How To Train Chatgpt On Your Own Data (2 Best Methods)




Have you ever imagined having a chatbot that speaks in your tone, knows your business, and can handle intricate questions like a seasoned professional? Dream no more! With the incredible platform of Aii.CX, the future is now, and it’s more accessible than you might think. I promise, by the end of this guide, you’ll be soaring on the wings of technological advancement.

Method 1: For Non-programmers

Discover the Magic of Aii.CX: Train chatGPT on Your Documents

If you’ve ever wondered “how to train chatgpt on your own data”, you’re in the right place. Like many before you, I was enthralled by the potential of Aii.CX to harness the power of GPT in a user-friendly way. Let’s begin your journey:

  1. Visit Aii.CX and navigate the straightforward registration process.
  2. Upon entry, click the inviting “Create Chat” button.
  3. Name your chatbot—make it as creative or straightforward as you like.

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Training ChatGPT with Your Data: Personalizing the AI Experience

Aii.CX stands out because of its adaptability. Imagine tailoring your chatbot’s knowledge base using documents, web pages, or even specific text snippets. Here’s your step-by-step guide on how to train ChatGPT on your own data:

  1. Upload your preferred files, documents, or directly paste website links.
  2. Have a whole website’s data? Input the sitemap and witness the AI assimilation.
  3. Click “Start Training“. Soon after, you’ll notice the successful absorption of your data into the chatbot.

Character Building: Customizing Your Chatbot’s Personality

A chatbot is not just about data; its character matters too. Here’s how to impart a unique touch to your AI:

  1. Click on the “NEXT” button.
  2. Define a behavioral template, such as “Respond like an 18th-century poet.”
  3. Add sample questions to guide your users in their interactions.

Enhancements: Delving into Advanced Features

For those looking to further tweak their chatbot’s capabilities:

  1. Select your desired AI model. Opt for GPT-4 if you’re seeking advanced interactions.
  2. Enable chat history for recalling previous interactions.
  3. Turn on the “Show source links after the answer” feature for transparency.

Launch: Your Chatbot Goes Live

After refining your chatbot, click on “Publish“. Share its link, embed it, or integrate it as you see fit.

Continuous Learning: Updating and Improving Your Chatbot

A chatbot’s evolution is ongoing. Regularly train, update, and refine it. Check chat histories to ensure it remains relevant and efficient.

If assistance or new features are required, the talented team at Aii.CX is available via Telegram. They’re enthusiastic about turning your AI dreams into reality.

Embrace this world of AI opportunities. Dive deep, experiment, and watch your creation flourish.

Method 2: For Programmers

Try to use following instructions:

Step 1: Install Python

You need Python 3.0+ to begin. Before diving into the installation, I recommend checking if you already have Python3. Use the command below:

python3 --version

If a version appears after executing the command, it indicates you already have Python3. Otherwise, if you receive a ‘command not found’ error, proceed with the installation:

  1. Visit the Python downloads page.
  2. Download and run the installer.
  3. After installation, run the above command again to confirm the Python version.

Step 2: Upgrade Pip

While Python includes pip by default, it’s a good idea to ensure you have the latest version. Pip is a package manager for Python, similar to composer for PHP. Upgrade pip with the command below:

python3 -m pip install -U pip

If pip is already up-to-date, a warning will be displayed. To check the pip version and location, use the following:

pip3 --version

If the pip installation directory isn’t in your PATH variable, you can add it by editing the bash profile:

nano ~/.bash_profile

Step 3: Install Libraries

Before proceeding, you’ll need specific libraries. Execute the commands below in your terminal:

  1. pip3 install openai – For the OpenAI library.
  2. pip3 install gpt_index – Known as LlamaIndex, this connects LLM to external data.
  3. pip3 install PyPDF2 – A Python-based PDF parser.
  4. pip3 install gradio – To create a UI for AI ChatGPT.

Once the libraries are installed, you’re ready to create the training script and prepare your data.

Step 4: Get OpenAI Key

Before working on the script, acquire an API key from OpenAI. If you haven’t logged in, the platform will prompt you. Once inside, click ‘Create new secret key’ to generate your script’s key.

Note: After the key is generated, ensure you copy and save it securely, as you won’t see it again.

Step 5: Prepare Data

Create a new directory named ‘docs’ and place PDF, TXT, or CSV files inside. The number of files isn’t restricted, but remember: the more data, the more tokens consumed. Free accounts receive $18 worth of tokens for use.

Step 6: Create Script

With all prerequisites in place, our next step is to craft a Python script to train the chatbot using custom data. This script will process files from the ‘docs’ directory, which we previously created, and generate a JSON file.

Choose any text editor for this purpose. For macOS users, the default TextEdit will suffice. However, if you have Visual Studio Code, it’s an even better choice.

Begin by creating a new file and copying the following code:

from gpt_index import SimpleDirectoryReader, GPTListIndex, GPTSimpleVectorIndex, LLMPredictor, PromptHelper
from langchain import OpenAI
import gradio as gr
import sys
import os

os.environ["OPENAI_API_KEY"] = ''

def construct_index(directory_path):
    max_input_size = 4096
    num_outputs = 512
    max_chunk_overlap = 20
    chunk_size_limit = 600

    prompt_helper = PromptHelper(max_input_size, num_outputs, max_chunk_overlap, chunk_size_limit=chunk_size_limit)

    llm_predictor = LLMPredictor(llm=OpenAI(temperature=0.7, model_name="text-davinci-003", max_tokens=num_outputs))

    documents = SimpleDirectoryReader(directory_path).load_data()

    index = GPTSimpleVectorIndex(documents, llm_predictor=llm_predictor, prompt_helper=prompt_helper)


    return index

def chatbot(input_text):
    index = GPTSimpleVectorIndex.load_from_disk('index.json')
    response = index.query(input_text, response_mode="compact")
    return response.response

iface = gr.Interface(fn=chatbot,
                     inputs=gr.inputs.Textbox(lines=7, label="Enter your text"),
                     title="My AI Chatbot")

index = construct_index("docs")

Once you’ve copied the code, don’t forget to add your OpenAI key before saving the file. You’ll notice a placeholder for OPENAI_API_KEY in the code. Replace ‘your-key-goes-here’ with the actual OpenAI key we obtained in Step 5, as shown:

os.environ["OPENAI_API_KEY"] = 'your-key-goes-here'

After this modification, save the file as ensuring it’s located in the same directory as your ‘docs’ folder. It’s crucial for ‘docs’ and to be on the same directory level.

Step 7: Let the Fun Begin

With all components now in position, it’s time to execute the script and witness the results.

First, navigate to the directory containing both the and docs folders. In my instance, it’s located in the train directory on the desktop. This is illustrated in the previous screenshot.

To begin, launch the Terminal and enter the command:

cd path-to-your-train-directory

For example, if it’s located on your desktop:

cd ~/Desktop/train

Now, with your current location set to the ‘train’ directory, run the Python script:


Initiating this command commences the chatbot’s training. Depending on the volume of data you’re using, this could require some time. Once complete, a link will display allowing you to test the bot via a straightforward UI.

For example, the output might look like: This URL can be accessed through any browser, and you’ll be able to test your uniquely trained chatbot. Note: The port number might differ in your case.

The interface is intuitive: pose your questions on the left, and the bot’s replies will appear on the right. But, keep in mind that each query will consume tokens from your OpenAI account. Furthermore, the training process also expends tokens based on the data volume.

To adjust or add training data, simply halt the program using CTRL + C, make your changes, and rerun the Python script.

If you found this article beneficial, I’d be grateful for a thumbs up or share. I’m eager to delve deeper into ChatGPT and will soon share insights on creating a custom bot UI for integration into websites.

Happy AI Journey! With Our Guide How To Train Chatgpt On Your Own Data

Tags: ChatGPT training, Custom data training, Personalized AI models, OpenAI fine-tuning, Training datasets, ChatGPT best practices, Transfer learning, Data preprocessing for ChatGPT, Custom model deployment, OpenAI guidelines, Chatbot training methods, GPT-4 fine-tuning, Model optimization, Data collection for training, ChatGPT personalization, AI training tools, Step-by-step ChatGPT training, Best methods for AI training, Chatbot dataset creation, Customizing ChatGPT.

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One response to “How To Train Chatgpt On Your Own Data (2 Best Methods)”

  1. JoyMaker

    The idea of being able to leverage such technology without needing extensive technical know-how is truly empowering. Can’t wait to delve deeper into this guide and see how it can transform my business interactions. Kudos for bringing this insight to the forefront!

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