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In this guide, we will walk through the process of uploading data sources to your chatbot using the GPT-trainer API.

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Prerequisites

Before you begin, make sure you have the following:
  • An API key for accessing the GPT-trainer API.
  • A development environment or tool for making HTTP requests, such as Curl or a programming language like Python.

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API Endpoint

The API endpoint for uploading data sources to your chatbot is: https://app.gpt-trainer.com/api/v1/chatbot/{uuid}/data-source/url.
Make sure to replace {uuid} with the UUID of your chatbot..

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Request Body

To upload a data source, you need to send a POST request to the API endpoint with a JSON request body containing the URL of the data source. Here’s an example request body: 
{
  "url": "https://example.com/data-source"
}
  • url (string, required): Provide the URL of the data source you want to upload.

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Example Request

Here’re example command sto create a chatbot using the GPT-trainer API:
Replace token with your actual API key.
curl --location --request POST 'https://app.gpt-trainer.com/api/v1/chatbot/{uuid}/data-source/url' \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer <token>' \
--data-raw '{
  "url": "string"
}'

import requests

url = 'https://app.gpt-trainer.com/api/v1/chatbot/{uuid}/data-source/url'
headers = {
    'Content-Type': 'application/json',
    'Authorization': 'Bearer <token>'
}

params = {
    'uuid': '<chatbot-uuid>'
}

data = {
    "url": "string",
}

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

if response.status_code == 200:
    print("Request successful!")
    print(response.json())
else:
    print("Request failed with status code:", response.status_code)
    print(response.text)

const axios = require('axios');

const url = 'https://app.gpt-trainer.com/api/v1/chatbot/{uuid}/data-source/url';
const headers = {
    'Content-Type': 'application/json',
    'Authorization': 'Bearer <token>'
};

const params = {
    uuid: '<chatbot-uuid>'
};

const data = {
    "url": "string",
};

axios.post(url, data, { headers, params })
    .then(response => {
        console.log('Request successful!');
        console.log(response.data);
    })
    .catch(error => {
        console.error('Request failed:', error);
    });
That’s it! You’ve now learned how to upload data sources to your chatbot using the GPT-trainer API.