How to build an AI chatbot with personalized customer data using Open AI API and GPT Index

By harnessing the potential of AI-powered chatbots and personalized interactions, businesses can revolutionize their customer experience, fostering stronger relationships and driving customer loyalty. Implementing cutting-edge technologies like OpenAI API enables the creation of intelligent chatbots capable of understanding and adapting to individual customer needs, preferences, and concerns. As a result, customers receive tailored support, enjoy seamless interactions, and feel valued, significantly enhancing their overall experience. By prioritizing customer-centric innovation, businesses can position themselves as industry leaders and create lasting, positive impacts on customer satisfaction and brand reputation.

What are OpenAI API and GPT Index?

OpenAI API is a powerful tool provided by OpenAI, a leading AI research organization, that enables developers to access state-of-the-art AI models like GPT-4. GPT, which stands for Generative Pre-trained Transformer, is a groundbreaking language model that has revolutionized natural language processing (NLP) with its ability to generate human-like text.

The GPT Index is a catalog of fine-tuned models, allowing developers to pick and choose the most suitable model for their specific needs. By leveraging OpenAI API and the GPT Index, you can create AI chatbots that provide personalized customer experiences, enhancing user engagement and satisfaction.

How to Fine-tune with OpenAI API?

Fine-tuning is training a pre-trained AI model on a custom dataset to make it more suitable for a specific task or application. In the context of chatbots, this can involve training the model on customer interactions, preferences, and other relevant data. Here’s a step-by-step guide to fine-tuning the OpenAI API:

Gather your data: Collect customer data, including conversation transcripts, customer preferences, and other relevant information. Ensure that your data is cleaned and formatted for training.

Prepare your dataset: Split your data into training and validation sets. The training set will be used to fine-tune the model, while the validation set will help you evaluate the model’s performance.

Select a base model: Choose a suitable base model from the GPT Index, considering factors like size, performance, and language capabilities. The base model will serve as the foundation for your custom chatbot.

Fine-tune the model: Use the OpenAI API to fine-tune your selected model on your dataset. This involves uploading your dataset, specifying the base model, and setting the training parameters. The API will then train the model on your data, adjusting its weights and biases to understand better and generate personalized customer interactions.

Evaluate and iterate: Once the fine-tuning is complete, evaluate the model’s performance on the validation set. Adjust the training parameters and fine-tune the model to improve its performance if necessary.

Integrate the chatbot: After achieving satisfactory performance, integrate your fine-tuned AI chatbot into your desired platform, such as a website or a mobile app, and start providing personalized customer experiences.

Is it possible to fine-tune the new GPT-4 model?

The OpenAI GPT-4 model is anticipated to have the capability for task or domain-specific fine-tuning, akin to its predecessor, GPT-3. However, OpenAI has not yet disclosed the particulars of fine-tuning GPT-4. As the model undergoes further exploration and experimentation by researchers and developers, it is expected that more details about fine-tuning GPT-4 will be revealed in the future.

Let’s dive into my actual fine-tuning experiment.

I created an example training the Open AI API with two articles from my blog about ChatGPT, titled “How can you earn money with ChatGPT and Power BI?” and “Open AI released the new ChatGPT API” this week.” I used this site tool to convert my post contents into text and stored it on my GitHub page. You can add as many files as needed to train your model or easily convert this process to store into a database. If you want to open directly in Google Colab, click this link.

#This is ro run in the Google Colab notebook and has all the code you need to create your own chatbot with custom knowledge base using GPT-3. 

#Follow the instructions for each steps and then run the code sample. In order to run the code, you need to press "play" button near each code sample.

#Download the data for your custom knowledge base
#For the demonstration purposes we are going to use ----- as our knowledge base. You can download them to your local folder from the github repository by running the code below.
#Alternatively, you can put your own custom data into the local folder.

! git clone

# Install the dependicies
#Run the code below to install the depencies we need for our functions

!pip install llama-index
!pip install langchain

# Define the functions
#The following code defines the functions we need to construct the index and query it

from llama_index import SimpleDirectoryReader, GPTListIndex, readers, GPTSimpleVectorIndex, LLMPredictor, PromptHelper, ServiceContext
from langchain import OpenAI
import sys
import os
from IPython.display import Markdown, display

def construct_index(directory_path):
    # set maximum input size
    max_input_size = 4096
    # set number of output tokens
    num_outputs = 2000
    # set maximum chunk overlap
    max_chunk_overlap = 20
    # set chunk size limit
    chunk_size_limit = 600 

    # define prompt helper
    prompt_helper = PromptHelper(max_input_size, num_outputs, max_chunk_overlap, chunk_size_limit=chunk_size_limit)

    # define LLM
    llm_predictor = LLMPredictor(llm=OpenAI(temperature=0.5, model_name="text-davinci-003", max_tokens=num_outputs))
    documents = SimpleDirectoryReader(directory_path).load_data()
    service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor, prompt_helper=prompt_helper)
    index = GPTSimpleVectorIndex.from_documents(documents, service_context=service_context)


    return index

def ask_ai():
    index = GPTSimpleVectorIndex.load_from_disk('index.json')
    while True: 
        query = input("What do you want to ask? ")
        response = index.query(query)
        display(Markdown(f"Response: <b>{response.response}</b>"))

# Set OpenAI API Key
#You need an OPENAI API key to be able to run this code.

If you don't have one yet, get it by [signing up]( Then click your account icon on the top right of the screen and select "View API Keys". Create an API key.

#Then run the code below and paste your API key into the text input.

os.environ["OPENAI_API_KEY"] = input("Paste your OpenAI key here and hit enter:")

#Construct an index
#Now we are ready to construct the index. This will take every file in the folder 'data', split it into chunks, and embed it with OpenAI's embeddings API.

#*Notice:** running this code will cost you credits on your OpenAPI account ($0.02 for every 1,000 tokens). If you've just set up your account, the free credits that you have should be more than enough for this experiment.


#Ask questions
#It's time to have fun and test our AI. Run the function that queries GPT and type your question into the input. 

#If you've used the provided example data for your custom knowledge base, here are a few questions that you can ask:
#1. What is the new API?
#2. ChatGPT is designed to handle what?
#3. What is BotGPT?
#4. Tell me what are the benefits of BotGPT?
#5. What the relations with Power BI and ChatGPT?
#6. How to make money with chatGPT and Power BI?


Build AI chatbot with custom knowledge base using Open AI API and GPT index


Developing an AI chatbot that leverages personalized client information through the OpenAI API and GPT Index has the potential to transform customer experiences by providing customized interactions, education, and assistance. This solution offers numerous business prospects, including training models to comprehend a company’s areas of expertise, processes, policies, and protocols. By fine-tuning a model from the GPT Index with the OpenAI API, you can harness the power of AI to enhance user engagement and satisfaction, setting your business apart from the competition.

Embrace the world of AI chatbots and start providing a truly personalized experience for your customers today.

That’s it for today!

Here are the sources I used to create this article:

Open AI – Oficial Fine-tuning documentation

Author: Lawrence Teixeira

Lawrence is a veteran senior technology delivery lead with over 17 years of experience as a CTO and CIO in intellectual property companies. He has experience in both Agile and Waterfall development methodologies. He has a solid technical background in IT and excellent management skills with over 25 years in the area, delivering advanced systems projects and data analytics. Lawrence has hands-on experience building and deploying intellectual property systems, business intelligence, data warehousing, and building bots for RPA and data collection. He also knows PMP, Agile, Scrum, DevOps, ITIL, CMMI, and ISO/IEC 27001.

Leave a Reply

%d bloggers like this: