{ "openapi": "3.0.3", "info": { "title": "AnyAPI API", "description":"Universal API Integration Platform - Connect any API to any API with intelligent data transformation and workflow orchestration.", "version": "1.0.0", "contact": { "name": "AnyAPI Support", "email": "support@anyapi.ai", "url": "https://anyapi.ai/support" }, "license": { "name": "MIT", "url": "https://opensource.org/licenses/MIT" } }, "servers": [ { "url": "https://api.anyapi.ai/v1", "description": "Production server" } ], "security": [ { "bearerAuth": [] } ], "paths": { "/chat/completions": { "post": { "summary": "Create chat completion", "description": "Creates a model response for the given chat conversation", "operationId": "createChatCompletion", "tags": ["Text"], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/CreateChatCompletionRequest" }, "examples": { "Base request": { "summary": "Base request", "description": "Minimal request with mandatory parameters. Used for simple questions without additional settings.", "value": { "model": "openai/gpt-4", "messages": [ { "role": "user", "content": "Sample text goes here" } ] } }, "With a system prompt": { "summary": "With a system prompt", "description": "Adds a system message that sets the behavior and context for the assistant throughout the dialogue.", "value": { "model": "openai/gpt-4", "messages": [ { "role": "system", "content": "You are a helpful assistant." }, { "role": "user", "content": "What is the capital of France?" } ] } }, "Multi-message conversation": { "summary": "A multi-message conversation", "description": "Presents a history of dialogue with multiple messages from the user and assistant for contextual communication.", "value": { "model": "openai/gpt-4", "messages": [ { "role": "system", "content": "You are a helpful assistant." }, { "role": "user", "content": "What is the capital of France?" }, { "role": "assistant", "content": "The capital of France is Paris." }, { "role": "user", "content": "What is its population?" } ] } }, "With temperature and token settings": { "summary": "With temperature and token settings", "description": "Adjusting the creativity of the response (temperature), maximum length (max_tokens), and other sampling parameters to control the quality of the generation.", "value": { "model": "openai/gpt-4", "messages": [ { "role": "user", "content": "Write a creative story about a robot." } ], "temperature": 0.7, "max_tokens": 500, "top_p": 1, "frequency_penalty": 0, "presence_penalty": 0 } }, "Streaming response": { "summary": "Streaming response", "description": "Includes streaming response transmission (as in ChatGPT), where tokens arrive gradually rather than all at once.", "value": { "model": "openai/gpt-4", "messages": [ { "role": "user", "content": "Tell me a story." } ], "stream": true } }, "Vision - URL": { "summary": "Vision - URL", "description": "Sending an image via URL for analysis using the multimodal GPT-4 Vision model.", "value": { "model": "google/gemini-2.5-pro", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "What's in this image?" }, { "type": "image_url", "image_url": { "url": "https://example.com/image.jpg" } } ] } ], "max_tokens": 300 } }, "Vision base64": { "summary": "Vision base64", "description": "Sending an image in base64 format for analysis. Used when the image is not available via URL or is stored locally.", "value": { "model": "mistralai/pixtral-12b", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image" }, { "type": "image_url", "image_url": { "url": "data:image/jpeg;base64,/9j/4AAQSkZJRg...", "detail": "high" } } ] } ] } }, "Function calling": { "summary": "Function Calling - function definition", "description": "Defining the tools (functions) that the model can call to obtain external data (e.g., weather, database).", "value": { "model": "openai/gpt-4", "messages": [ { "role": "user", "content": "What's the weather in Boston?" } ], "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" } }, "Function call response": { "summary": "With a response to Function Call", "description": "Continuation of the dialogue after the model has called the function - transfer of the function execution result back to the model.", "value": { "model": "openai/gpt-4", "messages": [ { "role": "user", "content": "What's the weather in Boston?" }, { "role": "assistant", "content": null, "tool_calls": [ { "id": "call_abc123", "type": "function", "function": { "name": "get_current_weather", "arguments": "{\"location\": \"Boston, MA\"}" } } ] }, { "role": "tool", "tool_call_id": "call_abc123", "content": "{\"temperature\": 22, \"unit\": \"celsius\", \"description\": \"Sunny\"}" } ], "tools": [ { "type": "function", "function": { "name": "get_current_weather", "description": "Get the current weather", "parameters": { "type": "object", "properties": { "location": {"type": "string"} } } } } ] } }, "JSON mode": { "summary": "JSON Mode", "description": "Forces the model to return only valid JSON. Useful for parsing structured data.", "value": { "model": "openai/gpt-4-turbo", "messages": [ { "role": "system", "content": "You are a helpful assistant designed to output JSON." }, { "role": "user", "content": "Extract the name and age from this text: 'John is 25 years old.'" } ], "response_format": { "type": "json_object" } } }, "Structured outputs": { "summary": "Structured Outputs (JSON Schema)", "description": "Ensures that the response conforms to the exact JSON schema. A stricter version of JSON Mode with structure validation.", "value": { "model": "openai/gpt-4o-2024-08-06", "messages": [ { "role": "user", "content": "Extract user information from: 'John Doe, 30 years old, john@example.com'" } ], "response_format": { "type": "json_schema", "json_schema": { "name": "user_info", "strict": true, "schema": { "type": "object", "properties": { "name": {"type": "string"}, "age": {"type": "number"}, "email": {"type": "string"} }, "required": ["name", "age", "email"], "additionalProperties": false } } } } }, "Logprobs": { "summary": "With Probability Logging (logprobs)", "description": "Returns probabilities for each generated token. Useful for analyzing model confidence.", "value": { "model": "openai/gpt-4", "messages": [ { "role": "user", "content": "Say hello" } ], "logprobs": true, "top_logprobs": 2 } }, "Multiple options": { "summary": "With multiple options (n > 1)", "description": "Generates several alternative answers to a single query. Useful for selecting the best option.", "value": { "model": "openai/gpt-4", "messages": [ { "role": "user", "content": "Write a tagline for an ice cream shop." } ], "n": 3, "temperature": 0.9 } }, "Stop sequences": { "summary": "Stop sequences", "description": "Specifies character sequences at which generation should stop. Useful for formatting.", "value": { "model": "openai/gpt-4", "messages": [ { "role": "user", "content": "List three colors:" } ], "stop": ["\n", "4."], "max_tokens": 100 } }, "Seed reproducibility": { "summary": "With seed for reproducibility", "description": "Sets the seed for deterministic generation. With identical parameters, it will produce the same result.", "value": { "model": "openai/gpt-4", "messages": [ { "role": "user", "content": "Generate a random story." } ], "seed": 12345, "temperature": 1 } }, "UserId": { "summary": "With user ID", "description": "Transmits the user ID for monitoring and preventing abuse. Helps OpenAI track violations.", "value": { "model": "openai/gpt-4", "messages": [ { "role": "user", "content": "Sample text goes here" } ], "user": "user-123456" } }, "Parallel function calling": { "summary": "Parallel Function Calling", "description": "Allows the model to call multiple functions simultaneously in a single response for efficiency.", "value": { "model": "openai/gpt-4-turbo", "messages": [ { "role": "user", "content": "What's the weather in Boston and New York?" } ], "tools": [ { "type": "function", "function": { "name": "get_current_weather", "description": "Get weather", "parameters": { "type": "object", "properties": { "location": {"type": "string"} }, "required": ["location"] } } } ], "parallel_tool_calls": true } }, "o1 models": { "summary": "O1 models (reasoning)", "description": "Using O1 models with advanced reasoning capabilities for complex tasks (mathematics, logic).", "value": { "model": "openai/o1", "messages": [ { "role": "user", "content": "Solve this complex math problem: Find the integral of x^2 * e^x dx" } ], "max_completion_tokens": 5000 } }, "Audio input (base)": { "summary": "Audio input (base)", "description": "Sending audio to a model for transcription or voice analysis. Audio is encoded in base64.", "value": { "model": "google/gemini-2.5-pro", "modalities": ["text", "audio"], "audio": { "voice": "alloy", "format": "wav" }, "messages": [ { "role": "user", "content": [ { "type": "input_audio", "input_audio": { "data": "base64_encoded_audio_data_here...", "format": "wav" } } ] } ] } }, "Audio input + text": { "summary": "Audio input + text", "description": "A combination of text and audio in a single message for multimodal interaction.", "value": { "model": "google/gemini-2.5-pro", "modalities": ["text", "audio"], "audio": { "voice": "echo", "format": "mp3" }, "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Please transcribe and summarize this audio:" }, { "type": "input_audio", "input_audio": { "data": "base64_encoded_audio...", "format": "mp3" } } ] } ] } }, "Stream option": { "summary": "Stream Options", "description": "Additional options for streaming mode, such as enabling token usage information.", "value": { "model": "openai/gpt-4", "messages": [ { "role": "user", "content": "Hello" } ], "stream": true, "stream_options": { "include_usage": true } } }, "Logit bias": { "summary": "Logit Bias", "description": "Changes the probability of certain tokens appearing. Positive values increase it, negative values decrease it.", "value": { "model": "openai/gpt-4", "messages": [ { "role": "user", "content": "Write a sentence" } ], "logit_bias": { "50256": -100, "13": 10 } } }, "Multiple images": { "summary": "Multiple images in one post", "description": "Sending multiple images simultaneously for comparison or comprehensive analysis.", "value": { "model": "google/gemini-2.5-pro", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Compare these images:" }, { "type": "image_url", "image_url": {"url": "https://example.com/image1.jpg"} }, { "type": "image_url", "image_url": {"url": "https://example.com/image2.jpg"} }, { "type": "image_url", "image_url": {"url": "https://example.com/image3.jpg"} } ] } ], "max_tokens": 500 } }, "Image detail level": { "summary": "Image detail level", "description": "Control the level of detail in image analysis. 'high' - detailed analysis, 'low' - quick overview.", "value": { "model": "google/gemini-2.5-pro", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Analyze in detail" }, { "type": "image_url", "image_url": { "url": "https://example.com/image.jpg", "detail": "high" } } ] } ] } }, "Tool choice (specific)": { "summary": "Tool Choice - Specific Function", "description": "Forced invocation of a specific function instead of automatic selection by the model.", "value": { "model": "openai/gpt-4", "messages": [ { "role": "user", "content": "What's the weather?" } ], "tools": [ { "type": "function", "function": { "name": "get_weather", "description": "Get weather", "parameters": { "type": "object", "properties": { "location": {"type": "string"} } } } } ], "tool_choice": { "type": "function", "function": { "name": "get_weather" } } } }, "Tool choice": { "summary": "Tool Choice - none", "description": "Prohibition of using tools. The model must respond only with text, even if functions are defined.", "value": { "model": "openai/gpt-4", "messages": [ { "role": "user", "content": "What's the weather in Paris?" } ], "tools": [ { "type": "function", "function": { "name": "get_weather", "description": "Get weather", "parameters": { "type": "object", "properties": { "location": {"type": "string"} } } } } ], "tool_choice": "none" } }, "Tool choice (required)": { "summary": "Tool Choice - required", "description": "Requires the mandatory use of at least one tool. The model cannot respond with text alone.", "value": { "model": "openai/gpt-4", "messages": [ { "role": "user", "content": "What's the weather in Paris?" } ], "tools": [ { "type": "function", "function": { "name": "get_weather", "description": "Get weather", "parameters": { "type": "object", "properties": { "location": {"type": "string"} } } } } ], "tool_choice": "required" } }, "Multi-user chats": { "summary": "Name in messages (multi-user chats)", "description": "Specifying the sender's name for group chats or dialogues with multiple users.", "value": { "model": "openai/gpt-4", "messages": [ { "role": "system", "content": "You are in a group chat" }, { "role": "user", "name": "Alice", "content": "Hello everyone!" }, { "role": "user", "name": "Bob", "content": "Hi Alice!" } ] } }, "Metadata": { "summary": "Metadata", "description": "Arbitrary metadata for tracking and analytics. Does not affect generation, used for organization.", "value": { "model": "openai/gpt-4", "messages": [ { "role": "user", "content": "Sample text goes here" } ], "metadata": { "user_id": "user_123", "conversation_id": "conv_456", "custom_field": "value" } } }, "Store": { "summary": "Store (saving conversations)", "description": "Saving conversations for subsequent use in model training (with user consent).", "value": { "model": "openai/gpt-4", "messages": [ { "role": "user", "content": "Sample text goes here" } ], "store": true } }, "Reasoning effort": { "summary": "Reasoning Effort (O1 models)", "description": "Control of 'thinking' time for O1 models. More effort = deeper analysis, but slower.", "value": { "model": "openai/o1", "messages": [ { "role": "user", "content": "Solve this complex problem: optimize the traveling salesman for 20 cities" } ], "reasoning_effort": "high" } }, "Developer messages": { "summary": "Developer Messages (O1)", "description": "Use the 'developer' role for system instructions in O1 models (instead of 'system').", "value": { "model": "openai/o1", "messages": [ { "role": "developer", "content": "You are a helpful assistant." }, { "role": "user", "content": "Hello" } ] } }, "Service tier": { "summary": "Service Tier", "description": "Select the service level for prioritizing requests (default or auto).", "value": { "model": "openai/gpt-4", "messages": [ { "role": "user", "content": "Sample text goes here" } ], "service_tier": "default" } }, "Prediction (prompt caching)": { "summary": "Prediction (Prompt Caching)", "description": "Optimization for prompt caching. Continuation prediction to speed up generation.", "value": { "model": "openai/gpt-4", "messages": [ { "role": "user", "content": "Continue this story: Once upon a time in a magical forest..." } ], "prediction": { "type": "content", "content": "There lived a wise old owl who knew all the secrets of the woods." } } }, "Parallel tool calls - disabled": { "summary": "Parallel Tool Calls - disabled", "description": "Disable parallel function calls. The model will call functions sequentially.", "value": { "model": "openai/gpt-4-turbo", "messages": [ { "role": "user", "content": "Get weather in Boston and New York" } ], "tools": [ { "type": "function", "function": { "name": "get_weather", "description": "Get weather", "parameters": { "type": "object", "properties": { "location": {"type": "string"} } } } } ], "parallel_tool_calls": false } }, "Refusal": { "summary": "Refusal in assistant messages", "description": "A clear indication of the model's refusal in the previous response to continue the dialogue.", "value": { "model": "openai/gpt-4", "messages": [ { "role": "user", "content": "Tell me how to hack" }, { "role": "assistant", "refusal": "I cannot help with that request." }, { "role": "user", "content": "Okay, how about legal cybersecurity?" } ] } }, "All parameters": { "summary": "Complex request - all parameters", "description": "Demonstration of the maximum number of parameters in a single request for complete control over generation.", "value": { "model": "openai/gpt-4-turbo", "messages": [ { "role": "system", "content": "You are a helpful assistant." }, { "role": "user", "name": "Alice", "content": "Help me with weather and calculations" } ], "temperature": 0.7, "top_p": 0.9, "max_tokens": 1000, "n": 1, "stream": false, "stop": null, "presence_penalty": 0.1, "frequency_penalty": 0.1, "logit_bias": {}, "user": "user-12345", "seed": 42, "tools": [ { "type": "function", "function": { "name": "get_weather", "description": "Get weather", "parameters": { "type": "object", "properties": { "location": {"type": "string"} } } } } ], "tool_choice": "auto", "parallel_tool_calls": true, "response_format": {"type": "text"}, "metadata": { "conversation_id": "conv_789" }, "store": false } }, "Structured outputs with description": { "summary": "Structured Outputs with Description", "description": "JSON Schema with detailed description for precise control of response structure with validation.", "value": { "model": "openai/gpt-4o-2024-08-06", "messages": [ { "role": "user", "content": "Calculate 15 * 27 step by step" } ], "response_format": { "type": "json_schema", "json_schema": { "name": "math_response", "description": "A mathematical calculation response", "strict": true, "schema": { "type": "object", "properties": { "result": { "type": "number", "description": "The final answer" }, "steps": { "type": "array", "description": "Step by step calculations", "items": {"type": "string"} } }, "required": ["result", "steps"], "additionalProperties": false } } } } } } } } }, "responses": { "200": { "description": "Successful response", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/CreateChatCompletionResponse" } }, "text/event-stream": { "schema": { "$ref": "#/components/schemas/CreateChatCompletionStreamResponse" } } } }, "400": { "description": "Bad request", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/ErrorResponse" } } } }, "401": { "description": "Unauthorized", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/ErrorResponse" } } } } } } }, "/completions": { "post": { "summary": "[LEGACY] Create completion", "description": "Create text completion from a single prompt\n.", "operationId": "createCompletion", "tags": ["Text"], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/CreateCompletionRequest" } } } }, "responses": { "200": { "description": "Successful response", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/CreateCompletionResponse" } } } }, "400": { "description": "Bad request", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/Error" } } } } } } }, "/images/generations": { "post": { "summary": "Create image", "description": "Creates an image given a text prompt. Supports streaming responses for real-time generation updates.", "operationId": "createImageGeneration", "tags": ["Images"], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/ImageGenerationRequest" }, "examples": { "Generation": { "summary": "Basic image generation", "value": { "model": "MODEL_EXAMPLE", "prompt": "A cute baby sea otter", "n": 1, "size": "1024x1024" } }, "Streaming": { "summary": "Streaming image generation", "value": { "model": "MODEL_EXAMPLE", "prompt": "A cute baby sea otter", "n": 1, "size": "1024x1024", "stream": true } } } } } }, "responses": { "200": { "description": "Successful response", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/ImageGenerationResponse" } }, "text/event-stream": { "schema": { "$ref": "#/components/schemas/ImageGenerationStreamResponse" } } } }, "400": { "description": "Bad request", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/Error" } } } }, "401": { "description": "Unauthorized", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/Error" } } } }, "429": { "description": "Rate limit exceeded", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/Error" } } } }, "500": { "description": "Internal server error", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/Error" } } } } } } }, "/embeddings": { "post": { "summary": "Create embeddings", "description": "Get a vector representation of a given input that can be easily consumed by machine learning models and algorithms.", "operationId": "createEmbedding", "tags": ["Embeddings"], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/EmbeddingRequest" }, "examples": { "basic_embedding": { "summary": "Basic text embedding", "value": { "input": "The food was delicious and the waiter...", "model": "text-embedding-ada-002", "encoding_format": "float" } } } } } }, "responses": { "200": { "description": "Successful response", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/EmbeddingResponse" } } } }, "400": { "description": "Bad request - Invalid input parameters" }, "401": { "description": "Unauthorized - Invalid or missing API key" }, "429": { "description": "Rate limit exceeded" }, "500": { "description": "Internal server error" } } } }, "/models": { "get": { "summary": "List models", "description": "Lists the currently available models, and provides basic information about each one such as the owner and availability.", "operationId": "listModels", "tags": ["Models"], "responses": { "200": { "description": "Successful response", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/ListModelsResponse" } } } }, "401": { "description": "Invalid request parameters", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/Error" } } } }, "429": { "description": "Invalid request parameters", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/Error" } } } } } } }, "/responses": { "post": { "summary": "Create response", "description": "Creates a comprehensive AI response with support for conversations, tools, files, and streaming", "operationId": "createResponse", "tags": ["Responses"], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/CreateResponseRequest" }, "examples": { "Basic": { "summary": "Basic request", "description": "Simple request with model and input text", "value": { "model": "openai/gpt-4o", "input": "Tell me about AI" } }, "With temperature": { "summary": "With temperature", "description": "Adjusts creativity level using temperature parameter", "value": { "model": "openai/gpt-4o", "input": "Write a creative story about a robot", "temperature": 0.9 } }, "With max_tokens": { "summary": "With max_tokens", "description": "Limits the length of the response using max_tokens", "value": { "model": "openai/gpt-4o", "input": "Explain quantum computing", "max_tokens": 500 } }, "Conversation": { "summary": "Conversation with message history", "description": "Sends full conversation history with multiple messages", "value": { "model": "openai/gpt-4o", "conversation": { "messages": [ { "role": "user", "content": "Hello! How are you?" }, { "role": "assistant", "content": "I'm doing well, thank you! How can I help you today?" }, { "role": "user", "content": "Tell me about the weather" } ] } } }, "Conversation ID": { "summary": "Using conversation ID", "description": "References existing conversation by ID for context continuity", "value": { "model": "openai/gpt-4o", "conversation": "conv_abc123xyz", "input": "Can you elaborate on that?" } }, "New dialog with system message": { "summary": "System message for context", "description": "Starts conversation with system message to set assistant behavior", "value": { "model": "openai/gpt-4o", "conversation": { "messages": [ { "role": "system", "content": "You are a helpful math tutor. Explain concepts clearly." }, { "role": "user", "content": "What is calculus?" } ] } } }, "Web Search": { "summary": "Web search tool", "description": "Enables web search capability to find current information", "value": { "model": "openai/gpt-4o", "input": "What are the latest news about AI today?", "tools": [ { "type": "web_search" } ] } }, "File Search": { "summary": "File search tool", "description": "Searches through uploaded documents for relevant information", "value": { "model": "openai/gpt-4o", "input": "Find information about quarterly sales in the uploaded documents", "tools": [ { "type": "file_search" } ] } }, "Code Interpreter": { "summary": "Code interpreter tool", "description": "Executes Python code for calculations and data analysis", "value": { "model": "openai/gpt-4o", "input": "Calculate the first 10 Fibonacci numbers", "tools": [ { "type": "code_interpreter" } ] } }, "Multiple tools": { "summary": "Multiple tools combined", "description": "Uses multiple tools together for complex tasks", "value": { "model": "openai/gpt-4o", "input": "Research the latest Python libraries and create a comparison chart", "tools": [ { "type": "web_search" }, { "type": "code_interpreter" } ] } }, "All tools": { "summary": "All available tools", "description": "Enables all three tools for maximum capability", "value": { "model": "openai/gpt-4o", "input": "Find recent research papers on machine learning, summarize them, and create visualizations", "tools": [ { "type": "web_search" }, { "type": "file_search" }, { "type": "code_interpreter" } ] } }, "Include web_search sources": { "summary": "Include web search sources", "description": "Returns web search sources in the response", "value": { "model": "openai/gpt-4o", "input": "What happened in tech news yesterday?", "tools": [ { "type": "web_search" } ], "include": ["web_search"] } }, "Include code_interpreter outputs": { "summary": "Include code outputs", "description": "Returns code execution outputs and generated files", "value": { "model": "openai/gpt-4o", "input": "Plot a sine wave from 0 to 2π", "tools": [ { "type": "code_interpreter" } ], "include": ["code_interpreter.outputs"] } }, "Include multiple": { "summary": "Include multiple tool outputs", "description": "Returns outputs from multiple tools in the response", "value": { "model": "openai/gpt-4o", "input": "Research AI trends and create a visualization", "tools": [ { "type": "web_search" }, { "type": "code_interpreter" } ], "include": ["web_search", "code_interpreter.outputs"] } }, "Single image URL": { "summary": "Image from URL", "description": "Analyzes image provided via URL", "value": { "model": "openai/gpt-4o", "input": [ { "type": "text", "text": "What's in this image?" }, { "type": "image_url", "image_url": { "url": "https://example.com/image.jpg" } } ] } }, "Image base64": { "summary": "Image in base64 format", "description": "Analyzes image provided as base64-encoded data", "value": { "model": "openai/gpt-4o", "input": [ { "type": "text", "text": "Describe this image" }, { "type": "image_url", "image_url": { "url": "data:image/jpeg;base64,/9j/4AAQSkZJRg...", "detail": "high" } } ] } }, "Multiple images": { "summary": "Multiple images", "description": "Analyzes and compares multiple images", "value": { "model": "openai/gpt-4o", "input": [ { "type": "text", "text": "Compare these two images" }, { "type": "image_url", "image_url": { "url": "https://example.com/image1.jpg" } }, { "type": "image_url", "image_url": { "url": "https://example.com/image2.jpg" } } ] } }, "File input (base64)": { "summary": "File input (base64)", "description": "Provides a document directly in the input as a base64-encoded data URL", "value": { "model": "openai/gpt-4o", "input": [ { "type": "text", "text": "Summarize this document" }, { "type": "file", "file": { "file_data": "data:application/pdf;base64,JVBERi0xLjQKJ..." } } ] } }, "File input (URL)": { "summary": "File input (public URL)", "description": "Provides a document via a publicly accessible URL", "value": { "model": "openai/gpt-4o", "input": [ { "type": "text", "text": "Summarize this document" }, { "type": "file", "file": { "file_id": "https://example.com/report.pdf" } } ] } }, "Background processing": { "summary": "Background processing", "description": "Processes request in background for long-running tasks", "value": { "model": "openai/gpt-4o", "input": "Generate a comprehensive market analysis report", "tools": [ { "type": "web_search" }, { "type": "code_interpreter" } ], "background": true } }, "Background with callback": { "summary": "Background with webhook", "description": "Background processing with webhook callback when complete", "value": { "model": "openai/gpt-4o", "input": "Process this large dataset and create visualizations", "tools": [ { "type": "code_interpreter" } ], "background": true, "webhook_url": "https://your-app.com/webhook" } }, "With metadata": { "summary": "Request with metadata", "description": "Includes custom metadata for tracking and organization", "value": { "model": "openai/gpt-4o", "input": "Analyze customer feedback", "metadata": { "user_id": "user_12345", "session_id": "session_abc", "request_type": "analysis" } } }, "Web research with conversation": { "summary": "Web research in conversation", "description": "Combines conversation context with web search capabilities", "value": { "model": "openai/gpt-4o", "conversation": { "messages": [ { "role": "user", "content": "I'm researching renewable energy" } ] }, "input": "What are the latest developments in solar panel technology?", "tools": [ { "type": "web_search" } ], "include": ["web_search"], "temperature": 0.7 } }, "Image analysis with code interpreter": { "summary": "Image + code analysis", "description": "Analyzes images and generates code or visualizations", "value": { "model": "openai/gpt-4o", "input": [ { "type": "text", "text": "Analyze the data in this chart and recreate it with updated styling" }, { "type": "image_url", "image_url": { "url": "https://example.com/chart.png" } } ], "tools": [ { "type": "code_interpreter" } ], "include": ["code_interpreter.outputs"] } }, "Document analysis with web search": { "summary": "Document + web search", "description": "Compares document content with current online information", "value": { "model": "openai/gpt-4o", "input": "Compare the information in this document with current online sources", "tools": [ { "type": "file_search" }, { "type": "web_search" } ], "include": ["web_search"] } }, "Multi-step research task": { "summary": "Complex multi-step research", "description": "Comprehensive research task with system instructions and multiple tools", "value": { "model": "openai/gpt-4o", "conversation": { "messages": [ { "role": "system", "content": "You are a research assistant. Provide detailed, well-sourced analysis." }, { "role": "user", "content": "I need to understand market trends" } ] }, "input": "Research competitors in the EV market, analyze their strategies, and create comparison charts", "tools": [ { "type": "web_search" }, { "type": "code_interpreter" } ], "include": ["web_search", "code_interpreter.outputs"], "max_tokens": 2000, "temperature": 0.5 } }, "Code analysis with file": { "summary": "Code file analysis", "description": "Reviews code files for bugs and improvements", "value": { "model": "openai/gpt-4o", "input": "Review this code file for bugs and suggest improvements", "tools": [ { "type": "code_interpreter" }, { "type": "file_search" } ], "include": ["code_interpreter.outputs"] } }, "Data visualization task": { "summary": "Data visualization", "description": "Creates visualizations and dashboards from data files", "value": { "model": "openai/gpt-4o", "input": "Create a dashboard with sales trends from the uploaded CSV", "tools": [ { "type": "code_interpreter" } ], "include": ["code_interpreter.outputs"], "temperature": 0.3 } }, "Stream mode": { "summary": "Streaming response", "description": "Streams the response in real-time as it's generated", "value": { "model": "openai/gpt-4o", "input": "Write a long article about artificial intelligence", "stream": true } }, "Stream with tools": { "summary": "Streaming with tools", "description": "Streams response while using tools", "value": { "model": "openai/gpt-4o", "input": "Research and write about quantum computing advances", "tools": [ { "type": "web_search" } ], "include": ["web_search"], "stream": true } }, "Multi-modal analysis": { "summary": "Multi-modal comprehensive analysis", "description": "Combines images, conversation context, and multiple tools", "value": { "model": "openai/gpt-4o", "conversation": { "messages": [ { "role": "system", "content": "You are a data analyst expert" } ] }, "input": [ { "type": "text", "text": "Analyze this chart, search for related market data, and provide insights" }, { "type": "image_url", "image_url": { "url": "https://example.com/market-chart.png", "detail": "high" } } ], "tools": [ { "type": "web_search" }, { "type": "code_interpreter" } ], "include": ["web_search", "code_interpreter.outputs"], "max_tokens": 3000 } }, "Research paper analysis": { "summary": "Research paper analysis", "description": "Analyzes research papers and compares with latest findings", "value": { "model": "openai/gpt-4o", "input": "Summarize key findings and compare with latest research", "tools": [ { "type": "file_search" }, { "type": "web_search" } ], "include": ["web_search"], "metadata": { "project": "literature-review", "topic": "machine-learning" } } }, "Automated report generation": { "summary": "Automated reporting", "description": "Generates comprehensive reports with charts in background", "value": { "model": "openai/gpt-4o", "input": "Generate a comprehensive weekly report with charts and data analysis", "tools": [ { "type": "code_interpreter" }, { "type": "web_search" } ], "include": ["code_interpreter.outputs", "web_search"], "background": true, "webhook_url": "https://your-app.com/reports/webhook", "metadata": { "report_type": "weekly", "department": "sales" } } }, "Maximum parameters": { "summary": "All available parameters", "description": "Demonstrates maximum capability with all parameters combined", "value": { "model": "openai/gpt-4o", "conversation": { "messages": [ { "role": "system", "content": "You are an expert research analyst" }, { "role": "user", "content": "I need comprehensive market analysis" } ] }, "input": [ { "type": "text", "text": "Analyze this market data, search for competitor information, and create visualizations" }, { "type": "image_url", "image_url": { "url": "https://example.com/data-chart.png", "detail": "high" } } ], "tools": [ { "type": "web_search" }, { "type": "file_search" }, { "type": "code_interpreter" } ], "include": ["web_search", "code_interpreter.outputs"], "max_tokens": 4000, "temperature": 0.7, "stream": false, "background": false, "metadata": { "user_id": "user_xyz", "project": "market-analysis", "priority": "high" } } } } } } }, "responses": { "200": { "description": "Successful response", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/CreateResponseResponse" } }, "text/event-stream": { "schema": { "$ref": "#/components/schemas/CreateResponseStreamResponse" } } } }, "400": { "description": "Bad request", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/ErrorResponse" } } } }, "401": { "description": "Unauthorized", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/ErrorResponse" } } } } } } }, "/moderations": { "post": { "summary": "Check content moderation", "description": "The moderations endpoint is a tool you can use to check whether content complies with an LLM Providers policies", "operationId": "checkModeration", "tags": ["Moderation"], "requestBody": { "required": true, "content": { "application/json": { "schema": { "type": "object", "required": [ "input", "model" ], "properties": { "input": { "type": "string", "description": "The text to check for moderation", "example": "Sample text goes here" }, "model": { "type": "string", "description": "The moderation model to use", "example": "text-moderation-stable" } } }, "example": { "input": "Sample text goes here", "model": "text-moderation-stable" } } } }, "responses": { "200": { "description": "Successful response", "content": { "application/json": { "schema": { "type": "object", "properties": { "id": { "type": "string", "description": "Response identifier" }, "model": { "type": "string", "description": "Model used for moderation" }, "results": { "type": "array", "items": { "type": "object", "properties": { "flagged": { "type": "boolean", "description": "Whether the content was flagged" }, "categories": { "type": "object", "description": "Categories that were checked", "additionalProperties": { "type": "boolean" } }, "category_scores": { "type": "object", "description": "Scores for each category", "additionalProperties": { "type": "number" } } } } } } } } } }, "400": { "description": "Bad request - invalid input" }, "401": { "description": "Unauthorized - invalid API key" } } } } }, "components": { "securitySchemes": { "bearerAuth": { "type": "http", "scheme": "bearer", "bearerFormat": "JWT", "description": "Bearer token authentication. Get your API key from the dashboard." } }, "parameters":{ "AcceptHeader":{ "name":"accept", "in":"header", "required":true, "schema":{ "type":"string", "enum":[ "application/json" ] } }, "ContentTypeHeader":{ "name":"content", "in":"header", "required":true, "schema":{ "type":"string", "enum":[ "application/json" ] } } }, "schemas": { "TextContentPart": { "type": "object", "required": ["type", "text"], "properties": { "type": { "type": "string", "enum": ["text"] }, "text": { "type": "string", "description": "The text content" } } }, "ImageUrlContentPart": { "type": "object", "required": ["type", "image_url"], "properties": { "type": { "type": "string", "enum": ["image_url"] }, "image_url": { "$ref": "#/components/schemas/ImageUrl" } } }, "ImageUrl": { "type": "object", "required": ["url"], "properties": { "url": { "type": "string", "format": "uri", "description": "Either a URL of the image or the base64 encoded image data" }, "detail": { "type": "string", "enum": ["auto", "low", "high"], "description": "Specifies the detail level of the image", "default": "auto" } } }, "Tool": { "type": "object", "required": ["type", "function"], "properties": { "type": { "type": "string", "enum": ["function"] }, "function": { "$ref": "#/components/schemas/Function" } } }, "Function": { "type": "object", "required": ["name"], "properties": { "name": { "type": "string", "description": "The name of the function to be called" }, "description": { "type": "string", "description": "A description of what the function does" }, "parameters": { "type": "object", "description": "The parameters the functions accepts, described as a JSON Schema object" } } }, "ToolChoice": { "type": "object", "required": ["type", "function"], "properties": { "type": { "type": "string", "enum": ["function"] }, "function": { "type": "object", "required": ["name"], "properties": { "name": { "type": "string", "description": "The name of the function to call" } } } } }, "Usage": { "type": "object", "required": ["prompt_tokens", "completion_tokens", "total_tokens"], "properties": { "prompt_tokens": { "type": "integer", "description": "Number of tokens in the prompt" }, "completion_tokens": { "type": "integer", "description": "Number of tokens in the generated completion" }, "total_tokens": { "type": "integer", "description": "Total number of tokens used in the request" } } }, "ChatCompletionStreamResponse": { "type": "object", "description": "Represents a streamed chunk of a chat completion response", "required": ["id", "object", "created", "model", "choices"], "properties": { "id": { "type": "string", "description": "A unique identifier for the chat completion" }, "object": { "type": "string", "enum": ["chat.completion.chunk"], "description": "The object type, which is always 'chat.completion.chunk'" }, "created": { "type": "integer", "description": "The Unix timestamp (in seconds) of when the chat completion was created" }, "model": { "type": "string", "description": "The model used for the chat completion" }, "choices": { "type": "array", "description": "A list of chat completion choices", "items": { "$ref": "#/components/schemas/StreamChoice" } } } }, "StreamChoice": { "type": "object", "required": ["index", "delta"], "properties": { "index": { "type": "integer", "description": "The index of the choice in the list of choices" }, "delta": { "$ref": "#/components/schemas/ChatMessageDelta" }, "finish_reason": { "type": "string", "enum": ["stop", "length", "function_call", "content_filter", "tool_calls"], "description": "The reason the model stopped generating tokens", "nullable": true } } }, "ChatMessageDelta": { "type": "object", "description": "A chat completion delta generated by streamed model responses", "properties": { "role": { "type": "string", "enum": ["assistant"], "description": "The role of the author of this message" }, "content": { "type": "string", "description": "The contents of the chunk message", "nullable": true } } }, "TranslationRequest": { "type": "object", "required": ["file", "model"], "properties": { "file": { "type": "string", "format": "binary", "description": "The audio file object (not file name) to translate, in one of these formats: flac, mp3, mp4, mpeg, mpga, m4a, ogg, wav, or webm." }, "model": { "type": "string", "description": "ID of the model to use", "example": "whisper-1" }, "prompt": { "type": "string", "description": "An optional text to guide the model's style or continue a previous audio segment. The prompt should be in English." }, "response_format": { "type": "string", "description": "The format of the transcript output", "enum": ["json", "text", "srt", "verbose_json", "vtt"], "default": "json" }, "temperature": { "type": "number", "description": "The sampling temperature, between 0 and 1", "minimum": 0, "maximum": 1, "default": 0 } } }, "TranslationResponse": { "type": "object", "required": ["text"], "properties": { "text": { "type": "string", "description": "The translated text in English" }, "language": { "type": "string", "description": "The language of the input audio" }, "duration": { "type": "number", "description": "The duration of the input audio in seconds" } } }, "ImageEditRequest": { "type": "object", "required": ["model", "image"], "properties": { "model": { "type": "string", "description": "The model to use for image editing", "example": "gpt-image-1" }, "image": { "oneOf": [ { "type": "string", "format": "binary", "description": "The image to edit. Must be a valid PNG file, less than 4MB, and square." }, { "type": "array", "items": { "type": "string", "format": "binary" }, "description": "Multiple images to use in the edit. Each must be a valid PNG file, less than 4MB, and square." } ] }, "mask": { "type": "string", "format": "binary", "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." }, "prompt": { "type": "string", "description": "A text description of the desired image(s). The maximum length is 1000 characters.", "maxLength": 1000 }, "n": { "type": "integer", "description": "The number of images to generate", "minimum": 1, "maximum": 10, "default": 1 }, "size": { "type": "string", "description": "The size of the generated images", "enum": ["256x256", "512x512", "1024x1024"], "default": "1024x1024" }, "response_format": { "type": "string", "description": "The format in which the generated images are returned", "enum": ["url", "b64_json"], "default": "url" }, "user": { "type": "string", "description": "A unique identifier representing your end-user" } } }, "ImageGenerationRequest": { "type": "object", "required": ["model", "prompt"], "properties": { "model": { "type": "string", "description": "The model to use for image generation", "example": "gpt-image-1" }, "prompt": { "type": "string", "description": "A text description of the desired image(s)", "example": "A cute baby sea otter", "maxLength": 1000 }, "n": { "type": "integer", "description": "The number of images to generate", "minimum": 1, "maximum": 10, "default": 1 }, "size": { "type": "string", "description": "The size of the generated images", "enum": ["256x256", "512x512", "1024x1024", "1792x1024", "1024x1792"], "default": "1024x1024" }, "stream": { "type": "boolean", "description": "If set, generation progress updates will be sent as server-sent events", "default": false }, "response_format": { "type": "string", "description": "The format in which the generated images are returned", "enum": ["url", "b64_json"], "default": "url" } } }, "ImageGenerationResponse": { "type": "object", "required": ["created", "data"], "properties": { "created": { "type": "integer", "description": "The Unix timestamp (in seconds) of when the images were generated" }, "data": { "type": "array", "description": "Array of generated image objects", "items": { "$ref": "#/components/schemas/ImageObject" } } } }, "ImageObject": { "type": "object", "properties": { "url": { "type": "string", "format": "uri", "description": "The URL of the generated image, if response_format is url (default)" }, "b64_json": { "type": "string", "description": "The base64-encoded JSON of the generated image, if response_format is b64_json" }, "revised_prompt": { "type": "string", "description": "The prompt that was used to generate the image, if there was any revision to the prompt" } } }, "ImageGenerationStreamResponse": { "type": "object", "description": "Represents a streamed chunk of an image generation response", "required": ["id", "object", "created"], "properties": { "id": { "type": "string", "description": "A unique identifier for the image generation" }, "object": { "type": "string", "enum": ["image.generation.chunk"], "description": "The object type, which is always 'image.generation.chunk'" }, "created": { "type": "integer", "description": "The Unix timestamp (in seconds) of when the image generation was created" }, "progress": { "type": "number", "description": "The progress of the image generation as a percentage (0-100)", "minimum": 0, "maximum": 100 }, "status": { "type": "string", "enum": ["pending", "generating", "completed", "failed"], "description": "The current status of the image generation" }, "data": { "type": "array", "description": "Array of generated image objects (only present when completed)", "items": { "$ref": "#/components/schemas/ImageObject" } } } }, "ImageVariationRequest": { "type": "object", "required": ["image"], "properties": { "image": { "type": "string", "format": "binary", "description": "The image to use as the basis for the variation(s). Must be a valid PNG file, less than 4MB, and square." }, "model": { "type": "string", "description": "The model to use for image variation generation", "example": "dall-e-2" }, "n": { "type": "integer", "description": "The number of images to generate", "minimum": 1, "maximum": 10, "default": 1 }, "response_format": { "type": "string", "description": "The format in which the generated images are returned", "enum": ["url", "b64_json"], "default": "url" }, "size": { "type": "string", "description": "The size of the generated images", "enum": ["256x256", "512x512", "1024x1024"], "default": "1024x1024" }, "user": { "type": "string", "description": "A unique identifier representing your end-user" } } }, "SpeechRequest": { "type": "object", "required": ["model", "input", "voice"], "properties": { "model": { "type": "string", "description": "One of the available TTS models", "example": "gpt-4o-mini-tts" }, "input": { "type": "string", "description": "The text to generate audio for. The maximum length is 4096 characters", "maxLength": 4096 }, "voice": { "type": "string", "description": "The voice to use when generating the audio", "enum": ["alloy", "echo", "fable", "onyx", "nova", "shimmer"] }, "response_format": { "type": "string", "description": "The format to audio in", "enum": ["mp3", "opus", "aac", "flac", "wav", "pcm"], "default": "mp3" }, "speed": { "type": "number", "description": "The speed of the generated audio", "minimum": 0.25, "maximum": 4.0, "default": 1.0 } } }, "TranscriptionRequest": { "type": "object", "required": ["file", "model"], "properties": { "file": { "type": "string", "format": "binary", "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." }, "model": { "type": "string", "description": "ID of the model to use", "example": "gpt-4o-transcribe" }, "language": { "type": "string", "description": "The language of the input audio. Supplying the input language in ISO-639-1 format will improve accuracy and latency" }, "prompt": { "type": "string", "description": "An optional text to guide the model's style or continue a previous audio segment" }, "response_format": { "type": "string", "description": "The format of the transcript output", "enum": ["json", "text", "srt", "verbose_json", "vtt"], "default": "json" }, "temperature": { "type": "number", "description": "The sampling temperature, between 0 and 1", "minimum": 0, "maximum": 1, "default": 0 }, "timestamp_granularities": { "type": "array", "description": "The timestamp granularities to populate for this transcription", "items": { "type": "string", "enum": ["word", "segment"] } }, "stream": { "type": "boolean", "description": "If set, partial transcription results will be sent as server-sent events", "default": false }, "include": { "type": "array", "description": "Additional data to include in the response", "items": { "type": "string", "enum": ["logprobs"] } } } }, "TranscriptionResponse": { "type": "object", "required": ["text"], "properties": { "text": { "type": "string", "description": "The transcribed text" }, "language": { "type": "string", "description": "The language of the input audio" }, "duration": { "type": "number", "description": "The duration of the input audio in seconds" }, "words": { "type": "array", "description": "Extracted words and their corresponding timestamps (when timestamp_granularities includes 'word')", "items": { "$ref": "#/components/schemas/TranscriptionWord" } }, "segments": { "type": "array", "description": "Segments of the transcribed text and their corresponding details (when timestamp_granularities includes 'segment')", "items": { "$ref": "#/components/schemas/TranscriptionSegment" } } } }, "TranscriptionWord": { "type": "object", "required": ["word", "start", "end"], "properties": { "word": { "type": "string", "description": "The text content of the word" }, "start": { "type": "number", "description": "Start time of the word in seconds" }, "end": { "type": "number", "description": "End time of the word in seconds" } } }, "TranscriptionSegment": { "type": "object", "required": ["id", "seek", "start", "end", "text", "tokens"], "properties": { "id": { "type": "integer", "description": "Unique identifier of the segment" }, "seek": { "type": "integer", "description": "Seek offset of the segment" }, "start": { "type": "number", "description": "Start time of the segment in seconds" }, "end": { "type": "number", "description": "End time of the segment in seconds" }, "text": { "type": "string", "description": "Text content of the segment" }, "tokens": { "type": "array", "description": "Array of token IDs for the text content", "items": { "type": "integer" } }, "temperature": { "type": "number", "description": "Temperature parameter used for the segment" }, "avg_logprob": { "type": "number", "description": "Average log probability of the segment" }, "compression_ratio": { "type": "number", "description": "Compression ratio of the segment" }, "no_speech_prob": { "type": "number", "description": "Probability of no speech" } } }, "TranscriptionStreamResponse": { "type": "object", "description": "Represents a streamed chunk of a transcription response", "required": ["id", "object", "created"], "properties": { "id": { "type": "string", "description": "A unique identifier for the transcription" }, "object": { "type": "string", "enum": ["transcription.chunk"], "description": "The object type, which is always 'transcription.chunk'" }, "created": { "type": "integer", "description": "The Unix timestamp (in seconds) of when the transcription was created" }, "model": { "type": "string", "description": "The model used for transcription" }, "delta": { "$ref": "#/components/schemas/TranscriptionDelta" } } }, "TranscriptionDelta": { "type": "object", "description": "A transcription delta generated by streamed model responses", "properties": { "text": { "type": "string", "description": "The partial transcribed text", "nullable": true } } }, "EmbeddingRequest": { "type": "object", "required": ["input", "model"], "properties": { "input": { "oneOf": [ { "type": "string", "description": "Input text to embed" }, { "type": "array", "items": { "type": "string" }, "description": "Array of input texts to embed" } ] }, "model": { "type": "string", "description": "ID of the model to use", "example": "text-embedding-ada-002" }, "encoding_format": { "type": "string", "description": "The format to return the embeddings in", "enum": ["float", "base64"], "default": "float" }, "dimensions": { "type": "integer", "description": "The number of dimensions the resulting embeddings should have", "minimum": 1 }, "user": { "type": "string", "description": "A unique identifier representing your end-user" } } }, "EmbeddingResponse": { "type": "object", "required": ["object", "data", "model", "usage"], "properties": { "object": { "type": "string", "enum": ["list"], "description": "The object type, which is always 'list'" }, "data": { "type": "array", "description": "Array of embedding objects", "items": { "$ref": "#/components/schemas/Embedding" } }, "model": { "type": "string", "description": "The name of the model used to generate the embedding" }, "usage": { "$ref": "#/components/schemas/Usage" } } }, "Embedding": { "type": "object", "required": ["object", "embedding", "index"], "properties": { "object": { "type": "string", "enum": ["embedding"], "description": "The object type, which is always 'embedding'" }, "embedding": { "type": "array", "description": "The embedding vector", "items": { "type": "number" } }, "index": { "type": "integer", "description": "The index of the embedding in the list of embeddings" } } }, "BatchRequest": { "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" }, "endpoint": { "type": "string", "description": "The endpoint to be used for all requests in the batch", "enum": ["/v1/chat/completions", "/v1/embeddings", "/v1/completions"] }, "completion_window": { "type": "string", "description": "The time frame within which the batch should be processed", "enum": ["24h"] }, "metadata": { "type": "object", "description": "Optional custom metadata for the batch", "additionalProperties": { "type": "string" } } } }, "BatchObject": { "type": "object", "required": ["id", "object", "endpoint", "input_file_id", "completion_window", "status", "created_at"], "properties": { "id": { "type": "string", "description": "The batch identifier" }, "object": { "type": "string", "enum": ["batch"], "description": "The object type, which is always 'batch'" }, "endpoint": { "type": "string", "description": "The endpoint used for the batch" }, "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", "nullable": true }, "error_file_id": { "type": "string", "description": "The ID of the file containing the outputs of requests with errors", "nullable": true }, "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", "nullable": true }, "expires_at": { "type": "integer", "description": "The Unix timestamp (in seconds) for when the batch will expire", "nullable": true }, "finalizing_at": { "type": "integer", "description": "The Unix timestamp (in seconds) for when the batch started finalizing", "nullable": true }, "completed_at": { "type": "integer", "description": "The Unix timestamp (in seconds) for when the batch was completed", "nullable": true }, "failed_at": { "type": "integer", "description": "The Unix timestamp (in seconds) for when the batch failed", "nullable": true }, "expired_at": { "type": "integer", "description": "The Unix timestamp (in seconds) for when the batch expired", "nullable": true }, "cancelling_at": { "type": "integer", "description": "The Unix timestamp (in seconds) for when the batch started cancelling", "nullable": true }, "cancelled_at": { "type": "integer", "description": "The Unix timestamp (in seconds) for when the batch was cancelled", "nullable": true }, "request_counts": { "$ref": "#/components/schemas/BatchRequestCounts" }, "metadata": { "type": "object", "description": "Set of key-value pairs attached to the object", "additionalProperties": { "type": "string" }, "nullable": true } } }, "BatchRequestCounts": { "type": "object", "required": ["total", "completed", "failed"], "properties": { "total": { "type": "integer", "description": "Total number of requests in the batch" }, "completed": { "type": "integer", "description": "Number of requests that have been completed successfully" }, "failed": { "type": "integer", "description": "Number of requests that have failed" } } }, "BatchList": { "type": "object", "required": ["object", "data"], "properties": { "object": { "type": "string", "enum": ["list"], "description": "The object type, which is always 'list'" }, "data": { "type": "array", "description": "Array of batch objects", "items": { "$ref": "#/components/schemas/BatchObject" } }, "first_id": { "type": "string", "description": "First ID in the retrieved list" }, "last_id": { "type": "string", "description": "Last ID in the retrieved list" }, "has_more": { "type": "boolean", "description": "Whether there are more batches available" } }}, "FineTuningJobRequest": { "type": "object", "required": ["model", "training_file"], "properties": { "model": { "type": "string", "description": "The name of the model to fine-tune", "example": "openai/gpt-4o-mini" }, "training_file": { "type": "string", "description": "The ID of an uploaded file that contains training data" }, "hyperparameters": { "$ref": "#/components/schemas/FineTuningHyperparameters" }, "suffix": { "type": "string", "description": "A string of up to 18 characters that will be added to your fine-tuned model name", "maxLength": 18 }, "validation_file": { "type": "string", "description": "The ID of an uploaded file that contains validation data" }, "integrations": { "type": "array", "description": "A list of integrations to enable for your fine-tuning job", "items": { "$ref": "#/components/schemas/FineTuningIntegration" } }, "seed": { "type": "integer", "description": "The seed controls the reproducibility of the job", "minimum": 0, "maximum": 2147483647 }, "method": { "$ref": "#/components/schemas/FineTuningMethod" } } }, "FineTuningMethod": { "type": "object", "required": ["type"], "properties": { "type": { "type": "string", "enum": ["supervised", "dpo", "reinforcement"], "description": "The fine-tuning method to use" }, "supervised": { "$ref": "#/components/schemas/SupervisedFineTuning" }, "dpo": { "$ref": "#/components/schemas/DPOFineTuning" }, "reinforcement": { "$ref": "#/components/schemas/ReinforcementFineTuning" } } }, "SupervisedFineTuning": { "type": "object", "properties": { "hyperparameters": { "type": "object", "properties": { "n_epochs": { "type": "integer", "description": "The number of epochs to train the model for", "minimum": 1, "maximum": 50, "default": 3 }, "learning_rate_multiplier": { "type": "number", "description": "Scaling factor for the learning rate", "minimum": 0.02, "maximum": 2, "default": 1 }, "batch_size": { "type": "integer", "description": "Number of examples in each batch", "minimum": 1, "maximum": 256, "default": 8 } } } } }, "DPOFineTuning": { "type": "object", "properties": { "hyperparameters": { "type": "object", "properties": { "beta": { "type": "number", "description": "The beta parameter for DPO loss", "minimum": 0, "maximum": 5, "default": 0.1 }, "learning_rate_multiplier": { "type": "number", "description": "Scaling factor for the learning rate", "minimum": 0.02, "maximum": 2, "default": 1 }, "n_epochs": { "type": "integer", "description": "The number of epochs to train the model for", "minimum": 1, "maximum": 50, "default": 3 } } } } }, "ReinforcementFineTuning": { "type": "object", "required": ["grader"], "properties": { "grader": { "$ref": "#/components/schemas/FineTuningGrader" }, "hyperparameters": { "type": "object", "properties": { "reasoning_effort": { "type": "string", "enum": ["low", "medium", "high"], "description": "The reasoning effort level for the model", "default": "medium" } } } } }, "FineTuningGrader": { "type": "object", "required": ["type", "name", "input", "reference", "operation"], "properties": { "type": { "type": "string", "enum": ["string_check"], "description": "The type of grader" }, "name": { "type": "string", "description": "The name of the grader" }, "input": { "type": "string", "description": "The input template for the grader" }, "reference": { "type": "string", "description": "The reference template for comparison" }, "operation": { "type": "string", "enum": ["eq", "ne", "contains"], "description": "The comparison operation" } } }, "FineTuningHyperparameters": { "type": "object", "properties": { "batch_size": { "oneOf": [ { "type": "string", "enum": ["auto"] }, { "type": "integer", "minimum": 1, "maximum": 256 } ], "description": "Number of examples in each batch", "default": "auto" }, "learning_rate_multiplier": { "oneOf": [ { "type": "string", "enum": ["auto"] }, { "type": "number", "minimum": 0.02, "maximum": 2 } ], "description": "Scaling factor for the learning rate", "default": "auto" }, "n_epochs": { "oneOf": [ { "type": "string", "enum": ["auto"] }, { "type": "integer", "minimum": 1, "maximum": 50 } ], "description": "The number of epochs to train the model for", "default": "auto" } } }, "FineTuningIntegration": { "type": "object", "required": ["type"], "properties": { "type": { "type": "string", "enum": ["wandb"], "description": "The type of integration" }, "wandb": { "$ref": "#/components/schemas/WandbIntegration" } } }, "WandbIntegration": { "type": "object", "required": ["project"], "properties": { "project": { "type": "string", "description": "The name of the project that the new run will be created under" }, "name": { "type": "string", "description": "A display name to set for the run" }, "entity": { "type": "string", "description": "The entity to use for the run" }, "tags": { "type": "array", "items": { "type": "string" }, "description": "A list of tags to be attached to the newly created run" } } }, "FineTuningJob": { "type": "object", "required": ["id", "object", "created_at", "finished_at", "fine_tuned_model", "hyperparameters", "model", "organization_id", "result_files", "status", "trained_tokens", "training_file", "validation_file"], "properties": { "id": { "type": "string", "description": "The object identifier" }, "object": { "type": "string", "enum": ["fine_tuning.job"], "description": "The object type, which is always 'fine_tuning.job'" }, "created_at": { "type": "integer", "description": "The Unix timestamp (in seconds) for when the fine-tuning job was created" }, "error": { "$ref": "#/components/schemas/FineTuningJobError" }, "fine_tuned_model": { "type": "string", "description": "The name of the fine-tuned model that is being created", "nullable": true }, "finished_at": { "type": "integer", "description": "The Unix timestamp (in seconds) for when the fine-tuning job was finished", "nullable": true }, "hyperparameters": { "$ref": "#/components/schemas/FineTuningHyperparameters" }, "model": { "type": "string", "description": "The base model that is being fine-tuned" }, "organization_id": { "type": "string", "description": "The organization that owns the fine-tuning job" }, "result_files": { "type": "array", "items": { "type": "string" }, "description": "The compiled results file ID(s) for the fine-tuning job" }, "status": { "type": "string", "enum": ["validating_files", "queued", "running", "succeeded", "failed", "cancelled"], "description": "The current status of the fine-tuning job" }, "trained_tokens": { "type": "integer", "description": "The total number of billable tokens processed by this fine-tuning job", "nullable": true }, "training_file": { "type": "string", "description": "The file ID used for training" }, "validation_file": { "type": "string", "description": "The file ID used for validation", "nullable": true }, "integrations": { "type": "array", "items": { "$ref": "#/components/schemas/FineTuningIntegration" }, "description": "A list of integrations enabled for this fine-tuning job" }, "seed": { "type": "integer", "description": "The seed used for the fine-tuning job" }, "estimated_finish": { "type": "integer", "description": "The Unix timestamp (in seconds) for when the fine-tuning job is estimated to finish", "nullable": true } } }, "FineTuningJobError": { "type": "object", "required": ["code", "message", "param"], "properties": { "code": { "type": "string", "description": "A machine-readable error code" }, "message": { "type": "string", "description": "A human-readable error message" }, "param": { "type": "string", "description": "The parameter that was invalid", "nullable": true } } }, "FineTuningJobList": { "type": "object", "required": ["object", "data"], "properties": { "object": { "type": "string", "enum": ["list"], "description": "The object type, which is always 'list'" }, "data": { "type": "array", "items": { "$ref": "#/components/schemas/FineTuningJob" }, "description": "Array of fine-tuning job objects" }, "has_more": { "type": "boolean", "description": "Whether there are more fine-tuning jobs available" } } }, "FineTuningJobEvent": { "type": "object", "required": ["id", "object", "created_at", "level", "message"], "properties": { "id": { "type": "string", "description": "The object identifier" }, "object": { "type": "string", "enum": ["fine_tuning.job.event"], "description": "The object type, which is always 'fine_tuning.job.event'" }, "created_at": { "type": "integer", "description": "The Unix timestamp (in seconds) for when the fine-tuning job event was created" }, "level": { "type": "string", "enum": ["info", "warn", "error"], "description": "The log level of the event" }, "message": { "type": "string", "description": "The event message" }, "data": { "type": "object", "description": "Additional event data", "nullable": true }, "type": { "type": "string", "enum": ["message", "metrics"], "description": "The type of event" } } }, "FineTuningJobEventList": { "type": "object", "required": ["object", "data"], "properties": { "object": { "type": "string", "enum": ["list"], "description": "The object type, which is always 'list'" }, "data": { "type": "array", "items": { "$ref": "#/components/schemas/FineTuningJobEvent" }, "description": "Array of fine-tuning job event objects" }, "has_more": { "type": "boolean", "description": "Whether there are more events available" } } }, "ChatCompletionMessageFunctionCall": { "type": "object", "properties": { "name": { "type": "string" }, "arguments": { "type": "string" } }, "required": ["name", "arguments"] }, "CreateChatCompletionResponse": { "type": "object", "required": ["id", "object", "created", "model", "choices"], "properties": { "id": { "type": "string", "description": "A unique identifier for the chat completion" }, "object": { "type": "string", "enum": ["chat.completion"] }, "created": { "type": "integer", "description": "The Unix timestamp (in seconds) of when the chat completion was created" }, "model": { "type": "string", "description": "The model used for the chat completion" }, "choices": { "type": "array", "items": { "$ref": "#/components/schemas/ChatCompletionChoice" } }, "usage": { "$ref": "#/components/schemas/CompletionUsage" }, "system_fingerprint": { "type": "string", "description": "This fingerprint represents the backend configuration that the model runs with" } } }, "CreateChatCompletionStreamResponse": { "type": "object", "required": ["id", "object", "created", "model", "choices"], "properties": { "id": { "type": "string", "description": "A unique identifier for the chat completion" }, "object": { "type": "string", "enum": ["chat.completion.chunk"] }, "created": { "type": "integer", "description": "The Unix timestamp (in seconds) of when the chat completion was created" }, "model": { "type": "string", "description": "The model used for the chat completion" }, "choices": { "type": "array", "items": { "$ref": "#/components/schemas/ChatCompletionStreamChoice" } }, "system_fingerprint": { "type": "string", "description": "This fingerprint represents the backend configuration that the model runs with" } } }, "CreateResponseRequest": { "type": "object", "required": ["model"], "properties": { "model": { "type": "string", "description": "ID of the model to use", "example": "openai/gpt-4o" }, "input": { "oneOf": [ { "type": "string", "description": "Text input for the model" }, { "type": "array", "description": "Array of content parts (text, images, and/or files)", "items": { "oneOf": [ { "$ref": "#/components/schemas/ChatCompletionMessageContentPartText" }, { "$ref": "#/components/schemas/ChatCompletionMessageContentPartImage" }, { "$ref": "#/components/schemas/ChatCompletionMessageContentPartFile" } ] } } ] }, "conversation": { "oneOf": [ { "type": "string", "description": "Conversation ID to continue an existing conversation" }, { "type": "object", "description": "Conversation object with message history", "properties": { "messages": { "type": "array", "items": { "$ref": "#/components/schemas/ChatCompletionMessage" } } } } ] }, "tools": { "type": "array", "description": "List of tools available to the model", "items": { "type": "object", "required": ["type"], "properties": { "type": { "type": "string", "enum": ["web_search", "file_search", "code_interpreter"], "description": "Type of tool to enable" } } } }, "files": { "type": "array", "description": "List of files to attach to the request", "items": { "type": "object", "required": ["file_id"], "properties": { "file_id": { "type": "string", "description": "The ID of the file to use" } } } }, "include": { "type": "array", "description": "List of additional data to include in the response", "items": { "type": "string", "enum": ["web_search", "file_search", "code_interpreter.outputs"] } }, "max_tokens": { "type": "integer", "description": "Maximum number of tokens to generate", "minimum": 1 }, "temperature": { "type": "number", "description": "Sampling temperature between 0 and 2", "minimum": 0, "maximum": 2, "default": 1 }, "stream": { "type": "boolean", "description": "Whether to stream the response", "default": false }, "background": { "type": "boolean", "description": "Process request in background and notify via webhook", "default": false }, "webhook_url": { "type": "string", "format": "uri", "description": "Webhook URL for background processing notifications" }, "metadata": { "type": "object", "description": "Arbitrary metadata to attach to the request", "additionalProperties": true } } }, "CreateResponseResponse": { "type": "object", "required": ["id", "object", "created", "model", "content"], "properties": { "id": { "type": "string", "description": "Unique identifier for the response" }, "object": { "type": "string", "enum": ["response"], "description": "Object type, always 'response'" }, "created": { "type": "integer", "description": "Unix timestamp of when the response was created" }, "model": { "type": "string", "description": "The model used for generating the response" }, "content": { "type": "string", "description": "The generated response content" }, "conversation_id": { "type": "string", "description": "ID of the conversation if part of a conversation" }, "tools_used": { "type": "array", "description": "List of tools that were used in generating the response", "items": { "type": "object", "properties": { "type": { "type": "string", "enum": ["web_search", "file_search", "code_interpreter"] }, "result": { "type": "object", "description": "Tool execution result" } } } }, "usage": { "$ref": "#/components/schemas/CompletionUsage" }, "metadata": { "type": "object", "description": "Metadata attached to the request", "additionalProperties": true } } }, "CreateResponseStreamResponse": { "type": "object", "required": ["id", "object", "created", "model", "delta"], "properties": { "id": { "type": "string", "description": "Unique identifier for the response" }, "object": { "type": "string", "enum": ["response.chunk"], "description": "Object type, always 'response.chunk'" }, "created": { "type": "integer", "description": "Unix timestamp of when the response was created" }, "model": { "type": "string", "description": "The model used for generating the response" }, "delta": { "type": "object", "description": "Incremental content update", "properties": { "content": { "type": "string", "description": "Content chunk", "nullable": true } } }, "finish_reason": { "type": "string", "enum": ["stop", "length", "tool_calls", "content_filter"], "description": "Reason why the stream ended", "nullable": true } } }, "CreateChatCompletionRequest": { "type": "object", "required": ["model", "messages"], "properties": { "model": { "type": "string", "description": "ID of the model to use", "example": ["openai/gpt-4.1", "openai/gpt-5"] }, "messages": { "type": "array", "description": "A list of messages comprising the conversation so far", "items": { "$ref": "#/components/schemas/ChatCompletionMessage" }, "minItems": 1 }, "max_tokens": { "type": "integer", "description": "The maximum number of tokens to generate in the chat completion", "minimum": 1, "example": 300 }, "stream": { "type": "boolean", "description": "If set, partial message deltas will be sent as data-only server-sent events", "default": false }, "tools": { "type": "array", "description": "A list of tools the model may call", "items": { "$ref": "#/components/schemas/ChatCompletionTool" } }, "tool_choice": { "oneOf": [ { "type": "string", "enum": ["none", "auto"], "description": "Controls which (if any) tool is called by the model" }, { "$ref": "#/components/schemas/ChatCompletionToolChoice" } ], "default": "auto" }, "logprobs": { "type": "boolean", "description": "Whether to return log probabilities of the output tokens", "default": false }, "top_logprobs": { "type": "integer", "description": "An integer between 0 and 20 specifying the number of most likely tokens to return at each token position", "minimum": 0, "maximum": 20 }, "modalities": { "type": "array", "description": "Output types the model should generate for this request", "items": { "type": "string", "enum": ["text", "audio"] } }, "audio": { "type": "object", "description": "Parameters for audio output. Required when modalities includes audio", "properties": { "voice": { "type": "string", "description": "The voice to use for audio output" }, "format": { "type": "string", "enum": ["wav", "mp3"], "description": "The output audio format" } } } } }, "ChatCompletionTool": { "type": "object", "required": ["type", "function"], "properties": { "type": { "type": "string", "enum": ["function"] }, "function": { "$ref": "#/components/schemas/FunctionDefinition" } } }, "ChatCompletionMessage": { "type": "object", "required": ["role", "content"], "properties": { "role": { "type": "string", "enum": ["user", "assistant", "developer", "tool"], "description": "The role of the messages author" }, "content": { "oneOf": [ { "type": "string", "description": "The contents of the message" }, { "type": "array", "description": "An array of content parts with a defined type", "items": { "$ref": "#/components/schemas/ChatCompletionMessageContentPart" } } ] }, "name": { "type": "string", "description": "An optional name for the participant" }, "tool_calls": { "type": "array", "description": "The tool calls generated by the model", "items": { "$ref": "#/components/schemas/ChatCompletionMessageToolCall" } }, "tool_call_id": { "type": "string", "description": "Tool call that this message is responding to" } } }, "ChatCompletionMessageContentPart": { "oneOf": [ { "$ref": "#/components/schemas/ChatCompletionMessageContentPartText" }, { "$ref": "#/components/schemas/ChatCompletionMessageContentPartImage" }, { "$ref": "#/components/schemas/ChatCompletionMessageContentPartFile" }, { "$ref": "#/components/schemas/ChatCompletionMessageContentPartInputAudio" } ], "discriminator": { "propertyName": "type", "mapping": { "text": "#/components/schemas/ChatCompletionMessageContentPartText", "image_url": "#/components/schemas/ChatCompletionMessageContentPartImage", "file": "#/components/schemas/ChatCompletionMessageContentPartFile", "input_audio": "#/components/schemas/ChatCompletionMessageContentPartInputAudio" } } }, "ChatCompletionMessageContentPartText": { "type": "object", "required": ["type", "text"], "properties": { "type": { "type": "string", "enum": ["text"] }, "text": { "type": "string", "description": "The text content" } } }, "ChatCompletionMessageContentPartImage": { "type": "object", "required": ["type", "image_url"], "properties": { "type": { "type": "string", "enum": ["image_url"] }, "image_url": { "type": "object", "required": ["url"], "properties": { "url": { "type": "string", "format": "uri", "description": "Either a URL of the image or the base64 encoded image data" }, "detail": { "type": "string", "enum": ["auto", "low", "high"], "description": "Specifies the detail level of the image", "default": "auto" } } } } }, "ChatCompletionMessageContentPartFile": { "type": "object", "required": ["type", "file"], "properties": { "type": { "type": "string", "enum": ["file"] }, "file": { "type": "object", "properties": { "file_data": { "type": "string", "description": "A base64-encoded data URL of the file (e.g. data:application/pdf;base64,...)" }, "file_id": { "type": "string", "description": "A public URL to the file" } }, "description": "Provide either file_data (base64 data URL) or file_id (public URL). Supported formats vary by model, e.g. PDF is broadly supported, while TXT/CSV/DOCX/XLSX support depends on the model." } } }, "ChatCompletionMessageContentPartInputAudio": { "type": "object", "required": ["type", "input_audio"], "properties": { "type": { "type": "string", "enum": ["input_audio"] }, "input_audio": { "type": "object", "required": ["data", "format"], "properties": { "data": { "type": "string", "description": "Base64-encoded audio data" }, "format": { "type": "string", "enum": ["wav", "mp3"], "description": "The format of the encoded audio data" } } } } }, "FunctionDefinition": { "type": "object", "required": ["name"], "properties": { "name": { "type": "string", "description": "The name of the function to be called" }, "description": { "type": "string", "description": "A description of what the function does" }, "parameters": { "type": "object", "description": "The parameters the functions accepts, described as a JSON Schema object" } } }, "ChatCompletionToolChoice": { "type": "object", "required": ["type", "function"], "properties": { "type": { "type": "string", "enum": ["function"] }, "function": { "type": "object", "required": ["name"], "properties": { "name": { "type": "string", "description": "The name of the function to call" } } } } }, "ChatCompletionStreamChoice": { "type": "object", "required": ["index", "delta"], "properties": { "index": { "type": "integer", "description": "The index of the choice in the list of choices" }, "delta": { "$ref": "#/components/schemas/ChatCompletionStreamChoiceDelta" }, "finish_reason": { "type": "string", "enum": ["stop", "length", "tool_calls", "content_filter", "function_call"], "description": "The reason the model stopped generating tokens" }, "logprobs": { "$ref": "#/components/schemas/ChatCompletionLogprobs" } } }, "ChatCompletionLogprobs": { "type": "object", "properties": { "content": { "type": "array", "items": { "$ref": "#/components/schemas/ChatCompletionTokenLogprob" } } } }, "ChatCompletionTokenLogprob": { "type": "object", "required": ["token", "logprob", "bytes", "top_logprobs"], "properties": { "token": { "type": "string", "description": "The token" }, "logprob": { "type": "number", "description": "The log probability of this token" }, "bytes": { "type": "array", "items": { "type": "integer" }, "description": "A list of integers representing the UTF-8 bytes representation of the token" }, "top_logprobs": { "type": "array", "items": { "$ref": "#/components/schemas/ChatCompletionTopLogprob" }, "description": "List of the most likely tokens and their log probabilities" } } }, "ChatCompletionTopLogprob": { "type": "object", "required": ["token", "logprob", "bytes"], "properties": { "token": { "type": "string", "description": "The token" }, "logprob": { "type": "number", "description": "The log probability of this token" }, "bytes": { "type": "array", "items": { "type": "integer" }, "description": "A list of integers representing the UTF-8 bytes representation of the token" } } }, "ChatCompletionStreamChoiceDelta": { "type": "object", "properties": { "role": { "type": "string", "enum": ["system", "user", "assistant", "tool"], "description": "The role of the author of this message" }, "content": { "type": "string", "description": "The contents of the chunk message" }, "tool_calls": { "type": "array", "items": { "$ref": "#/components/schemas/ChatCompletionMessageToolCallChunk" } } } }, "ChatCompletionMessageToolCallChunk": { "type": "object", "required": ["index"], "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", "enum": ["function"] }, "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" } } } } }, "ChatCompletionMessageToolCall": { "type": "object", "required": ["id", "type", "function"], "properties": { "id": { "type": "string", "description": "The ID of the tool call" }, "type": { "type": "string", "enum": ["function"] }, "function": { "type": "object", "required": ["name", "arguments"], "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" } } } } }, "ChatCompletionChoice": { "type": "object", "properties": { "index": { "type": "integer" }, "message": { "$ref": "#/components/schemas/ChatCompletionResponseMessage" }, "finish_reason": { "type": "string", "enum": ["stop", "length", "function_call", "content_filter"] } }, "required": ["index", "message", "finish_reason"] }, "ChatCompletionResponseMessage": { "type": "object", "properties": { "role": { "type": "string", "enum": ["assistant"] }, "content": { "type": "string" }, "function_call": { "$ref": "#/components/schemas/ChatCompletionMessageFunctionCall" } }, "required": ["role"] }, "CreateCompletionRequest": { "type": "object", "properties": { "model": { "type": "string", "description": "ID of the model to use" }, "prompt": { "description": "The prompt(s) to generate completions for" } }, "required": ["model", "prompt"] }, "CreateCompletionResponse": { "type": "object", "properties": { "id": { "type": "string" }, "object": { "type": "string", "enum": ["text_completion"] }, "created": { "type": "integer" }, "model": { "type": "string" }, "choices": { "type": "array", "items": { "$ref": "#/components/schemas/CompletionChoice" } }, "usage": { "$ref": "#/components/schemas/CompletionUsage" } }, "required": ["id", "object", "created", "model", "choices"] }, "CompletionChoice": { "type": "object", "properties": { "text": { "type": "string" }, "index": { "type": "integer" }, "logprobs": { "type": "object", "properties": { "tokens": { "type": "array", "items": { "type": "string" } }, "token_logprobs": { "type": "array", "items": { "type": "number" } }, "top_logprobs": { "type": "array", "items": { "type": "object", "additionalProperties": { "type": "number" } } }, "text_offset": { "type": "array", "items": { "type": "integer" } } } }, "finish_reason": { "type": "string", "enum": ["stop", "length", "content_filter"] } }, "required": ["text", "index", "finish_reason"] }, "CreateTranscriptionRequest": { "type": "object", "properties": { "file": { "type": "string", "format": "binary", "description": "The audio file object to transcribe" }, "model": { "type": "string", "description": "ID of the model to use", "enum": ["whisper-1"] }, "language": { "type": "string", "description": "The language of the input audio" }, "prompt": { "type": "string", "description": "An optional text to guide the model's style or continue a previous audio segment" }, "response_format": { "type": "string", "enum": ["json", "text", "srt", "verbose_json", "vtt"], "default": "json", "description": "The format of the transcript output" }, "temperature": { "type": "number", "minimum": 0, "maximum": 1, "default": 0, "description": "The sampling temperature, between 0 and 1" } }, "required": ["file", "model"] }, "CreateTranscriptionResponse": { "type": "object", "properties": { "text": { "type": "string", "description": "The transcribed text" } }, "required": ["text"] }, "ListModelsResponse": { "type": "object", "properties": { "object": { "type": "string", "enum": ["list"] }, "data": { "type": "array", "items": { "$ref": "#/components/schemas/Model" } } }, "required": ["object", "data"] }, "Model": { "type": "object", "properties": { "id": { "type": "string", "description": "The model identifier" }, "object": { "type": "string", "enum": ["model"], "description": "The object type" }, "created": { "type": "integer", "description": "The Unix timestamp (in seconds) when the model was created" }, "owned_by": { "type": "string", "description": "The organization that owns the model" } }, "required": ["id", "object", "created", "owned_by"] }, "CompletionUsage": { "type": "object", "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" } }, "required": ["completion_tokens", "prompt_tokens", "total_tokens"] }, "Error": { "type": "object", "properties": { "error": { "type": "object", "properties": { "code": { "type": "string" }, "message": { "type": "string" }, "param": { "type": "string" }, "type": { "type": "string" } }, "required": ["message", "type"] } }, "required": ["error"] }, "ErrorResponse": { "type": "object", "required": ["error"], "properties": { "error": { "type": "object", "required": ["message", "type"], "properties": { "message": { "type": "string", "description": "A human-readable error message" }, "type": { "type": "string", "description": "The type of error" }, "code": { "type": "string", "description": "An error code identifying the error type" } } } } } } }, "tags": [ { "name": "Text", "description": "Generate text completions" }, { "name": "Images", "description": "Generate and manipulate images" }, { "name": "Audio", "description": "Generate speech and transcribe audio" }, { "name": "Music", "description": "List and retrieve model information" }, { "name": "Video", "description": "Create vector embeddings for text" }, { "name": "Vision", "description": "Create vector embeddings for text" }, { "name": "3D Models", "description": "Create vector embeddings for text" }, { "name": "Embeddings", "description": "Create vector embeddings for text" }, { "name": "Batches", "description": "Batches" }, { "name": "Moderation", "description": "Moderation" }, { "name": "Other", "description": "Other models" } ] }