Chat completion using Groq Endpoint

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

{
  "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"
}

POST

groq

chatcompletion

Chat completion using Groq Endpoint

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

{
  "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

array

required

Array of messages to send to the chat model.

Show properties

string

required

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

string

required

The content of the message.

string

required

The model to use for chat completion.

boolean

Whether to stream the response or not.

200 - Successful chat completion response

string

The unique identifier for the chat completion request.

string

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

integer

The timestamp of when the chat completion was created.

string

The model used for generating the chat completion.

string

Fingerprint of the system, if available.

array

The array of completion choices.

Show properties

integer

The index of the choice.

object

The message returned by the model.

Show properties

string

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

string

The content of the message.

string

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

object

Log probabilities of the tokens, if available.

object

Usage statistics for the request.

Show properties

integer

The number of tokens in the prompt.

integer

The number of tokens in the completion.

integer

The total number of tokens used in the request.

float

The time taken for the prompt processing.

float

The time taken for the completion processing.

float

The total time taken for the request.

400 - Error response

string

The error message explaining what went wrong.

{
  "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"
}

API Endpoints

Endpoint Examples

Chat completion using Groq Endpoint

Request Body

200 - Successful chat completion response

400 - Error response