Chat completion using Groq Endpoint

Sends a request for chat completions. For more details on parameters, visit Groq Cloud.

POST v1/groq/chatcompletion

Chat completion using Groq Endpoint

cURL

curl --request POST \
  --url http://localhost:9000/api/v1/groq/chatcompletion \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '
{
  "messages": [
    {
      "role": "<string>",
      "content": "<string>"
    }
  ],
  "model": "<string>",
  "stream": true
}
'

import requests

url = "http://localhost:9000/api/v1/groq/chatcompletion"

payload = {
    "messages": [
        {
            "role": "<string>",
            "content": "<string>"
        }
    ],
    "model": "<string>",
    "stream": True
}
headers = {
    "Authorization": "Bearer <token>",
    "Content-Type": "application/json"
}

response = requests.post(url, json=payload, headers=headers)

print(response.text)

const options = {
  method: 'POST',
  headers: {Authorization: 'Bearer <token>', 'Content-Type': 'application/json'},
  body: JSON.stringify({
    messages: [{role: '<string>', content: '<string>'}],
    model: '<string>',
    stream: true
  })
};

fetch('http://localhost:9000/api/v1/groq/chatcompletion', options)
  .then(res => res.json())
  .then(res => console.log(res))
  .catch(err => console.error(err));

<?php

$curl = curl_init();

curl_setopt_array($curl, [
  CURLOPT_PORT => "9000",
  CURLOPT_URL => "http://localhost:9000/api/v1/groq/chatcompletion",
  CURLOPT_RETURNTRANSFER => true,
  CURLOPT_ENCODING => "",
  CURLOPT_MAXREDIRS => 10,
  CURLOPT_TIMEOUT => 30,
  CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,
  CURLOPT_CUSTOMREQUEST => "POST",
  CURLOPT_POSTFIELDS => json_encode([
    'messages' => [
        [
                'role' => '<string>',
                'content' => '<string>'
        ]
    ],
    'model' => '<string>',
    'stream' => true
  ]),
  CURLOPT_HTTPHEADER => [
    "Authorization: Bearer <token>",
    "Content-Type: application/json"
  ],
]);

$response = curl_exec($curl);
$err = curl_error($curl);

curl_close($curl);

if ($err) {
  echo "cURL Error #:" . $err;
} else {
  echo $response;
}

package main

import (
	"fmt"
	"strings"
	"net/http"
	"io"
)

func main() {

	url := "http://localhost:9000/api/v1/groq/chatcompletion"

	payload := strings.NewReader("{\n  \"messages\": [\n    {\n      \"role\": \"<string>\",\n      \"content\": \"<string>\"\n    }\n  ],\n  \"model\": \"<string>\",\n  \"stream\": true\n}")

	req, _ := http.NewRequest("POST", url, payload)

	req.Header.Add("Authorization", "Bearer <token>")
	req.Header.Add("Content-Type", "application/json")

	res, _ := http.DefaultClient.Do(req)

	defer res.Body.Close()
	body, _ := io.ReadAll(res.Body)

	fmt.Println(string(body))

}

HttpResponse<String> response = Unirest.post("http://localhost:9000/api/v1/groq/chatcompletion")
  .header("Authorization", "Bearer <token>")
  .header("Content-Type", "application/json")
  .body("{\n  \"messages\": [\n    {\n      \"role\": \"<string>\",\n      \"content\": \"<string>\"\n    }\n  ],\n  \"model\": \"<string>\",\n  \"stream\": true\n}")
  .asString();

require 'uri'
require 'net/http'

url = URI("http://localhost:9000/api/v1/groq/chatcompletion")

http = Net::HTTP.new(url.host, url.port)

request = Net::HTTP::Post.new(url)
request["Authorization"] = 'Bearer <token>'
request["Content-Type"] = 'application/json'
request.body = "{\n  \"messages\": [\n    {\n      \"role\": \"<string>\",\n      \"content\": \"<string>\"\n    }\n  ],\n  \"model\": \"<string>\",\n  \"stream\": true\n}"

response = http.request(request)
puts response.read_body

200

{
  "id": "34a9110d-c39d-423b-9ab9-9c748747b204",
  "object": "chat.completion",
  "created": 1708045122,
  "model": "mixtral-8x7b-32768",
  "system_fingerprint": null,
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "Low latency Large Language Models (LLMs) are important in the field of artificial intelligence and natural language processing (NLP) for several reasons:\n\n1. Real-time applications: Low latency LLMs are essential for real-time applications such as chatbots, voice assistants, and real-time translation services. These applications require immediate responses, and high latency can lead to a poor user experience.\n\n2. Improved user experience: Low latency LLMs provide a more seamless and responsive user experience. Users are more likely to continue using a service that provides quick and accurate responses, leading to higher user engagement and satisfaction.\n\n3. Competitive advantage: In today's fast-paced digital world, businesses that can provide quick and accurate responses to customer inquiries have a competitive advantage. Low latency LLMs can help businesses respond to customer inquiries more quickly, potentially leading to increased sales and customer loyalty.\n\n4. Better decision-making: Low latency LLMs can provide real-time insights and recommendations, enabling businesses to make better decisions more quickly. This can be particularly important in industries such as finance, healthcare, and logistics, where quick decision-making can have a significant impact on business outcomes.\n\n5. Scalability: Low latency LLMs can handle a higher volume of requests, making them more scalable than high-latency models. This is particularly important for businesses that experience spikes in traffic or have a large user base.\n\nIn summary, low latency LLMs are essential for real-time applications, providing a better user experience, enabling quick decision-making, and improving scalability. As the demand for real-time NLP applications continues to grow, the importance of low latency LLMs will only become more critical."
      },
      "finish_reason": "stop",
      "logprobs": null
    }
  ],
  "usage": {
    "prompt_tokens": 24,
    "completion_tokens": 377,
    "total_tokens": 401,
    "prompt_time": 0.009,
    "completion_time": 0.774,
    "total_time": 0.783
  }
}

{
  "error": "error message"
}

Request Body

messages · array · required

Array of messages to send to the chat model.

Show properties

role · string · required

The role of the message sender (e.g., ‘user’ or ‘assistant’ or ‘system’).

content · string · required

The content of the message.

model · string · required

The model to use for chat completion.

stream · boolean

Whether to stream the response or not.

200 - Successful chat completion response

id · string

The unique identifier for the chat completion request.

object · string

The type of the object returned, typically ‘chat.completion’.

created · integer

The timestamp of when the chat completion was created.

model · string

The model used for generating the chat completion.

system_fingerprint · string

Fingerprint of the system, if available.

choices · array

The array of completion choices.

Show properties

index · integer

The index of the choice.

message · object

The message returned by the model.

Show properties

role · string

The role of the message sender (e.g., ‘assistant’).

content · string

The content of the message.

finish_reason · string

The reason the completion ended (e.g., ‘stop’).

logprobs · object

Log probabilities of the tokens, if available.

usage · object

Usage statistics for the request.

Show properties

prompt_tokens · integer

The number of tokens in the prompt.

completion_tokens · integer

The number of tokens in the completion.

total_tokens · integer

The total number of tokens used in the request.

prompt_time · float

The time taken for the prompt processing.

completion_time · float

The time taken for the completion processing.

total_time · float

The total time taken for the request.

400 - Error response

error · string

The error message explaining what went wrong.

200

{
  "id": "34a9110d-c39d-423b-9ab9-9c748747b204",
  "object": "chat.completion",
  "created": 1708045122,
  "model": "mixtral-8x7b-32768",
  "system_fingerprint": null,
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "Low latency Large Language Models (LLMs) are important in the field of artificial intelligence and natural language processing (NLP) for several reasons:\n\n1. Real-time applications: Low latency LLMs are essential for real-time applications such as chatbots, voice assistants, and real-time translation services. These applications require immediate responses, and high latency can lead to a poor user experience.\n\n2. Improved user experience: Low latency LLMs provide a more seamless and responsive user experience. Users are more likely to continue using a service that provides quick and accurate responses, leading to higher user engagement and satisfaction.\n\n3. Competitive advantage: In today's fast-paced digital world, businesses that can provide quick and accurate responses to customer inquiries have a competitive advantage. Low latency LLMs can help businesses respond to customer inquiries more quickly, potentially leading to increased sales and customer loyalty.\n\n4. Better decision-making: Low latency LLMs can provide real-time insights and recommendations, enabling businesses to make better decisions more quickly. This can be particularly important in industries such as finance, healthcare, and logistics, where quick decision-making can have a significant impact on business outcomes.\n\n5. Scalability: Low latency LLMs can handle a higher volume of requests, making them more scalable than high-latency models. This is particularly important for businesses that experience spikes in traffic or have a large user base.\n\nIn summary, low latency LLMs are essential for real-time applications, providing a better user experience, enabling quick decision-making, and improving scalability. As the demand for real-time NLP applications continues to grow, the importance of low latency LLMs will only become more critical."
      },
      "finish_reason": "stop",
      "logprobs": null
    }
  ],
  "usage": {
    "prompt_tokens": 24,
    "completion_tokens": 377,
    "total_tokens": 401,
    "prompt_time": 0.009,
    "completion_time": 0.774,
    "total_time": 0.783
  }
}

{
  "error": "error message"
}