Chat

Chat Completions

Calls Sarvam LLM API to get the chat completion. Supported model(s): sarvam-m.

Headers

AuthorizationstringRequired

Bearer token for authentication. Format: “Bearer <token>”

Request

This endpoint expects an object.
messageslist of objectsRequired

Messages are a list of objects that contain a role and content.

model"sarvam-m"Required

Model ID used to generate the response, like sarvam-m.

temperaturedoubleOptional>=0<=2

What sampling temperature to use, between 0 and 2. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic. We generally recommend altering this or top_p but not both.

top_pdoubleOptional>=0<=1

An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10% probability mass are considered.

We generally recommend altering this or temperature but not both.

reasoning_effortenumOptional

Enables the model’s internal ‘thinking mode’. While this field accepts multiple string values, all values behave identically. We do not currently support tuning different levels of reasoning effort — any provided value will simply enable the same enhanced reasoning process.

Allowed values:
max_tokensintegerOptional

The maximum number of tokens that can be generated in the chat completion.

streambooleanOptional

If set to true, the model response data will be streamed to the client as it is generated using server-sent events.

stopstring or list of stringsOptional

Up to 4 sequences where the API will stop generating further tokens. The returned text will not contain the stop sequence.

nintegerOptional>=1<=128

How many chat completion choices to generate for each input message. Note that you will be charged based on the number of generated tokens across all of the choices. Keep n as 1 to minimize costs.

seedintegerOptional

This feature is in Beta. If specified, our system will make a best effort to sample deterministically, such that repeated requests with the same seed and parameters should return the same result. Determinism is not guaranteed, and you should refer to the system_fingerprint response parameter to monitor changes in the backend.

frequency_penaltydoubleOptional>=-2<=2

Number between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model’s likelihood to repeat the same line verbatim.

presence_penaltydoubleOptional>=-2<=2

Number between -2.0 and 2.0. Positive values penalize new tokens based on whether they appear in the text so far, increasing the model’s likelihood to talk about new topics.

Response

Successful Response

idstring

A unique identifier for the chat completion.

choiceslist of objects

A list of chat completion choices. Can be more than one if n is greater than 1.

createdinteger

The Unix timestamp (in seconds) of when the chat completion was created.

modelstring

The model used for the chat completion.

object"chat.completion"

The object type, which is always chat.completion.

usageobjectOptional

Errors