Create completion

JavaScript

import SambaNova from 'sambanova';

const client = new SambaNova({
  apiKey: 'My API Key',
});

const completion = await client.completions.create({
  model: 'string',
  prompt:
    '<|begin_of_text|><|start_header_id|>system<|end_header_id|>\nYou are a helpful assistant.<|eot_id|><|start_header_id|>user<|end_header_id|>\ncreate a poem using palindromes<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n',
});

console.log(completion);

{
  "choices": [
    {
      "finish_reason": "stop",
      "index": 0,
      "text": "In madam moon's silver glow,  Aha, a palindrome to know,  Radar spins, a circular tale,  Level heads prevail, without fail.  A man, a plan, a canal, Panama!  Able was I ere I saw Elba,  A Santa at NASA, a curious sight,  Do geese see God, in the pale moonlight?  Mr. Owl ate my metal worm,  Do nine men interpret? Nine men, I nod,  Never odd or even, a palindrome's might,  Madam, in Eden, I'm Adam.  Aibohphobia, a fear to confess,  A palindrome's symmetry, I must address,  Refer, a word that reads the same,  A palindrome's beauty, in its circular game.  In the stillness of the night,  Ava, a palindrome, shining bright,  Hannah, a name that reads the same,  A palindrome's magic, in its circular flame.  Note: Please keep in mind that creating a poem using palindromes can be a challenging task,  and the resulting poem may not be as cohesive or flowing as one that doesn't rely on palindromes.  However, I hope you enjoy the attempt!"
    }
  ],
  "created": 1737583288.6076705,
  "id": "83a7809d-e18f-44f9-9ab7-2bc494c6c661",
  "model": "Meta-Llama-3.3-70B-Instruct",
  "object": "chat.completion",
  "system_fingerprint": "fastcoe",
  "usage": {
    "acceptance_rate": 4.058139324188232,
    "completion_tokens": 350,
    "completion_tokens_after_first_per_sec": 248.09314856382406,
    "completion_tokens_after_first_per_sec_first_ten": 249.67922929952655,
    "completion_tokens_per_sec": 238.91966176995348,
    "end_time": 1737583289.7345645,
    "is_last_response": true,
    "prompt_tokens": 43,
    "start_time": 1737583288.264706,
    "time_to_first_token": 0.06312894821166992,
    "total_latency": 1.4649275719174653,
    "total_tokens": 393,
    "total_tokens_per_sec": 268.27264878740493
  }
}

POST

completions

JavaScript

import SambaNova from 'sambanova';

const client = new SambaNova({
  apiKey: 'My API Key',
});

const completion = await client.completions.create({
  model: 'string',
  prompt:
    '<|begin_of_text|><|start_header_id|>system<|end_header_id|>\nYou are a helpful assistant.<|eot_id|><|start_header_id|>user<|end_header_id|>\ncreate a poem using palindromes<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n',
});

console.log(completion);

{
  "choices": [
    {
      "finish_reason": "stop",
      "index": 0,
      "text": "In madam moon's silver glow,  Aha, a palindrome to know,  Radar spins, a circular tale,  Level heads prevail, without fail.  A man, a plan, a canal, Panama!  Able was I ere I saw Elba,  A Santa at NASA, a curious sight,  Do geese see God, in the pale moonlight?  Mr. Owl ate my metal worm,  Do nine men interpret? Nine men, I nod,  Never odd or even, a palindrome's might,  Madam, in Eden, I'm Adam.  Aibohphobia, a fear to confess,  A palindrome's symmetry, I must address,  Refer, a word that reads the same,  A palindrome's beauty, in its circular game.  In the stillness of the night,  Ava, a palindrome, shining bright,  Hannah, a name that reads the same,  A palindrome's magic, in its circular flame.  Note: Please keep in mind that creating a poem using palindromes can be a challenging task,  and the resulting poem may not be as cohesive or flowing as one that doesn't rely on palindromes.  However, I hope you enjoy the attempt!"
    }
  ],
  "created": 1737583288.6076705,
  "id": "83a7809d-e18f-44f9-9ab7-2bc494c6c661",
  "model": "Meta-Llama-3.3-70B-Instruct",
  "object": "chat.completion",
  "system_fingerprint": "fastcoe",
  "usage": {
    "acceptance_rate": 4.058139324188232,
    "completion_tokens": 350,
    "completion_tokens_after_first_per_sec": 248.09314856382406,
    "completion_tokens_after_first_per_sec_first_ten": 249.67922929952655,
    "completion_tokens_per_sec": 238.91966176995348,
    "end_time": 1737583289.7345645,
    "is_last_response": true,
    "prompt_tokens": 43,
    "start_time": 1737583288.264706,
    "time_to_first_token": 0.06312894821166992,
    "total_latency": 1.4649275719174653,
    "total_tokens": 393,
    "total_tokens_per_sec": 268.27264878740493
  }
}

Authorizations

Authorization

string

header

required

SambaNova API Key

Body

application/json

text prompt and parameters

completions request object

model

required

The model ID to use (e.g. Meta-Llama-3.3-70B-Instruct). See available models

prompt

string

required

Prompt to send to the model.

Examples:

max_tokens

integer | null

The maximum number of tokens that can be generated in the chat completion. The total length of input tokens and generated tokens is limited by the model's context length.

Example:

2048

max_completion_tokens

integer | null

The maximum number of tokens that can be generated in the chat completion. The total length of input tokens and generated tokens is limited by the model's context length.

Example:

2048

temperature

number | null

default:0.7

What sampling temperature to use, determines the degree of randomness in the response. 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. Is recommended altering this, top_p or top_k but not more than one of these.

Required range: 0 <= x <= 1

Example:

0.7

top_p

number | null

Cumulative probability for token choices. 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. Is recommended altering this, top_k or temperature but not more than one of these.

Required range: 0 <= x <= 1

Example:

1

top_k

integer | null

Amount limit of token choices. An alternative to sampling with temperature, the model considers the results of the first K tokens with higher probability. So 10 means only the first 10 tokens with higher probability are considered. Is recommended altering this, top_p or temperature but not more than one of these.

Required range: 1 <= x <= 100

Example:

5

presence_penalty

number | null

default:0

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.

Required range: -2 <= x <= 2

frequency_penalty

number

default:0

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.

Required range: -2 <= x <= 2

do_sample

boolean | null

If true, sampling is enabled during output generation. If false, deterministic decoding is used.

stop

Sequences where the API will stop generating tokens. The returned text will not contain the stop sequence.

Example:

"\n"

stream

boolean | null

default:false

If set, partial message deltas will be sent. Tokens will be sent as data-only server-sent events as they become available, with the stream terminated by a data: [DONE] message.

stream_options

object | null

Options for streaming response. Only set this when setting stream as true

Show child attributes

logprobs

boolean | null

default:false

This is not yet supported by our models. Whether to return log probabilities of the output tokens or not. If true, returns the log probabilities of each output token returned in the content of message.

top_logprobs

integer | null

This is not yet supported by our models. An integer between 0 and 20 specifying the number of most likely tokens to return at each token position, each with an associated log probability. logprobs must be set to true if this parameter is used.

Required range: 0 <= x <= 20

integer | null

default:1

This is not yet supported by our models. How many chat completion choices to generate for each input message.

Required range: 1 <= x <= 1

Example:

1

logit_bias

object | null

This is not yet supported by our models. Modify the likelihood of specified tokens appearing in the completion.

Show child attributes

seed

integer | null

This is not yet supported by our models.

Response

Successful Response

Completion Response
Completion Stream Response

ompletion response returned by the model

choices

Completion Choice · object[]

required

Minimum length: 1

Show child attributes

created

number

required

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

string

required

A unique identifier for the chat completion.

model

string

required

The model used for the chat completion.

object

enum<string>

required

The object type, always chat.completion.

Available options:

Title	Const
Object	`chat.completion`

system_fingerprint

string

required

Backend configuration that the model runs with.

usage

object | null

required

Usage metrics for the completion, embeddings,transcription or translation request

Show child attributes

Examples:

{
  "acceptance_rate": 4.058139324188232,
  "completion_tokens": 350,
  "completion_tokens_after_first_per_sec": 248.09314856382406,
  "completion_tokens_after_first_per_sec_first_ten": 249.67922929952655,
  "completion_tokens_after_first_per_sec_graph": 452.5030493415834,
  "completion_tokens_per_sec": 238.91966176995348,
  "end_time": 1737583289.7345645,
  "is_last_response": true,
  "prompt_tokens_details": { "cached_tokens": 0 },
  "prompt_tokens": 43,
  "start_time": 1737583288.264706,
  "time_to_first_token": 0.06312894821166992,
  "total_latency": 1.4649275719174653,
  "total_tokens": 393,
  "total_tokens_per_sec": 268.27264878740493
}

{ "prompt_tokens": 43, "total_tokens": 393 }

Create chat-based completion Create embeddings

⌘I

Overview

Inference API

Using the API

Authorizations

Body

Response