POST
/
api
/
ai
/
content
/
v4
/
{modelType}
/
{modelId}
curl --request POST \
  --url https://api.worqhat.com/api/ai/content/v4/{modelType}/{modelId} \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '{
  "training_data": "You will be provided with customer service queries. Classify each query into a  primary and secondary category. Provide JSON output with keys: `primary` and `secondary`.\n",
  "response_type": "json",
  "question": "How do I reset my password?",
  "conversation_id": "conv_1724236791746",
  "preserve_history": false,
  "conversation_history": [
    {
      "user_prompt": "I need help with my bill.",
      "model_response": "Sure! Can you clarify what specific issue you'\''re facing?"
    }
  ]
}'
{
  "Text Output": {
    "value": {
      "content": "Hi there!  My name is Alex, and I'm happy to help you with anything you need for your tour.  What can I do for you today?",
      "processingTime": 3105.397454,
      "processingId": "8aa97481-20f9-48f4-a12d-d02eb6c1d62a",
      "processingCount": 84,
      "conversation_id": "conv_1724236791746",
      "model": "aicon-v4-alpha-160824"
    }
  },
  "JSON Structured Output": {
    "value": {
      "content": {
        "response": "Hi there!  My name is Alex, and I'm delighted to assist you with your travel needs. What can I help you with today?"
      },
      "processingTime": 2946.176112,
      "processingId": "5944ab45-8bd7-4151-853d-da6cf315d617",
      "processingCount": 90,
      "conversation_id": "conv_1724237541817",
      "model": "aicon-v4-alpha-160824"
    }
  },
  "Streaming Data": {
    "value": {
      "data": [
        {
          "content": "Hi",
          "model": "aicon-v4-alpha-160824",
          "timestamp": 1724240278523,
          "processing_id": "bc4cc3b9-d000-4866-83ad-ad71abf10c8f",
          "conversation_id": "conv_1724240276193"
        },
        {
          "content": " there! My name is Alex, and I'm excited to be your tour",
          "model": "aicon-v4-alpha-160824",
          "timestamp": 1724240278630,
          "processing_id": "bc4cc3b9-d000-4866-83ad-ad71abf10c8f",
          "conversation_id": "conv_1724240276193"
        },
        {
          "content": " guide today. What can I help you with? \n",
          "model": "aicon-v4-alpha-160824",
          "timestamp": 1724240278708,
          "processing_id": "bc4cc3b9-d000-4866-83ad-ad71abf10c8f",
          "conversation_id": "conv_1724240276193"
        },
        {
          "content": "",
          "model": "aicon-v4-alpha-160824",
          "timestamp": 1724240278712,
          "processing_id": "bc4cc3b9-d000-4866-83ad-ad71abf10c8f",
          "conversation_id": "conv_1724240276193"
        },
        {
          "content": "",
          "model": "aicon-v4-alpha-160824",
          "finishReason": "stop",
          "timestamp": 1724240278716,
          "processing_id": "bc4cc3b9-d000-4866-83ad-ad71abf10c8f",
          "conversation_id": "conv_1724240276193",
          "wordCount": 77
        }
      ]
    }
  }
}

​
Generate from Multimodal Input

WorqHat AiCon V4 Custom Model API allows you to generate text using multimodal inputs. You can choose from several base models, each with its own strengths:

  • aicon-v4-nano-160824 (finetuned included): Ideal for quick and efficient text generation, especially when resources are limited.
  • aicon-v4-large-160824 (finetuned included): Our smartest and highest accuracy model, suitable for a wide range of text generation tasks, complex thoughts and reasoning along with mathematical solutions.

Choose Your Response Style: You have the option to either stream the response (stream_data = true) or wait for the entire result to be generated (stream_data = false).

Benefit: Faster interactions. You receive partial results as they are generated, allowing you to start working with the text immediately.


Example:

    var files = document.querySelector('input[type="file"]').files;
var myHeaders = new Headers();
myHeaders.append('Authorization', 'Bearer sk-02e44************');

var formdata = new FormData();
formdata.append('question', 'what are the images about');
formdata.append(
  'training_data',
  'You are alex and you are one of the best Tour Guides. answer everything while starting with your name'
);

for (const file of files) {
  formdata.append('files', file);
}

formdata.append('stream_data', 'true');

var requestOptions = {
  method: 'POST',
  headers: myHeaders,
  body: formdata,
  redirect: 'follow',
};

fetch('https://api.worqhat.com/api/ai/content/v4/{modelType}/{modelId}', requestOptions)
  .then(response => response.text())
  .then(result => {
      console.log(result);
      alert('Response received: ' + result);
  })
  .catch(error => {
      console.log('error', error);
      alert('Error: ' + error);
  });

Key Points:

  • Replace YOUR_API_KEY with your actual WorqHat AiCon V4 API key.
  • Adjust the model parameter to select the desired model.
  • Modify the prompt to provide your text input. Try out other Capabilities of the WorqHat API
  • Build Multiturn Conversations or Chat Applications by having the model maintain history by default
  • Fetch real world information and data using the Alpha Models

Authorizations

​
Authorization
string
header
required

Bearer authentication header of the form Bearer <token>, where <token> is your auth token.

Path Parameters

​
modelType
enum<string>
default:nano
required

The type of the base model that you have used to train the data.

Available options:
nano,
large
​
modelId
string
required

The unique ID of the custom model that has been created.

Body

​
question
string
required

The message or question you want to pass to the model.

​
model
string
required

ID of the model to use. See the model endpoint compatibility table for details.

​
randomness
number
required

What model prediction randomness to use, between 0 and 1. Higher values (e.g., 0.8) make the output more random, while lower values (e.g., 0.2) make it more deterministic.

Required range: 0 <= x <= 1
​
stream_data
boolean
required

If set to true, partial message deltas will be sent to reduce waiting time. Tokens will be streamed as data-only server-sent events, terminating with [DONE].

​
training_data
string
required

Allows passing system messages or training data to influence model responses. Supports up to 750K fixed context window without limitations on input.

​
response_type
string
required

Specifies the output format. Setting response_type: json ensures the model adheres to JSON format. Without proper instruction in training_data, the model may generate an unending whitespace stream.

​
preserve_history
boolean
required

Enables manually maintaining conversation history instead of using conversation_id.

​
conversation_history
object[]
required

Manually maintain past conversation records.

​
conversation_id
string

A unique identifier for each conversation. If omitted, the model generates one. Recommended to use the conversation ID returned in the first response.

Response

200
application/json
Text Generated Successfully
​
content
string

The generated response text.

​
processing_count
integer

Usage count for billing.

​
processing_time
number

Time taken (in milliseconds) to process the request.

​
processing_id
string

Unique identifier for the process (for tracking support requests).

​
conversation_id
string

The conversation ID for multi-turn chat applications.

​
model
string

The model used for processing.

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