hf_text-generation-inference/docs/openapi.json

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JSON

{
"openapi": "3.0.3",
"info": {
"title": "Text Generation Inference",
"description": "Text Generation Webserver",
"contact": {
"name": "Olivier Dehaene"
},
"license": {
"name": "Apache 2.0",
"url": "https://www.apache.org/licenses/LICENSE-2.0"
},
"version": "2.0.1"
},
"paths": {
"/": {
"post": {
"tags": [
"Text Generation Inference"
],
"summary": "Generate tokens if `stream == false` or a stream of token if `stream == true`",
"description": "Generate tokens if `stream == false` or a stream of token if `stream == true`",
"operationId": "compat_generate",
"requestBody": {
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/CompatGenerateRequest"
}
}
},
"required": true
},
"responses": {
"200": {
"description": "Generated Text",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/GenerateResponse"
}
},
"text/event-stream": {
"schema": {
"$ref": "#/components/schemas/StreamResponse"
}
}
}
},
"422": {
"description": "Input validation error",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
},
"example": {
"error": "Input validation error"
}
}
}
},
"424": {
"description": "Generation Error",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
},
"example": {
"error": "Request failed during generation"
}
}
}
},
"429": {
"description": "Model is overloaded",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
},
"example": {
"error": "Model is overloaded"
}
}
}
},
"500": {
"description": "Incomplete generation",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
},
"example": {
"error": "Incomplete generation"
}
}
}
}
}
}
},
"/generate": {
"post": {
"tags": [
"Text Generation Inference"
],
"summary": "Generate tokens",
"description": "Generate tokens",
"operationId": "generate",
"requestBody": {
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/GenerateRequest"
}
}
},
"required": true
},
"responses": {
"200": {
"description": "Generated Text",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/GenerateResponse"
}
}
}
},
"422": {
"description": "Input validation error",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
},
"example": {
"error": "Input validation error"
}
}
}
},
"424": {
"description": "Generation Error",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
},
"example": {
"error": "Request failed during generation"
}
}
}
},
"429": {
"description": "Model is overloaded",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
},
"example": {
"error": "Model is overloaded"
}
}
}
},
"500": {
"description": "Incomplete generation",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
},
"example": {
"error": "Incomplete generation"
}
}
}
}
}
}
},
"/generate_stream": {
"post": {
"tags": [
"Text Generation Inference"
],
"summary": "Generate a stream of token using Server-Sent Events",
"description": "Generate a stream of token using Server-Sent Events",
"operationId": "generate_stream",
"requestBody": {
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/GenerateRequest"
}
}
},
"required": true
},
"responses": {
"200": {
"description": "Generated Text",
"content": {
"text/event-stream": {
"schema": {
"$ref": "#/components/schemas/StreamResponse"
}
}
}
},
"422": {
"description": "Input validation error",
"content": {
"text/event-stream": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
},
"example": {
"error": "Input validation error"
}
}
}
},
"424": {
"description": "Generation Error",
"content": {
"text/event-stream": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
},
"example": {
"error": "Request failed during generation"
}
}
}
},
"429": {
"description": "Model is overloaded",
"content": {
"text/event-stream": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
},
"example": {
"error": "Model is overloaded"
}
}
}
},
"500": {
"description": "Incomplete generation",
"content": {
"text/event-stream": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
},
"example": {
"error": "Incomplete generation"
}
}
}
}
}
}
},
"/health": {
"get": {
"tags": [
"Text Generation Inference"
],
"summary": "Health check method",
"description": "Health check method",
"operationId": "health",
"responses": {
"200": {
"description": "Everything is working fine"
},
"503": {
"description": "Text generation inference is down",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
},
"example": {
"error": "unhealthy",
"error_type": "healthcheck"
}
}
}
}
}
}
},
"/info": {
"get": {
"tags": [
"Text Generation Inference"
],
"summary": "Text Generation Inference endpoint info",
"description": "Text Generation Inference endpoint info",
"operationId": "get_model_info",
"responses": {
"200": {
"description": "Served model info",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/Info"
}
}
}
}
}
}
},
"/metrics": {
"get": {
"tags": [
"Text Generation Inference"
],
"summary": "Prometheus metrics scrape endpoint",
"description": "Prometheus metrics scrape endpoint",
"operationId": "metrics",
"responses": {
"200": {
"description": "Prometheus Metrics",
"content": {
"text/plain": {
"schema": {
"type": "string"
}
}
}
}
}
}
},
"/tokenize": {
"post": {
"tags": [
"Text Generation Inference"
],
"summary": "Tokenize inputs",
"description": "Tokenize inputs",
"operationId": "tokenize",
"requestBody": {
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/GenerateRequest"
}
}
},
"required": true
},
"responses": {
"200": {
"description": "Tokenized ids",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/TokenizeResponse"
}
}
}
},
"404": {
"description": "No tokenizer found",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
},
"example": {
"error": "No fast tokenizer available"
}
}
}
}
}
}
},
"/v1/chat/completions": {
"post": {
"tags": [
"Text Generation Inference"
],
"summary": "Generate tokens",
"description": "Generate tokens",
"operationId": "chat_completions",
"requestBody": {
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ChatRequest"
}
}
},
"required": true
},
"responses": {
"200": {
"description": "Generated Chat Completion",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ChatCompletion"
}
},
"text/event-stream": {
"schema": {
"$ref": "#/components/schemas/ChatCompletionChunk"
}
}
}
},
"422": {
"description": "Input validation error",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
},
"example": {
"error": "Input validation error"
}
}
}
},
"424": {
"description": "Generation Error",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
},
"example": {
"error": "Request failed during generation"
}
}
}
},
"429": {
"description": "Model is overloaded",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
},
"example": {
"error": "Model is overloaded"
}
}
}
},
"500": {
"description": "Incomplete generation",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
},
"example": {
"error": "Incomplete generation"
}
}
}
}
}
}
},
"/v1/completions": {
"post": {
"tags": [
"Text Generation Inference"
],
"summary": "Generate tokens",
"description": "Generate tokens",
"operationId": "completions",
"requestBody": {
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/CompletionRequest"
}
}
},
"required": true
},
"responses": {
"200": {
"description": "Generated Chat Completion",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/Completion"
}
},
"text/event-stream": {
"schema": {
"$ref": "#/components/schemas/CompletionCompleteChunk"
}
}
}
},
"422": {
"description": "Input validation error",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
},
"example": {
"error": "Input validation error"
}
}
}
},
"424": {
"description": "Generation Error",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
},
"example": {
"error": "Request failed during generation"
}
}
}
},
"429": {
"description": "Model is overloaded",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
},
"example": {
"error": "Model is overloaded"
}
}
}
},
"500": {
"description": "Incomplete generation",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
},
"example": {
"error": "Incomplete generation"
}
}
}
}
}
}
}
},
"components": {
"schemas": {
"BestOfSequence": {
"type": "object",
"required": [
"generated_text",
"finish_reason",
"generated_tokens",
"prefill",
"tokens"
],
"properties": {
"finish_reason": {
"$ref": "#/components/schemas/FinishReason"
},
"generated_text": {
"type": "string",
"example": "test"
},
"generated_tokens": {
"type": "integer",
"format": "int32",
"example": 1,
"minimum": 0
},
"prefill": {
"type": "array",
"items": {
"$ref": "#/components/schemas/PrefillToken"
}
},
"seed": {
"type": "integer",
"format": "int64",
"example": 42,
"nullable": true,
"minimum": 0
},
"tokens": {
"type": "array",
"items": {
"$ref": "#/components/schemas/Token"
}
},
"top_tokens": {
"type": "array",
"items": {
"type": "array",
"items": {
"$ref": "#/components/schemas/Token"
}
}
}
}
},
"ChatCompletion": {
"type": "object",
"required": [
"id",
"object",
"created",
"model",
"system_fingerprint",
"choices",
"usage"
],
"properties": {
"choices": {
"type": "array",
"items": {
"$ref": "#/components/schemas/ChatCompletionComplete"
}
},
"created": {
"type": "integer",
"format": "int64",
"example": "1706270835",
"minimum": 0
},
"id": {
"type": "string"
},
"model": {
"type": "string",
"example": "mistralai/Mistral-7B-Instruct-v0.2"
},
"object": {
"type": "string"
},
"system_fingerprint": {
"type": "string"
},
"usage": {
"$ref": "#/components/schemas/Usage"
}
}
},
"ChatCompletionChoice": {
"type": "object",
"required": [
"index",
"delta"
],
"properties": {
"delta": {
"$ref": "#/components/schemas/ChatCompletionDelta"
},
"finish_reason": {
"type": "string",
"nullable": true
},
"index": {
"type": "integer",
"format": "int32",
"minimum": 0
},
"logprobs": {
"allOf": [
{
"$ref": "#/components/schemas/ChatCompletionLogprobs"
}
],
"nullable": true
}
}
},
"ChatCompletionChunk": {
"type": "object",
"required": [
"id",
"object",
"created",
"model",
"system_fingerprint",
"choices"
],
"properties": {
"choices": {
"type": "array",
"items": {
"$ref": "#/components/schemas/ChatCompletionChoice"
}
},
"created": {
"type": "integer",
"format": "int64",
"example": "1706270978",
"minimum": 0
},
"id": {
"type": "string"
},
"model": {
"type": "string",
"example": "mistralai/Mistral-7B-Instruct-v0.2"
},
"object": {
"type": "string"
},
"system_fingerprint": {
"type": "string"
}
}
},
"ChatCompletionComplete": {
"type": "object",
"required": [
"index",
"message",
"finish_reason"
],
"properties": {
"finish_reason": {
"type": "string"
},
"index": {
"type": "integer",
"format": "int32",
"minimum": 0
},
"logprobs": {
"allOf": [
{
"$ref": "#/components/schemas/ChatCompletionLogprobs"
}
],
"nullable": true
},
"message": {
"$ref": "#/components/schemas/Message"
}
}
},
"ChatCompletionDelta": {
"type": "object",
"required": [
"role"
],
"properties": {
"content": {
"type": "string",
"example": "What is Deep Learning?",
"nullable": true
},
"role": {
"type": "string",
"example": "user"
},
"tool_calls": {
"allOf": [
{
"$ref": "#/components/schemas/DeltaToolCall"
}
],
"nullable": true
}
}
},
"ChatCompletionLogprob": {
"type": "object",
"required": [
"token",
"logprob",
"top_logprobs"
],
"properties": {
"logprob": {
"type": "number",
"format": "float"
},
"token": {
"type": "string"
},
"top_logprobs": {
"type": "array",
"items": {
"$ref": "#/components/schemas/ChatCompletionTopLogprob"
}
}
}
},
"ChatCompletionLogprobs": {
"type": "object",
"required": [
"content"
],
"properties": {
"content": {
"type": "array",
"items": {
"$ref": "#/components/schemas/ChatCompletionLogprob"
}
}
}
},
"ChatCompletionTopLogprob": {
"type": "object",
"required": [
"token",
"logprob"
],
"properties": {
"logprob": {
"type": "number",
"format": "float"
},
"token": {
"type": "string"
}
}
},
"ChatRequest": {
"type": "object",
"required": [
"model",
"messages"
],
"properties": {
"frequency_penalty": {
"type": "number",
"format": "float",
"description": "Number between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text so far,\ndecreasing the model's likelihood to repeat the same line verbatim.",
"example": "1.0",
"nullable": true
},
"logit_bias": {
"type": "array",
"items": {
"type": "number",
"format": "float"
},
"description": "UNUSED\nModify the likelihood of specified tokens appearing in the completion. Accepts a JSON object that maps tokens\n(specified by their token ID in the tokenizer) to an associated bias value from -100 to 100. Mathematically,\nthe bias is added to the logits generated by the model prior to sampling. The exact effect will vary per model,\nbut values between -1 and 1 should decrease or increase likelihood of selection; values like -100 or 100 should\nresult in a ban or exclusive selection of the relevant token.",
"nullable": true
},
"logprobs": {
"type": "boolean",
"description": "Whether to return log probabilities of the output tokens or not. If true, returns the log probabilities of each\noutput token returned in the content of message.",
"example": "false",
"nullable": true
},
"max_tokens": {
"type": "integer",
"format": "int32",
"description": "The maximum number of tokens that can be generated in the chat completion.",
"example": "32",
"nullable": true,
"minimum": 0
},
"messages": {
"type": "array",
"items": {
"$ref": "#/components/schemas/Message"
},
"description": "A list of messages comprising the conversation so far.",
"example": "[{\"role\": \"user\", \"content\": \"What is Deep Learning?\"}]"
},
"model": {
"type": "string",
"description": "[UNUSED] ID of the model to use. See the model endpoint compatibility table for details on which models work with the Chat API.",
"example": "mistralai/Mistral-7B-Instruct-v0.2"
},
"n": {
"type": "integer",
"format": "int32",
"description": "UNUSED\nHow many chat completion choices to generate for each input message. Note that you will be charged based on the\nnumber of generated tokens across all of the choices. Keep n as 1 to minimize costs.",
"example": "2",
"nullable": true,
"minimum": 0
},
"presence_penalty": {
"type": "number",
"format": "float",
"description": "Number between -2.0 and 2.0. Positive values penalize new tokens based on whether they appear in the text so far,\nincreasing the model's likelihood to talk about new topics",
"example": 0.1,
"nullable": true
},
"seed": {
"type": "integer",
"format": "int64",
"example": 42,
"nullable": true,
"minimum": 0
},
"stop": {
"type": "array",
"items": {
"type": "string"
},
"description": "Up to 4 sequences where the API will stop generating further tokens.",
"example": "null",
"nullable": true
},
"stream": {
"type": "boolean"
},
"temperature": {
"type": "number",
"format": "float",
"description": "What sampling temperature to use, between 0 and 2. Higher values like 0.8 will make the output more random, while\nlower values like 0.2 will make it more focused and deterministic.\n\nWe generally recommend altering this or `top_p` but not both.",
"example": 1.0,
"nullable": true
},
"tool_choice": {
"allOf": [
{
"$ref": "#/components/schemas/ToolType"
}
],
"nullable": true
},
"tool_prompt": {
"type": "string",
"description": "A prompt to be appended before the tools",
"example": "\"You will be presented with a JSON schema representing a set of tools.\nIf the user request lacks of sufficient information to make a precise tool selection: Do not invent any tool's properties, instead notify with an error message.\n\nJSON Schema:\n\"",
"nullable": true
},
"tools": {
"type": "array",
"items": {
"$ref": "#/components/schemas/Tool"
},
"description": "A list of tools the model may call. Currently, only functions are supported as a tool. Use this to provide a list of\nfunctions the model may generate JSON inputs for.",
"example": "null",
"nullable": true
},
"top_logprobs": {
"type": "integer",
"format": "int32",
"description": "An integer between 0 and 5 specifying the number of most likely tokens to return at each token position, each with\nan associated log probability. logprobs must be set to true if this parameter is used.",
"example": "5",
"nullable": true,
"minimum": 0
},
"top_p": {
"type": "number",
"format": "float",
"description": "An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the\ntokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10% probability mass are considered.",
"example": 0.95,
"nullable": true
}
}
},
"CompatGenerateRequest": {
"type": "object",
"required": [
"inputs"
],
"properties": {
"inputs": {
"type": "string",
"example": "My name is Olivier and I"
},
"parameters": {
"$ref": "#/components/schemas/GenerateParameters"
},
"stream": {
"type": "boolean",
"default": "false"
}
}
},
"CompletionComplete": {
"type": "object",
"required": [
"index",
"text",
"finish_reason"
],
"properties": {
"finish_reason": {
"type": "string"
},
"index": {
"type": "integer",
"format": "int32",
"minimum": 0
},
"logprobs": {
"type": "array",
"items": {
"type": "number",
"format": "float"
},
"nullable": true
},
"text": {
"type": "string"
}
}
},
"CompletionCompleteChunk": {
"type": "object",
"required": [
"id",
"object",
"created",
"choices",
"model",
"system_fingerprint"
],
"properties": {
"choices": {
"type": "array",
"items": {
"$ref": "#/components/schemas/CompletionComplete"
}
},
"created": {
"type": "integer",
"format": "int64",
"minimum": 0
},
"id": {
"type": "string"
},
"model": {
"type": "string"
},
"object": {
"type": "string"
},
"system_fingerprint": {
"type": "string"
}
}
},
"CompletionRequest": {
"type": "object",
"required": [
"model",
"prompt"
],
"properties": {
"frequency_penalty": {
"type": "number",
"format": "float",
"description": "Number between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text so far,\ndecreasing the model's likelihood to repeat the same line verbatim.",
"example": "1.0",
"nullable": true
},
"max_tokens": {
"type": "integer",
"format": "int32",
"description": "The maximum number of tokens that can be generated in the chat completion.",
"default": "32",
"nullable": true,
"minimum": 0
},
"model": {
"type": "string",
"description": "UNUSED\nID of the model to use. See the model endpoint compatibility table for details on which models work with the Chat API.",
"example": "mistralai/Mistral-7B-Instruct-v0.2"
},
"prompt": {
"type": "array",
"items": {
"type": "string"
},
"description": "The prompt to generate completions for.",
"example": "What is Deep Learning?"
},
"repetition_penalty": {
"type": "number",
"format": "float",
"nullable": true
},
"seed": {
"type": "integer",
"format": "int64",
"example": 42,
"nullable": true,
"minimum": 0
},
"stream": {
"type": "boolean"
},
"suffix": {
"type": "string",
"description": "The text to append to the prompt. This is useful for completing sentences or generating a paragraph of text.\nplease see the completion_template field in the model's tokenizer_config.json file for completion template.",
"nullable": true
},
"temperature": {
"type": "number",
"format": "float",
"description": "What sampling temperature to use, between 0 and 2. Higher values like 0.8 will make the output more random, while\nlower values like 0.2 will make it more focused and deterministic. We generally recommend altering this or `top_p` but not both.",
"example": 1.0,
"nullable": true
},
"top_p": {
"type": "number",
"format": "float",
"description": "An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the\ntokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10% probability mass are considered.",
"example": 0.95,
"nullable": true
}
}
},
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"model_device_type",
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}
},
"tags": [
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"description": "Hugging Face Text Generation Inference API"
}
]
}