Last Update: 2025/3/26

LLMVision Chat Completion API

The LLMVision Chat Completion API allows you to generate conversational responses using OpenAI's SDK. This document provides an overview of the API endpoints, request parameters, and response structure.

Endpoint

POST https://platform.llmprovider.ai/v1/chat/completions

Request Headers

Header	Value
Authorization	Bearer YOUR_API_KEY
Content-Type	application/json

Request Body

The request body should be a JSON object with the following parameters:

Parameter	Type	Description
model	string	The model to use (e.g., SenseCharacter-20250124-01).
messages	array	A list of message objects representing the conversation history.
stream	boolean	(Optional) Whether to stream the response as it is generated.
max_tokens	integer	(Optional) The maximum number of tokens to generate.
temperature	number	(Optional) Sampling temperature, between 0 and 1.
top_p	number	(Optional) Nucleus sampling probability, between 0 and 1.
stop	array	(Optional) Up to 4 sequences where the API will stop generating further tokens.
presence_penalty	number	(Optional) Penalty for new tokens based on their presence in the text so far.
frequency_penalty	number	(Optional) Penalty for new tokens based on their frequency in the text so far.

Example Request

{
  "model": "SenseCharacter-20250124-01",
  "messages": [
    {
      "role": "system",
      "content": "You are a helpful assistant."
    },
    {
      "role": "user",
      "content": "Tell me a joke."
    }
  ],
  "max_tokens": 50,
  "temperature": 0.7
}

Response Body

The response body will be a JSON object containing the generated completions and other metadata.

Field	Type	Description
id	string	Unique identifier for the completion.
object	string	The type of object returned, usually chat.completion.
created	integer	Timestamp of when the completion was created.
model	string	The model used for the completion.
choices	array	A list of generated completion choices.
usage	object	Token usage statistics for the request.

Example Response

{
  "id": "cmpl-6aF1d2e3G4H5I6J7K8L9M0N1",
  "object": "chat.completion",
  "created": 1678491234,
  "model": "SenseCharacter-20250124-01",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "Why don't scientists trust atoms? Because they make up everything!"
      },
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 10,
    "completion_tokens": 16,
    "total_tokens": 26
  }
}

Example Request

Shell

curl -X POST https://platform.llmprovider.ai/v1/chat/completions \
-H "Authorization: Bearer $YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "SenseCharacter-20250124-01",
"messages": [
  {
    "role": "user",
    "content": "Hello!"
  }
]
}'

nodejs

const axios = require('axios');
const apiKey = 'YOUR_API_KEY';
const url = 'https://platform.llmprovider.ai/v1/chat/completions';

const data = {
    conversation_id: '',
    model: 'SenseCharacter-20250124-01',
    messages: [
        {
            role: 'user',
            content: 'Hello!'
        }
    ]
};

const headers = {
    'Authorization': `Bearer ${apiKey}`,
    'Content-Type': 'application/json'
};

axios.post(url, data, { headers })
.then(response => {
    console.log('Response:', response.data);
})
.catch(error => {
    console.error('Error:', error);
});

python

import requests
import json

api_key = 'YOUR_API_KEY'
url = 'https://platform.llmprovider.ai/v1/chat/completions'

headers = {
    'Authorization': f'Bearer {api_key}',
    'Content-Type': 'application/json'
}

data = {
    'model': 'SenseCharacter-20250124-01',
    'messages': [
        {
            'role': 'user',
            'content': 'Hello!'
        }
    ]
}

response = requests.post(url, headers=headers, data=json.dumps(data))

if response.status_code == 200:
    print('Response:', response.json())
else:
    print('Error:', response.status_code, response.text)

For any questions or further assistance, please contact us at [email protected].