FriendliAI Dedicated.Chat API
The Dedicated.Chat API from FriendliAI — 2 operation(s) for dedicated.chat.
OpenAPI Specification
openapi: 3.1.0
info:
title: Friendli Suite API Reference Container.Audio Dedicated.Chat API
description: This is an OpenAPI reference of Friendli Suite API.
termsOfService: https://friendli.ai/terms-of-service
contact:
name: FriendliAI Support Team
email: support@friendli.ai
version: 0.1.0
servers:
- url: https://api.friendli.ai
tags:
- name: Dedicated.Chat
paths:
/dedicated/v1/chat/completions:
post:
tags:
- Dedicated.Chat
summary: Chat completions
description: Generate a model response from a list of messages comprising a conversation. Compatible with the OpenAI Chat Completions API, with support for streaming, tool calls, and structured outputs.
operationId: dedicatedChatComplete
security:
- token: []
parameters:
- name: X-Friendli-Team
in: header
required: false
schema:
anyOf:
- type: string
- type: 'null'
description: ID of team to run requests as (optional parameter).
title: X-Friendli-Team
description: ID of team to run requests as (optional parameter).
requestBody:
required: true
content:
application/json:
schema:
$ref: '#/components/schemas/DedicatedChatCompletionBody'
responses:
'200':
description: Successfully generated a chat response.
content:
application/json:
schema:
$ref: '#/components/schemas/DedicatedChatCompleteSuccess'
examples:
Example:
value:
id: chatcmpl-4b71d12c86d94e719c7e3984a7bb7941
model: (endpoint-id)
object: chat.completion
choices:
- index: 0
message:
role: assistant
content: Hello there, how may I assist you today?
finish_reason: stop
usage:
prompt_tokens: 9
completion_tokens: 11
total_tokens: 20
created: 1735722153
'422':
description: Unprocessable Entity
x-speakeasy-name-override: complete
x-mint:
metadata:
title: Dedicated Chat Completions
sidebarTitle: Chat Completions
og:title: Dedicated Chat Completions
description: Generate a model response from a list of messages comprising a conversation. Compatible with the OpenAI Chat Completions API, with support for streaming, tool calls, and structured outputs.
og:description: Generate a model response from a list of messages comprising a conversation. Compatible with the OpenAI Chat Completions API, with support for streaming, tool calls, and structured outputs.
href: /openapi/dedicated/inference/chat-completions
content: 'Generate a model response from a list of messages comprising a conversation. Compatible with the OpenAI Chat Completions API, with support for streaming, tool calls, and structured outputs.
To request successfully, it is mandatory to enter a **Personal API Key** (e.g. flp_XXX) value in the **Bearer Token** field.
Refer to the [authentication section](/openapi/introduction#authentication) on our introduction page to learn how to acquire this variable and [visit here](https://friendli.ai/suite/~/setting/keys) to generate your API Key.
When streaming mode is used (i.e., `stream` option is set to `true`), the response is in MIME type `text/event-stream`. Otherwise, the content type is `application/json`.
You can view the schema of the streamed sequence of chunk objects in streaming mode [here](/openapi/dedicated/inference/chat-completions-chunk-object).'
/dedicated/v1/chat/completions#stream:
post:
tags:
- Dedicated.Chat
summary: Stream chat completions
description: Generate a model response from a list of messages comprising a conversation. Compatible with the OpenAI Chat Completions API, with support for streaming, tool calls, and structured outputs.
operationId: dedicatedChatStream
security:
- token: []
parameters:
- name: X-Friendli-Team
in: header
required: false
schema:
anyOf:
- type: string
- type: 'null'
description: ID of team to run requests as (optional parameter).
title: X-Friendli-Team
description: ID of team to run requests as (optional parameter).
requestBody:
required: true
content:
application/json:
schema:
$ref: '#/components/schemas/DedicatedChatCompletionStreamBody'
responses:
'200':
description: Successfully generated a chat response.
content:
text/event-stream:
x-speakeasy-sse-sentinel: '[DONE]'
examples:
Example:
value: 'data: {"id":"chatcmpl-4b71d12c86d94e719c7e3984a7bb7941","object":"chat.completion.chunk","choices":[{"index":0,"delta":{"role":"assistant","content":"This"},"finish_reason":null,"logprobs":null}],"usage":null,"created":1726294381}
data: {"id":"chatcmpl-4b71d12c86d94e719c7e3984a7bb7941","object":"chat.completion.chunk","choices":[{"index":0,"delta":{"content":" is"},"finish_reason":null,"logprobs":null}],"usage":null,"created":1726294381}
...
data: {"id":"chatcmpl-4b71d12c86d94e719c7e3984a7bb7941","object":"chat.completion.chunk","choices":[{"index":0,"delta":{},"finish_reason":"stop","logprobs":null}],"usage":null,"created":1726294383}
data: {"id":"chatcmpl-4b71d12c86d94e719c7e3984a7bb7941","object":"chat.completion.chunk","choices":[],"usage":{"prompt_tokens":8,"completion_tokens":4,"total_tokens":12},"created":1726294402}
data: [DONE]
'
schema:
$ref: '#/components/schemas/DedicatedChatCompletionStreamSuccess'
'422':
description: Unprocessable Entity
x-speakeasy-name-override: stream
x-mint:
metadata:
title: Dedicated Stream Chat Completions
sidebarTitle: Stream Chat Completions
og:title: Dedicated Stream Chat Completions
description: Generate a model response from a list of messages comprising a conversation. Compatible with the OpenAI Chat Completions API, with support for streaming, tool calls, and structured outputs.
og:description: Generate a model response from a list of messages comprising a conversation. Compatible with the OpenAI Chat Completions API, with support for streaming, tool calls, and structured outputs.
content: 'Generate a model response from a list of messages comprising a conversation. Compatible with the OpenAI Chat Completions API, with support for streaming, tool calls, and structured outputs.
To request successfully, it is mandatory to enter a **Personal API Key** (e.g. flp_XXX) value in the **Bearer Token** field.
Refer to the [authentication section](/openapi/introduction#authentication) on our introduction page to learn how to acquire this variable and [visit here](https://friendli.ai/suite/~/setting/keys) to generate your API Key.
When streaming mode is used (i.e., `stream` option is set to `true`), the response is in MIME type `text/event-stream`. Otherwise, the content type is `application/json`.
You can view the schema of the streamed sequence of chunk objects in streaming mode [here](/openapi/dedicated/inference/chat-completions-chunk-object).'
components:
schemas:
VideoContent:
properties:
type:
type: string
const: video_url
title: Type
description: The type of the message content.
video_url:
$ref: '#/components/schemas/VideoData'
description: The video URL data.
type: object
required:
- type
- video_url
title: VideoContent
StreamedToolCallResult:
properties:
type:
type: string
const: function
title: Type
description: The type of the tool.
id:
anyOf:
- type: string
- type: 'null'
title: Id
description: The ID of the tool call.
index:
type: integer
title: Index
description: The index of the tool call being generated.
function:
$ref: '#/components/schemas/StreamedFunctionResult'
type: object
required:
- type
- index
- function
title: StreamedToolCallResult
ToolCallResult:
properties:
type:
type: string
const: function
title: Type
description: The type of the tool.
id:
type: string
title: Id
description: The ID of the tool call.
function:
$ref: '#/components/schemas/FunctionResult'
type: object
required:
- type
- id
- function
title: ToolCallResult
SystemMessage:
properties:
role:
type: string
const: system
title: Role
description: The role of the messages author.
content:
type: string
title: Content
description: The content of system message.
name:
anyOf:
- type: string
- type: 'null'
title: Name
description: The name for the participant to distinguish between participants with the same role.
type: object
required:
- role
- content
title: SystemMessage
Tool:
properties:
type:
type: string
const: function
title: Type
description: The type of the tool. Currently, only `function` is supported.
function:
$ref: '#/components/schemas/Function'
type: object
required:
- type
- function
title: Tool
AssistantMessageToolCall:
properties:
id:
type: string
title: Id
description: The ID of tool call.
type:
type: string
const: function
title: Type
description: The type of tool call.
function:
$ref: '#/components/schemas/AssistantMessageToolCallFunction'
description: The function specification
type: object
required:
- id
- type
- function
title: AssistantMessageToolCall
ImageContent:
properties:
type:
type: string
const: image_url
title: Type
description: The type of the message content.
image_url:
$ref: '#/components/schemas/ImageData'
description: The image URL data.
type: object
required:
- type
- image_url
title: ImageContent
StreamOptions:
properties:
include_usage:
anyOf:
- type: boolean
- type: 'null'
title: Include Usage
description: 'When set to `true`,
the number of tokens used will be included at the end of the stream result in the form of
`"usage": {"completion_tokens": number, "prompt_tokens": number, "total_tokens": number}`.
'
type: object
title: StreamOptions
DedicatedChatCompletionStreamBody:
properties:
model:
type: string
title: Model
description: ID of target endpoint. If you want to send request to specific adapter, use the format "YOUR_ENDPOINT_ID:YOUR_ADAPTER_ROUTE". Otherwise, you can just use "YOUR_ENDPOINT_ID" alone.
examples:
- (endpoint-id)
messages:
items:
$ref: '#/components/schemas/Message'
type: array
title: Messages
description: A list of messages comprising the conversation so far.
examples:
- - content: You are a helpful assistant.
role: system
- content: Hello!
role: user
chat_template_kwargs:
anyOf:
- additionalProperties: true
type: object
- type: 'null'
title: Chat Template Kwargs
description: Additional keyword arguments supplied to the template renderer. These parameters will be available for use within the chat template.
eos_token:
anyOf:
- items:
type: integer
type: array
- type: 'null'
title: Eos Token
description: A list of endpoint sentence tokens.
frequency_penalty:
anyOf:
- type: number
- type: 'null'
title: Frequency Penalty
description: Number between -2.0 and 2.0. Positive values penalizes tokens that have been sampled, taking into account their frequency in the preceding text. This penalization diminishes the model's tendency to reproduce identical lines verbatim.
logit_bias:
anyOf:
- additionalProperties: true
type: object
- type: 'null'
title: Logit Bias
description: Accepts a JSON object that maps tokens to an associated bias value. Mathematically, the bias is added to the logits generated by the model prior to sampling. The exact effect will vary per model.
logprobs:
anyOf:
- type: boolean
- type: 'null'
title: Logprobs
description: Whether to return log probabilities of the output tokens or not.
max_tokens:
anyOf:
- type: integer
- type: 'null'
title: Max Tokens
description: The maximum number of tokens to generate. For decoder-only models like GPT, the length of your input tokens plus `max_tokens` should not exceed the model's maximum length (e.g., 2048 for OpenAI GPT-3). For encoder-decoder models like T5 or BlenderBot, `max_tokens` should not exceed the model's maximum output length. This is similar to Hugging Face's [`max_new_tokens`](https://huggingface.co/docs/transformers/v4.26.0/en/main_classes/text_generation#transformers.GenerationConfig.max_new_tokens) argument.
min_p:
anyOf:
- type: number
- type: 'null'
title: Min P
description: A scaling factor used to determine the minimum token probability threshold. This threshold is calculated as `min_p` multiplied by the probability of the most likely token. Tokens with probabilities below this scaled threshold are excluded from sampling. Values range from 0.0 (inclusive) to 1.0 (inclusive). Higher values result in stricter filtering, while lower values allow for greater diversity. The default value of 0.0 disables filtering, allowing all tokens to be considered for sampling.
n:
anyOf:
- type: integer
- type: 'null'
title: N
description: The number of independently generated results for the prompt. Defaults to 1. This is similar to Hugging Face's [`num_return_sequences`](https://huggingface.co/docs/transformers/v4.26.0/en/main_classes/text_generation#transformers.GenerationConfig.num_return_sequences) argument.
parallel_tool_calls:
anyOf:
- type: boolean
- type: 'null'
title: Parallel Tool Calls
description: Whether to enable parallel function calling.
presence_penalty:
anyOf:
- type: number
- type: 'null'
title: Presence Penalty
description: Number between -2.0 and 2.0. Positive values penalizes tokens that have been sampled at least once in the existing text.
repetition_penalty:
anyOf:
- type: number
- type: 'null'
title: Repetition Penalty
description: Penalizes tokens that have already appeared in the generated result (plus the input tokens for decoder-only models). Should be positive value (1.0 means no penalty). See [keskar et al., 2019](https://arxiv.org/abs/1909.05858) for more details. This is similar to Hugging Face's [`repetition_penalty`](https://huggingface.co/docs/transformers/v4.26.0/en/main_classes/text_generation#transformers.generationconfig.repetition_penalty) argument.
reasoning_effort:
anyOf:
- type: string
enum:
- minimal
- low
- medium
- high
- xhigh
- max
- ultracode
- type: 'null'
title: Reasoning Effort
description: Sets how much reasoning the model does before answering. Higher values give more thorough responses but take longer. This affects reasoning models only, and the available options depend on the model.
reasoning_budget:
anyOf:
- type: integer
- type: 'null'
title: Reasoning Budget
description: Specifies a positive integer that defines a limit on the number of tokens used for internal reasoning tokens. This parameter is only effective for reasoning models.
seed:
anyOf:
- items:
type: integer
type: array
- type: integer
- type: 'null'
title: Seed
description: Seed to control random procedure. If nothing is given, random seed is used for sampling, and return the seed along with the generated result. When using the `n` argument, you can pass a list of seed values to control all of the independent generations.
stop:
anyOf:
- items:
type: string
type: array
- type: 'null'
title: Stop
description: When one of the stop phrases appears in the generation result, the API will stop generation. The stop phrases are excluded from the result. Defaults to empty list.
stream:
type: boolean
title: Stream
description: Whether to stream the generation result. When set to `true`, each token is sent as [server-sent events](https://developer.mozilla.org/en-US/docs/Web/API/Server-sent_events/Using_server-sent_events#event_stream_format) once generated.
default: true
stream_options:
anyOf:
- $ref: '#/components/schemas/StreamOptions'
- type: 'null'
description: 'Options related to stream.
It can only be used when `stream: true`.'
parse_reasoning:
anyOf:
- type: boolean
- type: 'null'
title: Parse Reasoning
description: 'Parses model reasoning into `reasoning_content` while keeping the answer in `content`. Default value may vary between endpoints.
For more detailed information, please refer [here](https://friendli.ai/docs/guides/reasoning#reasoning-parsing-with-friendli).'
include_reasoning:
anyOf:
- type: boolean
- type: 'null'
title: Include Reasoning
description: 'When `parse_reasoning=true`, include parsed reasoning (`reasoning_content`). Defaults to true.
For more detailed information, please refer [here](https://friendli.ai/docs/guides/reasoning#reasoning-parsing-with-friendli).'
temperature:
anyOf:
- type: number
- type: 'null'
title: Temperature
description: Sampling temperature. Smaller temperature makes the generation result closer to greedy, argmax (i.e., `top_k = 1`) sampling. Defaults to 1.0. This is similar to Hugging Face's [`temperature`](https://huggingface.co/docs/transformers/v4.26.0/en/main_classes/text_generation#transformers.generationconfig.temperature) argument.
tool_choice:
anyOf:
- $ref: '#/components/schemas/ChatCompleteBodyToolChoice'
- type: string
title: Tool Choice
description: 'Determines the tool calling behavior of the model.
When set to `none`, the model will bypass tool execution and generate a response directly.
In `auto` mode (the default), the model dynamically decides whether to call a tool or respond with a message.
Alternatively, setting `required` ensures that the model invokes at least one tool before responding to the user.
You can also specify a particular tool by `{"type": "function", "function": {"name": "my_function"}}`.
'
top_k:
anyOf:
- type: integer
- type: 'null'
title: Top K
description: Limits sampling to the top k tokens with the highest probabilities. Values range from 0 (no filtering) to the model's vocabulary size (inclusive). The default value of 0 applies no filtering, allowing all tokens.
top_logprobs:
anyOf:
- type: integer
- type: 'null'
title: Top Logprobs
description: The number of most likely tokens to return at each token position, each with an associated log probability. `logprobs` must be set to true if this parameter is used.
top_p:
anyOf:
- type: number
- type: 'null'
title: Top P
description: Keeps only the smallest set of tokens whose cumulative probabilities reach `top_p` or higher. Values range from 0.0 (exclusive) to 1.0 (inclusive). The default value of 1.0 includes all tokens, allowing maximum diversity.
xtc_threshold:
anyOf:
- type: number
- type: 'null'
title: Xtc Threshold
description: A probability threshold used to identify “top choice” tokens for exclusion in XTC (Exclude Top Choices) sampling. Tokens with probabilities at or above this threshold are considered viable candidates, and all but the least likely viable token are excluded from sampling. This option reduces the dominance of highly probable tokens while preserving some diversity by keeping the least confident “top choice.” Values range from 0.0 (inclusive) to 1.0 (inclusive). Higher values make the filtering more selective by requiring higher probabilities to trigger exclusion, while lower values apply filtering more broadly. The default value of 0.0 disables XTC filtering entirely.
xtc_probability:
anyOf:
- type: number
- type: 'null'
title: Xtc Probability
description: The probability that XTC (Exclude Top Choices) filtering will be applied for each sampling decision. When XTC is triggered, high-probability tokens above the `xtc_threshold` are excluded except for the least likely viable token. This stochastic activation allows for a balance between standard sampling and creativity-boosting exclusion filtering. Values range from 0.0 (inclusive) to 1.0 (inclusive), where 0.0 means XTC is never applied, 1.0 means XTC is always applied when viable tokens exist, and intermediate values provide probabilistic activation. The default value of 0.0 disables XTC filtering.
tools:
anyOf:
- items:
$ref: '#/components/schemas/Tool'
type: array
- type: 'null'
title: Tools
description: 'A list of tools the model may call.
Use this to provide a list of functions the model may generate JSON inputs for.
**When `tools` is specified, `min_tokens` and `response_format` fields are unsupported.**'
min_tokens:
anyOf:
- type: integer
- type: 'null'
title: Min Tokens
description: 'The minimum number of tokens to generate. Default value is 0. This is similar to Hugging Face''s [`min_new_tokens`](https://huggingface.co/docs/transformers/v4.26.0/en/main_classes/text_generation#transformers.generationconfig.min_new_tokens) argument.
**This field is unsupported when `tools` or `response_format` is specified.**'
response_format:
anyOf:
- $ref: '#/components/schemas/ResponseFormat'
- type: 'null'
description: 'The enforced format of the model''s output.
Note that the content of the output message may be truncated if it exceeds the `max_tokens`. You can check this by verifying that the `finish_reason` of the output message is `length`.
For more detailed information, please refer [here](https://friendli.ai/docs/guides/structured-outputs).
***Important***
You must explicitly instruct the model to produce the desired output format using a system prompt or user message (e.g., `You are an API generating a valid JSON as output.`).
Otherwise, the model may result in an unending stream of whitespace or other characters.
**This field is unsupported when `tools` is specified.**
**When `response_format` is specified, `min_tokens` field is unsupported.**'
type: object
required:
- model
- messages
title: DedicatedChatCompletionStreamBody
example:
messages:
- content: You are a helpful assistant.
role: system
- content: Hello!
role: user
model: (endpoint-id)
AssistantMessage:
properties:
role:
type: string
const: assistant
title: Role
description: The role of the messages author.
content:
anyOf:
- type: string
- type: 'null'
title: Content
description: The content of assistant message. Required unless `tool_calls` is specified.
reasoning_content:
anyOf:
- type: string
- type: 'null'
title: Reasoning Content
description: The intermediate reasoning content of assistant message.
reasoning:
anyOf:
- type: string
- type: 'null'
title: Reasoning
description: The intermediate reasoning content of assistant message. This field is a compatible option for the 'reasoning_content' field.
name:
anyOf:
- type: string
- type: 'null'
title: Name
description: The name for the participant to distinguish between participants with the same role.
tool_calls:
anyOf:
- items:
$ref: '#/components/schemas/AssistantMessageToolCall'
type: array
- type: 'null'
title: Tool Calls
type: object
required:
- role
title: AssistantMessage
Message:
oneOf:
- $ref: '#/components/schemas/SystemMessage'
title: System
- $ref: '#/components/schemas/UserMessage'
title: User
- $ref: '#/components/schemas/AssistantMessage'
title: Assistant
- $ref: '#/components/schemas/ToolMessage'
title: Tool
discriminator:
propertyName: role
mapping:
assistant: '#/components/schemas/AssistantMessage'
system: '#/components/schemas/SystemMessage'
tool: '#/components/schemas/ToolMessage'
user: '#/components/schemas/UserMessage'
ResponseFormatJsonSchemaSchema:
properties:
schema:
additionalProperties: true
type: object
title: Schema
description: The schema for the response format, described as a JSON Schema object.
type: object
required:
- schema
title: ResponseFormatJsonSchemaSchema
PromptTokensDetails:
properties:
cached_tokens:
anyOf:
- type: integer
- type: 'null'
title: Cached Tokens
description: Cached tokens present in the prompt.
type: object
title: PromptTokensDetails
DedicatedChatCompletionStreamSuccess:
$ref: '#/components/schemas/StreamedChatResult'
title: DedicatedChatCompletionStreamSuccess
FunctionResult:
properties:
arguments:
type: string
title: Arguments
description: 'The arguments for calling the function, generated by the model in JSON format.
Ensure to validate these arguments in your code before invoking the function since the model may not always produce valid JSON.'
name:
type: string
title: Name
description: The name of the function to call.
type: object
required:
- arguments
- name
title: FunctionResult
TextContent:
properties:
type:
type: string
const: text
title: Type
description: The type of the message content.
text:
type: string
title: Text
description: The text content of the message.
type: object
required:
- type
- text
title: TextContent
StreamedFunctionResult:
properties:
arguments:
type: string
title: Arguments
description: 'The arguments for calling the function, generated by the model in JSON format.
Ensure to validate these arguments in your code before invoking the function since the model may not always produce valid JSON.'
name:
anyOf:
- type: string
- type: 'null'
title: Name
description: The name of the function to call.
type: object
required:
- arguments
title: StreamedFunctionResult
ChatResult:
properties:
id:
type: string
title: Id
description: A unique ID of the chat completion.
choices:
items:
$ref: '#/components/schemas/ChatChoice'
type: array
title: Choices
usage:
$ref: '#/components/schemas/ChatUsage'
object:
type: string
const: chat.completion
title: Object
description: The object type, which is always set to `chat.completion`.
created:
type: integer
title: Created
description: The Unix timestamp (in seconds) for when the generation completed.
model:
anyOf:
- type: string
- type: 'null'
title: Model
description: The model to generate the completion. For dedicated endpoints, it returns the endpoint ID.
type: object
required:
- id
- choices
- usage
- object
- created
title: ChatResult
ResponseFormat:
oneOf:
- $ref: '#/components/schemas/ResponseFormatJsonSchema'
title: Json Schema
- $ref: '#/components/schemas/ResponseFormatJsonObject'
title: Json Object
- $ref: '#/components/schemas/ResponseFormatRegex'
title: Regex
- $ref: '#/components/schemas/ResponseFormatText'
title: Text
description: 'The enforced format of the model''s output.
Note that the content of the output message may be truncated if it exceeds the `max_tokens`. You can check this by verifying that the `finish_reason` of the output message is `length`.
For more detailed information, please refer [here](https://friendli.ai/docs/guides/structured-outputs).
***Important***
You must explicitly instruct the model to produce the desired output format using a system prompt or user message (e.g., `You are an API generating a valid JSON as output.`).
Otherwise, the model may result in an unending stream of whitespace or other characters.
**When `response_format` is specified, `min_tokens` field is unsupported.**'
discriminator:
propertyName: type
mapping:
json_object: '#/components/schemas/ResponseFormatJsonObject'
json_schema: '#/components/schemas/ResponseFormatJsonSchema'
regex: '#/components/schemas/ResponseFormatRegex'
text: '#/components/schemas/ResponseFormatText'
ChatChoiceMessage:
properties:
content:
anyOf:
- type: string
- type: 'null'
title: Content
description: The contents of the assistant message.
role:
type: string
title: Role
description: Role of the generated message author, in this case `assistant`.
tool_calls:
anyOf:
- items:
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