() { const stream = await openai.beta.threads.runs.create( "thread_123", { assistant_id: "asst_123", stream: true } ); for await (const event of stream) { console.log(event); } } main(); response: | event: thread.run.created data: {"id":"run_123","object":"thread.run","created_at":1710330640,"assistant_id":"asst_123","thread_id":"thread_123","status":"queued","started_at":null,"expires_at":1710331240,"cancelled_at":null,"failed_at":null,"completed_at":null,"required_action":null,"last_error":null,"model":"gpt-4-turbo","instructions":null,"tools":[],"metadata":{},"temperature":1.0,"top_p":1.0,"max_completion_tokens":null,"max_prompt_tokens":null,"truncation_strategy":{"type":"auto","last_messages":null},"incomplete_details":null,"usage":null,"response_format":"auto","tool_choice":"auto"}} event: thread.run.queued data: {"id":"run_123","object":"thread.run","created_at":1710330640,"assistant_id":"asst_123","thread_id":"thread_123","status":"queued","started_at":null,"expires_at":1710331240,"cancelled_at":null,"failed_at":null,"completed_at":null,"required_action":null,"last_error":null,"model":"gpt-4-turbo","instructions":null,"tools":[],"metadata":{},"temperature":1.0,"top_p":1.0,"max_completion_tokens":null,"max_prompt_tokens":null,"truncation_strategy":{"type":"auto","last_messages":null},"incomplete_details":null,"usage":null,"response_format":"auto","tool_choice":"auto"}} event: thread.run.in_progress data: {"id":"run_123","object":"thread.run","created_at":1710330640,"assistant_id":"asst_123","thread_id":"thread_123","status":"in_progress","started_at":1710330641,"expires_at":1710331240,"cancelled_at":null,"failed_at":null,"completed_at":null,"required_action":null,"last_error":null,"model":"gpt-4-turbo","instructions":null,"tools":[],"metadata":{},"temperature":1.0,"top_p":1.0,"max_completion_tokens":null,"max_prompt_tokens":null,"truncation_strategy":{"type":"auto","last_messages":null},"incomplete_details":null,"usage":null,"response_format":"auto","tool_choice":"auto"}} event: thread.run.step.created data: {"id":"step_001","object":"thread.run.step","created_at":1710330641,"run_id":"run_123","assistant_id":"asst_123","thread_id":"thread_123","type":"message_creation","status":"in_progress","cancelled_at":null,"completed_at":null,"expires_at":1710331240,"failed_at":null,"last_error":null,"step_details":{"type":"message_creation","message_creation":{"message_id":"msg_001"}},"usage":null} event: thread.run.step.in_progress data: {"id":"step_001","object":"thread.run.step","created_at":1710330641,"run_id":"run_123","assistant_id":"asst_123","thread_id":"thread_123","type":"message_creation","status":"in_progress","cancelled_at":null,"completed_at":null,"expires_at":1710331240,"failed_at":null,"last_error":null,"step_details":{"type":"message_creation","message_creation":{"message_id":"msg_001"}},"usage":null} event: thread.message.created data: {"id":"msg_001","object":"thread.message","created_at":1710330641,"assistant_id":"asst_123","thread_id":"thread_123","run_id":"run_123","status":"in_progress","incomplete_details":null,"incomplete_at":null,"completed_at":null,"role":"assistant","content":[],"metadata":{}} event: thread.message.in_progress data: {"id":"msg_001","object":"thread.message","created_at":1710330641,"assistant_id":"asst_123","thread_id":"thread_123","run_id":"run_123","status":"in_progress","incomplete_details":null,"incomplete_at":null,"completed_at":null,"role":"assistant","content":[],"metadata":{}} event: thread.message.delta data: {"id":"msg_001","object":"thread.message.delta","delta":{"content":[{"index":0,"type":"text","text":{"value":"Hello","annotations":[]}}]}} ... event: thread.message.delta data: {"id":"msg_001","object":"thread.message.delta","delta":{"content":[{"index":0,"type":"text","text":{"value":" today"}}]}} event: thread.message.delta data: {"id":"msg_001","object":"thread.message.delta","delta":{"content":[{"index":0,"type":"text","text":{"value":"?"}}]}} event: thread.message.completed data: {"id":"msg_001","object":"thread.message","created_at":1710330641,"assistant_id":"asst_123","thread_id":"thread_123","run_id":"run_123","status":"completed","incomplete_details":null,"incomplete_at":null,"completed_at":1710330642,"role":"assistant","content":[{"type":"text","text":{"value":"Hello! How can I assist you today?","annotations":[]}}],"metadata":{}} event: thread.run.step.completed data: {"id":"step_001","object":"thread.run.step","created_at":1710330641,"run_id":"run_123","assistant_id":"asst_123","thread_id":"thread_123","type":"message_creation","status":"completed","cancelled_at":null,"completed_at":1710330642,"expires_at":1710331240,"failed_at":null,"last_error":null,"step_details":{"type":"message_creation","message_creation":{"message_id":"msg_001"}},"usage":{"prompt_tokens":20,"completion_tokens":11,"total_tokens":31}} event: thread.run.completed data: {"id":"run_123","object":"thread.run","created_at":1710330640,"assistant_id":"asst_123","thread_id":"thread_123","status":"completed","started_at":1710330641,"expires_at":null,"cancelled_at":null,"failed_at":null,"completed_at":1710330642,"required_action":null,"last_error":null,"model":"gpt-4-turbo","instructions":null,"tools":[],"metadata":{},"temperature":1.0,"top_p":1.0,"max_completion_tokens":null,"max_prompt_tokens":null,"truncation_strategy":{"type":"auto","last_messages":null},"incomplete_details":null,"usage":{"prompt_tokens":20,"completion_tokens":11,"total_tokens":31},"response_format":"auto","tool_choice":"auto"}} event: done data: [DONE] - title: Streaming with Functions request: curl: | curl https://api.openai.com/v1/threads/thread_abc123/runs \ -H "Authorization: Bearer $OPENAI_API_KEY" \ -H "Content-Type: application/json" \ -H "OpenAI-Beta: assistants=v2" \ -d '{ "assistant_id": "asst_abc123", "tools": [ { "type": "function", "function": { "name": "get_current_weather", "description": "Get the current weather in a given location", "parameters": { "type": "object", "properties": { "location": { "type": "string", "description": "The city and state, e.g. San Francisco, CA" }, "unit": { "type": "string", "enum": ["celsius", "fahrenheit"] } }, "required": ["location"] } } } ], "stream": true }' python: | from openai import OpenAI client = OpenAI() tools = [ { "type": "function", "function": { "name": "get_current_weather", "description": "Get the current weather in a given location", "parameters": { "type": "object", "properties": { "location": { "type": "string", "description": "The city and state, e.g. San Francisco, CA", }, "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}, }, "required": ["location"], }, } } ] stream = client.beta.threads.runs.create( thread_id="thread_abc123", assistant_id="asst_abc123", tools=tools, stream=True ) for event in stream: print(event) node.js: | import OpenAI from "openai"; const openai = new OpenAI(); const tools = [ { "type": "function", "function": { "name": "get_current_weather", "description": "Get the current weather in a given location", "parameters": { "type": "object", "properties": { "location": { "type": "string", "description": "The city and state, e.g. San Francisco, CA", }, "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}, }, "required": ["location"], }, } } ]; async function main() { const stream = await openai.beta.threads.runs.create( "thread_abc123", { assistant_id: "asst_abc123", tools: tools, stream: true } ); for await (const event of stream) { console.log(event); } } main(); response: | event: thread.run.created data: {"id":"run_123","object":"thread.run","created_at":1710348075,"assistant_id":"asst_123","thread_id":"thread_123","status":"queued","started_at":null,"expires_at":1710348675,"cancelled_at":null,"failed_at":null,"completed_at":null,"required_action":null,"last_error":null,"model":"gpt-4-turbo","instructions":null,"tools":[],"metadata":{},"temperature":1.0,"top_p":1.0,"max_completion_tokens":null,"max_prompt_tokens":null,"truncation_strategy":{"type":"auto","last_messages":null},"incomplete_details":null,"usage":null,"response_format":"auto","tool_choice":"auto"}} event: thread.run.queued data: {"id":"run_123","object":"thread.run","created_at":1710348075,"assistant_id":"asst_123","thread_id":"thread_123","status":"queued","started_at":null,"expires_at":1710348675,"cancelled_at":null,"failed_at":null,"completed_at":null,"required_action":null,"last_error":null,"model":"gpt-4-turbo","instructions":null,"tools":[],"metadata":{},"temperature":1.0,"top_p":1.0,"max_completion_tokens":null,"max_prompt_tokens":null,"truncation_strategy":{"type":"auto","last_messages":null},"incomplete_details":null,"usage":null,"response_format":"auto","tool_choice":"auto"}} event: thread.run.in_progress data: {"id":"run_123","object":"thread.run","created_at":1710348075,"assistant_id":"asst_123","thread_id":"thread_123","status":"in_progress","started_at":1710348075,"expires_at":1710348675,"cancelled_at":null,"failed_at":null,"completed_at":null,"required_action":null,"last_error":null,"model":"gpt-4-turbo","instructions":null,"tools":[],"metadata":{},"temperature":1.0,"top_p":1.0,"max_completion_tokens":null,"max_prompt_tokens":null,"truncation_strategy":{"type":"auto","last_messages":null},"incomplete_details":null,"usage":null,"response_format":"auto","tool_choice":"auto"}} event: thread.run.step.created data: {"id":"step_001","object":"thread.run.step","created_at":1710348076,"run_id":"run_123","assistant_id":"asst_123","thread_id":"thread_123","type":"message_creation","status":"in_progress","cancelled_at":null,"completed_at":null,"expires_at":1710348675,"failed_at":null,"last_error":null,"step_details":{"type":"message_creation","message_creation":{"message_id":"msg_001"}},"usage":null} event: thread.run.step.in_progress data: {"id":"step_001","object":"thread.run.step","created_at":1710348076,"run_id":"run_123","assistant_id":"asst_123","thread_id":"thread_123","type":"message_creation","status":"in_progress","cancelled_at":null,"completed_at":null,"expires_at":1710348675,"failed_at":null,"last_error":null,"step_details":{"type":"message_creation","message_creation":{"message_id":"msg_001"}},"usage":null} event: thread.message.created data: {"id":"msg_001","object":"thread.message","created_at":1710348076,"assistant_id":"asst_123","thread_id":"thread_123","run_id":"run_123","status":"in_progress","incomplete_details":null,"incomplete_at":null,"completed_at":null,"role":"assistant","content":[],"metadata":{}} event: thread.message.in_progress data: {"id":"msg_001","object":"thread.message","created_at":1710348076,"assistant_id":"asst_123","thread_id":"thread_123","run_id":"run_123","status":"in_progress","incomplete_details":null,"incomplete_at":null,"completed_at":null,"role":"assistant","content":[],"metadata":{}} event: thread.message.delta data: {"id":"msg_001","object":"thread.message.delta","delta":{"content":[{"index":0,"type":"text","text":{"value":"Hello","annotations":[]}}]}} ... event: thread.message.delta data: {"id":"msg_001","object":"thread.message.delta","delta":{"content":[{"index":0,"type":"text","text":{"value":" today"}}]}} event: thread.message.delta data: {"id":"msg_001","object":"thread.message.delta","delta":{"content":[{"index":0,"type":"text","text":{"value":"?"}}]}} event: thread.message.completed data: {"id":"msg_001","object":"thread.message","created_at":1710348076,"assistant_id":"asst_123","thread_id":"thread_123","run_id":"run_123","status":"completed","incomplete_details":null,"incomplete_at":null,"completed_at":1710348077,"role":"assistant","content":[{"type":"text","text":{"value":"Hello! How can I assist you today?","annotations":[]}}],"metadata":{}} event: thread.run.step.completed data: {"id":"step_001","object":"thread.run.step","created_at":1710348076,"run_id":"run_123","assistant_id":"asst_123","thread_id":"thread_123","type":"message_creation","status":"completed","cancelled_at":null,"completed_at":1710348077,"expires_at":1710348675,"failed_at":null,"last_error":null,"step_details":{"type":"message_creation","message_creation":{"message_id":"msg_001"}},"usage":{"prompt_tokens":20,"completion_tokens":11,"total_tokens":31}} event: thread.run.completed data: {"id":"run_123","object":"thread.run","created_at":1710348075,"assistant_id":"asst_123","thread_id":"thread_123","status":"completed","started_at":1710348075,"expires_at":null,"cancelled_at":null,"failed_at":null,"completed_at":1710348077,"required_action":null,"last_error":null,"model":"gpt-4-turbo","instructions":null,"tools":[],"metadata":{},"temperature":1.0,"top_p":1.0,"max_completion_tokens":null,"max_prompt_tokens":null,"truncation_strategy":{"type":"auto","last_messages":null},"incomplete_details":null,"usage":{"prompt_tokens":20,"completion_tokens":11,"total_tokens":31},"response_format":"auto","tool_choice":"auto"}} event: done data: [DONE] /threads/{thread_id}/runs/{run_id}: get: operationId: getRun tags: - Assistants summary: Retrieves a run. parameters: - in: path name: thread_id required: true schema: type: string description: The ID of the [thread](/docs/api-reference/threads) that was run. - in: path name: run_id required: true schema: type: string description: The ID of the run to retrieve. responses: "200": description: OK content: application/json: schema: $ref: "#/components/schemas/RunObject" x-oaiMeta: name: Retrieve run group: threads beta: true returns: The [run](/docs/api-reference/runs/object) object matching the specified ID. examples: request: curl: | curl https://api.openai.com/v1/threads/thread_abc123/runs/run_abc123 \ -H "Authorization: Bearer $OPENAI_API_KEY" \ -H "OpenAI-Beta: assistants=v2" python: | from openai import OpenAI client = OpenAI() run = client.beta.threads.runs.retrieve( thread_id="thread_abc123", run_id="run_abc123" ) print(run) node.js: | import OpenAI from "openai"; const openai = new OpenAI(); async function main() { const run = await openai.beta.threads.runs.retrieve( "thread_abc123", "run_abc123" ); console.log(run); } main(); response: | { "id": "run_abc123", "object": "thread.run", "created_at": 1699075072, "assistant_id": "asst_abc123", "thread_id": "thread_abc123", "status": "completed", "started_at": 1699075072, "expires_at": null, "cancelled_at": null, "failed_at": null, "completed_at": 1699075073, "last_error": null, "model": "gpt-4-turbo", "instructions": null, "incomplete_details": null, "tools": [ { "type": "code_interpreter" } ], "metadata": {}, "usage": { "prompt_tokens": 123, "completion_tokens": 456, "total_tokens": 579 }, "temperature": 1.0, "top_p": 1.0, "max_prompt_tokens": 1000, "max_completion_tokens": 1000, "truncation_strategy": { "type": "auto", "last_messages": null }, "response_format": "auto", "tool_choice": "auto" } post: operationId: modifyRun tags: - Assistants summary: Modifies a run. parameters: - in: path name: thread_id required: true schema: type: string description: The ID of the [thread](/docs/api-reference/threads) that was run. - in: path name: run_id required: true schema: type: string description: The ID of the run to modify. requestBody: required: true content: application/json: schema: $ref: "#/components/schemas/ModifyRunRequest" responses: "200": description: OK content: application/json: schema: $ref: "#/components/schemas/RunObject" x-oaiMeta: name: Modify run group: threads beta: true returns: The modified [run](/docs/api-reference/runs/object) object matching the specified ID. examples: request: curl: | curl https://api.openai.com/v1/threads/thread_abc123/runs/run_abc123 \ -H "Authorization: Bearer $OPENAI_API_KEY" \ -H "Content-Type: application/json" \ -H "OpenAI-Beta: assistants=v2" \ -d '{ "metadata": { "user_id": "user_abc123" } }' python: | from openai import OpenAI client = OpenAI() run = client.beta.threads.runs.update( thread_id="thread_abc123", run_id="run_abc123", metadata={"user_id": "user_abc123"}, ) print(run) node.js: | import OpenAI from "openai"; const openai = new OpenAI(); async function main() { const run = await openai.beta.threads.runs.update( "thread_abc123", "run_abc123", { metadata: { user_id: "user_abc123", }, } ); console.log(run); } main(); response: | { "id": "run_abc123", "object": "thread.run", "created_at": 1699075072, "assistant_id": "asst_abc123", "thread_id": "thread_abc123", "status": "completed", "started_at": 1699075072, "expires_at": null, "cancelled_at": null, "failed_at": null, "completed_at": 1699075073, "last_error": null, "model": "gpt-4-turbo", "instructions": null, "incomplete_details": null, "tools": [ { "type": "code_interpreter" } ], "tool_resources": { "code_interpreter": { "file_ids": [ "file-abc123", "file-abc456" ] } }, "metadata": { "user_id": "user_abc123" }, "usage": { "prompt_tokens": 123, "completion_tokens": 456, "total_tokens": 579 }, "temperature": 1.0, "top_p": 1.0, "max_prompt_tokens": 1000, "max_completion_tokens": 1000, "truncation_strategy": { "type": "auto", "last_messages": null }, "response_format": "auto", "tool_choice": "auto" } /threads/{thread_id}/runs/{run_id}/submit_tool_outputs: post: operationId: submitToolOuputsToRun tags: - Assistants summary: | When a run has the `status: "requires_action"` and `required_action.type` is `submit_tool_outputs`, this endpoint can be used to submit the outputs from the tool calls once they're all completed. All outputs must be submitted in a single request. parameters: - in: path name: thread_id required: true schema: type: string description: The ID of the [thread](/docs/api-reference/threads) to which this run belongs. - in: path name: run_id required: true schema: type: string description: The ID of the run that requires the tool output submission. requestBody: required: true content: application/json: schema: $ref: "#/components/schemas/SubmitToolOutputsRunRequest" responses: "200": description: OK content: application/json: schema: $ref: "#/components/schemas/RunObject" x-oaiMeta: name: Submit tool outputs to run group: threads beta: true returns: The modified [run](/docs/api-reference/runs/object) object matching the specified ID. examples: - title: Default request: curl: | curl https://api.openai.com/v1/threads/thread_123/runs/run_123/submit_tool_outputs \ -H "Authorization: Bearer $OPENAI_API_KEY" \ -H "Content-Type: application/json" \ -H "OpenAI-Beta: assistants=v2" \ -d '{ "tool_outputs": [ { "tool_call_id": "call_001", "output": "70 degrees and sunny." } ] }' python: | from openai import OpenAI client = OpenAI() run = client.beta.threads.runs.submit_tool_outputs( thread_id="thread_123", run_id="run_123", tool_outputs=[ { "tool_call_id": "call_001", "output": "70 degrees and sunny." } ] ) print(run) node.js: | import OpenAI from "openai"; const openai = new OpenAI(); async function main() { const run = await openai.beta.threads.runs.submitToolOutputs( "thread_123", "run_123", { tool_outputs: [ { tool_call_id: "call_001", output: "70 degrees and sunny.", }, ], } ); console.log(run); } main(); response: | { "id": "run_123", "object": "thread.run", "created_at": 1699075592, "assistant_id": "asst_123", "thread_id": "thread_123", "status": "queued", "started_at": 1699075592, "expires_at": 1699076192, "cancelled_at": null, "failed_at": null, "completed_at": null, "last_error": null, "model": "gpt-4-turbo", "instructions": null, "tools": [ { "type": "function", "function": { "name": "get_current_weather", "description": "Get the current weather in a given location", "parameters": { "type": "object", "properties": { "location": { "type": "string", "description": "The city and state, e.g. San Francisco, CA" }, "unit": { "type": "string", "enum": ["celsius", "fahrenheit"] } }, "required": ["location"] } } } ], "metadata": {}, "usage": null, "temperature": 1.0, "top_p": 1.0, "max_prompt_tokens": 1000, "max_completion_tokens": 1000, "truncation_strategy": { "type": "auto", "last_messages": null }, "response_format": "auto", "tool_choice": "auto" } - title: Streaming request: curl: | curl https://api.openai.com/v1/threads/thread_123/runs/run_123/submit_tool_outputs \ -H "Authorization: Bearer $OPENAI_API_KEY" \ -H "Content-Type: application/json" \ -H "OpenAI-Beta: assistants=v2" \ -d '{ "tool_outputs": [ { "tool_call_id": "call_001", "output": "70 degrees and sunny." } ], "stream": true }' python: | from openai import OpenAI client = OpenAI() stream = client.beta.threads.runs.submit_tool_outputs( thread_id="thread_123", run_id="run_123", tool_outputs=[ { "tool_call_id": "call_001", "output": "70 degrees and sunny." } ], stream=True ) for event in stream: print(event) node.js: | import OpenAI from "openai"; const openai = new OpenAI(); async function main() { const stream = await openai.beta.threads.runs.submitToolOutputs( "thread_123", "run_123", { tool_outputs: [ { tool_call_id: "call_001", output: "70 degrees and sunny.", }, ], } ); for await (const event of stream) { console.log(event); } } main(); response: | event: thread.run.step.completed data: {"id":"step_001","object":"thread.run.step","created_at":1710352449,"run_id":"run_123","assistant_id":"asst_123","thread_id":"thread_123","type":"tool_calls","status":"completed","cancelled_at":null,"completed_at":1710352475,"expires_at":1710353047,"failed_at":null,"last_error":null,"step_details":{"type":"tool_calls","tool_calls":[{"id":"call_iWr0kQ2EaYMaxNdl0v3KYkx7","type":"function","function":{"name":"get_current_weather","arguments":"{\"location\":\"San Francisco, CA\",\"unit\":\"fahrenheit\"}","output":"70 degrees and sunny."}}]},"usage":{"prompt_tokens":291,"completion_tokens":24,"total_tokens":315}} event: thread.run.queued data: {"id":"run_123","object":"thread.run","created_at":1710352447,"assistant_id":"asst_123","thread_id":"thread_123","status":"queued","started_at":1710352448,"expires_at":1710353047,"cancelled_at":null,"failed_at":null,"completed_at":null,"required_action":null,"last_error":null,"model":"gpt-4-turbo","instructions":null,"tools":[{"type":"function","function":{"name":"get_current_weather","description":"Get the current weather in a given location","parameters":{"type":"object","properties":{"location":{"type":"string","description":"The city and state, e.g. San Francisco, CA"},"unit":{"type":"string","enum":["celsius","fahrenheit"]}},"required":["location"]}}}],"metadata":{},"temperature":1.0,"top_p":1.0,"max_completion_tokens":null,"max_prompt_tokens":null,"truncation_strategy":{"type":"auto","last_messages":null},"incomplete_details":null,"usage":null,"response_format":"auto","tool_choice":"auto"}} event: thread.run.in_progress data: {"id":"run_123","object":"thread.run","created_at":1710352447,"assistant_id":"asst_123","thread_id":"thread_123","status":"in_progress","started_at":1710352475,"expires_at":1710353047,"cancelled_at":null,"failed_at":null,"completed_at":null,"required_action":null,"last_error":null,"model":"gpt-4-turbo","instructions":null,"tools":[{"type":"function","function":{"name":"get_current_weather","description":"Get the current weather in a given location","parameters":{"type":"object","properties":{"location":{"type":"string","description":"The city and state, e.g. San Francisco, CA"},"unit":{"type":"string","enum":["celsius","fahrenheit"]}},"required":["location"]}}}],"metadata":{},"temperature":1.0,"top_p":1.0,"max_completion_tokens":null,"max_prompt_tokens":null,"truncation_strategy":{"type":"auto","last_messages":null},"incomplete_details":null,"usage":null,"response_format":"auto","tool_choice":"auto"}} event: thread.run.step.created data: {"id":"step_002","object":"thread.run.step","created_at":1710352476,"run_id":"run_123","assistant_id":"asst_123","thread_id":"thread_123","type":"message_creation","status":"in_progress","cancelled_at":null,"completed_at":null,"expires_at":1710353047,"failed_at":null,"last_error":null,"step_details":{"type":"message_creation","message_creation":{"message_id":"msg_002"}},"usage":null} event: thread.run.step.in_progress data: {"id":"step_002","object":"thread.run.step","created_at":1710352476,"run_id":"run_123","assistant_id":"asst_123","thread_id":"thread_123","type":"message_creation","status":"in_progress","cancelled_at":null,"completed_at":null,"expires_at":1710353047,"failed_at":null,"last_error":null,"step_details":{"type":"message_creation","message_creation":{"message_id":"msg_002"}},"usage":null} event: thread.message.created data: {"id":"msg_002","object":"thread.message","created_at":1710352476,"assistant_id":"asst_123","thread_id":"thread_123","run_id":"run_123","status":"in_progress","incomplete_details":null,"incomplete_at":null,"completed_at":null,"role":"assistant","content":[],"metadata":{}} event: thread.message.in_progress data: {"id":"msg_002","object":"thread.message","created_at":1710352476,"assistant_id":"asst_123","thread_id":"thread_123","run_id":"run_123","status":"in_progress","incomplete_details":null,"incomplete_at":null,"completed_at":null,"role":"assistant","content":[],"metadata":{}} event: thread.message.delta data: {"id":"msg_002","object":"thread.message.delta","delta":{"content":[{"index":0,"type":"text","text":{"value":"The","annotations":[]}}]}} event: thread.message.delta data: {"id":"msg_002","object":"thread.message.delta","delta":{"content":[{"index":0,"type":"text","text":{"value":" current"}}]}} event: thread.message.delta data: {"id":"msg_002","object":"thread.message.delta","delta":{"content":[{"index":0,"type":"text","text":{"value":" weather"}}]}} ... event: thread.message.delta data: {"id":"msg_002","object":"thread.message.delta","delta":{"content":[{"index":0,"type":"text","text":{"value":" sunny"}}]}} event: thread.message.delta data: {"id":"msg_002","object":"thread.message.delta","delta":{"content":[{"index":0,"type":"text","text":{"value":"."}}]}} event: thread.message.completed data: {"id":"msg_002","object":"thread.message","created_at":1710352476,"assistant_id":"asst_123","thread_id":"thread_123","run_id":"run_123","status":"completed","incomplete_details":null,"incomplete_at":null,"completed_at":1710352477,"role":"assistant","content":[{"type":"text","text":{"value":"The current weather in San Francisco, CA is 70 degrees Fahrenheit and sunny.","annotations":[]}}],"metadata":{}} event: thread.run.step.completed data: {"id":"step_002","object":"thread.run.step","created_at":1710352476,"run_id":"run_123","assistant_id":"asst_123","thread_id":"thread_123","type":"message_creation","status":"completed","cancelled_at":null,"completed_at":1710352477,"expires_at":1710353047,"failed_at":null,"last_error":null,"step_details":{"type":"message_creation","message_creation":{"message_id":"msg_002"}},"usage":{"prompt_tokens":329,"completion_tokens":18,"total_tokens":347}} event: thread.run.completed data: {"id":"run_123","object":"thread.run","created_at":1710352447,"assistant_id":"asst_123","thread_id":"thread_123","status":"completed","started_at":1710352475,"expires_at":null,"cancelled_at":null,"failed_at":null,"completed_at":1710352477,"required_action":null,"last_error":null,"model":"gpt-4-turbo","instructions":null,"tools":[{"type":"function","function":{"name":"get_current_weather","description":"Get the current weather in a given location","parameters":{"type":"object","properties":{"location":{"type":"string","description":"The city and state, e.g. San Francisco, CA"},"unit":{"type":"string","enum":["celsius","fahrenheit"]}},"required":["location"]}}}],"metadata":{},"temperature":1.0,"top_p":1.0,"max_completion_tokens":null,"max_prompt_tokens":null,"truncation_strategy":{"type":"auto","last_messages":null},"incomplete_details":null,"usage":{"prompt_tokens":20,"completion_tokens":11,"total_tokens":31},"response_format":"auto","tool_choice":"auto"}} event: done data: [DONE] /threads/{thread_id}/runs/{run_id}/cancel: post: operationId: cancelRun tags: - Assistants summary: Cancels a run that is `in_progress`. parameters: - in: path name: thread_id required: true schema: type: string description: The ID of the thread to which this run belongs. - in: path name: run_id required: true schema: type: string description: The ID of the run to cancel. responses: "200": description: OK content: application/json: schema: $ref: "#/components/schemas/RunObject" x-oaiMeta: name: Cancel a run group: threads beta: true returns: The modified [run](/docs/api-reference/runs/object) object matching the specified ID. examples: request: curl: | curl https://api.openai.com/v1/threads/thread_abc123/runs/run_abc123/cancel \ -H "Authorization: Bearer $OPENAI_API_KEY" \ -H "OpenAI-Beta: assistants=v2" \ -X POST python: | from openai import OpenAI client = OpenAI() run = client.beta.threads.runs.cancel( thread_id="thread_abc123", run_id="run_abc123" ) print(run) node.js: | import OpenAI from "openai"; const openai = new OpenAI(); async function main() { const run = await openai.beta.threads.runs.cancel( "thread_abc123", "run_abc123" ); console.log(run); } main(); response: | { "id": "run_abc123", "object": "thread.run", "created_at": 1699076126, "assistant_id": "asst_abc123", "thread_id": "thread_abc123", "status": "cancelling", "started_at": 1699076126, "expires_at": 1699076726, "cancelled_at": null, "failed_at": null, "completed_at": null, "last_error": null, "model": "gpt-4-turbo", "instructions": "You summarize books.", "tools": [ { "type": "file_search" } ], "tool_resources": { "file_search": { "vector_store_ids": ["vs_123"] } }, "metadata": {}, "usage": null, "temperature": 1.0, "top_p": 1.0, "response_format": "auto" } /threads/{thread_id}/runs/{run_id}/steps: get: operationId: listRunSteps tags: - Assistants summary: Returns a list of run steps belonging to a run. parameters: - name: thread_id in: path required: true schema: type: string description: The ID of the thread the run and run steps belong to. - name: run_id in: path required: true schema: type: string description: The ID of the run the run steps belong to. - name: limit in: query description: *pagination_limit_param_description required: false schema: type: integer default: 20 - name: order in: query description: *pagination_order_param_description schema: type: string default: desc enum: ["asc", "desc"] - name: after in: query description: *pagination_after_param_description schema: type: string - name: before in: query description: *pagination_before_param_description schema: type: string responses: "200": description: OK content: application/json: schema: $ref: "#/components/schemas/ListRunStepsResponse" x-oaiMeta: name: List run steps group: threads beta: true returns: A list of [run step](/docs/api-reference/runs/step-object) objects. examples: request: curl: | curl https://api.openai.com/v1/threads/thread_abc123/runs/run_abc123/steps \ -H "Authorization: Bearer $OPENAI_API_KEY" \ -H "Content-Type: application/json" \ -H "OpenAI-Beta: assistants=v2" python: | from openai import OpenAI client = OpenAI() run_steps = client.beta.threads.runs.steps.list( thread_id="thread_abc123", run_id="run_abc123" ) print(run_steps) node.js: | import OpenAI from "openai"; const openai = new OpenAI(); async function main() { const runStep = await openai.beta.threads.runs.steps.list( "thread_abc123", "run_abc123" ); console.log(runStep); } main(); response: | { "object": "list", "data": [ { "id": "step_abc123", "object": "thread.run.step", "created_at": 1699063291, "run_id": "run_abc123", "assistant_id": "asst_abc123", "thread_id": "thread_abc123", "type": "message_creation", "status": "completed", "cancelled_at": null, "completed_at": 1699063291, "expired_at": null, "failed_at": null, "last_error": null, "step_details": { "type": "message_creation", "message_creation": { "message_id": "msg_abc123" } }, "usage": { "prompt_tokens": 123, "completion_tokens": 456, "total_tokens": 579 } } ], "first_id": "step_abc123", "last_id": "step_abc456", "has_more": false } /threads/{thread_id}/runs/{run_id}/steps/{step_id}: get: operationId: getRunStep tags: - Assistants summary: Retrieves a run step. parameters: - in: path name: thread_id required: true schema: type: string description: The ID of the thread to which the run and run step belongs. - in: path name: run_id required: true schema: type: string description: The ID of the run to which the run step belongs. - in: path name: step_id required: true schema: type: string description: The ID of the run step to retrieve. responses: "200": description: OK content: application/json: schema: $ref: "#/components/schemas/RunStepObject" x-oaiMeta: name: Retrieve run step group: threads beta: true returns: The [run step](/docs/api-reference/runs/step-object) object matching the specified ID. examples: request: curl: | curl https://api.openai.com/v1/threads/thread_abc123/runs/run_abc123/steps/step_abc123 \ -H "Authorization: Bearer $OPENAI_API_KEY" \ -H "Content-Type: application/json" \ -H "OpenAI-Beta: assistants=v2" python: | from openai import OpenAI client = OpenAI() run_step = client.beta.threads.runs.steps.retrieve( thread_id="thread_abc123", run_id="run_abc123", step_id="step_abc123" ) print(run_step) node.js: | import OpenAI from "openai"; const openai = new OpenAI(); async function main() { const runStep = await openai.beta.threads.runs.steps.retrieve( "thread_abc123", "run_abc123", "step_abc123" ); console.log(runStep); } main(); response: &run_step_object_example | { "id": "step_abc123", "object": "thread.run.step", "created_at": 1699063291, "run_id": "run_abc123", "assistant_id": "asst_abc123", "thread_id": "thread_abc123", "type": "message_creation", "status": "completed", "cancelled_at": null, "completed_at": 1699063291, "expired_at": null, "failed_at": null, "last_error": null, "step_details": { "type": "message_creation", "message_creation": { "message_id": "msg_abc123" } }, "usage": { "prompt_tokens": 123, "completion_tokens": 456, "total_tokens": 579 } } /vector_stores: get: operationId: listVectorStores tags: - Vector Stores summary: Returns a list of vector stores. parameters: - name: limit in: query description: *pagination_limit_param_description required: false schema: type: integer default: 20 - name: order in: query description: *pagination_order_param_description schema: type: string default: desc enum: ["asc", "desc"] - name: after in: query description: *pagination_after_param_description schema: type: string - name: before in: query description: *pagination_before_param_description schema: type: string responses: "200": description: OK content: application/json: schema: $ref: "#/components/schemas/ListVectorStoresResponse" x-oaiMeta: name: List vector stores group: vector_stores beta: true returns: A list of [vector store](/docs/api-reference/vector-stores/object) objects. examples: request: curl: | curl https://api.openai.com/v1/vector_stores \ -H "Authorization: Bearer $OPENAI_API_KEY" \ -H "Content-Type: application/json" \ -H "OpenAI-Beta: assistants=v2" python: | from openai import OpenAI client = OpenAI() vector_stores = client.beta.vector_stores.list() print(vector_stores) node.js: | import OpenAI from "openai"; const openai = new OpenAI(); async function main() { const vectorStores = await openai.beta.vectorStores.list(); console.log(vectorStores); } main(); response: | { "object": "list", "data": [ { "id": "vs_abc123", "object": "vector_store", "created_at": 1699061776, "name": "Support FAQ", "bytes": 139920, "file_counts": { "in_progress": 0, "completed": 3, "failed": 0, "cancelled": 0, "total": 3 } }, { "id": "vs_abc456", "object": "vector_store", "created_at": 1699061776, "name": "Support FAQ v2", "bytes": 139920, "file_counts": { "in_progress": 0, "completed": 3, "failed": 0, "cancelled": 0, "total": 3 } } ], "first_id": "vs_abc123", "last_id": "vs_abc456", "has_more": false } post: operationId: createVectorStore tags: - Vector Stores summary: Create a vector store. requestBody: required: true content: application/json: schema: $ref: "#/components/schemas/CreateVectorStoreRequest" responses: "200": description: OK content: application/json: schema: $ref: "#/components/schemas/VectorStoreObject" x-oaiMeta: name: Create vector store group: vector_stores beta: true returns: A [vector store](/docs/api-reference/vector-stores/object) object. examples: request: curl: | curl https://api.openai.com/v1/vector_stores \ -H "Authorization: Bearer $OPENAI_API_KEY" \ -H "Content-Type: application/json" \ -H "OpenAI-Beta: assistants=v2" -d '{ "name": "Support FAQ" }' python: | from openai import OpenAI client = OpenAI() vector_store = client.beta.vector_stores.create( name="Support FAQ" ) print(vector_store) node.js: | import OpenAI from "openai"; const openai = new OpenAI(); async function main() { const vectorStore = await openai.beta.vectorStores.create({ name: "Support FAQ" }); console.log(vectorStore); } main(); response: | { "id": "vs_abc123", "object": "vector_store", "created_at": 1699061776, "name": "Support FAQ", "bytes": 139920, "file_counts": { "in_progress": 0, "completed": 3, "failed": 0, "cancelled": 0, "total": 3 } } /vector_stores/{vector_store_id}: get: operationId: getVectorStore tags: - Vector Stores summary: Retrieves a vector store. parameters: - in: path name: vector_store_id required: true schema: type: string description: The ID of the vector store to retrieve. responses: "200": description: OK content: application/json: schema: $ref: "#/components/schemas/VectorStoreObject" x-oaiMeta: name: Retrieve vector store group: vector_stores beta: true returns: The [vector store](/docs/api-reference/vector-stores/object) object matching the specified ID. examples: request: curl: | curl https://api.openai.com/v1/vector_stores/vs_abc123 \ -H "Authorization: Bearer $OPENAI_API_KEY" \ -H "Content-Type: application/json" \ -H "OpenAI-Beta: assistants=v2" python: | from openai import OpenAI client = OpenAI() vector_store = client.beta.vector_stores.retrieve( vector_store_id="vs_abc123" ) print(vector_store) node.js: | import OpenAI from "openai"; const openai = new OpenAI(); async function main() { const vectorStore = await openai.beta.vectorStores.retrieve( "vs_abc123" ); console.log(vectorStore); } main(); response: | { "id": "vs_abc123", "object": "vector_store", "created_at": 1699061776 } post: operationId: modifyVectorStore tags: - Vector Stores summary: Modifies a vector store. parameters: - in: path name: vector_store_id required: true schema: type: string description: The ID of the vector store to modify. requestBody: required: true content: application/json: schema: $ref: "#/components/schemas/UpdateVectorStoreRequest" responses: "200": description: OK content: application/json: schema: $ref: "#/components/schemas/VectorStoreObject" x-oaiMeta: name: Modify vector store group: vector_stores beta: true returns: The modified [vector store](/docs/api-reference/vector-stores/object) object. examples: request: curl: | curl https://api.openai.com/v1/vector_stores/vs_abc123 \ -H "Authorization: Bearer $OPENAI_API_KEY" \ -H "Content-Type: application/json" \ -H "OpenAI-Beta: assistants=v2" -d '{ "name": "Support FAQ" }' python: | from openai import OpenAI client = OpenAI() vector_store = client.beta.vector_stores.update( vector_store_id="vs_abc123", name="Support FAQ" ) print(vector_store) node.js: | import OpenAI from "openai"; const openai = new OpenAI(); async function main() { const vectorStore = await openai.beta.vectorStores.update( "vs_abc123", { name: "Support FAQ" } ); console.log(vectorStore); } main(); response: | { "id": "vs_abc123", "object": "vector_store", "created_at": 1699061776, "name": "Support FAQ", "bytes": 139920, "file_counts": { "in_progress": 0, "completed": 3, "failed": 0, "cancelled": 0, "total": 3 } } delete: operationId: deleteVectorStore tags: - Vector Stores summary: Delete a vector store. parameters: - in: path name: vector_store_id required: true schema: type: string description: The ID of the vector store to delete. responses: "200": description: OK content: application/json: schema: $ref: "#/components/schemas/DeleteVectorStoreResponse" x-oaiMeta: name: Delete vector store group: vector_stores beta: true returns: Deletion status examples: request: curl: | curl https://api.openai.com/v1/vector_stores/vs_abc123 \ -H "Authorization: Bearer $OPENAI_API_KEY" \ -H "Content-Type: application/json" \ -H "OpenAI-Beta: assistants=v2" \ -X DELETE python: | from openai import OpenAI client = OpenAI() deleted_vector_store = client.beta.vector_stores.delete( vector_store_id="vs_abc123" ) print(deleted_vector_store) node.js: | import OpenAI from "openai"; const openai = new OpenAI(); async function main() { const deletedVectorStore = await openai.beta.vectorStores.del( "vs_abc123" ); console.log(deletedVectorStore); } main(); response: | { id: "vs_abc123", object: "vector_store.deleted", deleted: true } /vector_stores/{vector_store_id}/files: get: operationId: listVectorStoreFiles tags: - Vector Stores summary: Returns a list of vector store files. parameters: - name: vector_store_id in: path description: The ID of the vector store that the files belong to. required: true schema: type: string - name: limit in: query description: *pagination_limit_param_description required: false schema: type: integer default: 20 - name: order in: query description: *pagination_order_param_description schema: type: string default: desc enum: ["asc", "desc"] - name: after in: query description: *pagination_after_param_description schema: type: string - name: before in: query description: *pagination_before_param_description schema: type: string - name: filter in: query description: "Filter by file status. One of `in_progress`, `completed`, `failed`, `cancelled`." schema: type: string enum: ["in_progress", "completed", "failed", "cancelled"] responses: "200": description: OK content: application/json: schema: $ref: "#/components/schemas/ListVectorStoreFilesResponse" x-oaiMeta: name: List vector store files group: vector_stores beta: true returns: A list of [vector store file](/docs/api-reference/vector-stores-files/file-object) objects. examples: request: curl: | curl https://api.openai.com/v1/vector_stores/vs_abc123/files \ -H "Authorization: Bearer $OPENAI_API_KEY" \ -H "Content-Type: application/json" \ -H "OpenAI-Beta: assistants=v2" python: | from openai import OpenAI client = OpenAI() vector_store_files = client.beta.vector_stores.files.list( vector_store_id="vs_abc123" ) print(vector_store_files) node.js: | import OpenAI from "openai"; const openai = new OpenAI(); async function main() { const vectorStoreFiles = await openai.beta.vectorStores.files.list( "vs_abc123" ); console.log(vectorStoreFiles); } main(); response: | { "object": "list", "data": [ { "id": "file-abc123", "object": "vector_store.file", "created_at": 1699061776, "vector_store_id": "vs_abc123" }, { "id": "file-abc456", "object": "vector_store.file", "created_at": 1699061776, "vector_store_id": "vs_abc123" } ], "first_id": "file-abc123", "last_id": "file-abc456", "has_more": false } post: operationId: createVectorStoreFile tags: - Vector Stores summary: Create a vector store file by attaching a [File](/docs/api-reference/files) to a [vector store](/docs/api-reference/vector-stores/object). parameters: - in: path name: vector_store_id required: true schema: type: string example: vs_abc123 description: | The ID of the vector store for which to create a File. requestBody: required: true content: application/json: schema: $ref: "#/components/schemas/CreateVectorStoreFileRequest" responses: "200": description: OK content: application/json: schema: $ref: "#/components/schemas/VectorStoreFileObject" x-oaiMeta: name: Create vector store file group: vector_stores beta: true returns: A [vector store file](/docs/api-reference/vector-stores-files/file-object) object. examples: request: curl: | curl https://api.openai.com/v1/vector_stores/vs_abc123/files \ -H "Authorization: Bearer $OPENAI_API_KEY" \ -H "Content-Type: application/json" \ -H "OpenAI-Beta: assistants=v2" \ -d '{ "file_id": "file-abc123" }' python: | from openai import OpenAI client = OpenAI() vector_store_file = client.beta.vector_stores.files.create( vector_store_id="vs_abc123", file_id="file-abc123" ) print(vector_store_file) node.js: | import OpenAI from "openai"; const openai = new OpenAI(); async function main() { const myVectorStoreFile = await openai.beta.vectorStores.files.create( "vs_abc123", { file_id: "file-abc123" } ); console.log(myVectorStoreFile); } main(); response: | { "id": "file-abc123", "object": "vector_store.file", "created_at": 1699061776, "usage_bytes": 1234, "vector_store_id": "vs_abcd", "status": "completed", "last_error": null } /vector_stores/{vector_store_id}/files/{file_id}: get: operationId: getVectorStoreFile tags: - Vector Stores summary: Retrieves a vector store file. parameters: - in: path name: vector_store_id required: true schema: type: string example: vs_abc123 description: The ID of the vector store that the file belongs to. - in: path name: file_id required: true schema: type: string example: file-abc123 description: The ID of the file being retrieved. responses: "200": description: OK content: application/json: schema: $ref: "#/components/schemas/VectorStoreFileObject" x-oaiMeta: name: Retrieve vector store file group: vector_stores beta: true returns: The [vector store file](/docs/api-reference/vector-stores-files/file-object) object. examples: request: curl: | curl https://api.openai.com/v1/vector_stores/vs_abc123/files/file-abc123 \ -H "Authorization: Bearer $OPENAI_API_KEY" \ -H "Content-Type: application/json" \ -H "OpenAI-Beta: assistants=v2" python: | from openai import OpenAI client = OpenAI() vector_store_file = client.beta.vector_stores.files.retrieve( vector_store_id="vs_abc123", file_id="file-abc123" ) print(vector_store_file) node.js: | import OpenAI from "openai"; const openai = new OpenAI(); async function main() { const vectorStoreFile = await openai.beta.vectorStores.files.retrieve( "vs_abc123", "file-abc123" ); console.log(vectorStoreFile); } main(); response: | { "id": "file-abc123", "object": "vector_store.file", "created_at": 1699061776, "vector_store_id": "vs_abcd", "status": "completed", "last_error": null } delete: operationId: deleteVectorStoreFile tags: - Vector Stores summary: Delete a vector store file. This will remove the file from the vector store but the file itself will not be deleted. To delete the file, use the [delete file](/docs/api-reference/files/delete) endpoint. parameters: - in: path name: vector_store_id required: true schema: type: string description: The ID of the vector store that the file belongs to. - in: path name: file_id required: true schema: type: string description: The ID of the file to delete. responses: "200": description: OK content: application/json: schema: $ref: "#/components/schemas/DeleteVectorStoreFileResponse" x-oaiMeta: name: Delete vector store file group: vector_stores beta: true returns: Deletion status examples: request: curl: | curl https://api.openai.com/v1/vector_stores/vs_abc123/files/file-abc123 \ -H "Authorization: Bearer $OPENAI_API_KEY" \ -H "Content-Type: application/json" \ -H "OpenAI-Beta: assistants=v2" \ -X DELETE python: | from openai import OpenAI client = OpenAI() deleted_vector_store_file = client.beta.vector_stores.files.delete( vector_store_id="vs_abc123", file_id="file-abc123" ) print(deleted_vector_store_file) node.js: | import OpenAI from "openai"; const openai = new OpenAI(); async function main() { const deletedVectorStoreFile = await openai.beta.vectorStores.files.del( "vs_abc123", "file-abc123" ); console.log(deletedVectorStoreFile); } main(); response: | { id: "file-abc123", object: "vector_store.file.deleted", deleted: true } /vector_stores/{vector_store_id}/file_batches: post: operationId: createVectorStoreFileBatch tags: - Vector Stores summary: Create a vector store file batch. parameters: - in: path name: vector_store_id required: true schema: type: string example: vs_abc123 description: | The ID of the vector store for which to create a File Batch. requestBody: required: true content: application/json: schema: $ref: "#/components/schemas/CreateVectorStoreFileBatchRequest" responses: "200": description: OK content: application/json: schema: $ref: "#/components/schemas/VectorStoreFileBatchObject" x-oaiMeta: name: Create vector store file batch group: vector_stores beta: true returns: A [vector store file batch](/docs/api-reference/vector-stores-file-batches/batch-object) object. examples: request: curl: | curl https://api.openai.com/v1/vector_stores/vs_abc123/file_batches \ -H "Authorization: Bearer $OPENAI_API_KEY" \ -H "Content-Type: application/json \ -H "OpenAI-Beta: assistants=v2" \ -d '{ "file_ids": ["file-abc123", "file-abc456"] }' python: | from openai import OpenAI client = OpenAI() vector_store_file_batch = client.beta.vector_stores.file_batches.create( vector_store_id="vs_abc123", file_ids=["file-abc123", "file-abc456"] ) print(vector_store_file_batch) node.js: | import OpenAI from "openai"; const openai = new OpenAI(); async function main() { const myVectorStoreFileBatch = await openai.beta.vectorStores.fileBatches.create( "vs_abc123", { file_ids: ["file-abc123", "file-abc456"] } ); console.log(myVectorStoreFileBatch); } main(); response: | { "id": "vsfb_abc123", "object": "vector_store.file_batch", "created_at": 1699061776, "vector_store_id": "vs_abc123", "status": "in_progress", "file_counts": { "in_progress": 1, "completed": 1, "failed": 0, "cancelled": 0, "total": 0, } } /vector_stores/{vector_store_id}/file_batches/{batch_id}: get: operationId: getVectorStoreFileBatch tags: - Vector Stores summary: Retrieves a vector store file batch. parameters: - in: path name: vector_store_id required: true schema: type: string example: vs_abc123 description: The ID of the vector store that the file batch belongs to. - in: path name: batch_id required: true schema: type: string example: vsfb_abc123 description: The ID of the file batch being retrieved. responses: "200": description: OK content: application/json: schema: $ref: "#/components/schemas/VectorStoreFileBatchObject" x-oaiMeta: name: Retrieve vector store file batch group: vector_stores beta: true returns: The [vector store file batch](/docs/api-reference/vector-stores-file-batches/batch-object) object. examples: request: curl: | curl https://api.openai.com/v1/vector_stores/vs_abc123/files_batches/vsfb_abc123 \ -H "Authorization: Bearer $OPENAI_API_KEY" \ -H "Content-Type: application/json" \ -H "OpenAI-Beta: assistants=v2" python: | from openai import OpenAI client = OpenAI() vector_store_file_batch = client.beta.vector_stores.file_batches.retrieve( vector_store_id="vs_abc123", batch_id="vsfb_abc123" ) print(vector_store_file_batch) node.js: | import OpenAI from "openai"; const openai = new OpenAI(); async function main() { const vectorStoreFileBatch = await openai.beta.vectorStores.fileBatches.retrieve( "vs_abc123", "vsfb_abc123" ); console.log(vectorStoreFileBatch); } main(); response: | { "id": "vsfb_abc123", "object": "vector_store.file_batch", "created_at": 1699061776, "vector_store_id": "vs_abc123", "status": "in_progress", "file_counts": { "in_progress": 1, "completed": 1, "failed": 0, "cancelled": 0, "total": 0, } } /vector_stores/{vector_store_id}/file_batches/{batch_id}/cancel: post: operationId: cancelVectorStoreFileBatch tags: - Vector Stores summary: Cancel a vector store file batch. This attempts to cancel the processing of files in this batch as soon as possible. parameters: - in: path name: vector_store_id required: true schema: type: string description: The ID of the vector store that the file batch belongs to. - in: path name: batch_id required: true schema: type: string description: The ID of the file batch to cancel. responses: "200": description: OK content: application/json: schema: $ref: "#/components/schemas/VectorStoreFileBatchObject" x-oaiMeta: name: Cancel vector store file batch group: vector_stores beta: true returns: The modified vector store file batch object. examples: request: curl: | curl https://api.openai.com/v1/vector_stores/vs_abc123/files_batches/vsfb_abc123/cancel \ -H "Authorization: Bearer $OPENAI_API_KEY" \ -H "Content-Type: application/json" \ -H "OpenAI-Beta: assistants=v2" \ -X POST python: | from openai import OpenAI client = OpenAI() deleted_vector_store_file_batch = client.beta.vector_stores.file_batches.cancel( vector_store_id="vs_abc123", file_batch_id="vsfb_abc123" ) print(deleted_vector_store_file_batch) node.js: | import OpenAI from "openai"; const openai = new OpenAI(); async function main() { const deletedVectorStoreFileBatch = await openai.vector_stores.fileBatches.cancel( "vs_abc123", "vsfb_abc123" ); console.log(deletedVectorStoreFileBatch); } main(); response: | { "id": "vsfb_abc123", "object": "vector_store.file_batch", "created_at": 1699061776, "vector_store_id": "vs_abc123", "status": "cancelling", "file_counts": { "in_progress": 12, "completed": 3, "failed": 0, "cancelled": 0, "total": 15, } } /vector_stores/{vector_store_id}/file_batches/{batch_id}/files: get: operationId: listFilesInVectorStoreBatch tags: - Vector Stores summary: Returns a list of vector store files in a batch. parameters: - name: vector_store_id in: path description: The ID of the vector store that the files belong to. required: true schema: type: string - name: batch_id in: path description: The ID of the file batch that the files belong to. required: true schema: type: string - name: limit in: query description: *pagination_limit_param_description required: false schema: type: integer default: 20 - name: order in: query description: *pagination_order_param_description schema: type: string default: desc enum: ["asc", "desc"] - name: after in: query description: *pagination_after_param_description schema: type: string - name: before in: query description: *pagination_before_param_description schema: type: string - name: filter in: query description: "Filter by file status. One of `in_progress`, `completed`, `failed`, `cancelled`." schema: type: string enum: ["in_progress", "completed", "failed", "cancelled"] responses: "200": description: OK content: application/json: schema: $ref: "#/components/schemas/ListVectorStoreFilesResponse" x-oaiMeta: name: List vector store files in a batch group: vector_stores beta: true returns: A list of [vector store file](/docs/api-reference/vector-stores-files/file-object) objects. examples: request: curl: | curl https://api.openai.com/v1/vector_stores/vs_abc123/files_batches/vsfb_abc123/files \ -H "Authorization: Bearer $OPENAI_API_KEY" \ -H "Content-Type: application/json" \ -H "OpenAI-Beta: assistants=v2" python: | from openai import OpenAI client = OpenAI() vector_store_files = client.beta.vector_stores.file_batches.list_files( vector_store_id="vs_abc123", batch_id="vsfb_abc123" ) print(vector_store_files) node.js: | import OpenAI from "openai"; const openai = new OpenAI(); async function main() { const vectorStoreFiles = await openai.beta.vectorStores.fileBatches.listFiles( "vs_abc123", "vsfb_abc123" ); console.log(vectorStoreFiles); } main(); response: | { "object": "list", "data": [ { "id": "file-abc123", "object": "vector_store.file", "created_at": 1699061776, "vector_store_id": "vs_abc123" }, { "id": "file-abc456", "object": "vector_store.file", "created_at": 1699061776, "vector_store_id": "vs_abc123" } ], "first_id": "file-abc123", "last_id": "file-abc456", "has_more": false } /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/requestInput), and must be uploaded with the purpose `batch`. The file can contain up to 50,000 requests, and can be up to 100 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: type: object additionalProperties: type: string description: Optional custom metadata for the batch. nullable: true 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: curl: | curl https://api.openai.com/v1/batches \ -H "Authorization: Bearer $OPENAI_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "input_file_id": "file-abc123", "endpoint": "/v1/chat/completions", "completion_window": "24h" }' python: | from openai import OpenAI client = OpenAI() client.batches.create( input_file_id="file-abc123", endpoint="/v1/chat/completions", completion_window="24h" ) node: | import OpenAI from "openai"; const openai = new OpenAI(); async function main() { const batch = await openai.batches.create({ input_file_id: "file-abc123", endpoint: "/v1/chat/completions", completion_window: "24h" }); console.log(batch); } main(); response: | { "id": "batch_abc123", "object": "batch", "endpoint": "/v1/chat/completions", "errors": null, "input_file_id": "file-abc123", "completion_window": "24h", "status": "validating", "output_file_id": null, "error_file_id": null, "created_at": 1711471533, "in_progress_at": null, "expires_at": null, "finalizing_at": null, "completed_at": null, "failed_at": null, "expired_at": null, "cancelling_at": null, "cancelled_at": null, "request_counts": { "total": 0, "completed": 0, "failed": 0 }, "metadata": { "customer_id": "user_123456789", "batch_description": "Nightly eval job", } } get: operationId: listBatches tags: - Batch summary: List your organization's batches. parameters: - in: query name: after required: false schema: type: string description: *pagination_after_param_description - name: limit in: query description: *pagination_limit_param_description required: false schema: type: integer default: 20 responses: "200": description: Batch listed successfully. content: application/json: schema: $ref: "#/components/schemas/ListBatchesResponse" x-oaiMeta: name: List batch group: batch returns: A list of paginated [Batch](/docs/api-reference/batch/object) objects. examples: request: curl: | curl https://api.openai.com/v1/batches?limit=2 \ -H "Authorization: Bearer $OPENAI_API_KEY" \ -H "Content-Type: application/json" python: | from openai import OpenAI client = OpenAI() client.batches.list() node: | import OpenAI from "openai"; const openai = new OpenAI(); async function main() { const list = await openai.batches.list(); for await (const batch of list) { console.log(batch); } } main(); response: | { "object": "list", "data": [ { "id": "batch_abc123", "object": "batch", "endpoint": "/v1/chat/completions", "errors": null, "input_file_id": "file-abc123", "completion_window": "24h", "status": "completed", "output_file_id": "file-cvaTdG", "error_file_id": "file-HOWS94", "created_at": 1711471533, "in_progress_at": 1711471538, "expires_at": 1711557933, "finalizing_at": 1711493133, "completed_at": 1711493163, "failed_at": null, "expired_at": null, "cancelling_at": null, "cancelled_at": null, "request_counts": { "total": 100, "completed": 95, "failed": 5 }, "metadata": { "customer_id": "user_123456789", "batch_description": "Nightly job", } }, { ... }, ], "first_id": "batch_abc123", "last_id": "batch_abc456", "has_more": true } /batches/{batch_id}: get: operationId: retrieveBatch tags: - Batch summary: Retrieves a batch. parameters: - in: path name: batch_id required: true schema: type: string description: The ID of the batch to retrieve. responses: "200": description: Batch retrieved successfully. content: application/json: schema: $ref: "#/components/schemas/Batch" x-oaiMeta: name: Retrieve batch group: batch returns: The [Batch](/docs/api-reference/batch/object) object matching the specified ID. examples: request: curl: | curl https://api.openai.com/v1/batches/batch_abc123 \ -H "Authorization: Bearer $OPENAI_API_KEY" \ -H "Content-Type: application/json" \ python: | from openai import OpenAI client = OpenAI() client.batches.retrieve("batch_abc123") node: | import OpenAI from "openai"; const openai = new OpenAI(); async function main() { const batch = await openai.batches.retrieve("batch_abc123"); console.log(batch); } main(); response: &batch_object | { "id": "batch_abc123", "object": "batch", "endpoint": "/v1/completions", "errors": null, "input_file_id": "file-abc123", "completion_window": "24h", "status": "completed", "output_file_id": "file-cvaTdG", "error_file_id": "file-HOWS94", "created_at": 1711471533, "in_progress_at": 1711471538, "expires_at": 1711557933, "finalizing_at": 1711493133, "completed_at": 1711493163, "failed_at": null, "expired_at": null, "cancelling_at": null, "cancelled_at": null, "request_counts": { "total": 100, "completed": 95, "failed": 5 }, "metadata": { "customer_id": "user_123456789", "batch_description": "Nightly eval job", } } /batches/{batch_id}/cancel: post: operationId: cancelBatch tags: - Batch summary: Cancels an in-progress batch. parameters: - in: path name: batch_id required: true schema: type: string description: The ID of the batch to cancel. responses: "200": description: Batch is cancelling. Returns the cancelling batch's details. content: application/json: schema: $ref: "#/components/schemas/Batch" x-oaiMeta: name: Cancel batch group: batch returns: The [Batch](/docs/api-reference/batch/object) object matching the specified ID. examples: request: curl: | curl https://api.openai.com/v1/batches/batch_abc123/cancel \ -H "Authorization: Bearer $OPENAI_API_KEY" \ -H "Content-Type: application/json" \ -X POST python: | from openai import OpenAI client = OpenAI() client.batches.cancel("batch_abc123") node: | import OpenAI from "openai"; const openai = new OpenAI(); async function main() { const batch = await openai.batches.cancel("batch_abc123"); console.log(batch); } main(); response: | { "id": "batch_abc123", "object": "batch", "endpoint": "/v1/chat/completions", "errors": null, "input_file_id": "file-abc123", "completion_window": "24h", "status": "cancelling", "output_file_id": null, "error_file_id": null, "created_at": 1711471533, "in_progress_at": 1711471538, "expires_at": 1711557933, "finalizing_at": null, "completed_at": null, "failed_at": null, "expired_at": null, "cancelling_at": 1711475133, "cancelled_at": null, "request_counts": { "total": 100, "completed": 23, "failed": 1 }, "metadata": { "customer_id": "user_123456789", "batch_description": "Nightly eval job", } } components: securitySchemes: ApiKeyAuth: type: http scheme: "bearer" schemas: Error: type: object properties: code: type: string nullable: true message: type: string nullable: false param: type: string nullable: true type: type: string nullable: false required: - type - message - param - code ErrorResponse: type: object properties: error: $ref: "#/components/schemas/Error" required: - error ListModelsResponse: type: object properties: object: type: string enum: [list] data: type: array items: $ref: "#/components/schemas/Model" required: - object - data DeleteModelResponse: type: object properties: id: type: string deleted: type: boolean object: type: string required: - id - object - deleted CreateCompletionRequest: type: object properties: model: description: &model_description | ID of the model to use. You can use the [List models](/docs/api-reference/models/list) API to see all of your available models, or see our [Model overview](/docs/models/overview) for descriptions of them. anyOf: - type: string - type: string enum: ["gpt-3.5-turbo-instruct", "davinci-002", "babbage-002"] x-oaiTypeLabel: string prompt: description: &completions_prompt_description | The prompt(s) to generate completions for, encoded as a string, array of strings, array of tokens, or array of token arrays. Note that <|endoftext|> is the document separator that the model sees during training, so if a prompt is not specified the model will generate as if from the beginning of a new document. default: "<|endoftext|>" nullable: true oneOf: - type: string default: "" example: "This is a test." - type: array items: type: string default: "" example: "This is a test." - type: array minItems: 1 items: type: integer example: "[1212, 318, 257, 1332, 13]" - type: array minItems: 1 items: type: array minItems: 1 items: type: integer example: "[[1212, 318, 257, 1332, 13]]" 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`. echo: type: boolean default: false nullable: true description: &completions_echo_description > Echo back the prompt in addition to the completion frequency_penalty: type: number default: 0 minimum: -2 maximum: 2 nullable: true description: &completions_frequency_penalty_description | Number between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model's likelihood to repeat the same line verbatim. [See more information about frequency and presence penalties.](/docs/guides/text-generation/parameter-details) logit_bias: &completions_logit_bias type: object x-oaiTypeLabel: map default: null nullable: true additionalProperties: type: integer description: &completions_logit_bias_description | Modify the likelihood of specified tokens appearing in the completion. Accepts a JSON object that maps tokens (specified by their token ID in the GPT tokenizer) to an associated bias value from -100 to 100. You can use this [tokenizer tool](/tokenizer?view=bpe) 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. 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 output 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. max_tokens: type: integer minimum: 0 default: 16 example: 16 nullable: true description: &completions_max_tokens_description | The maximum number of [tokens](/tokenizer) that can be generated in the completion. The token count of your prompt plus `max_tokens` cannot exceed the model's context length. [Example Python code](https://cookbook.openai.com/examples/how_to_count_tokens_with_tiktoken) for counting tokens. 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`. presence_penalty: type: number default: 0 minimum: -2 maximum: 2 nullable: true description: &completions_presence_penalty_description | Number between -2.0 and 2.0. Positive values penalize new tokens based on whether they appear in the text so far, increasing the model's likelihood to talk about new topics. [See more information about frequency and presence penalties.](/docs/guides/text-generation/parameter-details) seed: &completions_seed_param type: integer minimum: -9223372036854775808 maximum: 9223372036854775807 nullable: true description: | If specified, our system will make a best effort to sample deterministically, such that repeated requests with the same `seed` and parameters should return the same result. Determinism is not guaranteed, and you should refer to the `system_fingerprint` response parameter to monitor changes in the backend. 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"]' stream: description: > Whether to stream back partial progress. If set, tokens will be sent as data-only [server-sent events](https://developer.mozilla.org/en-US/docs/Web/API/Server-sent_events/Using_server-sent_events#Event_stream_format) as they become available, with the stream terminated by a `data: [DONE]` message. [Example Python code](https://cookbook.openai.com/examples/how_to_stream_completions). type: boolean nullable: true default: false stream_options: $ref: "#/components/schemas/ChatCompletionStreamOptions" suffix: description: | The suffix that comes after a completion of inserted text. This parameter is only supported for `gpt-3.5-turbo-instruct`. default: null nullable: true type: string example: "test." temperature: type: number minimum: 0 maximum: 2 default: 1 example: 1 nullable: true description: &completions_temperature_description | What sampling temperature to use, between 0 and 2. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic. We generally recommend altering this or `top_p` but not both. top_p: type: number minimum: 0 maximum: 1 default: 1 example: 1 nullable: true description: &completions_top_p_description | An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10% probability mass are considered. We generally recommend altering this or `temperature` but not both. user: &end_user_param_configuration type: string example: user-1234 description: | A unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse. [Learn more](/docs/guides/safety-best-practices/end-user-ids). required: - model - prompt CreateCompletionResponse: type: object description: | Represents a completion response from the API. Note: both the streamed and non-streamed response objects share the same shape (unlike the chat endpoint). properties: id: type: string description: A unique identifier for the completion. choices: type: array description: The list of completion choices the model generated for the input prompt. items: type: object required: - finish_reason - index - logprobs - text properties: finish_reason: type: string description: &completion_finish_reason_description | The reason the model stopped generating tokens. This will be `stop` if the model hit a natural stop point or a provided stop sequence, `length` if the maximum number of tokens specified in the request was reached, or `content_filter` if content was omitted due to a flag from our content filters. enum: ["stop", "length", "content_filter"] index: type: integer logprobs: type: object nullable: true properties: text_offset: type: array items: type: integer token_logprobs: type: array items: type: number tokens: type: array items: type: string top_logprobs: type: array items: type: object additionalProperties: type: number text: type: string created: type: integer description: The Unix timestamp (in seconds) of when the completion was created. model: type: string description: The model used for completion. system_fingerprint: type: string description: | This fingerprint represents the backend configuration that the model runs with. Can be used in conjunction with the `seed` request parameter to understand when backend changes have been made that might impact determinism. object: type: string description: The object type, which is always "text_completion" enum: [text_completion] usage: $ref: "#/components/schemas/CompletionUsage" required: - id - object - created - model - choices x-oaiMeta: name: The completion object legacy: true example: | { "id": "cmpl-uqkvlQyYK7bGYrRHQ0eXlWi7", "object": "text_completion", "created": 1589478378, "model": "gpt-4-turbo", "choices": [ { "text": "\n\nThis is indeed a test", "index": 0, "logprobs": null, "finish_reason": "length" } ], "usage": { "prompt_tokens": 5, "completion_tokens": 7, "total_tokens": 12 } } ChatCompletionRequestMessageContentPart: oneOf: - $ref: "#/components/schemas/ChatCompletionRequestMessageContentPartText" - $ref: "#/components/schemas/ChatCompletionRequestMessageContentPartImage" x-oaiExpandable: true ChatCompletionRequestMessageContentPartImage: type: object title: Image content part properties: type: type: string enum: ["image_url"] description: The type of the content part. image_url: type: object properties: url: type: string description: Either a URL of the image or the base64 encoded image data. format: uri detail: type: string description: Specifies the detail level of the image. Learn more in the [Vision guide](/docs/guides/vision/low-or-high-fidelity-image-understanding). enum: ["auto", "low", "high"] default: "auto" required: - url required: - type - image_url ChatCompletionRequestMessageContentPartText: type: object title: Text content part properties: type: type: string enum: ["text"] description: The type of the content part. text: type: string description: The text content. required: - type - text ChatCompletionRequestMessage: oneOf: - $ref: "#/components/schemas/ChatCompletionRequestSystemMessage" - $ref: "#/components/schemas/ChatCompletionRequestUserMessage" - $ref: "#/components/schemas/ChatCompletionRequestAssistantMessage" - $ref: "#/components/schemas/ChatCompletionRequestToolMessage" - $ref: "#/components/schemas/ChatCompletionRequestFunctionMessage" x-oaiExpandable: true ChatCompletionRequestSystemMessage: type: object title: System message properties: content: description: The contents of the system message. type: string role: type: string enum: ["system"] description: The role of the messages author, in this case `system`. name: type: string description: An optional name for the participant. Provides the model information to differentiate between participants of the same role. required: - content - role ChatCompletionRequestUserMessage: type: object title: User message properties: content: description: | The contents of the user message. oneOf: - type: string description: The text contents of the message. title: Text content - type: array description: An array of content parts with a defined type, each can be of type `text` or `image_url` when passing in images. You can pass multiple images by adding multiple `image_url` content parts. Image input is only supported when using the `gpt-4-visual-preview` model. title: Array of content parts items: $ref: "#/components/schemas/ChatCompletionRequestMessageContentPart" minItems: 1 x-oaiExpandable: true role: type: string enum: ["user"] description: The role of the messages author, in this case `user`. name: type: string description: An optional name for the participant. Provides the model information to differentiate between participants of the same role. required: - content - role ChatCompletionRequestAssistantMessage: type: object title: Assistant message properties: content: nullable: true type: string description: | The contents of the assistant message. Required unless `tool_calls` or `function_call` is specified. role: type: string enum: ["assistant"] description: The role of the messages author, in this case `assistant`. name: type: string description: An optional name for the participant. Provides the model information to differentiate between participants of the same role. tool_calls: $ref: "#/components/schemas/ChatCompletionMessageToolCalls" function_call: type: object deprecated: true description: "Deprecated and replaced by `tool_calls`. The name and arguments of a function that should be called, as generated by the model." properties: arguments: type: string description: The arguments to call the function with, as generated by the model in JSON format. Note that the model does not always generate valid JSON, and may hallucinate parameters not defined by your function schema. Validate the arguments in your code before calling your function. name: type: string description: The name of the function to call. required: - arguments - name required: - role ChatCompletionRequestToolMessage: type: object title: Tool message properties: role: type: string enum: ["tool"] description: The role of the messages author, in this case `tool`. content: type: string description: The contents of the tool message. tool_call_id: type: string description: Tool call that this message is responding to. required: - role - content - tool_call_id ChatCompletionRequestFunctionMessage: type: object title: Function message deprecated: true properties: role: type: string enum: ["function"] description: The role of the messages author, in this case `function`. content: nullable: true type: string description: The contents of the function message. name: type: string description: The name of the function to call. required: - role - content - name FunctionParameters: type: object description: "The parameters the functions accepts, described as a JSON Schema object. See the [guide](/docs/guides/text-generation/function-calling) for examples, and the [JSON Schema reference](https://json-schema.org/understanding-json-schema/) for documentation about the format. \n\nOmitting `parameters` defines a function with an empty parameter list." additionalProperties: true ChatCompletionFunctions: type: object deprecated: true properties: description: type: string description: A description of what the function does, used by the model to choose when and how to call the function. name: type: string description: The name of the function to be called. Must be a-z, A-Z, 0-9, or contain underscores and dashes, with a maximum length of 64. parameters: $ref: "#/components/schemas/FunctionParameters" required: - name ChatCompletionFunctionCallOption: type: object description: > Specifying a particular function via `{"name": "my_function"}` forces the model to call that function. properties: name: type: string description: The name of the function to call. required: - name ChatCompletionTool: type: object properties: type: type: string enum: ["function"] description: The type of the tool. Currently, only `function` is supported. function: $ref: "#/components/schemas/FunctionObject" required: - type - function FunctionObject: type: object properties: description: type: string description: A description of what the function does, used by the model to choose when and how to call the function. name: type: string description: The name of the function to be called. Must be a-z, A-Z, 0-9, or contain underscores and dashes, with a maximum length of 64. parameters: $ref: "#/components/schemas/FunctionParameters" required: - name ChatCompletionToolChoiceOption: description: | Controls which (if any) tool is called by the model. `none` means the model will not call any tool and instead generates a message. `auto` means the model can pick between generating a message or calling one or more tools. `required` means the model must call one or more tools. Specifying a particular tool via `{"type": "function", "function": {"name": "my_function"}}` forces the model to call that tool. `none` is the default when no tools are present. `auto` is the default if tools are present. oneOf: - type: string description: > `none` means the model will not call any tool and instead generates a message. `auto` means the model can pick between generating a message or calling one or more tools. `required` means the model must call one or more tools. enum: [none, auto, required] - $ref: "#/components/schemas/ChatCompletionNamedToolChoice" x-oaiExpandable: true ChatCompletionNamedToolChoice: type: object description: Specifies a tool the model should use. Use to force the model to call a specific function. properties: type: type: string enum: ["function"] description: The type of the tool. Currently, only `function` is supported. function: type: object properties: name: type: string description: The name of the function to call. required: - name required: - type - function ChatCompletionMessageToolCalls: type: array description: The tool calls generated by the model, such as function calls. items: $ref: "#/components/schemas/ChatCompletionMessageToolCall" ChatCompletionMessageToolCall: type: object properties: # TODO: index included when streaming id: type: string description: The ID of the tool call. type: type: string enum: ["function"] description: The type of the tool. Currently, only `function` is supported. function: type: object description: The function that the model called. properties: name: type: string description: The name of the function to call. arguments: type: string description: The arguments to call the function with, as generated by the model in JSON format. Note that the model does not always generate valid JSON, and may hallucinate parameters not defined by your function schema. Validate the arguments in your code before calling your function. required: - name - arguments required: - id - type - function ChatCompletionMessageToolCallChunk: type: object properties: index: type: integer id: type: string description: The ID of the tool call. type: type: string enum: ["function"] description: The type of the tool. Currently, only `function` is supported. function: type: object properties: name: type: string description: The name of the function to call. arguments: type: string description: The arguments to call the function with, as generated by the model in JSON format. Note that the model does not always generate valid JSON, and may hallucinate parameters not defined by your function schema. Validate the arguments in your code before calling your function. required: - index # Note, this isn't referenced anywhere, but is kept as a convenience to record all possible roles in one place. ChatCompletionRole: type: string description: The role of the author of a message enum: - system - user - assistant - tool - function ChatCompletionStreamOptions: description: | Options for streaming response. Only set this when you set `stream: true`. type: object nullable: true default: null properties: include_usage: type: boolean description: | If set, an additional chunk will be streamed before the `data: [DONE]` message. The `usage` field on this chunk shows the token usage statistics for the entire request, and the `choices` field will always be an empty array. All other chunks will also include a `usage` field, but with a null value. ChatCompletionResponseMessage: type: object description: A chat completion message generated by the model. properties: content: type: string description: The contents of the message. nullable: true tool_calls: $ref: "#/components/schemas/ChatCompletionMessageToolCalls" role: type: string enum: ["assistant"] description: The role of the author of this message. function_call: type: object deprecated: true description: "Deprecated and replaced by `tool_calls`. The name and arguments of a function that should be called, as generated by the model." properties: arguments: type: string description: The arguments to call the function with, as generated by the model in JSON format. Note that the model does not always generate valid JSON, and may hallucinate parameters not defined by your function schema. Validate the arguments in your code before calling your function. name: type: string description: The name of the function to call. required: - name - arguments required: - role - content ChatCompletionStreamResponseDelta: type: object description: A chat completion delta generated by streamed model responses. properties: content: type: string description: The contents of the chunk message. nullable: true function_call: deprecated: true type: object description: "Deprecated and replaced by `tool_calls`. The name and arguments of a function that should be called, as generated by the model." properties: arguments: type: string description: The arguments to call the function with, as generated by the model in JSON format. Note that the model does not always generate valid JSON, and may hallucinate parameters not defined by your function schema. Validate the arguments in your code before calling your function. name: type: string description: The name of the function to call. tool_calls: type: array items: $ref: "#/components/schemas/ChatCompletionMessageToolCallChunk" role: type: string enum: ["system", "user", "assistant", "tool"] description: The role of the author of this message. CreateChatCompletionRequest: type: object properties: messages: description: A list of messages comprising the conversation so far. [Example Python code](https://cookbook.openai.com/examples/how_to_format_inputs_to_chatgpt_models). type: array minItems: 1 items: $ref: "#/components/schemas/ChatCompletionRequestMessage" model: description: ID of the model to use. See the [model endpoint compatibility](/docs/models/model-endpoint-compatibility) table for details on which models work with the Chat API. example: "gpt-4-turbo" anyOf: - type: string - type: string enum: [ "gpt-4o", "gpt-4o-2024-05-13", "gpt-4-turbo", "gpt-4-turbo-2024-04-09", "gpt-4-0125-preview", "gpt-4-turbo-preview", "gpt-4-1106-preview", "gpt-4-vision-preview", "gpt-4", "gpt-4-0314", "gpt-4-0613", "gpt-4-32k", "gpt-4-32k-0314", "gpt-4-32k-0613", "gpt-3.5-turbo", "gpt-3.5-turbo-16k", "gpt-3.5-turbo-0301", "gpt-3.5-turbo-0613", "gpt-3.5-turbo-1106", "gpt-3.5-turbo-0125", "gpt-3.5-turbo-16k-0613", ] x-oaiTypeLabel: string frequency_penalty: type: number default: 0 minimum: -2 maximum: 2 nullable: true description: *completions_frequency_penalty_description logit_bias: type: object x-oaiTypeLabel: map default: null nullable: true additionalProperties: type: integer description: | Modify the likelihood of specified tokens appearing in the completion. Accepts a JSON object that maps tokens (specified by their token ID in the tokenizer) to an associated bias value from -100 to 100. Mathematically, the bias is added to the logits generated by the model prior to sampling. The exact effect will vary per model, but values between -1 and 1 should decrease or increase likelihood of selection; values like -100 or 100 should result in a ban or exclusive selection of the relevant token. logprobs: description: Whether to return log probabilities of the output tokens or not. If true, returns the log probabilities of each output token returned in the `content` of `message`. type: boolean default: false nullable: true top_logprobs: description: An integer between 0 and 20 specifying the number of most likely tokens to return at each token position, each with an associated log probability. `logprobs` must be set to `true` if this parameter is used. type: integer minimum: 0 maximum: 20 nullable: true max_tokens: description: | The maximum number of [tokens](/tokenizer) that can be generated in the chat completion. The total length of input tokens and generated tokens is limited by the model's context length. [Example Python code](https://cookbook.openai.com/examples/how_to_count_tokens_with_tiktoken) for counting tokens. type: integer nullable: true n: type: integer minimum: 1 maximum: 128 default: 1 example: 1 nullable: true description: How many chat completion choices to generate for each input message. Note that you will be charged based on the number of generated tokens across all of the choices. Keep `n` as `1` to minimize costs. presence_penalty: type: number default: 0 minimum: -2 maximum: 2 nullable: true description: *completions_presence_penalty_description response_format: type: object description: | An object specifying the format that the model must output. Compatible with [GPT-4 Turbo](/docs/models/gpt-4-and-gpt-4-turbo) and all GPT-3.5 Turbo models newer than `gpt-3.5-turbo-1106`. Setting to `{ "type": "json_object" }` enables JSON mode, which guarantees the message the model generates is valid JSON. **Important:** when using JSON mode, you **must** also instruct the model to produce JSON yourself via a system or user message. Without this, the model may generate an unending stream of whitespace until the generation reaches the token limit, resulting in a long-running and seemingly "stuck" request. Also note that the message content may be partially cut off if `finish_reason="length"`, which indicates the generation exceeded `max_tokens` or the conversation exceeded the max context length. properties: type: type: string enum: ["text", "json_object"] example: "json_object" default: "text" description: Must be one of `text` or `json_object`. seed: type: integer minimum: -9223372036854775808 maximum: 9223372036854775807 nullable: true description: | This feature is in Beta. If specified, our system will make a best effort to sample deterministically, such that repeated requests with the same `seed` and parameters should return the same result. Determinism is not guaranteed, and you should refer to the `system_fingerprint` response parameter to monitor changes in the backend. x-oaiMeta: beta: true stop: description: | Up to 4 sequences where the API will stop generating further tokens. default: null oneOf: - type: string nullable: true - type: array minItems: 1 maxItems: 4 items: type: string stream: description: > If set, partial message deltas will be sent, like in ChatGPT. Tokens will be sent as data-only [server-sent events](https://developer.mozilla.org/en-US/docs/Web/API/Server-sent_events/Using_server-sent_events#Event_stream_format) as they become available, with the stream terminated by a `data: [DONE]` message. [Example Python code](https://cookbook.openai.com/examples/how_to_stream_completions). type: boolean nullable: true default: false stream_options: $ref: "#/components/schemas/ChatCompletionStreamOptions" 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 tools: type: array description: > A list of tools the model may call. Currently, only functions are supported as a tool. Use this to provide a list of functions the model may generate JSON inputs for. A max of 128 functions are supported. items: $ref: "#/components/schemas/ChatCompletionTool" tool_choice: $ref: "#/components/schemas/ChatCompletionToolChoiceOption" user: *end_user_param_configuration function_call: deprecated: true description: | Deprecated in favor of `tool_choice`. Controls which (if any) function is called by the model. `none` means the model will not call a function and instead generates a message. `auto` means the model can pick between generating a message or calling a function. Specifying a particular function via `{"name": "my_function"}` forces the model to call that function. `none` is the default when no functions are present. `auto` is the default if functions are present. oneOf: - type: string description: > `none` means the model will not call a function and instead generates a message. `auto` means the model can pick between generating a message or calling a function. enum: [none, auto] - $ref: "#/components/schemas/ChatCompletionFunctionCallOption" x-oaiExpandable: true functions: deprecated: true description: | Deprecated in favor of `tools`. A list of functions the model may generate JSON inputs for. type: array minItems: 1 maxItems: 128 items: $ref: "#/components/schemas/ChatCompletionFunctions" required: - model - messages CreateChatCompletionResponse: type: object description: Represents a chat completion response returned by model, based on the provided input. properties: id: type: string description: A unique identifier for the chat completion. choices: type: array description: A list of chat completion choices. Can be more than one if `n` is greater than 1. items: type: object required: - finish_reason - index - message - logprobs properties: finish_reason: type: string description: &chat_completion_finish_reason_description | The reason the model stopped generating tokens. This will be `stop` if the model hit a natural stop point or a provided stop sequence, `length` if the maximum number of tokens specified in the request was reached, `content_filter` if content was omitted due to a flag from our content filters, `tool_calls` if the model called a tool, or `function_call` (deprecated) if the model called a function. enum: [ "stop", "length", "tool_calls", "content_filter", "function_call", ] index: type: integer description: The index of the choice in the list of choices. message: $ref: "#/components/schemas/ChatCompletionResponseMessage" logprobs: &chat_completion_response_logprobs description: Log probability information for the choice. type: object nullable: true properties: content: description: A list of message content tokens with log probability information. type: array items: $ref: "#/components/schemas/ChatCompletionTokenLogprob" nullable: true required: - content created: type: integer description: The Unix timestamp (in seconds) of when the chat completion was created. model: type: string description: The model used for the chat completion. system_fingerprint: type: string description: | This fingerprint represents the backend configuration that the model runs with. Can be used in conjunction with the `seed` request parameter to understand when backend changes have been made that might impact determinism. object: type: string description: The object type, which is always `chat.completion`. enum: [chat.completion] usage: $ref: "#/components/schemas/CompletionUsage" required: - choices - created - id - model - object x-oaiMeta: name: The chat completion object group: chat example: *chat_completion_example CreateChatCompletionFunctionResponse: type: object description: Represents a chat completion response returned by model, based on the provided input. properties: id: type: string description: A unique identifier for the chat completion. choices: type: array description: A list of chat completion choices. Can be more than one if `n` is greater than 1. items: type: object required: - finish_reason - index - message - logprobs properties: finish_reason: type: string description: &chat_completion_function_finish_reason_description | The reason the model stopped generating tokens. This will be `stop` if the model hit a natural stop point or a provided stop sequence, `length` if the maximum number of tokens specified in the request was reached, `content_filter` if content was omitted due to a flag from our content filters, or `function_call` if the model called a function. enum: ["stop", "length", "function_call", "content_filter"] index: type: integer description: The index of the choice in the list of choices. message: $ref: "#/components/schemas/ChatCompletionResponseMessage" created: type: integer description: The Unix timestamp (in seconds) of when the chat completion was created. model: type: string description: The model used for the chat completion. system_fingerprint: type: string description: | This fingerprint represents the backend configuration that the model runs with. Can be used in conjunction with the `seed` request parameter to understand when backend changes have been made that might impact determinism. object: type: string description: The object type, which is always `chat.completion`. enum: [chat.completion] usage: $ref: "#/components/schemas/CompletionUsage" required: - choices - created - id - model - object x-oaiMeta: name: The chat completion object group: chat example: *chat_completion_function_example ChatCompletionTokenLogprob: type: object properties: token: &chat_completion_response_logprobs_token description: The token. type: string logprob: &chat_completion_response_logprobs_token_logprob description: The log probability of this token, if it is within the top 20 most likely tokens. Otherwise, the value `-9999.0` is used to signify that the token is very unlikely. type: number bytes: &chat_completion_response_logprobs_bytes description: A list of integers representing the UTF-8 bytes representation of the token. Useful in instances where characters are represented by multiple tokens and their byte representations must be combined to generate the correct text representation. Can be `null` if there is no bytes representation for the token. type: array items: type: integer nullable: true top_logprobs: description: List of the most likely tokens and their log probability, at this token position. In rare cases, there may be fewer than the number of requested `top_logprobs` returned. type: array items: type: object properties: token: *chat_completion_response_logprobs_token logprob: *chat_completion_response_logprobs_token_logprob bytes: *chat_completion_response_logprobs_bytes required: - token - logprob - bytes required: - token - logprob - bytes - top_logprobs ListPaginatedFineTuningJobsResponse: type: object properties: data: type: array items: $ref: "#/components/schemas/FineTuningJob" has_more: type: boolean object: type: string enum: [list] required: - object - data - has_more CreateChatCompletionStreamResponse: type: object description: Represents a streamed chunk of a chat completion response returned by model, based on the provided input. properties: id: type: string description: A unique identifier for the chat completion. Each chunk has the same ID. choices: type: array description: | A list of chat completion choices. Can contain more than one elements if `n` is greater than 1. Can also be empty for the last chunk if you set `stream_options: {"include_usage": true}`. items: type: object required: - delta - finish_reason - index properties: delta: $ref: "#/components/schemas/ChatCompletionStreamResponseDelta" logprobs: *chat_completion_response_logprobs finish_reason: type: string description: *chat_completion_finish_reason_description enum: [ "stop", "length", "tool_calls", "content_filter", "function_call", ] nullable: true index: type: integer description: The index of the choice in the list of choices. created: type: integer description: The Unix timestamp (in seconds) of when the chat completion was created. Each chunk has the same timestamp. model: type: string description: The model to generate the completion. system_fingerprint: type: string description: | This fingerprint represents the backend configuration that the model runs with. Can be used in conjunction with the `seed` request parameter to understand when backend changes have been made that might impact determinism. object: type: string description: The object type, which is always `chat.completion.chunk`. enum: [chat.completion.chunk] usage: type: object description: | An optional field that will only be present when you set `stream_options: {"include_usage": true}` in your request. When present, it contains a null value except for the last chunk which contains the token usage statistics for the entire request. properties: completion_tokens: type: integer description: Number of tokens in the generated completion. prompt_tokens: type: integer description: Number of tokens in the prompt. total_tokens: type: integer description: Total number of tokens used in the request (prompt + completion). required: - prompt_tokens - completion_tokens - total_tokens required: - choices - created - id - model - object x-oaiMeta: name: The chat completion chunk object group: chat example: *chat_completion_chunk_example CreateChatCompletionImageResponse: type: object description: Represents a streamed chunk of a chat completion response returned by model, based on the provided input. x-oaiMeta: name: The chat completion chunk object group: chat example: *chat_completion_image_example CreateImageRequest: type: object properties: prompt: description: A text description of the desired image(s). The maximum length is 1000 characters for `dall-e-2` and 4000 characters for `dall-e-3`. type: string example: "A cute baby sea otter" model: anyOf: - type: string - type: string enum: ["dall-e-2", "dall-e-3"] x-oaiTypeLabel: string default: "dall-e-2" example: "dall-e-3" nullable: true description: The model to use for image generation. 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. For `dall-e-3`, only `n=1` is supported. quality: type: string enum: ["standard", "hd"] default: "standard" example: "standard" description: The quality of the image that will be generated. `hd` creates images with finer details and greater consistency across the image. This param is only supported for `dall-e-3`. 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`. URLs are only valid for 60 minutes after the image has been generated. size: &images_size type: string enum: ["256x256", "512x512", "1024x1024", "1792x1024", "1024x1792"] default: "1024x1024" example: "1024x1024" nullable: true description: The size of the generated images. Must be one of `256x256`, `512x512`, or `1024x1024` for `dall-e-2`. Must be one of `1024x1024`, `1792x1024`, or `1024x1792` for `dall-e-3` models. style: type: string enum: ["vivid", "natural"] default: "vivid" example: "vivid" nullable: true description: The style of the generated images. Must be one of `vivid` or `natural`. Vivid causes the model to lean towards generating hyper-real and dramatic images. Natural causes the model to produce more natural, less hyper-real looking images. This param is only supported for `dall-e-3`. user: *end_user_param_configuration required: - prompt ImagesResponse: properties: created: type: integer data: type: array items: $ref: "#/components/schemas/Image" required: - created - data Image: type: object description: Represents the url or the content of an image generated by the OpenAI API. properties: b64_json: type: string description: The base64-encoded JSON of the generated image, if `response_format` is `b64_json`. url: type: string description: The URL of the generated image, if `response_format` is `url` (default). revised_prompt: type: string description: The prompt that was used to generate the image, if there was any revision to the prompt. x-oaiMeta: name: The image object example: | { "url": "...", "revised_prompt": "..." } 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 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" 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 model: anyOf: - type: string - type: string enum: ["dall-e-2"] x-oaiTypeLabel: string default: "dall-e-2" example: "dall-e-2" nullable: true description: The model to use for image generation. Only `dall-e-2` is supported at this time. 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: &dalle2_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 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 model: anyOf: - type: string - type: string enum: ["dall-e-2"] x-oaiTypeLabel: string default: "dall-e-2" example: "dall-e-2" nullable: true description: The model to use for image generation. Only `dall-e-2` is supported at this time. n: *images_n response_format: *images_response_format size: *dalle2_images_size user: *end_user_param_configuration required: - image CreateModerationRequest: type: object properties: input: description: The input text to classify oneOf: - type: string default: "" example: "I want to kill them." - type: array items: type: string default: "" example: "I want to kill them." model: description: | Two content moderations models are available: `text-moderation-stable` and `text-moderation-latest`. The default is `text-moderation-latest` which will be automatically upgraded over time. This ensures you are always using our most accurate model. If you use `text-moderation-stable`, we will provide advanced notice before updating the model. Accuracy of `text-moderation-stable` may be slightly lower than for `text-moderation-latest`. nullable: false default: "text-moderation-latest" example: "text-moderation-stable" anyOf: - type: string - type: string enum: ["text-moderation-latest", "text-moderation-stable"] x-oaiTypeLabel: string required: - input CreateModerationResponse: type: object description: Represents if a given text input is potentially harmful. properties: id: type: string description: The unique identifier for the moderation request. model: type: string description: The model used to generate the moderation results. results: type: array description: A list of moderation objects. items: type: object properties: flagged: type: boolean description: Whether any of the below categories are flagged. categories: type: object description: A list of the categories, and whether they are flagged or not. properties: hate: type: boolean description: Content that expresses, incites, or promotes hate based on race, gender, ethnicity, religion, nationality, sexual orientation, disability status, or caste. Hateful content aimed at non-protected groups (e.g., chess players) is harassment. hate/threatening: type: boolean description: Hateful content that also includes violence or serious harm towards the targeted group based on race, gender, ethnicity, religion, nationality, sexual orientation, disability status, or caste. harassment: type: boolean description: Content that expresses, incites, or promotes harassing language towards any target. harassment/threatening: type: boolean description: Harassment content that also includes violence or serious harm towards any target. self-harm: type: boolean description: Content that promotes, encourages, or depicts acts of self-harm, such as suicide, cutting, and eating disorders. self-harm/intent: type: boolean description: Content where the speaker expresses that they are engaging or intend to engage in acts of self-harm, such as suicide, cutting, and eating disorders. self-harm/instructions: type: boolean description: Content that encourages performing acts of self-harm, such as suicide, cutting, and eating disorders, or that gives instructions or advice on how to commit such acts. sexual: type: boolean description: Content meant to arouse sexual excitement, such as the description of sexual activity, or that promotes sexual services (excluding sex education and wellness). sexual/minors: type: boolean description: Sexual content that includes an individual who is under 18 years old. violence: type: boolean description: Content that depicts death, violence, or physical injury. violence/graphic: type: boolean description: Content that depicts death, violence, or physical injury in graphic detail. required: - hate - hate/threatening - harassment - harassment/threatening - self-harm - self-harm/intent - self-harm/instructions - sexual - sexual/minors - violence - violence/graphic category_scores: type: object description: A list of the categories along with their scores as predicted by model. properties: hate: type: number description: The score for the category 'hate'. hate/threatening: type: number description: The score for the category 'hate/threatening'. harassment: type: number description: The score for the category 'harassment'. harassment/threatening: type: number description: The score for the category 'harassment/threatening'. self-harm: type: number description: The score for the category 'self-harm'. self-harm/intent: type: number description: The score for the category 'self-harm/intent'. self-harm/instructions: type: number description: The score for the category 'self-harm/instructions'. sexual: type: number description: The score for the category 'sexual'. sexual/minors: type: number description: The score for the category 'sexual/minors'. violence: type: number description: The score for the category 'violence'. violence/graphic: type: number description: The score for the category 'violence/graphic'. required: - hate - hate/threatening - harassment - harassment/threatening - self-harm - self-harm/intent - self-harm/instructions - sexual - sexual/minors - violence - violence/graphic required: - flagged - categories - category_scores required: - id - model - results x-oaiMeta: name: The moderation object example: *moderation_example ListFilesResponse: type: object properties: data: type: array items: $ref: "#/components/schemas/OpenAIFile" object: type: string enum: [list] required: - object - data CreateFileRequest: type: object additionalProperties: false properties: file: description: | The File object (not file name) to be uploaded. type: string format: binary purpose: description: | The intended purpose of the uploaded file. Use "assistants" for [Assistants](/docs/api-reference/assistants) and [Message](/docs/api-reference/messages) files, "vision" for Assistants image file inputs, "batch" for [Batch API](/docs/guides/batch), and "fine-tune" for [Fine-tuning](/docs/api-reference/fine-tuning). type: string enum: ["assistants", "batch", "fine-tune"] required: - file - purpose DeleteFileResponse: type: object properties: id: type: string object: type: string enum: [file] deleted: type: boolean required: - id - object - deleted CreateFineTuningJobRequest: type: object properties: model: description: | The name of the model to fine-tune. You can select one of the [supported models](/docs/guides/fine-tuning/what-models-can-be-fine-tuned). example: "gpt-3.5-turbo" anyOf: - type: string - type: string enum: ["babbage-002", "davinci-002", "gpt-3.5-turbo"] x-oaiTypeLabel: string training_file: description: | The ID of an uploaded file that contains training data. See [upload file](/docs/api-reference/files/create) for how to upload a file. Your dataset must be formatted as a JSONL file. Additionally, you must upload your file with the purpose `fine-tune`. See the [fine-tuning guide](/docs/guides/fine-tuning) for more details. type: string example: "file-abc123" hyperparameters: type: object description: The hyperparameters used for the fine-tuning job. properties: batch_size: description: | Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance. oneOf: - type: string enum: [auto] - type: integer minimum: 1 maximum: 256 default: auto learning_rate_multiplier: description: | Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting. oneOf: - type: string enum: [auto] - type: number minimum: 0 exclusiveMinimum: true default: auto n_epochs: description: | The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset. oneOf: - type: string enum: [auto] - type: integer minimum: 1 maximum: 50 default: auto suffix: description: | A string of up to 18 characters that will be added to your fine-tuned model name. For example, a `suffix` of "custom-model-name" would produce a model name like `ft:gpt-3.5-turbo:openai:custom-model-name:7p4lURel`. type: string minLength: 1 maxLength: 40 default: null nullable: true validation_file: description: | The ID of an uploaded file that contains validation data. If you provide this file, the data is used to generate validation metrics periodically during fine-tuning. These metrics can be viewed in the fine-tuning results file. The same data should not be present in both train and validation files. Your dataset must be formatted as a JSONL file. You must upload your file with the purpose `fine-tune`. See the [fine-tuning guide](/docs/guides/fine-tuning) for more details. type: string nullable: true example: "file-abc123" integrations: type: array description: A list of integrations to enable for your fine-tuning job. nullable: true items: type: object required: - type - wandb properties: type: description: | The type of integration to enable. Currently, only "wandb" (Weights and Biases) is supported. oneOf: - type: string enum: [wandb] wandb: type: object description: | The settings for your integration with Weights and Biases. This payload specifies the project that metrics will be sent to. Optionally, you can set an explicit display name for your run, add tags to your run, and set a default entity (team, username, etc) to be associated with your run. required: - project properties: project: description: | The name of the project that the new run will be created under. type: string example: "my-wandb-project" name: description: | A display name to set for the run. If not set, we will use the Job ID as the name. nullable: true type: string entity: description: | The entity to use for the run. This allows you to set the team or username of the WandB user that you would like associated with the run. If not set, the default entity for the registered WandB API key is used. nullable: true type: string tags: description: | A list of tags to be attached to the newly created run. These tags are passed through directly to WandB. Some default tags are generated by OpenAI: "openai/finetune", "openai/{base-model}", "openai/{ftjob-abcdef}". type: array items: type: string example: "custom-tag" seed: description: | The seed controls the reproducibility of the job. Passing in the same seed and job parameters should produce the same results, but may differ in rare cases. If a seed is not specified, one will be generated for you. type: integer nullable: true minimum: 0 maximum: 2147483647 example: 42 required: - model - training_file ListFineTuningJobEventsResponse: type: object properties: data: type: array items: $ref: "#/components/schemas/FineTuningJobEvent" object: type: string enum: [list] required: - object - data ListFineTuningJobCheckpointsResponse: type: object properties: data: type: array items: $ref: "#/components/schemas/FineTuningJobCheckpoint" object: type: string enum: [list] first_id: type: string nullable: true last_id: type: string nullable: true has_more: type: boolean required: - object - data - has_more CreateEmbeddingRequest: type: object additionalProperties: false properties: input: description: | Input text to embed, encoded as a string or array of tokens. To embed multiple inputs in a single request, pass an array of strings or array of token arrays. The input must not exceed the max input tokens for the model (8192 tokens for `text-embedding-ada-002`), cannot be an empty string, and any array must be 2048 dimensions or less. [Example Python code](https://cookbook.openai.com/examples/how_to_count_tokens_with_tiktoken) for counting tokens. example: "The quick brown fox jumped over the lazy dog" oneOf: - type: string title: string description: The string that will be turned into an embedding. default: "" example: "This is a test." - type: array title: array description: The array of strings that will be turned into an embedding. minItems: 1 maxItems: 2048 items: type: string default: "" example: "['This is a test.']" - type: array title: array description: The array of integers that will be turned into an embedding. minItems: 1 maxItems: 2048 items: type: integer example: "[1212, 318, 257, 1332, 13]" - type: array title: array description: The array of arrays containing integers that will be turned into an embedding. minItems: 1 maxItems: 2048 items: type: array minItems: 1 items: type: integer example: "[[1212, 318, 257, 1332, 13]]" x-oaiExpandable: true model: description: *model_description example: "text-embedding-3-small" anyOf: - type: string - type: string enum: [ "text-embedding-ada-002", "text-embedding-3-small", "text-embedding-3-large", ] x-oaiTypeLabel: string encoding_format: description: "The format to return the embeddings in. Can be either `float` or [`base64`](https://pypi.org/project/pybase64/)." example: "float" default: "float" type: string enum: ["float", "base64"] dimensions: description: | The number of dimensions the resulting output embeddings should have. Only supported in `text-embedding-3` and later models. type: integer minimum: 1 user: *end_user_param_configuration required: - model - input CreateEmbeddingResponse: type: object properties: data: type: array description: The list of embeddings generated by the model. items: $ref: "#/components/schemas/Embedding" model: type: string description: The name of the model used to generate the embedding. object: type: string description: The object type, which is always "list". enum: [list] usage: type: object description: The usage information for the request. properties: prompt_tokens: type: integer description: The number of tokens used by the prompt. total_tokens: type: integer description: The total number of tokens used by the request. required: - prompt_tokens - total_tokens required: - object - model - data - usage CreateTranscriptionRequest: type: object additionalProperties: false properties: file: description: | The audio file object (not file name) to transcribe, in one of these formats: flac, mp3, mp4, mpeg, mpga, m4a, ogg, wav, or webm. type: string x-oaiTypeLabel: file format: binary model: description: | ID of the model to use. Only `whisper-1` (which is powered by our open source Whisper V2 model) is currently available. example: whisper-1 anyOf: - type: string - type: string enum: ["whisper-1"] x-oaiTypeLabel: string language: description: | The language of the input audio. Supplying the input language in [ISO-639-1](https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes) format will improve accuracy and latency. type: string prompt: description: | An optional text to guide the model's style or continue a previous audio segment. The [prompt](/docs/guides/speech-to-text/prompting) should match the audio language. type: string response_format: description: | The format of the transcript output, in one of these options: `json`, `text`, `srt`, `verbose_json`, or `vtt`. type: string enum: - json - text - srt - verbose_json - vtt default: json temperature: description: | The sampling temperature, between 0 and 1. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic. If set to 0, the model will use [log probability](https://en.wikipedia.org/wiki/Log_probability) to automatically increase the temperature until certain thresholds are hit. type: number default: 0 timestamp_granularities[]: description: | The timestamp granularities to populate for this transcription. `response_format` must be set `verbose_json` to use timestamp granularities. Either or both of these options are supported: `word`, or `segment`. Note: There is no additional latency for segment timestamps, but generating word timestamps incurs additional latency. type: array items: type: string enum: - word - segment default: [segment] required: - file - model # Note: This does not currently support the non-default response format types. CreateTranscriptionResponseJson: type: object description: Represents a transcription response returned by model, based on the provided input. properties: text: type: string description: The transcribed text. required: - text x-oaiMeta: name: The transcription object group: audio example: *basic_transcription_response_example TranscriptionSegment: type: object properties: id: type: integer description: Unique identifier of the segment. seek: type: integer description: Seek offset of the segment. start: type: number format: float description: Start time of the segment in seconds. end: type: number format: float description: End time of the segment in seconds. text: type: string description: Text content of the segment. tokens: type: array items: type: integer description: Array of token IDs for the text content. temperature: type: number format: float description: Temperature parameter used for generating the segment. avg_logprob: type: number format: float description: Average logprob of the segment. If the value is lower than -1, consider the logprobs failed. compression_ratio: type: number format: float description: Compression ratio of the segment. If the value is greater than 2.4, consider the compression failed. no_speech_prob: type: number format: float description: Probability of no speech in the segment. If the value is higher than 1.0 and the `avg_logprob` is below -1, consider this segment silent. required: - id - seek - start - end - text - tokens - temperature - avg_logprob - compression_ratio - no_speech_prob TranscriptionWord: type: object properties: word: type: string description: The text content of the word. start: type: number format: float description: Start time of the word in seconds. end: type: number format: float description: End time of the word in seconds. required: [word, start, end] CreateTranscriptionResponseVerboseJson: type: object description: Represents a verbose json transcription response returned by model, based on the provided input. properties: language: type: string description: The language of the input audio. duration: type: string description: The duration of the input audio. text: type: string description: The transcribed text. words: type: array description: Extracted words and their corresponding timestamps. items: $ref: "#/components/schemas/TranscriptionWord" segments: type: array description: Segments of the transcribed text and their corresponding details. items: $ref: "#/components/schemas/TranscriptionSegment" required: [language, duration, text] x-oaiMeta: name: The transcription object group: audio example: *verbose_transcription_response_example CreateTranslationRequest: type: object additionalProperties: false properties: file: description: | The audio file object (not file name) translate, in one of these formats: flac, mp3, mp4, mpeg, mpga, m4a, ogg, wav, or webm. type: string x-oaiTypeLabel: file format: binary model: description: | ID of the model to use. Only `whisper-1` (which is powered by our open source Whisper V2 model) is currently available. example: whisper-1 anyOf: - type: string - type: string enum: ["whisper-1"] x-oaiTypeLabel: string prompt: description: | An optional text to guide the model's style or continue a previous audio segment. The [prompt](/docs/guides/speech-to-text/prompting) should be in English. type: string response_format: description: | The format of the transcript output, in one of these options: `json`, `text`, `srt`, `verbose_json`, or `vtt`. type: string default: json temperature: description: | The sampling temperature, between 0 and 1. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic. If set to 0, the model will use [log probability](https://en.wikipedia.org/wiki/Log_probability) to automatically increase the temperature until certain thresholds are hit. type: number default: 0 required: - file - model # Note: This does not currently support the non-default response format types. CreateTranslationResponseJson: type: object properties: text: type: string required: - text CreateTranslationResponseVerboseJson: type: object properties: language: type: string description: The language of the output translation (always `english`). duration: type: string description: The duration of the input audio. text: type: string description: The translated text. segments: type: array description: Segments of the translated text and their corresponding details. items: $ref: "#/components/schemas/TranscriptionSegment" required: [language, duration, text] CreateSpeechRequest: type: object additionalProperties: false properties: model: description: | One of the available [TTS models](/docs/models/tts): `tts-1` or `tts-1-hd` anyOf: - type: string - type: string enum: ["tts-1", "tts-1-hd"] x-oaiTypeLabel: string input: type: string description: The text to generate audio for. The maximum length is 4096 characters. maxLength: 4096 voice: description: The voice to use when generating the audio. Supported voices are `alloy`, `echo`, `fable`, `onyx`, `nova`, and `shimmer`. Previews of the voices are available in the [Text to speech guide](/docs/guides/text-to-speech/voice-options). type: string enum: ["alloy", "echo", "fable", "onyx", "nova", "shimmer"] response_format: description: "The format to audio in. Supported formats are `mp3`, `opus`, `aac`, `flac`, `wav`, and `pcm`." default: "mp3" type: string enum: ["mp3", "opus", "aac", "flac", "wav", "pcm"] speed: description: "The speed of the generated audio. Select a value from `0.25` to `4.0`. `1.0` is the default." type: number default: 1.0 minimum: 0.25 maximum: 4.0 required: - model - input - voice Model: title: Model description: Describes an OpenAI model offering that can be used with the API. properties: id: type: string description: The model identifier, which can be referenced in the API endpoints. created: type: integer description: The Unix timestamp (in seconds) when the model was created. object: type: string description: The object type, which is always "model". enum: [model] owned_by: type: string description: The organization that owns the model. required: - id - object - created - owned_by x-oaiMeta: name: The model object example: *retrieve_model_response OpenAIFile: title: OpenAIFile description: The `File` object represents a document that has been uploaded to OpenAI. properties: id: type: string description: The file identifier, which can be referenced in the API endpoints. bytes: type: integer description: The size of the file, in bytes. created_at: type: integer description: The Unix timestamp (in seconds) for when the file was created. filename: type: string description: The name of the file. object: type: string description: The object type, which is always `file`. enum: ["file"] purpose: type: string description: The intended purpose of the file. Supported values are `assistants`, `assistants_output`, `batch`, `batch_output`, `fine-tune`, `fine-tune-results` and `vision`. enum: [ "assistants", "assistants_output", "batch", "batch_output", "fine-tune", "fine-tune-results", "vision" ] status: type: string deprecated: true description: Deprecated. The current status of the file, which can be either `uploaded`, `processed`, or `error`. enum: ["uploaded", "processed", "error"] status_details: type: string deprecated: true description: Deprecated. For details on why a fine-tuning training file failed validation, see the `error` field on `fine_tuning.job`. required: - id - object - bytes - created_at - filename - purpose - status x-oaiMeta: name: The file object example: | { "id": "file-abc123", "object": "file", "bytes": 120000, "created_at": 1677610602, "filename": "salesOverview.pdf", "purpose": "assistants", } Embedding: type: object description: | Represents an embedding vector returned by embedding endpoint. properties: index: type: integer description: The index of the embedding in the list of embeddings. embedding: type: array description: | The embedding vector, which is a list of floats. The length of vector depends on the model as listed in the [embedding guide](/docs/guides/embeddings). items: type: number object: type: string description: The object type, which is always "embedding". enum: [embedding] required: - index - object - embedding x-oaiMeta: name: The embedding object example: | { "object": "embedding", "embedding": [ 0.0023064255, -0.009327292, .... (1536 floats total for ada-002) -0.0028842222, ], "index": 0 } FineTuningJob: type: object title: FineTuningJob description: | The `fine_tuning.job` object represents a fine-tuning job that has been created through the API. properties: id: type: string description: The object identifier, which can be referenced in the API endpoints. created_at: type: integer description: The Unix timestamp (in seconds) for when the fine-tuning job was created. error: type: object nullable: true description: For fine-tuning jobs that have `failed`, this will contain more information on the cause of the failure. properties: code: type: string description: A machine-readable error code. message: type: string description: A human-readable error message. param: type: string description: The parameter that was invalid, usually `training_file` or `validation_file`. This field will be null if the failure was not parameter-specific. nullable: true required: - code - message - param fine_tuned_model: type: string nullable: true description: The name of the fine-tuned model that is being created. The value will be null if the fine-tuning job is still running. finished_at: type: integer nullable: true description: The Unix timestamp (in seconds) for when the fine-tuning job was finished. The value will be null if the fine-tuning job is still running. hyperparameters: type: object description: The hyperparameters used for the fine-tuning job. See the [fine-tuning guide](/docs/guides/fine-tuning) for more details. properties: n_epochs: oneOf: - type: string enum: [auto] - type: integer minimum: 1 maximum: 50 default: auto description: The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset. "auto" decides the optimal number of epochs based on the size of the dataset. If setting the number manually, we support any number between 1 and 50 epochs. required: - n_epochs model: type: string description: The base model that is being fine-tuned. object: type: string description: The object type, which is always "fine_tuning.job". enum: [fine_tuning.job] organization_id: type: string description: The organization that owns the fine-tuning job. result_files: type: array description: The compiled results file ID(s) for the fine-tuning job. You can retrieve the results with the [Files API](/docs/api-reference/files/retrieve-contents). items: type: string example: file-abc123 status: type: string description: The current status of the fine-tuning job, which can be either `validating_files`, `queued`, `running`, `succeeded`, `failed`, or `cancelled`. enum: [ "validating_files", "queued", "running", "succeeded", "failed", "cancelled", ] trained_tokens: type: integer nullable: true description: The total number of billable tokens processed by this fine-tuning job. The value will be null if the fine-tuning job is still running. training_file: type: string description: The file ID used for training. You can retrieve the training data with the [Files API](/docs/api-reference/files/retrieve-contents). validation_file: type: string nullable: true description: The file ID used for validation. You can retrieve the validation results with the [Files API](/docs/api-reference/files/retrieve-contents). integrations: type: array nullable: true description: A list of integrations to enable for this fine-tuning job. maxItems: 5 items: oneOf: - $ref: "#/components/schemas/FineTuningIntegration" x-oaiExpandable: true seed: type: integer description: The seed used for the fine-tuning job. estimated_finish: type: integer nullable: true description: The Unix timestamp (in seconds) for when the fine-tuning job is estimated to finish. The value will be null if the fine-tuning job is not running. required: - created_at - error - finished_at - fine_tuned_model - hyperparameters - id - model - object - organization_id - result_files - status - trained_tokens - training_file - validation_file - seed x-oaiMeta: name: The fine-tuning job object example: *fine_tuning_example FineTuningIntegration: type: object title: Fine-Tuning Job Integration required: - type - wandb properties: type: type: string description: "The type of the integration being enabled for the fine-tuning job" enum: ["wandb"] wandb: type: object description: | The settings for your integration with Weights and Biases. This payload specifies the project that metrics will be sent to. Optionally, you can set an explicit display name for your run, add tags to your run, and set a default entity (team, username, etc) to be associated with your run. required: - project properties: project: description: | The name of the project that the new run will be created under. type: string example: "my-wandb-project" name: description: | A display name to set for the run. If not set, we will use the Job ID as the name. nullable: true type: string entity: description: | The entity to use for the run. This allows you to set the team or username of the WandB user that you would like associated with the run. If not set, the default entity for the registered WandB API key is used. nullable: true type: string tags: description: | A list of tags to be attached to the newly created run. These tags are passed through directly to WandB. Some default tags are generated by OpenAI: "openai/finetune", "openai/{base-model}", "openai/{ftjob-abcdef}". type: array items: type: string example: "custom-tag" FineTuningJobEvent: type: object description: Fine-tuning job event object properties: id: type: string created_at: type: integer level: type: string enum: ["info", "warn", "error"] message: type: string object: type: string enum: [fine_tuning.job.event] required: - id - object - created_at - level - message x-oaiMeta: name: The fine-tuning job event object example: | { "object": "fine_tuning.job.event", "id": "ftevent-abc123" "created_at": 1677610602, "level": "info", "message": "Created fine-tuning job" } FineTuningJobCheckpoint: type: object title: FineTuningJobCheckpoint description: | The `fine_tuning.job.checkpoint` object represents a model checkpoint for a fine-tuning job that is ready to use. properties: id: type: string description: The checkpoint identifier, which can be referenced in the API endpoints. created_at: type: integer description: The Unix timestamp (in seconds) for when the checkpoint was created. fine_tuned_model_checkpoint: type: string description: The name of the fine-tuned checkpoint model that is created. step_number: type: integer description: The step number that the checkpoint was created at. metrics: type: object description: Metrics at the step number during the fine-tuning job. properties: step: type: number train_loss: type: number train_mean_token_accuracy: type: number valid_loss: type: number valid_mean_token_accuracy: type: number full_valid_loss: type: number full_valid_mean_token_accuracy: type: number fine_tuning_job_id: type: string description: The name of the fine-tuning job that this checkpoint was created from. object: type: string description: The object type, which is always "fine_tuning.job.checkpoint". enum: [fine_tuning.job.checkpoint] required: - created_at - fine_tuning_job_id - fine_tuned_model_checkpoint - id - metrics - object - step_number x-oaiMeta: name: The fine-tuning job checkpoint object example: | { "object": "fine_tuning.job.checkpoint", "id": "ftckpt_qtZ5Gyk4BLq1SfLFWp3RtO3P", "created_at": 1712211699, "fine_tuned_model_checkpoint": "ft:gpt-3.5-turbo-0125:my-org:custom_suffix:9ABel2dg:ckpt-step-88", "fine_tuning_job_id": "ftjob-fpbNQ3H1GrMehXRf8cO97xTN", "metrics": { "step": 88, "train_loss": 0.478, "train_mean_token_accuracy": 0.924, "valid_loss": 10.112, "valid_mean_token_accuracy": 0.145, "full_valid_loss": 0.567, "full_valid_mean_token_accuracy": 0.944 }, "step_number": 88 } CompletionUsage: type: object description: Usage statistics for the completion request. properties: completion_tokens: type: integer description: Number of tokens in the generated completion. prompt_tokens: type: integer description: Number of tokens in the prompt. total_tokens: type: integer description: Total number of tokens used in the request (prompt + completion). required: - prompt_tokens - completion_tokens - total_tokens RunCompletionUsage: type: object description: Usage statistics related to the run. This value will be `null` if the run is not in a terminal state (i.e. `in_progress`, `queued`, etc.). properties: completion_tokens: type: integer description: Number of completion tokens used over the course of the run. prompt_tokens: type: integer description: Number of prompt tokens used over the course of the run. total_tokens: type: integer description: Total number of tokens used (prompt + completion). required: - prompt_tokens - completion_tokens - total_tokens nullable: true RunStepCompletionUsage: type: object description: Usage statistics related to the run step. This value will be `null` while the run step's status is `in_progress`. properties: completion_tokens: type: integer description: Number of completion tokens used over the course of the run step. prompt_tokens: type: integer description: Number of prompt tokens used over the course of the run step. total_tokens: type: integer description: Total number of tokens used (prompt + completion). required: - prompt_tokens - completion_tokens - total_tokens nullable: true AssistantsApiResponseFormatOption: description: | Specifies the format that the model must output. Compatible with [GPT-4o](/docs/models/gpt-4o), [GPT-4 Turbo](/docs/models/gpt-4-turbo-and-gpt-4), and all GPT-3.5 Turbo models since `gpt-3.5-turbo-1106`. Setting to `{ "type": "json_object" }` enables JSON mode, which guarantees the message the model generates is valid JSON. **Important:** when using JSON mode, you **must** also instruct the model to produce JSON yourself via a system or user message. Without this, the model may generate an unending stream of whitespace until the generation reaches the token limit, resulting in a long-running and seemingly "stuck" request. Also note that the message content may be partially cut off if `finish_reason="length"`, which indicates the generation exceeded `max_tokens` or the conversation exceeded the max context length. oneOf: - type: string description: > `auto` is the default value enum: [none, auto] - $ref: "#/components/schemas/AssistantsApiResponseFormat" x-oaiExpandable: true AssistantsApiResponseFormat: type: object description: | An object describing the expected output of the model. If `json_object` only `function` type `tools` are allowed to be passed to the Run. If `text` the model can return text or any value needed. properties: type: type: string enum: ["text", "json_object"] example: "json_object" default: "text" description: Must be one of `text` or `json_object`. AssistantObject: type: object title: Assistant description: Represents an `assistant` that can call the model and use tools. properties: id: description: The identifier, which can be referenced in API endpoints. type: string object: description: The object type, which is always `assistant`. type: string enum: [assistant] created_at: description: The Unix timestamp (in seconds) for when the assistant was created. type: integer name: description: &assistant_name_param_description | The name of the assistant. The maximum length is 256 characters. type: string maxLength: 256 nullable: true description: description: &assistant_description_param_description | The description of the assistant. The maximum length is 512 characters. type: string maxLength: 512 nullable: true model: description: *model_description type: string instructions: description: &assistant_instructions_param_description | The system instructions that the assistant uses. The maximum length is 256,000 characters. type: string maxLength: 256000 nullable: true tools: description: &assistant_tools_param_description | A list of tool enabled on the assistant. There can be a maximum of 128 tools per assistant. Tools can be of types `code_interpreter`, `file_search`, or `function`. default: [] type: array maxItems: 128 items: oneOf: - $ref: "#/components/schemas/AssistantToolsCode" - $ref: "#/components/schemas/AssistantToolsFileSearch" - $ref: "#/components/schemas/AssistantToolsFunction" x-oaiExpandable: true tool_resources: type: object description: | A set of resources that are used by the assistant's tools. The resources are specific to the type of tool. For example, the `code_interpreter` tool requires a list of file IDs, while the `file_search` tool requires a list of vector store IDs. properties: code_interpreter: type: object properties: file_ids: type: array description: | A list of [file](/docs/api-reference/files) IDs made available to the `code_interpreter`` tool. There can be a maximum of 20 files associated with the tool. default: [] maxItems: 20 items: type: string file_search: type: object properties: vector_store_ids: type: array description: | The ID of the [vector store](/docs/api-reference/vector-stores/object) attached to this assistant. There can be a maximum of 1 vector store attached to the assistant. maxItems: 1 items: type: string nullable: true metadata: description: &metadata_description | Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format. Keys can be a maximum of 64 characters long and values can be a maxium of 512 characters long. type: object x-oaiTypeLabel: map nullable: true temperature: description: &run_temperature_description | What sampling temperature to use, between 0 and 2. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic. type: number minimum: 0 maximum: 2 default: 1 example: 1 nullable: true top_p: type: number minimum: 0 maximum: 1 default: 1 example: 1 nullable: true description: &run_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. response_format: $ref: "#/components/schemas/AssistantsApiResponseFormatOption" nullable: true required: - id - object - created_at - name - description - model - instructions - tools - metadata x-oaiMeta: name: The assistant object beta: true example: *create_assistants_example CreateAssistantRequest: type: object additionalProperties: false properties: model: description: *model_description example: "gpt-4-turbo" anyOf: - type: string - type: string enum: [ "gpt-4o", "gpt-4o-2024-05-13", "gpt-4-turbo", "gpt-4-turbo-2024-04-09", "gpt-4-0125-preview", "gpt-4-turbo-preview", "gpt-4-1106-preview", "gpt-4-vision-preview", "gpt-4", "gpt-4-0314", "gpt-4-0613", "gpt-4-32k", "gpt-4-32k-0314", "gpt-4-32k-0613", "gpt-3.5-turbo", "gpt-3.5-turbo-16k", "gpt-3.5-turbo-0613", "gpt-3.5-turbo-1106", "gpt-3.5-turbo-0125", "gpt-3.5-turbo-16k-0613", ] x-oaiTypeLabel: string name: description: *assistant_name_param_description type: string nullable: true maxLength: 256 description: description: *assistant_description_param_description type: string nullable: true maxLength: 512 instructions: description: *assistant_instructions_param_description type: string nullable: true maxLength: 256000 tools: description: *assistant_tools_param_description default: [] type: array maxItems: 128 items: oneOf: - $ref: "#/components/schemas/AssistantToolsCode" - $ref: "#/components/schemas/AssistantToolsFileSearch" - $ref: "#/components/schemas/AssistantToolsFunction" x-oaiExpandable: true tool_resources: type: object description: | A set of resources that are used by the assistant's tools. The resources are specific to the type of tool. For example, the `code_interpreter` tool requires a list of file IDs, while the `file_search` tool requires a list of vector store IDs. properties: code_interpreter: type: object properties: file_ids: type: array description: | A list of [file](/docs/api-reference/files) IDs made available to the `code_interpreter` tool. There can be a maximum of 20 files associated with the tool. default: [] maxItems: 20 items: type: string file_search: type: object properties: vector_store_ids: type: array description: | The [vector store](/docs/api-reference/vector-stores/object) attached to this assistant. There can be a maximum of 1 vector store attached to the assistant. maxItems: 1 items: type: string vector_stores: type: array description: | A helper to create a [vector store](/docs/api-reference/vector-stores/object) with file_ids and attach it to this assistant. There can be a maximum of 1 vector store attached to the assistant. maxItems: 1 items: type: object properties: file_ids: type: array description: | A list of [file](/docs/api-reference/files) IDs to add to the vector store. There can be a maximum of 10000 files in a vector store. maxItems: 10000 items: type: string metadata: type: object description: | Set of 16 key-value pairs that can be attached to a vector store. This can be useful for storing additional information about the vector store in a structured format. Keys can be a maximum of 64 characters long and values can be a maxium of 512 characters long. x-oaiTypeLabel: map oneOf: - required: [vector_store_ids] - required: [vector_stores] nullable: true metadata: description: *metadata_description type: object x-oaiTypeLabel: map nullable: true temperature: description: &run_temperature_description | What sampling temperature to use, between 0 and 2. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic. type: number minimum: 0 maximum: 2 default: 1 example: 1 nullable: true top_p: type: number minimum: 0 maximum: 1 default: 1 example: 1 nullable: true description: &run_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. response_format: $ref: "#/components/schemas/AssistantsApiResponseFormatOption" nullable: true required: - model ModifyAssistantRequest: type: object additionalProperties: false properties: model: description: *model_description anyOf: - type: string name: description: *assistant_name_param_description type: string nullable: true maxLength: 256 description: description: *assistant_description_param_description type: string nullable: true maxLength: 512 instructions: description: *assistant_instructions_param_description type: string nullable: true maxLength: 256000 tools: description: *assistant_tools_param_description default: [] type: array maxItems: 128 items: oneOf: - $ref: "#/components/schemas/AssistantToolsCode" - $ref: "#/components/schemas/AssistantToolsFileSearch" - $ref: "#/components/schemas/AssistantToolsFunction" x-oaiExpandable: true tool_resources: type: object description: | A set of resources that are used by the assistant's tools. The resources are specific to the type of tool. For example, the `code_interpreter` tool requires a list of file IDs, while the `file_search` tool requires a list of vector store IDs. properties: code_interpreter: type: object properties: file_ids: type: array description: | Overrides the list of [file](/docs/api-reference/files) IDs made available to the `code_interpreter` tool. There can be a maximum of 20 files associated with the tool. default: [] maxItems: 20 items: type: string file_search: type: object properties: vector_store_ids: type: array description: | Overrides the [vector store](/docs/api-reference/vector-stores/object) attached to this assistant. There can be a maximum of 1 vector store attached to the assistant. maxItems: 1 items: type: string nullable: true metadata: description: *metadata_description type: object x-oaiTypeLabel: map nullable: true temperature: description: *run_temperature_description type: number minimum: 0 maximum: 2 default: 1 example: 1 nullable: true top_p: type: number minimum: 0 maximum: 1 default: 1 example: 1 nullable: true description: &run_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. response_format: $ref: "#/components/schemas/AssistantsApiResponseFormatOption" nullable: true DeleteAssistantResponse: type: object properties: id: type: string deleted: type: boolean object: type: string enum: [assistant.deleted] required: - id - object - deleted ListAssistantsResponse: type: object properties: object: type: string example: "list" data: type: array items: $ref: "#/components/schemas/AssistantObject" first_id: type: string example: "asst_abc123" last_id: type: string example: "asst_abc456" has_more: type: boolean example: false required: - object - data - first_id - last_id - has_more x-oaiMeta: name: List assistants response object group: chat example: *list_assistants_example AssistantToolsCode: type: object title: Code interpreter tool properties: type: type: string description: "The type of tool being defined: `code_interpreter`" enum: ["code_interpreter"] required: - type AssistantToolsFileSearch: type: object title: FileSearch tool properties: type: type: string description: "The type of tool being defined: `file_search`" enum: ["file_search"] required: - type AssistantToolsFunction: type: object title: Function tool properties: type: type: string description: "The type of tool being defined: `function`" enum: ["function"] function: $ref: "#/components/schemas/FunctionObject" required: - type - function TruncationObject: type: object title: Thread Truncation Controls description: Controls for how a thread will be truncated prior to the run. Use this to control the intial context window of the run. properties: type: type: string description: The truncation strategy to use for the thread. The default is `auto`. If set to `last_messages`, the thread will be truncated to the n most recent messages in the thread. When set to `auto`, messages in the middle of the thread will be dropped to fit the context length of the model, `max_prompt_tokens`. enum: ["auto", "last_messages"] last_messages: type: integer description: The number of most recent messages from the thread when constructing the context for the run. minimum: 1 nullable: true required: - type AssistantsApiToolChoiceOption: description: | Controls which (if any) tool is called by the model. `none` means the model will not call any tools and instead generates a message. `auto` is the default value and means the model can pick between generating a message or calling one or more tools. `required` means the model must call one or more tools before responding to the user. Specifying a particular tool like `{"type": "file_search"}` or `{"type": "function", "function": {"name": "my_function"}}` forces the model to call that tool. oneOf: - type: string description: > `none` means the model will not call any tools and instead generates a message. `auto` means the model can pick between generating a message or calling one or more tools. `required` means the model must call one or more tools before responding to the user. enum: [none, auto, required] - $ref: "#/components/schemas/AssistantsNamedToolChoice" x-oaiExpandable: true AssistantsNamedToolChoice: type: object description: Specifies a tool the model should use. Use to force the model to call a specific tool. properties: type: type: string enum: ["function", "code_interpreter", "file_search"] description: The type of the tool. If type is `function`, the function name must be set function: type: object properties: name: type: string description: The name of the function to call. required: - name required: - type RunObject: type: object title: A run on a thread description: Represents an execution run on a [thread](/docs/api-reference/threads). properties: id: description: The identifier, which can be referenced in API endpoints. type: string object: description: The object type, which is always `thread.run`. type: string enum: ["thread.run"] created_at: description: The Unix timestamp (in seconds) for when the run was created. type: integer thread_id: description: The ID of the [thread](/docs/api-reference/threads) that was executed on as a part of this run. type: string assistant_id: description: The ID of the [assistant](/docs/api-reference/assistants) used for execution of this run. type: string status: description: The status of the run, which can be either `queued`, `in_progress`, `requires_action`, `cancelling`, `cancelled`, `failed`, `completed`, `incomplete`, or `expired`. type: string enum: [ "queued", "in_progress", "requires_action", "cancelling", "cancelled", "failed", "completed", "incomplete", "expired", ] required_action: type: object description: Details on the action required to continue the run. Will be `null` if no action is required. nullable: true properties: type: description: For now, this is always `submit_tool_outputs`. type: string enum: ["submit_tool_outputs"] submit_tool_outputs: type: object description: Details on the tool outputs needed for this run to continue. properties: tool_calls: type: array description: A list of the relevant tool calls. items: $ref: "#/components/schemas/RunToolCallObject" required: - tool_calls required: - type - submit_tool_outputs last_error: type: object description: The last error associated with this run. Will be `null` if there are no errors. nullable: true properties: code: type: string description: One of `server_error`, `rate_limit_exceeded`, or `invalid_prompt`. enum: ["server_error", "rate_limit_exceeded", "invalid_prompt"] message: type: string description: A human-readable description of the error. required: - code - message expires_at: description: The Unix timestamp (in seconds) for when the run will expire. type: integer nullable: true started_at: description: The Unix timestamp (in seconds) for when the run was started. type: integer nullable: true cancelled_at: description: The Unix timestamp (in seconds) for when the run was cancelled. type: integer nullable: true failed_at: description: The Unix timestamp (in seconds) for when the run failed. type: integer nullable: true completed_at: description: The Unix timestamp (in seconds) for when the run was completed. type: integer nullable: true incomplete_details: description: Details on why the run is incomplete. Will be `null` if the run is not incomplete. type: object nullable: true properties: reason: description: The reason why the run is incomplete. This will point to which specific token limit was reached over the course of the run. type: string enum: ["max_completion_tokens", "max_prompt_tokens"] model: description: The model that the [assistant](/docs/api-reference/assistants) used for this run. type: string instructions: description: The instructions that the [assistant](/docs/api-reference/assistants) used for this run. type: string tools: description: The list of tools that the [assistant](/docs/api-reference/assistants) used for this run. default: [] type: array maxItems: 20 items: oneOf: - $ref: "#/components/schemas/AssistantToolsCode" - $ref: "#/components/schemas/AssistantToolsFileSearch" - $ref: "#/components/schemas/AssistantToolsFunction" x-oaiExpandable: true metadata: description: *metadata_description type: object x-oaiTypeLabel: map nullable: true usage: $ref: "#/components/schemas/RunCompletionUsage" temperature: description: The sampling temperature used for this run. If not set, defaults to 1. type: number nullable: true top_p: description: The nucleus sampling value used for this run. If not set, defaults to 1. type: number nullable: true max_prompt_tokens: type: integer nullable: true description: | The maximum number of prompt tokens specified to have been used over the course of the run. minimum: 256 max_completion_tokens: type: integer nullable: true description: | The maximum number of completion tokens specified to have been used over the course of the run. minimum: 256 truncation_strategy: $ref: "#/components/schemas/TruncationObject" nullable: true tool_choice: $ref: "#/components/schemas/AssistantsApiToolChoiceOption" nullable: true response_format: $ref: "#/components/schemas/AssistantsApiResponseFormatOption" nullable: true required: - id - object - created_at - thread_id - assistant_id - status - required_action - last_error - expires_at - started_at - cancelled_at - failed_at - completed_at - model - instructions - tools - metadata - usage - incomplete_details - max_prompt_tokens - max_completion_tokens - truncation_strategy - tool_choice - response_format x-oaiMeta: name: The run object beta: true example: | { "id": "run_abc123", "object": "thread.run", "created_at": 1698107661, "assistant_id": "asst_abc123", "thread_id": "thread_abc123", "status": "completed", "started_at": 1699073476, "expires_at": null, "cancelled_at": null, "failed_at": null, "completed_at": 1699073498, "last_error": null, "model": "gpt-4-turbo", "instructions": null, "tools": [{"type": "file_search"}, {"type": "code_interpreter"}], "metadata": {}, "incomplete_details": null, "usage": { "prompt_tokens": 123, "completion_tokens": 456, "total_tokens": 579 }, "temperature": 1.0, "top_p": 1.0, "max_prompt_tokens": 1000, "max_completion_tokens": 1000, "truncation_strategy": { "type": "auto", "last_messages": null }, "response_format": "auto", "tool_choice": "auto" } CreateRunRequest: type: object additionalProperties: false properties: assistant_id: description: The ID of the [assistant](/docs/api-reference/assistants) to use to execute this run. type: string model: description: The ID of the [Model](/docs/api-reference/models) to be used to execute this run. If a value is provided here, it will override the model associated with the assistant. If not, the model associated with the assistant will be used. example: "gpt-4-turbo" anyOf: - type: string - type: string enum: [ "gpt-4o", "gpt-4o-2024-05-13", "gpt-4-turbo", "gpt-4-turbo-2024-04-09", "gpt-4-0125-preview", "gpt-4-turbo-preview", "gpt-4-1106-preview", "gpt-4-vision-preview", "gpt-4", "gpt-4-0314", "gpt-4-0613", "gpt-4-32k", "gpt-4-32k-0314", "gpt-4-32k-0613", "gpt-3.5-turbo", "gpt-3.5-turbo-16k", "gpt-3.5-turbo-0613", "gpt-3.5-turbo-1106", "gpt-3.5-turbo-0125", "gpt-3.5-turbo-16k-0613", ] x-oaiTypeLabel: string nullable: true instructions: description: Overrides the [instructions](/docs/api-reference/assistants/createAssistant) of the assistant. This is useful for modifying the behavior on a per-run basis. type: string nullable: true additional_instructions: description: Appends additional instructions at the end of the instructions for the run. This is useful for modifying the behavior on a per-run basis without overriding other instructions. type: string nullable: true additional_messages: description: Adds additional messages to the thread before creating the run. type: array items: $ref: "#/components/schemas/CreateMessageRequest" nullable: true tools: description: Override the tools the assistant can use for this run. This is useful for modifying the behavior on a per-run basis. nullable: true type: array maxItems: 20 items: oneOf: - $ref: "#/components/schemas/AssistantToolsCode" - $ref: "#/components/schemas/AssistantToolsFileSearch" - $ref: "#/components/schemas/AssistantToolsFunction" x-oaiExpandable: true metadata: description: *metadata_description type: object x-oaiTypeLabel: map nullable: true temperature: type: number minimum: 0 maximum: 2 default: 1 example: 1 nullable: true description: *run_temperature_description top_p: type: number minimum: 0 maximum: 1 default: 1 example: 1 nullable: true description: &run_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. stream: type: boolean nullable: true description: | If `true`, returns a stream of events that happen during the Run as server-sent events, terminating when the Run enters a terminal state with a `data: [DONE]` message. max_prompt_tokens: type: integer nullable: true description: | The maximum number of prompt tokens that may be used over the course of the run. The run will make a best effort to use only the number of prompt tokens specified, across multiple turns of the run. If the run exceeds the number of prompt tokens specified, the run will end with status `incomplete`. See `incomplete_details` for more info. minimum: 256 max_completion_tokens: type: integer nullable: true description: | The maximum number of completion tokens that may be used over the course of the run. The run will make a best effort to use only the number of completion tokens specified, across multiple turns of the run. If the run exceeds the number of completion tokens specified, the run will end with status `incomplete`. See `incomplete_details` for more info. minimum: 256 truncation_strategy: $ref: "#/components/schemas/TruncationObject" nullable: true tool_choice: $ref: "#/components/schemas/AssistantsApiToolChoiceOption" nullable: true response_format: $ref: "#/components/schemas/AssistantsApiResponseFormatOption" nullable: true required: - thread_id - assistant_id ListRunsResponse: type: object properties: object: type: string example: "list" data: type: array items: $ref: "#/components/schemas/RunObject" first_id: type: string example: "run_abc123" last_id: type: string example: "run_abc456" has_more: type: boolean example: false required: - object - data - first_id - last_id - has_more ModifyRunRequest: type: object additionalProperties: false properties: metadata: description: *metadata_description type: object x-oaiTypeLabel: map nullable: true SubmitToolOutputsRunRequest: type: object additionalProperties: false properties: tool_outputs: description: A list of tools for which the outputs are being submitted. type: array items: type: object properties: tool_call_id: type: string description: The ID of the tool call in the `required_action` object within the run object the output is being submitted for. output: type: string description: The output of the tool call to be submitted to continue the run. stream: type: boolean nullable: true description: | If `true`, returns a stream of events that happen during the Run as server-sent events, terminating when the Run enters a terminal state with a `data: [DONE]` message. required: - tool_outputs RunToolCallObject: type: object description: Tool call objects properties: id: type: string description: The ID of the tool call. This ID must be referenced when you submit the tool outputs in using the [Submit tool outputs to run](/docs/api-reference/runs/submitToolOutputs) endpoint. type: type: string description: The type of tool call the output is required for. For now, this is always `function`. enum: ["function"] function: type: object description: The function definition. properties: name: type: string description: The name of the function. arguments: type: string description: The arguments that the model expects you to pass to the function. required: - name - arguments required: - id - type - function CreateThreadAndRunRequest: type: object additionalProperties: false properties: assistant_id: description: The ID of the [assistant](/docs/api-reference/assistants) to use to execute this run. type: string thread: $ref: "#/components/schemas/CreateThreadRequest" description: If no thread is provided, an empty thread will be created. model: description: The ID of the [Model](/docs/api-reference/models) to be used to execute this run. If a value is provided here, it will override the model associated with the assistant. If not, the model associated with the assistant will be used. example: "gpt-4-turbo" anyOf: - type: string - type: string enum: [ "gpt-4o", "gpt-4o-2024-05-13", "gpt-4-turbo", "gpt-4-turbo-2024-04-09", "gpt-4-0125-preview", "gpt-4-turbo-preview", "gpt-4-1106-preview", "gpt-4-vision-preview", "gpt-4", "gpt-4-0314", "gpt-4-0613", "gpt-4-32k", "gpt-4-32k-0314", "gpt-4-32k-0613", "gpt-3.5-turbo", "gpt-3.5-turbo-16k", "gpt-3.5-turbo-0613", "gpt-3.5-turbo-1106", "gpt-3.5-turbo-0125", "gpt-3.5-turbo-16k-0613", ] x-oaiTypeLabel: string nullable: true instructions: description: Override the default system message of the assistant. This is useful for modifying the behavior on a per-run basis. type: string nullable: true tools: description: Override the tools the assistant can use for this run. This is useful for modifying the behavior on a per-run basis. nullable: true type: array maxItems: 20 items: oneOf: - $ref: "#/components/schemas/AssistantToolsCode" - $ref: "#/components/schemas/AssistantToolsFileSearch" - $ref: "#/components/schemas/AssistantToolsFunction" tool_resources: type: object description: | A set of resources that are used by the assistant's tools. The resources are specific to the type of tool. For example, the `code_interpreter` tool requires a list of file IDs, while the `file_search` tool requires a list of vector store IDs. properties: code_interpreter: type: object properties: file_ids: type: array description: | A list of [file](/docs/api-reference/files) IDs made available to the `code_interpreter` tool. There can be a maximum of 20 files associated with the tool. default: [] maxItems: 20 items: type: string file_search: type: object properties: vector_store_ids: type: array description: | The ID of the [vector store](/docs/api-reference/vector-stores/object) attached to this assistant. There can be a maximum of 1 vector store attached to the assistant. maxItems: 1 items: type: string nullable: true metadata: description: *metadata_description type: object x-oaiTypeLabel: map nullable: true temperature: type: number minimum: 0 maximum: 2 default: 1 example: 1 nullable: true description: *run_temperature_description top_p: type: number minimum: 0 maximum: 1 default: 1 example: 1 nullable: true description: *run_top_p_description stream: type: boolean nullable: true description: | If `true`, returns a stream of events that happen during the Run as server-sent events, terminating when the Run enters a terminal state with a `data: [DONE]` message. max_prompt_tokens: type: integer nullable: true description: | The maximum number of prompt tokens that may be used over the course of the run. The run will make a best effort to use only the number of prompt tokens specified, across multiple turns of the run. If the run exceeds the number of prompt tokens specified, the run will end with status `incomplete`. See `incomplete_details` for more info. minimum: 256 max_completion_tokens: type: integer nullable: true description: | The maximum number of completion tokens that may be used over the course of the run. The run will make a best effort to use only the number of completion tokens specified, across multiple turns of the run. If the run exceeds the number of completion tokens specified, the run will end with status `incomplete`. See `incomplete_details` for more info. minimum: 256 truncation_strategy: $ref: "#/components/schemas/TruncationObject" nullable: true tool_choice: $ref: "#/components/schemas/AssistantsApiToolChoiceOption" nullable: true response_format: $ref: "#/components/schemas/AssistantsApiResponseFormatOption" nullable: true required: - thread_id - assistant_id ThreadObject: type: object title: Thread description: Represents a thread that contains [messages](/docs/api-reference/messages). properties: id: description: The identifier, which can be referenced in API endpoints. type: string object: description: The object type, which is always `thread`. type: string enum: ["thread"] created_at: description: The Unix timestamp (in seconds) for when the thread was created. type: integer tool_resources: type: object description: | A set of resources that are made available to the assistant's tools in this thread. The resources are specific to the type of tool. For example, the `code_interpreter` tool requires a list of file IDs, while the `file_search` tool requires a list of vector store IDs. properties: code_interpreter: type: object properties: file_ids: type: array description: | A list of [file](/docs/api-reference/files) IDs made available to the `code_interpreter` tool. There can be a maximum of 20 files associated with the tool. default: [] maxItems: 20 items: type: string file_search: type: object properties: vector_store_ids: type: array description: | The [vector store](/docs/api-reference/vector-stores/object) attached to this thread. There can be a maximum of 1 vector store attached to the thread. maxItems: 1 items: type: string nullable: true metadata: description: *metadata_description type: object x-oaiTypeLabel: map nullable: true required: - id - object - created_at - tool_resources - metadata x-oaiMeta: name: The thread object beta: true example: | { "id": "thread_abc123", "object": "thread", "created_at": 1698107661, "metadata": {} } CreateThreadRequest: type: object additionalProperties: false properties: messages: description: A list of [messages](/docs/api-reference/messages) to start the thread with. type: array items: $ref: "#/components/schemas/CreateMessageRequest" tool_resources: type: object description: | A set of resources that are made available to the assistant's tools in this thread. The resources are specific to the type of tool. For example, the `code_interpreter` tool requires a list of file IDs, while the `file_search` tool requires a list of vector store IDs. properties: code_interpreter: type: object properties: file_ids: type: array description: | A list of [file](/docs/api-reference/files) IDs made available to the `code_interpreter` tool. There can be a maximum of 20 files associated with the tool. default: [] maxItems: 20 items: type: string file_search: type: object properties: vector_store_ids: type: array description: | The [vector store](/docs/api-reference/vector-stores/object) attached to this thread. There can be a maximum of 1 vector store attached to the thread. maxItems: 1 items: type: string vector_stores: type: array description: | A helper to create a [vector store](/docs/api-reference/vector-stores/object) with file_ids and attach it to this thread. There can be a maximum of 1 vector store attached to the thread. maxItems: 1 items: type: object properties: file_ids: type: array description: | A list of [file](/docs/api-reference/files) IDs to add to the vector store. There can be a maximum of 10000 files in a vector store. maxItems: 10000 items: type: string metadata: type: object description: | Set of 16 key-value pairs that can be attached to a vector store. This can be useful for storing additional information about the vector store in a structured format. Keys can be a maximum of 64 characters long and values can be a maxium of 512 characters long. x-oaiTypeLabel: map oneOf: - required: [vector_store_ids] - required: [vector_stores] nullable: true metadata: description: *metadata_description type: object x-oaiTypeLabel: map nullable: true ModifyThreadRequest: type: object additionalProperties: false properties: tool_resources: type: object description: | A set of resources that are made available to the assistant's tools in this thread. The resources are specific to the type of tool. For example, the `code_interpreter` tool requires a list of file IDs, while the `file_search` tool requires a list of vector store IDs. properties: code_interpreter: type: object properties: file_ids: type: array description: | A list of [file](/docs/api-reference/files) IDs made available to the `code_interpreter` tool. There can be a maximum of 20 files associated with the tool. default: [] maxItems: 20 items: type: string file_search: type: object properties: vector_store_ids: type: array description: | The [vector store](/docs/api-reference/vector-stores/object) attached to this thread. There can be a maximum of 1 vector store attached to the thread. maxItems: 1 items: type: string nullable: true metadata: description: *metadata_description type: object x-oaiTypeLabel: map nullable: true DeleteThreadResponse: type: object properties: id: type: string deleted: type: boolean object: type: string enum: [thread.deleted] required: - id - object - deleted ListThreadsResponse: properties: object: type: string example: "list" data: type: array items: $ref: "#/components/schemas/ThreadObject" first_id: type: string example: "asst_abc123" last_id: type: string example: "asst_abc456" has_more: type: boolean example: false required: - object - data - first_id - last_id - has_more MessageObject: type: object title: The message object description: Represents a message within a [thread](/docs/api-reference/threads). properties: id: description: The identifier, which can be referenced in API endpoints. type: string object: description: The object type, which is always `thread.message`. type: string enum: ["thread.message"] created_at: description: The Unix timestamp (in seconds) for when the message was created. type: integer thread_id: description: The [thread](/docs/api-reference/threads) ID that this message belongs to. type: string status: description: The status of the message, which can be either `in_progress`, `incomplete`, or `completed`. type: string enum: ["in_progress", "incomplete", "completed"] incomplete_details: description: On an incomplete message, details about why the message is incomplete. type: object properties: reason: type: string description: The reason the message is incomplete. enum: [ "content_filter", "max_tokens", "run_cancelled", "run_expired", "run_failed", ] nullable: true required: - reason completed_at: description: The Unix timestamp (in seconds) for when the message was completed. type: integer nullable: true incomplete_at: description: The Unix timestamp (in seconds) for when the message was marked as incomplete. type: integer nullable: true role: description: The entity that produced the message. One of `user` or `assistant`. type: string enum: ["user", "assistant"] content: description: The content of the message in array of text and/or images. type: array items: oneOf: - $ref: "#/components/schemas/MessageContentImageFileObject" - $ref: "#/components/schemas/MessageContentImageUrlObject" - $ref: "#/components/schemas/MessageContentTextObject" x-oaiExpandable: true assistant_id: description: If applicable, the ID of the [assistant](/docs/api-reference/assistants) that authored this message. type: string nullable: true run_id: description: The ID of the [run](/docs/api-reference/runs) associated with the creation of this message. Value is `null` when messages are created manually using the create message or create thread endpoints. type: string nullable: true attachments: type: array items: type: object properties: file_id: type: string description: The ID of the file to attach to the message. tools: description: The tools to add this file to. type: array items: oneOf: - $ref: "#/components/schemas/AssistantToolsCode" - $ref: "#/components/schemas/AssistantToolsFileSearch" x-oaiExpandable: true description: A list of files attached to the message, and the tools they were added to. nullable: true metadata: description: *metadata_description type: object x-oaiTypeLabel: map nullable: true required: - id - object - created_at - thread_id - status - incomplete_details - completed_at - incomplete_at - role - content - assistant_id - run_id - attachments - metadata x-oaiMeta: name: The message object beta: true example: | { "id": "msg_abc123", "object": "thread.message", "created_at": 1698983503, "thread_id": "thread_abc123", "role": "assistant", "content": [ { "type": "text", "text": { "value": "Hi! How can I help you today?", "annotations": [] } } ], "assistant_id": "asst_abc123", "run_id": "run_abc123", "attachments": [], "metadata": {} } MessageDeltaObject: type: object title: Message delta object description: | Represents a message delta i.e. any changed fields on a message during streaming. properties: id: description: The identifier of the message, which can be referenced in API endpoints. type: string object: description: The object type, which is always `thread.message.delta`. type: string enum: ["thread.message.delta"] delta: description: The delta containing the fields that have changed on the Message. type: object properties: role: description: The entity that produced the message. One of `user` or `assistant`. type: string enum: ["user", "assistant"] content: description: The content of the message in array of text and/or images. type: array items: oneOf: - $ref: "#/components/schemas/MessageDeltaContentImageFileObject" - $ref: "#/components/schemas/MessageDeltaContentTextObject" - $ref: "#/components/schemas/MessageDeltaContentImageUrlObject" x-oaiExpandable: true required: - id - object - delta x-oaiMeta: name: The message delta object beta: true example: | { "id": "msg_123", "object": "thread.message.delta", "delta": { "content": [ { "index": 0, "type": "text", "text": { "value": "Hello", "annotations": [] } } ] } } CreateMessageRequest: type: object additionalProperties: false required: - role - content properties: role: type: string enum: ["user", "assistant"] description: | The role of the entity that is creating the message. Allowed values include: - `user`: Indicates the message is sent by an actual user and should be used in most cases to represent user-generated messages. - `assistant`: Indicates the message is generated by the assistant. Use this value to insert messages from the assistant into the conversation. content: oneOf: - type: string description: The text contents of the message. title: Text content - type: array description: An array of content parts with a defined type, each can be of type `text` or images can be passed with `image_url` or `image_file`. Image types are only supported on [Vision-compatible models](/docs/models/overview). title: Array of content parts items: oneOf: - $ref: "#/components/schemas/MessageContentImageFileObject" - $ref: "#/components/schemas/MessageContentImageUrlObject" - $ref: "#/components/schemas/MessageRequestContentTextObject" x-oaiExpandable: true minItems: 1 x-oaiExpandable: true attachments: type: array items: type: object properties: file_id: type: string description: The ID of the file to attach to the message. tools: description: The tools to add this file to. type: array items: oneOf: - $ref: "#/components/schemas/AssistantToolsCode" - $ref: "#/components/schemas/AssistantToolsFileSearch" x-oaiExpandable: true description: A list of files attached to the message, and the tools they should be added to. required: - file_id - tools nullable: true metadata: description: *metadata_description type: object x-oaiTypeLabel: map nullable: true ModifyMessageRequest: type: object additionalProperties: false properties: metadata: description: *metadata_description type: object x-oaiTypeLabel: map nullable: true DeleteMessageResponse: type: object properties: id: type: string deleted: type: boolean object: type: string enum: [thread.message.deleted] required: - id - object - deleted ListMessagesResponse: properties: object: type: string example: "list" data: type: array items: $ref: "#/components/schemas/MessageObject" first_id: type: string example: "msg_abc123" last_id: type: string example: "msg_abc123" has_more: type: boolean example: false required: - object - data - first_id - last_id - has_more MessageContentImageFileObject: title: Image file type: object description: References an image [File](/docs/api-reference/files) in the content of a message. properties: type: description: Always `image_file`. type: string enum: ["image_file"] image_file: type: object properties: file_id: description: The [File](/docs/api-reference/files) ID of the image in the message content. Set `purpose="vision"` when uploading the File if you need to later display the file content. type: string detail: type: string description: Specifies the detail level of the image if specified by the user. `low` uses fewer tokens, you can opt in to high resolution using `high`. enum: ["auto", "low", "high"] default: "auto" required: - file_id required: - type - image_file MessageDeltaContentImageFileObject: title: Image file type: object description: References an image [File](/docs/api-reference/files) in the content of a message. properties: index: type: integer description: The index of the content part in the message. type: description: Always `image_file`. type: string enum: ["image_file"] image_file: type: object properties: file_id: description: The [File](/docs/api-reference/files) ID of the image in the message content. Set `purpose="vision"` when uploading the File if you need to later display the file content. type: string detail: type: string description: Specifies the detail level of the image if specified by the user. `low` uses fewer tokens, you can opt in to high resolution using `high`. enum: ["auto", "low", "high"] default: "auto" required: - index - type MessageContentImageUrlObject: title: Image URL type: object description: References an image URL in the content of a message. properties: type: type: string enum: ["image_url"] description: The type of the content part. image_url: type: object properties: url: type: string description: "The external URL of the image, must be a supported image types: jpeg, jpg, png, gif, webp." format: uri detail: type: string description: Specifies the detail level of the image. `low` uses fewer tokens, you can opt in to high resolution using `high`. Default value is `auto` enum: ["auto", "low", "high"] default: "auto" required: - url required: - type - image_url MessageDeltaContentImageUrlObject: title: Image URL type: object description: References an image URL in the content of a message. properties: index: type: integer description: The index of the content part in the message. type: description: Always `image_url`. type: string enum: ["image_url"] image_url: type: object properties: url: description: "The URL of the image, must be a supported image types: jpeg, jpg, png, gif, webp." type: string detail: type: string description: Specifies the detail level of the image. `low` uses fewer tokens, you can opt in to high resolution using `high`. enum: ["auto", "low", "high"] default: "auto" required: - index - type MessageContentTextObject: title: Text type: object description: The text content that is part of a message. properties: type: description: Always `text`. type: string enum: ["text"] text: type: object properties: value: description: The data that makes up the text. type: string annotations: type: array items: oneOf: - $ref: "#/components/schemas/MessageContentTextAnnotationsFileCitationObject" - $ref: "#/components/schemas/MessageContentTextAnnotationsFilePathObject" x-oaiExpandable: true required: - value - annotations required: - type - text MessageRequestContentTextObject: title: Text type: object description: The text content that is part of a message. properties: type: description: Always `text`. type: string enum: ["text"] text: type: string description: Text content to be sent to the model required: - type - text MessageContentTextAnnotationsFileCitationObject: title: File citation type: object description: A citation within the message that points to a specific quote from a specific File associated with the assistant or the message. Generated when the assistant uses the "file_search" tool to search files. properties: type: description: Always `file_citation`. type: string enum: ["file_citation"] text: description: The text in the message content that needs to be replaced. type: string file_citation: type: object properties: file_id: description: The ID of the specific File the citation is from. type: string quote: description: The specific quote in the file. type: string required: - file_id - quote start_index: type: integer minimum: 0 end_index: type: integer minimum: 0 required: - type - text - file_citation - start_index - end_index MessageContentTextAnnotationsFilePathObject: title: File path type: object description: A URL for the file that's generated when the assistant used the `code_interpreter` tool to generate a file. properties: type: description: Always `file_path`. type: string enum: ["file_path"] text: description: The text in the message content that needs to be replaced. type: string file_path: type: object properties: file_id: description: The ID of the file that was generated. type: string required: - file_id start_index: type: integer minimum: 0 end_index: type: integer minimum: 0 required: - type - text - file_path - start_index - end_index MessageDeltaContentTextObject: title: Text type: object description: The text content that is part of a message. properties: index: type: integer description: The index of the content part in the message. type: description: Always `text`. type: string enum: ["text"] text: type: object properties: value: description: The data that makes up the text. type: string annotations: type: array items: oneOf: - $ref: "#/components/schemas/MessageDeltaContentTextAnnotationsFileCitationObject" - $ref: "#/components/schemas/MessageDeltaContentTextAnnotationsFilePathObject" x-oaiExpandable: true required: - index - type MessageDeltaContentTextAnnotationsFileCitationObject: title: File citation type: object description: A citation within the message that points to a specific quote from a specific File associated with the assistant or the message. Generated when the assistant uses the "file_search" tool to search files. properties: index: type: integer description: The index of the annotation in the text content part. type: description: Always `file_citation`. type: string enum: ["file_citation"] text: description: The text in the message content that needs to be replaced. type: string file_citation: type: object properties: file_id: description: The ID of the specific File the citation is from. type: string quote: description: The specific quote in the file. type: string start_index: type: integer minimum: 0 end_index: type: integer minimum: 0 required: - index - type MessageDeltaContentTextAnnotationsFilePathObject: title: File path type: object description: A URL for the file that's generated when the assistant used the `code_interpreter` tool to generate a file. properties: index: type: integer description: The index of the annotation in the text content part. type: description: Always `file_path`. type: string enum: ["file_path"] text: description: The text in the message content that needs to be replaced. type: string file_path: type: object properties: file_id: description: The ID of the file that was generated. type: string start_index: type: integer minimum: 0 end_index: type: integer minimum: 0 required: - index - type RunStepObject: type: object title: Run steps description: | Represents a step in execution of a run. properties: id: description: The identifier of the run step, which can be referenced in API endpoints. type: string object: description: The object type, which is always `thread.run.step`. type: string enum: ["thread.run.step"] created_at: description: The Unix timestamp (in seconds) for when the run step was created. type: integer assistant_id: description: The ID of the [assistant](/docs/api-reference/assistants) associated with the run step. type: string thread_id: description: The ID of the [thread](/docs/api-reference/threads) that was run. type: string run_id: description: The ID of the [run](/docs/api-reference/runs) that this run step is a part of. type: string type: description: The type of run step, which can be either `message_creation` or `tool_calls`. type: string enum: ["message_creation", "tool_calls"] status: description: The status of the run step, which can be either `in_progress`, `cancelled`, `failed`, `completed`, or `expired`. type: string enum: ["in_progress", "cancelled", "failed", "completed", "expired"] step_details: type: object description: The details of the run step. oneOf: - $ref: "#/components/schemas/RunStepDetailsMessageCreationObject" - $ref: "#/components/schemas/RunStepDetailsToolCallsObject" x-oaiExpandable: true last_error: type: object description: The last error associated with this run step. Will be `null` if there are no errors. nullable: true properties: code: type: string description: One of `server_error` or `rate_limit_exceeded`. enum: ["server_error", "rate_limit_exceeded"] message: type: string description: A human-readable description of the error. required: - code - message expired_at: description: The Unix timestamp (in seconds) for when the run step expired. A step is considered expired if the parent run is expired. type: integer nullable: true cancelled_at: description: The Unix timestamp (in seconds) for when the run step was cancelled. type: integer nullable: true failed_at: description: The Unix timestamp (in seconds) for when the run step failed. type: integer nullable: true completed_at: description: The Unix timestamp (in seconds) for when the run step completed. type: integer nullable: true metadata: description: *metadata_description type: object x-oaiTypeLabel: map nullable: true usage: $ref: "#/components/schemas/RunStepCompletionUsage" required: - id - object - created_at - assistant_id - thread_id - run_id - type - status - step_details - last_error - expired_at - cancelled_at - failed_at - completed_at - metadata - usage x-oaiMeta: name: The run step object beta: true example: *run_step_object_example RunStepDeltaObject: type: object title: Run step delta object description: | Represents a run step delta i.e. any changed fields on a run step during streaming. properties: id: description: The identifier of the run step, which can be referenced in API endpoints. type: string object: description: The object type, which is always `thread.run.step.delta`. type: string enum: ["thread.run.step.delta"] delta: description: The delta containing the fields that have changed on the run step. type: object properties: step_details: type: object description: The details of the run step. oneOf: - $ref: "#/components/schemas/RunStepDeltaStepDetailsMessageCreationObject" - $ref: "#/components/schemas/RunStepDeltaStepDetailsToolCallsObject" x-oaiExpandable: true required: - id - object - delta x-oaiMeta: name: The run step delta object beta: true example: | { "id": "step_123", "object": "thread.run.step.delta", "delta": { "step_details": { "type": "tool_calls", "tool_calls": [ { "index": 0, "id": "call_123", "type": "code_interpreter", "code_interpreter": { "input": "", "outputs": [] } } ] } } } ListRunStepsResponse: properties: object: type: string example: "list" data: type: array items: $ref: "#/components/schemas/RunStepObject" first_id: type: string example: "step_abc123" last_id: type: string example: "step_abc456" has_more: type: boolean example: false required: - object - data - first_id - last_id - has_more RunStepDetailsMessageCreationObject: title: Message creation type: object description: Details of the message creation by the run step. properties: type: description: Always `message_creation`. type: string enum: ["message_creation"] message_creation: type: object properties: message_id: type: string description: The ID of the message that was created by this run step. required: - message_id required: - type - message_creation RunStepDeltaStepDetailsMessageCreationObject: title: Message creation type: object description: Details of the message creation by the run step. properties: type: description: Always `message_creation`. type: string enum: ["message_creation"] message_creation: type: object properties: message_id: type: string description: The ID of the message that was created by this run step. required: - type RunStepDetailsToolCallsObject: title: Tool calls type: object description: Details of the tool call. properties: type: description: Always `tool_calls`. type: string enum: ["tool_calls"] tool_calls: type: array description: | An array of tool calls the run step was involved in. These can be associated with one of three types of tools: `code_interpreter`, `file_search`, or `function`. items: oneOf: - $ref: "#/components/schemas/RunStepDetailsToolCallsCodeObject" - $ref: "#/components/schemas/RunStepDetailsToolCallsFileSearchObject" - $ref: "#/components/schemas/RunStepDetailsToolCallsFunctionObject" x-oaiExpandable: true required: - type - tool_calls RunStepDeltaStepDetailsToolCallsObject: title: Tool calls type: object description: Details of the tool call. properties: type: description: Always `tool_calls`. type: string enum: ["tool_calls"] tool_calls: type: array description: | An array of tool calls the run step was involved in. These can be associated with one of three types of tools: `code_interpreter`, `file_search`, or `function`. items: oneOf: - $ref: "#/components/schemas/RunStepDeltaStepDetailsToolCallsCodeObject" - $ref: "#/components/schemas/RunStepDeltaStepDetailsToolCallsFileSearchObject" - $ref: "#/components/schemas/RunStepDeltaStepDetailsToolCallsFunctionObject" x-oaiExpandable: true required: - type RunStepDetailsToolCallsCodeObject: title: Code Interpreter tool call type: object description: Details of the Code Interpreter tool call the run step was involved in. properties: id: type: string description: The ID of the tool call. type: type: string description: The type of tool call. This is always going to be `code_interpreter` for this type of tool call. enum: ["code_interpreter"] code_interpreter: type: object description: The Code Interpreter tool call definition. required: - input - outputs properties: input: type: string description: The input to the Code Interpreter tool call. outputs: type: array description: The outputs from the Code Interpreter tool call. Code Interpreter can output one or more items, including text (`logs`) or images (`image`). Each of these are represented by a different object type. items: type: object oneOf: - $ref: "#/components/schemas/RunStepDetailsToolCallsCodeOutputLogsObject" - $ref: "#/components/schemas/RunStepDetailsToolCallsCodeOutputImageObject" x-oaiExpandable: true required: - id - type - code_interpreter RunStepDeltaStepDetailsToolCallsCodeObject: title: Code interpreter tool call type: object description: Details of the Code Interpreter tool call the run step was involved in. properties: index: type: integer description: The index of the tool call in the tool calls array. id: type: string description: The ID of the tool call. type: type: string description: The type of tool call. This is always going to be `code_interpreter` for this type of tool call. enum: ["code_interpreter"] code_interpreter: type: object description: The Code Interpreter tool call definition. properties: input: type: string description: The input to the Code Interpreter tool call. outputs: type: array description: The outputs from the Code Interpreter tool call. Code Interpreter can output one or more items, including text (`logs`) or images (`image`). Each of these are represented by a different object type. items: type: object oneOf: - $ref: "#/components/schemas/RunStepDeltaStepDetailsToolCallsCodeOutputLogsObject" - $ref: "#/components/schemas/RunStepDeltaStepDetailsToolCallsCodeOutputImageObject" x-oaiExpandable: true required: - index - type RunStepDetailsToolCallsCodeOutputLogsObject: title: Code Interpreter log output type: object description: Text output from the Code Interpreter tool call as part of a run step. properties: type: description: Always `logs`. type: string enum: ["logs"] logs: type: string description: The text output from the Code Interpreter tool call. required: - type - logs RunStepDeltaStepDetailsToolCallsCodeOutputLogsObject: title: Code interpreter log output type: object description: Text output from the Code Interpreter tool call as part of a run step. properties: index: type: integer description: The index of the output in the outputs array. type: description: Always `logs`. type: string enum: ["logs"] logs: type: string description: The text output from the Code Interpreter tool call. required: - index - type RunStepDetailsToolCallsCodeOutputImageObject: title: Code Interpreter image output type: object properties: type: description: Always `image`. type: string enum: ["image"] image: type: object properties: file_id: description: The [file](/docs/api-reference/files) ID of the image. type: string required: - file_id required: - type - image RunStepDeltaStepDetailsToolCallsCodeOutputImageObject: title: Code interpreter image output type: object properties: index: type: integer description: The index of the output in the outputs array. type: description: Always `image`. type: string enum: ["image"] image: type: object properties: file_id: description: The [file](/docs/api-reference/files) ID of the image. type: string required: - index - type RunStepDetailsToolCallsFileSearchObject: title: File search tool call type: object properties: id: type: string description: The ID of the tool call object. type: type: string description: The type of tool call. This is always going to be `file_search` for this type of tool call. enum: ["file_search"] file_search: type: object description: For now, this is always going to be an empty object. x-oaiTypeLabel: map required: - id - type - file_search RunStepDeltaStepDetailsToolCallsFileSearchObject: title: File search tool call type: object properties: index: type: integer description: The index of the tool call in the tool calls array. id: type: string description: The ID of the tool call object. type: type: string description: The type of tool call. This is always going to be `file_search` for this type of tool call. enum: ["file_search"] file_search: type: object description: For now, this is always going to be an empty object. x-oaiTypeLabel: map required: - index - type - file_search RunStepDetailsToolCallsFunctionObject: type: object title: Function tool call properties: id: type: string description: The ID of the tool call object. type: type: string description: The type of tool call. This is always going to be `function` for this type of tool call. enum: ["function"] function: type: object description: The definition of the function that was called. properties: name: type: string description: The name of the function. arguments: type: string description: The arguments passed to the function. output: type: string description: The output of the function. This will be `null` if the outputs have not been [submitted](/docs/api-reference/runs/submitToolOutputs) yet. nullable: true required: - name - arguments - output required: - id - type - function RunStepDeltaStepDetailsToolCallsFunctionObject: type: object title: Function tool call properties: index: type: integer description: The index of the tool call in the tool calls array. id: type: string description: The ID of the tool call object. type: type: string description: The type of tool call. This is always going to be `function` for this type of tool call. enum: ["function"] function: type: object description: The definition of the function that was called. properties: name: type: string description: The name of the function. arguments: type: string description: The arguments passed to the function. output: type: string description: The output of the function. This will be `null` if the outputs have not been [submitted](/docs/api-reference/runs/submitToolOutputs) yet. nullable: true required: - index - type VectorStoreExpirationAfter: type: object title: Vector store expiration policy description: The expiration policy for a vector store. properties: anchor: description: "Anchor timestamp after which the expiration policy applies. Supported anchors: `last_active_at`." type: string enum: ["last_active_at"] days: description: The number of days after the anchor time that the vector store will expire. type: integer minimum: 1 maximum: 365 required: - anchor - days VectorStoreObject: type: object title: Vector store description: A vector store is a collection of processed files can be used by the `file_search` tool. properties: id: description: The identifier, which can be referenced in API endpoints. type: string object: description: The object type, which is always `vector_store`. type: string enum: ["vector_store"] created_at: description: The Unix timestamp (in seconds) for when the vector store was created. type: integer name: description: The name of the vector store. type: string usage_bytes: description: The total number of bytes used by the files in the vector store. type: integer file_counts: type: object properties: in_progress: description: The number of files that are currently being processed. type: integer completed: description: The number of files that have been successfully processed. type: integer failed: description: The number of files that have failed to process. type: integer cancelled: description: The number of files that were cancelled. type: integer total: description: The total number of files. type: integer required: - in_progress - completed - failed - cancelled - total status: description: The status of the vector store, which can be either `expired`, `in_progress`, or `completed`. A status of `completed` indicates that the vector store is ready for use. type: string enum: ["expired", "in_progress", "completed"] expires_after: $ref: "#/components/schemas/VectorStoreExpirationAfter" expires_at: description: The Unix timestamp (in seconds) for when the vector store will expire. type: integer nullable: true last_active_at: description: The Unix timestamp (in seconds) for when the vector store was last active. type: integer nullable: true metadata: description: *metadata_description type: object x-oaiTypeLabel: map nullable: true required: - id - object - usage_bytes - created_at - status - last_active_at - name - file_counts - metadata x-oaiMeta: name: The vector store object beta: true example: | { "id": "vs_123", "object": "vector_store", "created_at": 1698107661, "usage_bytes": 123456, "last_active_at": 1698107661, "name": "my_vector_store", "status": "completed", "file_counts": { "in_progress": 0, "completed": 100, "cancelled": 0, "failed": 0, "total": 100 }, "metadata": {}, "last_used_at": 1698107661 } CreateVectorStoreRequest: type: object additionalProperties: false properties: file_ids: description: A list of [File](/docs/api-reference/files) IDs that the vector store should use. Useful for tools like `file_search` that can access files. type: array maxItems: 500 items: type: string name: description: The name of the vector store. type: string expires_after: $ref: "#/components/schemas/VectorStoreExpirationAfter" metadata: description: *metadata_description type: object x-oaiTypeLabel: map nullable: true UpdateVectorStoreRequest: type: object additionalProperties: false properties: name: description: The name of the vector store. type: string nullable: true expires_after: $ref: "#/components/schemas/VectorStoreExpirationAfter" nullable: true metadata: description: *metadata_description type: object x-oaiTypeLabel: map nullable: true ListVectorStoresResponse: properties: object: type: string example: "list" data: type: array items: $ref: "#/components/schemas/VectorStoreObject" first_id: type: string example: "vs_abc123" last_id: type: string example: "vs_abc456" has_more: type: boolean example: false required: - object - data - first_id - last_id - has_more DeleteVectorStoreResponse: type: object properties: id: type: string deleted: type: boolean object: type: string enum: [vector_store.deleted] required: - id - object - deleted VectorStoreFileObject: type: object title: Vector store files description: A list of files attached to a vector store. properties: id: description: The identifier, which can be referenced in API endpoints. type: string object: description: The object type, which is always `vector_store.file`. type: string enum: ["vector_store.file"] usage_bytes: description: The total vector store usage in bytes. Note that this may be different from the original file size. type: integer created_at: description: The Unix timestamp (in seconds) for when the vector store file was created. type: integer vector_store_id: description: The ID of the [vector store](/docs/api-reference/vector-stores/object) that the [File](/docs/api-reference/files) is attached to. type: string status: description: The status of the vector store file, which can be either `in_progress`, `completed`, `cancelled`, or `failed`. The status `completed` indicates that the vector store file is ready for use. type: string enum: ["in_progress", "completed", "cancelled", "failed"] last_error: type: object description: The last error associated with this vector store file. Will be `null` if there are no errors. nullable: true properties: code: type: string description: One of `server_error` or `rate_limit_exceeded`. enum: [ "internal_error", "file_not_found", "parsing_error", "unhandled_mime_type", ] message: type: string description: A human-readable description of the error. required: - code - message required: - id - object - usage_bytes - created_at - vector_store_id - status - last_error x-oaiMeta: name: The vector store file object beta: true example: | { "id": "file-abc123", "object": "vector_store.file", "usage_bytes": 1234, "created_at": 1698107661, "vector_store_id": "vs_abc123", "status": "completed", "last_error": null } CreateVectorStoreFileRequest: type: object additionalProperties: false properties: file_id: description: A [File](/docs/api-reference/files) ID that the vector store should use. Useful for tools like `file_search` that can access files. type: string required: - file_id ListVectorStoreFilesResponse: properties: object: type: string example: "list" data: type: array items: $ref: "#/components/schemas/VectorStoreFileObject" first_id: type: string example: "file-abc123" last_id: type: string example: "file-abc456" has_more: type: boolean example: false required: - object - data - first_id - last_id - has_more DeleteVectorStoreFileResponse: type: object properties: id: type: string deleted: type: boolean object: type: string enum: [vector_store.file.deleted] required: - id - object - deleted VectorStoreFileBatchObject: type: object title: Vector store file batch description: A batch of files attached to a vector store. properties: id: description: The identifier, which can be referenced in API endpoints. type: string object: description: The object type, which is always `vector_store.file_batch`. type: string enum: ["vector_store.files_batch"] created_at: description: The Unix timestamp (in seconds) for when the vector store files batch was created. type: integer vector_store_id: description: The ID of the [vector store](/docs/api-reference/vector-stores/object) that the [File](/docs/api-reference/files) is attached to. type: string status: description: The status of the vector store files batch, which can be either `in_progress`, `completed`, `cancelled` or `failed`. type: string enum: ["in_progress", "completed", "cancelled", "failed"] file_counts: type: object properties: in_progress: description: The number of files that are currently being processed. type: integer completed: description: The number of files that have been processed. type: integer failed: description: The number of files that have failed to process. type: integer cancelled: description: The number of files that where cancelled. type: integer total: description: The total number of files. type: integer required: - in_progress - completed - cancelled - failed - total required: - id - object - created_at - vector_store_id - status - file_counts x-oaiMeta: name: The vector store files batch object beta: true example: | { "id": "vsfb_123", "object": "vector_store.files_batch", "created_at": 1698107661, "vector_store_id": "vs_abc123", "status": "completed", "file_counts": { "in_progress": 0, "completed": 100, "failed": 0, "cancelled": 0, "total": 100 } } CreateVectorStoreFileBatchRequest: type: object additionalProperties: false properties: file_ids: description: A list of [File](/docs/api-reference/files) IDs that the vector store should use. Useful for tools like `file_search` that can access files. type: array minItems: 1 maxItems: 500 items: type: string required: - file_ids AssistantStreamEvent: description: | Represents an event emitted when streaming a Run. Each event in a server-sent events stream has an `event` and `data` property: ``` event: thread.created data: {"id": "thread_123", "object": "thread", ...} ``` We emit events whenever a new object is created, transitions to a new state, or is being streamed in parts (deltas). For example, we emit `thread.run.created` when a new run is created, `thread.run.completed` when a run completes, and so on. When an Assistant chooses to create a message during a run, we emit a `thread.message.created event`, a `thread.message.in_progress` event, many `thread.message.delta` events, and finally a `thread.message.completed` event. We may add additional events over time, so we recommend handling unknown events gracefully in your code. See the [Assistants API quickstart](/docs/assistants/overview) to learn how to integrate the Assistants API with streaming. oneOf: - $ref: "#/components/schemas/ThreadStreamEvent" - $ref: "#/components/schemas/RunStreamEvent" - $ref: "#/components/schemas/RunStepStreamEvent" - $ref: "#/components/schemas/MessageStreamEvent" - $ref: "#/components/schemas/ErrorEvent" - $ref: "#/components/schemas/DoneEvent" x-oaiMeta: name: Assistant stream events beta: true ThreadStreamEvent: oneOf: - type: object properties: event: type: string enum: ["thread.created"] data: $ref: "#/components/schemas/ThreadObject" required: - event - data description: Occurs when a new [thread](/docs/api-reference/threads/object) is created. x-oaiMeta: dataDescription: "`data` is a [thread](/docs/api-reference/threads/object)" RunStreamEvent: oneOf: - type: object properties: event: type: string enum: ["thread.run.created"] data: $ref: "#/components/schemas/RunObject" required: - event - data description: Occurs when a new [run](/docs/api-reference/runs/object) is created. x-oaiMeta: dataDescription: "`data` is a [run](/docs/api-reference/runs/object)" - type: object properties: event: type: string enum: ["thread.run.queued"] data: $ref: "#/components/schemas/RunObject" required: - event - data description: Occurs when a [run](/docs/api-reference/runs/object) moves to a `queued` status. x-oaiMeta: dataDescription: "`data` is a [run](/docs/api-reference/runs/object)" - type: object properties: event: type: string enum: ["thread.run.in_progress"] data: $ref: "#/components/schemas/RunObject" required: - event - data description: Occurs when a [run](/docs/api-reference/runs/object) moves to an `in_progress` status. x-oaiMeta: dataDescription: "`data` is a [run](/docs/api-reference/runs/object)" - type: object properties: event: type: string enum: ["thread.run.requires_action"] data: $ref: "#/components/schemas/RunObject" required: - event - data description: Occurs when a [run](/docs/api-reference/runs/object) moves to a `requires_action` status. x-oaiMeta: dataDescription: "`data` is a [run](/docs/api-reference/runs/object)" - type: object properties: event: type: string enum: ["thread.run.completed"] data: $ref: "#/components/schemas/RunObject" required: - event - data description: Occurs when a [run](/docs/api-reference/runs/object) is completed. x-oaiMeta: dataDescription: "`data` is a [run](/docs/api-reference/runs/object)" - type: object properties: event: type: string enum: ["thread.run.failed"] data: $ref: "#/components/schemas/RunObject" required: - event - data description: Occurs when a [run](/docs/api-reference/runs/object) fails. x-oaiMeta: dataDescription: "`data` is a [run](/docs/api-reference/runs/object)" - type: object properties: event: type: string enum: ["thread.run.cancelling"] data: $ref: "#/components/schemas/RunObject" required: - event - data description: Occurs when a [run](/docs/api-reference/runs/object) moves to a `cancelling` status. x-oaiMeta: dataDescription: "`data` is a [run](/docs/api-reference/runs/object)" - type: object properties: event: type: string enum: ["thread.run.cancelled"] data: $ref: "#/components/schemas/RunObject" required: - event - data description: Occurs when a [run](/docs/api-reference/runs/object) is cancelled. x-oaiMeta: dataDescription: "`data` is a [run](/docs/api-reference/runs/object)" - type: object properties: event: type: string enum: ["thread.run.expired"] data: $ref: "#/components/schemas/RunObject" required: - event - data description: Occurs when a [run](/docs/api-reference/runs/object) expires. x-oaiMeta: dataDescription: "`data` is a [run](/docs/api-reference/runs/object)" RunStepStreamEvent: oneOf: - type: object properties: event: type: string enum: ["thread.run.step.created"] data: $ref: "#/components/schemas/RunStepObject" required: - event - data description: Occurs when a [run step](/docs/api-reference/runs/step-object) is created. x-oaiMeta: dataDescription: "`data` is a [run step](/docs/api-reference/runs/step-object)" - type: object properties: event: type: string enum: ["thread.run.step.in_progress"] data: $ref: "#/components/schemas/RunStepObject" required: - event - data description: Occurs when a [run step](/docs/api-reference/runs/step-object) moves to an `in_progress` state. x-oaiMeta: dataDescription: "`data` is a [run step](/docs/api-reference/runs/step-object)" - type: object properties: event: type: string enum: ["thread.run.step.delta"] data: $ref: "#/components/schemas/RunStepDeltaObject" required: - event - data description: Occurs when parts of a [run step](/docs/api-reference/runs/step-object) are being streamed. x-oaiMeta: dataDescription: "`data` is a [run step delta](/docs/api-reference/assistants-streaming/run-step-delta-object)" - type: object properties: event: type: string enum: ["thread.run.step.completed"] data: $ref: "#/components/schemas/RunStepObject" required: - event - data description: Occurs when a [run step](/docs/api-reference/runs/step-object) is completed. x-oaiMeta: dataDescription: "`data` is a [run step](/docs/api-reference/runs/step-object)" - type: object properties: event: type: string enum: ["thread.run.step.failed"] data: $ref: "#/components/schemas/RunStepObject" required: - event - data description: Occurs when a [run step](/docs/api-reference/runs/step-object) fails. x-oaiMeta: dataDescription: "`data` is a [run step](/docs/api-reference/runs/step-object)" - type: object properties: event: type: string enum: ["thread.run.step.cancelled"] data: $ref: "#/components/schemas/RunStepObject" required: - event - data description: Occurs when a [run step](/docs/api-reference/runs/step-object) is cancelled. x-oaiMeta: dataDescription: "`data` is a [run step](/docs/api-reference/runs/step-object)" - type: object properties: event: type: string enum: ["thread.run.step.expired"] data: $ref: "#/components/schemas/RunStepObject" required: - event - data description: Occurs when a [run step](/docs/api-reference/runs/step-object) expires. x-oaiMeta: dataDescription: "`data` is a [run step](/docs/api-reference/runs/step-object)" MessageStreamEvent: oneOf: - type: object properties: event: type: string enum: ["thread.message.created"] data: $ref: "#/components/schemas/MessageObject" required: - event - data description: Occurs when a [message](/docs/api-reference/messages/object) is created. x-oaiMeta: dataDescription: "`data` is a [message](/docs/api-reference/messages/object)" - type: object properties: event: type: string enum: ["thread.message.in_progress"] data: $ref: "#/components/schemas/MessageObject" required: - event - data description: Occurs when a [message](/docs/api-reference/messages/object) moves to an `in_progress` state. x-oaiMeta: dataDescription: "`data` is a [message](/docs/api-reference/messages/object)" - type: object properties: event: type: string enum: ["thread.message.delta"] data: $ref: "#/components/schemas/MessageDeltaObject" required: - event - data description: Occurs when parts of a [Message](/docs/api-reference/messages/object) are being streamed. x-oaiMeta: dataDescription: "`data` is a [message delta](/docs/api-reference/assistants-streaming/message-delta-object)" - type: object properties: event: type: string enum: ["thread.message.completed"] data: $ref: "#/components/schemas/MessageObject" required: - event - data description: Occurs when a [message](/docs/api-reference/messages/object) is completed. x-oaiMeta: dataDescription: "`data` is a [message](/docs/api-reference/messages/object)" - type: object properties: event: type: string enum: ["thread.message.incomplete"] data: $ref: "#/components/schemas/MessageObject" required: - event - data description: Occurs when a [message](/docs/api-reference/messages/object) ends before it is completed. x-oaiMeta: dataDescription: "`data` is a [message](/docs/api-reference/messages/object)" ErrorEvent: type: object properties: event: type: string enum: ["error"] data: $ref: "#/components/schemas/Error" required: - event - data description: Occurs when an [error](/docs/guides/error-codes/api-errors) occurs. This can happen due to an internal server error or a timeout. x-oaiMeta: dataDescription: "`data` is an [error](/docs/guides/error-codes/api-errors)" DoneEvent: type: object properties: event: type: string enum: ["done"] data: type: string enum: ["[DONE]"] required: - event - data description: Occurs when a stream ends. x-oaiMeta: dataDescription: "`data` is `[DONE]`" Batch: type: object properties: id: type: string object: type: string enum: [batch] description: The object type, which is always `batch`. endpoint: type: string description: The OpenAI API endpoint used by the batch. errors: type: object properties: object: type: string description: The object type, which is always `list`. data: type: array items: type: object properties: code: type: string description: An error code identifying the error type. message: type: string description: A human-readable message providing more details about the error. param: type: string description: The name of the parameter that caused the error, if applicable. nullable: true line: type: integer description: The line number of the input file where the error occurred, if applicable. nullable: true input_file_id: type: string description: The ID of the input file for the batch. completion_window: type: string description: The time frame within which the batch should be processed. status: type: string description: The current status of the batch. enum: - validating - failed - in_progress - finalizing - completed - expired - cancelling - cancelled output_file_id: type: string description: The ID of the file containing the outputs of successfully executed requests. error_file_id: type: string description: The ID of the file containing the outputs of requests with errors. created_at: type: integer description: The Unix timestamp (in seconds) for when the batch was created. in_progress_at: type: integer description: The Unix timestamp (in seconds) for when the batch started processing. expires_at: type: integer description: The Unix timestamp (in seconds) for when the batch will expire. finalizing_at: type: integer description: The Unix timestamp (in seconds) for when the batch started finalizing. completed_at: type: integer description: The Unix timestamp (in seconds) for when the batch was completed. failed_at: type: integer description: The Unix timestamp (in seconds) for when the batch failed. expired_at: type: integer description: The Unix timestamp (in seconds) for when the batch expired. cancelling_at: type: integer description: The Unix timestamp (in seconds) for when the batch started cancelling. cancelled_at: type: integer description: The Unix timestamp (in seconds) for when the batch was cancelled. request_counts: type: object properties: total: type: integer description: Total number of requests in the batch. completed: type: integer description: Number of requests that have been completed successfully. failed: type: integer description: Number of requests that have failed. required: - total - completed - failed description: The request counts for different statuses within the batch. metadata: description: *metadata_description type: object x-oaiTypeLabel: map nullable: true required: - id - object - endpoint - input_file_id - completion_window - status - created_at x-oaiMeta: name: The batch object example: *batch_object BatchRequestInput: type: object description: The per-line object of the batch input file properties: custom_id: type: string description: A developer-provided per-request id that will be used to match outputs to inputs. Must be unique for each request in a batch. method: type: string enum: ["POST"] description: The HTTP method to be used for the request. Currently only `POST` is supported. url: type: string description: The OpenAI API relative URL to be used for the request. Currently `/v1/chat/completions`, `/v1/embeddings`, and `/v1/completions` are supported. x-oaiMeta: name: The request input object example: | {"custom_id": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "gpt-3.5-turbo", "messages": [{"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "What is 2+2?"}]}} BatchRequestOutput: type: object description: The per-line object of the batch output and error files properties: id: type: string custom_id: type: string description: A developer-provided per-request id that will be used to match outputs to inputs. response: type: object nullable: true properties: status_code: type: integer description: The HTTP status code of the response request_id: type: string description: An unique identifier for the OpenAI API request. Please include this request ID when contacting support. body: type: object x-oaiTypeLabel: map description: The JSON body of the response error: type: object nullable: true description: For requests that failed with a non-HTTP error, this will contain more information on the cause of the failure. properties: code: type: string description: A machine-readable error code. message: type: string description: A human-readable error message. x-oaiMeta: name: The request output object example: | {"id": "batch_req_wnaDys", "custom_id": "request-2", "response": {"status_code": 200, "request_id": "req_c187b3", "body": {"id": "chatcmpl-9758Iw", "object": "chat.completion", "created": 1711475054, "model": "gpt-3.5-turbo", "choices": [{"index": 0, "message": {"role": "assistant", "content": "2 + 2 equals 4."}, "finish_reason": "stop"}], "usage": {"prompt_tokens": 24, "completion_tokens": 15, "total_tokens": 39}, "system_fingerprint": null}}, "error": null} ListBatchesResponse: type: object properties: data: type: array items: $ref: "#/components/schemas/Batch" first_id: type: string example: "batch_abc123" last_id: type: string example: "batch_abc456" has_more: type: boolean object: type: string enum: [list] required: - object - data - has_more security: - ApiKeyAuth: [] x-oaiMeta: navigationGroups: - id: endpoints title: Endpoints - id: assistants title: Assistants - id: legacy title: Legacy groups: # > General Notes # The `groups` section is used to generate the API reference pages and navigation, in the same # order listed below. Additionally, each `group` can have a list of `sections`, each of which # will become a navigation subroute and subsection under the group. Each section has: # - `type`: Currently, either an `endpoint` or `object`, depending on how the section needs to # be rendered # - `key`: The reference key that can be used to lookup the section definition # - `path`: The path (url) of the section, which is used to generate the navigation link. # # > The `object` sections maps to a schema component and the following fields are read for rendering # - `x-oaiMeta.name`: The name of the object, which will become the section title # - `x-oaiMeta.example`: The example object, which will be used to generate the example sample (always JSON) # - `description`: The description of the object, which will be used to generate the section description # # > The `endpoint` section maps to an operation path and the following fields are read for rendering: # - `x-oaiMeta.name`: The name of the endpoint, which will become the section title # - `x-oaiMeta.examples`: The endpoint examples, which can be an object (meaning a single variation, most # endpoints, or an array of objects, meaning multiple variations, e.g. the # chat completion and completion endpoints, with streamed and non-streamed examples. # - `x-oaiMeta.returns`: text describing what the endpoint returns. # - `summary`: The summary of the endpoint, which will be used to generate the section description - id: audio title: Audio description: | Learn how to turn audio into text or text into audio. Related guide: [Speech to text](/docs/guides/speech-to-text) navigationGroup: endpoints sections: - type: endpoint key: createSpeech path: createSpeech - type: endpoint key: createTranscription path: createTranscription - type: endpoint key: createTranslation path: createTranslation - type: object key: CreateTranscriptionResponseJson path: json-object - type: object key: CreateTranscriptionResponseVerboseJson path: verbose-json-object - id: chat title: Chat description: | Given a list of messages comprising a conversation, the model will return a response. Related guide: [Chat Completions](/docs/guides/text-generation) navigationGroup: endpoints sections: - type: endpoint key: createChatCompletion path: create - type: object key: CreateChatCompletionResponse path: object - type: object key: CreateChatCompletionStreamResponse path: streaming - id: 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) navigationGroup: endpoints sections: - type: endpoint key: createEmbedding path: create - type: object key: Embedding path: object - id: fine-tuning title: Fine-tuning description: | Manage fine-tuning jobs to tailor a model to your specific training data. Related guide: [Fine-tune models](/docs/guides/fine-tuning) navigationGroup: endpoints sections: - type: endpoint key: createFineTuningJob path: create - type: endpoint key: listPaginatedFineTuningJobs path: list - type: endpoint key: listFineTuningEvents path: list-events - type: endpoint key: listFineTuningJobCheckpoints path: list-checkpoints - type: endpoint key: retrieveFineTuningJob path: retrieve - type: endpoint key: cancelFineTuningJob path: cancel - type: object key: FineTuningJob path: object - type: object key: FineTuningJobEvent path: event-object - type: object key: FineTuningJobCheckpoint path: checkpoint-object - id: batch title: Batch description: | Create large batches of API requests for asynchronous processing. The Batch API returns completions within 24 hours for a 50% discount. Related guide: [Batch](/docs/guides/batch) navigationGroup: endpoints sections: - type: endpoint key: createBatch path: create - type: endpoint key: retrieveBatch path: retrieve - type: endpoint key: cancelBatch path: cancel - type: endpoint key: listBatches path: list - type: object key: Batch path: object - type: object key: BatchRequestInput path: requestInput - type: object key: BatchRequestOutput path: requestOutput - id: files title: Files description: | Files are used to upload documents that can be used with features like [Assistants](/docs/api-reference/assistants), [Fine-tuning](/docs/api-reference/fine-tuning), and [Batch API](/docs/guides/batch). navigationGroup: endpoints sections: - type: endpoint key: createFile path: create - type: endpoint key: listFiles path: list - type: endpoint key: retrieveFile path: retrieve - type: endpoint key: deleteFile path: delete - type: endpoint key: downloadFile path: retrieve-contents - type: object key: OpenAIFile path: object - id: images title: Images description: | Given a prompt and/or an input image, the model will generate a new image. Related guide: [Image generation](/docs/guides/images) navigationGroup: endpoints sections: - type: endpoint key: createImage path: create - type: endpoint key: createImageEdit path: createEdit - type: endpoint key: createImageVariation path: createVariation - type: object key: Image path: object - id: models title: Models description: | List and describe the various models available in the API. You can refer to the [Models](/docs/models) documentation to understand what models are available and the differences between them. navigationGroup: endpoints sections: - type: endpoint key: listModels path: list - type: endpoint key: retrieveModel path: retrieve - type: endpoint key: deleteModel path: delete - type: object key: Model path: object - id: moderations title: Moderations description: | Given some input text, outputs if the model classifies it as potentially harmful across several categories. Related guide: [Moderations](/docs/guides/moderation) navigationGroup: endpoints sections: - type: endpoint key: createModeration path: create - type: object key: CreateModerationResponse path: object - id: assistants title: Assistants beta: true description: | Build assistants that can call models and use tools to perform tasks. [Get started with the Assistants API](/docs/assistants) navigationGroup: assistants sections: - type: endpoint key: createAssistant path: createAssistant - type: endpoint key: listAssistants path: listAssistants - type: endpoint key: getAssistant path: getAssistant - type: endpoint key: modifyAssistant path: modifyAssistant - type: endpoint key: deleteAssistant path: deleteAssistant - type: object key: AssistantObject path: object - id: threads title: Threads beta: true description: | Create threads that assistants can interact with. Related guide: [Assistants](/docs/assistants/overview) navigationGroup: assistants sections: - type: endpoint key: createThread path: createThread - type: endpoint key: getThread path: getThread - type: endpoint key: modifyThread path: modifyThread - type: endpoint key: deleteThread path: deleteThread - type: object key: ThreadObject path: object - id: messages title: Messages beta: true description: | Create messages within threads Related guide: [Assistants](/docs/assistants/overview) navigationGroup: assistants sections: - type: endpoint key: createMessage path: createMessage - type: endpoint key: listMessages path: listMessages - type: endpoint key: getMessage path: getMessage - type: endpoint key: modifyMessage path: modifyMessage - type: endpoint key: deleteMessage path: deleteMessage - type: object key: MessageObject path: object - id: runs title: Runs beta: true description: | Represents an execution run on a thread. Related guide: [Assistants](/docs/assistants/overview) navigationGroup: assistants sections: - type: endpoint key: createRun path: createRun - type: endpoint key: createThreadAndRun path: createThreadAndRun - type: endpoint key: listRuns path: listRuns - type: endpoint key: getRun path: getRun - type: endpoint key: modifyRun path: modifyRun - type: endpoint key: submitToolOuputsToRun path: submitToolOutputs - type: endpoint key: cancelRun path: cancelRun - type: object key: RunObject path: object - id: run-steps title: Run Steps beta: true description: | Represents the steps (model and tool calls) taken during the run. Related guide: [Assistants](/docs/assistants/overview) navigationGroup: assistants sections: - type: endpoint key: listRunSteps path: listRunSteps - type: endpoint key: getRunStep path: getRunStep - type: object key: RunStepObject path: step-object - id: vector-stores title: Vector Stores beta: true description: | Vector stores are used to store files for use by the `file_search` tool. Related guide: [File Search](/docs/assistants/tools/file-search) navigationGroup: assistants sections: - type: endpoint key: createVectorStore path: create - type: endpoint key: listVectorStores path: list - type: endpoint key: getVectorStore path: retrieve - type: endpoint key: modifyVectorStore path: modify - type: endpoint key: deleteVectorStore path: delete - type: object key: VectorStoreObject path: object - id: vector-stores-files title: Vector Store Files beta: true description: | Vector store files represent files inside a vector store. Related guide: [File Search](/docs/assistants/tools/file-search) navigationGroup: assistants sections: - type: endpoint key: createVectorStoreFile path: createFile - type: endpoint key: listVectorStoreFiles path: listFiles - type: endpoint key: getVectorStoreFile path: getFile - type: endpoint key: deleteVectorStoreFile path: deleteFile - type: object key: VectorStoreFileObject path: file-object - id: vector-stores-file-batches title: Vector Store File Batches beta: true description: | Vector store file batches represent operations to add multiple files to a vector store. Related guide: [File Search](/docs/assistants/tools/file-search) navigationGroup: assistants sections: - type: endpoint key: createVectorStoreFileBatch path: createBatch - type: endpoint key: getVectorStoreFileBatch path: getBatch - type: endpoint key: cancelVectorStoreFileBatch path: cancelBatch - type: endpoint key: listFilesInVectorStoreBatch path: listBatchFiles - type: object key: VectorStoreFileBatchObject path: batch-object - id: assistants-streaming title: Streaming beta: true description: | Stream the result of executing a Run or resuming a Run after submitting tool outputs. You can stream events from the [Create Thread and Run](/docs/api-reference/runs/createThreadAndRun), [Create Run](/docs/api-reference/runs/createRun), and [Submit Tool Outputs](/docs/api-reference/runs/submitToolOutputs) endpoints by passing `"stream": true`. The response will be a [Server-Sent events](https://html.spec.whatwg.org/multipage/server-sent-events.html#server-sent-events) stream. Our Node and Python SDKs provide helpful utilities to make streaming easy. Reference the [Assistants API quickstart](/docs/assistants/overview) to learn more. navigationGroup: assistants sections: - type: object key: MessageDeltaObject path: message-delta-object - type: object key: RunStepDeltaObject path: run-step-delta-object - type: object key: AssistantStreamEvent path: events - id: completions title: Completions legacy: true navigationGroup: legacy description: | Given a prompt, the model will return one or more predicted completions along with the probabilities of alternative tokens at each position. Most developer should use our [Chat Completions API](/docs/guides/text-generation/text-generation-models) to leverage our best and newest models. sections: - type: endpoint key: createCompletion path: create - type: object key: CreateCompletionResponse path: object