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"
}
Endpoint Examples
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 --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
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
Show properties
object
Usage statistics for the request.
Show properties
Show properties
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"
}
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