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QnA with OpenAI GPT models on Personal / Business data using Embeddings and Completion

This project is inspired by 'knowledge base' from


With all the excellent capabilities of OpenAI GPT models, I have always wanted to build a Chatbot that learns from a knowledge base I provide, and answers my queries based on the knowledge base and its training data. It would serve as the perfect assistant for Tech Documentation, Scientific Papers, and Business Product Data.

After going through large documentations, code examples I have found this project by @gidgreen that allowed me to build this project. In this documentation I will try to explain all the steps and requirements to build this chatbot.


This project doesn't require any python knowledge (most of the code examples on OpenAI docs are in python but I write JS) and also no need to convert your text files to JSON/JSONL, this works fine with text files.

You need to know about NodeJS, basic Javascript, and OpenAI services.

Let's Code

We start by making a NodeJS project and installing all the necessary dependencies

mkdir QueryGPT
cd QueryGPT
npm init -y     // you can tweak your own settings or use -y for defaults
npm install @types/node dotenv openai

For the necessary folders

mkdir sourceData embeddedData utils

Don't worry if these doesn't make sense now, I'll explain everything in details.

Before starting to code we need to collect our api key from OpenAI. Then we create a .env file and paste the api key there.

In .env


We start by making a utility file for our openai package configuration.

In utils/helper.js :

require("dotenv").config(); // import variables from .env file
const { Configuration, OpenAIApi } = require("openai");

const configuration = new Configuration({
  apiKey: process.env.OPENAI_API_KEY,

const openai = new OpenAIApi(configuration);

module.exports = { openai };

Embedding our data

For this example, I will be using the wikipedia page on my University - University of Dhaka as my data. When asked ChatGPT, It can't answer correctly or it will make things up for intricate details.

There are some steps I followed to format my data which makes the results more accurate due to the approach we are using. The data needs to be split in small paragraphs, this helps with the embedding process. I saved my data in ./embeddedData/sourceData.txt

I made a new file embedding.js

The embedding.js file is necessarily large so I will be explaining the code using comments there. Please refer to that file

When executing the file with node embedding.js, the following gets printed in the console on successful embedding.

Embedding Started ⌛
Sent file over to OpenAI 🚀
Embedding finished ✨
Time taken : 19 seconds

You can inspect the ./embeddedData/embeddedFile.txt to gain more insights. What you see there are paragraphs as keys and their vector representation returned by the embedding model as value. We will use this key value pair later in completion.js to craft the perfect response.

Embeddings are numerical representations of words converted to number sequences, which make it easy for computers to understand the relationships between those concepts. The more related two words are, the more their vector value will be closer.

To learn more on this topic : Blinkdata Embedding Tutorial

Generating perfect response using embedding data and completion models

This is the most tricky yet 'woah' part.

We won't fine-tune the model which requires feeding it Questions and Answers. The approach we will take is also to embed the Question using the same embedding model.

The magic happens when we dot product (some math shit right!) the embedding vectors of two paragraphs - we get a scalar value that represents the similarity between the two paragraphs in terms of their semantic meaning and context.

So, we loop through each paragraph from our context and dot product its embedding vector with the one from the question. The paragraphs with more similarity get a higher score. Then we pull 3-5 of these paragraphs and send them over to the completion model, like the highly capable 'text-davinci-003' along with the questions, and with some prompt engineering, we get the correct response.

You can use any of the two prompts.

If you want it to answer with own knowledge if answer isn't present in provided data :

"Answer the following question, also use your own knowledge when necessary :\n\n" +
  "Context :\n" +
  paragraph.join("\n\n") +
  "\n\nQuestion :\n" +
  question +
  "?" +
  "\n\nAnswer :";

If you want it to only use provided data as knowledge base and not its own :

"Answer the following question from the context, if the answer can not be deduced from the context, say 'I dont know' :\n\n" +
  "Context :\n" +
  paragraph.join("\n\n") +
  "\n\nQuestion :\n" +
  question +
  "?" +
  "\n\nAnswer :";

The code is provided in completion.js along with explanation in comments

When we run completion.js with

generateCompletion("Who is acting dean of the Faculty of Business Studies");

In our console :

tsensei@desktop ~/D/QueryGPT (main)> node completion.js

Called completion function with prompt : Who is acting dean of the Faculty of Business Studies

Muhammad Abdul Moyeen is the acting dean of the Faculty of Business Studies.

Hurrah! We have successfully created a AI assistant that answers out queries based on our provided personalized and updated data!!


QnA with NodeJS & OpenAI GPT models on Personal / Business data using Embeddings and Completion






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