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
}'
{
  "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
  }
}

​
Request Body

​
messages
array
required
Array of messages to send to the chat model.
​
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.
​
usage
object
Usage statistics for the request.

​
400 - Error response

​
error
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
  }
}