Create an Intelligent KnowledgeBot with GPT-Index & LangChain!

Table of Contents

1. Introduction
2. Building a Question and Answer Bot
   1. Selecting a Popular Book
   2. Extracting Knowledge from the Book
   3. Creating a Q&A Bot
   4. Demos and Examples
3. Understanding Conversational AI
   1. Conversational AI in the Customer Support Space
   2. Building SOPs for Customer Support Agents
   3. Leveraging Large Language Models
4. Getting Started
   1. Installing Required Libraries
   2. Setting Up OpenAI API Key
   3. Getting the Data - Using TXT Files
5. Training and Embedding
   1. Fine-Tuning and Training Embeddings
   2. Creating a Prompt Helper Instance
   3. Defining the Large Language Model
   4. Using the Simple Directory Reader
   5. Building the Vector Index
6. Implementing the Q&A Bot
   1. Building the Ask Bot Function
   2. Loading the Index.json File
   3. Taking User Queries and Generating Responses
7. Conclusion
   1. Building a Knowledge Bot with GPT Index
   2. Future Possibilities and Applications

Building a Question and Answer Bot using GPT Index Language

In this tutorial, we will learn how to build a question and answer bot using GPT index language. We will use a popular book as our source for knowledge and build a simple bot that can answer questions Based on the content of the book. The focus of this tutorial is to understand the concept of building a Q&A bot, so we will not be creating a user interface. However, the code provided can serve as a foundation for building a more sophisticated bot with a UI in the future.

Introduction

Since the launch of chat GPT, there has been a growing interest in the field of conversational AI bots. People are eager to build their own conversational agents and automate various aspects of their personal and business lives. In the Context of business, customer support agents often handle repetitive tasks like answering customer queries. This workflow can be automated by building a bot that can understand and respond to customer queries based on a knowledge base, such as company documentation.

Selecting a Popular Book

To illustrate the process of building a knowledge bot, we will use the book "Meditations" by Marcus Aurelius as our source of knowledge. This book explores the importance of living a life of contentment and purpose and provides principles for achieving this through the art of adaptation. By using this book, we can demonstrate how the bot can answer questions based on the content of the selected book.

Extracting Knowledge from the Book

In order to build our knowledge bot, we need to extract the Relevant information from the book and convert it into a format that the bot can understand. We will leverage large language models, specifically models from OpenAI, to accomplish this task. Instead of directly using the OpenAI models, we will use two popular libraries, GPT index and Langchain. GPT index allows us to build a vector search index based on the document, which we can use to retrieve answers to user queries.

Creating a Q&A Bot

The process of creating a Q&A bot involves two main steps: training and embedding the knowledge base, and implementing the bot to generate responses to user queries. First, we'll use the Langchain library to fine-tune the OpenAI model and Create an embedding of the book's content. This embedding is stored as a JSON file, which serves as the vector index for the bot.

Next, we'll implement the Q&A bot using the GPT index library. The bot takes user queries as input and uses the vector index to retrieve relevant information from the book. The bot then generates a response based on the retrieved information and returns it to the user.

Demos and Examples

Throughout the tutorial, we will provide demos and examples to illustrate the functionality of the Q&A bot. We will demonstrate how the bot can answer questions about the book's content and provide insights on various topics. These demos will help You understand the capabilities of the bot and how it can be used to extract knowledge from a given text source.

Understanding Conversational AI

Before diving into the technical details of building the Q&A bot, it's important to understand the broader context of conversational AI and its applications. In the customer support space, conversational AI bots are widely used to automate customer interactions. Customer support agents rely on standardized procedures, known as SOPs (Standard Operating Procedures), to provide accurate responses to customer queries.

By leveraging large language models like GPT index, companies can transform their documentation and knowledge base into conversational AI bots. The bots can understand customer queries, retrieve relevant information from the knowledge base, and generate responses in a conversational manner. This workflow streamlines customer support processes and improves efficiency.

Getting Started

To get started with building the Q&A bot, we need to install the required libraries and set up the OpenAI API Key. The libraries we will be using are GPT index and Langchain. Both libraries can be installed using standard Python Package management tools.

Once the libraries are installed, we need to obtain an API key from OpenAI. The key is required to connect to OpenAI's large language models and retrieve responses to user queries. We will provide step-by-step instructions on how to obtain the API key from the OpenAI Website and set it as an environment variable.

Finally, we need to Gather the data that we will use to train and embed the knowledge base. In our case, we will use a folder containing TXT files, each representing a chapter or section of the selected book. The TXT files should be organized in a directory structure that reflects the logical structure of the book.

Training and Embedding

In this section, we will take a deep dive into the process of training and embedding the knowledge base using GPT index and Langchain. We will go through the step-by-step process of constructing the vector index, starting from the directory path where the TXT files are stored.

We will define the parameters for training and embedding, including the maximum input size, number of output tokens, chunk overlap, and chunk size limit. These parameters determine how the text is chunked and processed by the large language model. We will create a prompt helper instance to assist in generating Prompts for the model.

Next, we will define the large language model using the llm predictor from Langchain. We will specify the OpenAI model we want to use, keeping in mind that different models have different levels of accuracy and cost. We will also use the simple directory reader to load the Contents of the TXT files into a collection of documents.

Finally, we will build the vector index using the documents and the llm predictor. The vector index represents the embedded knowledge base, which the bot will use to retrieve answers to user queries. We will save the vector index as a JSON file for future use.

Implementing the Q&A Bot

With the vector index created, we can now implement the Q&A bot using the GPT index library. We will define a function called "ask bot" that takes the path to the index.json file as an argument. Inside this function, we will load the index from the specified path and enter an infinite loop to allow continuous user interaction.

In each iteration of the loop, we will prompt the user for a question and generate a response using the index.query method. The response will be displayed to the user, representing the bot's answer to their query. This process will Continue until the user decides to exit the program.

Conclusion

In this tutorial, we have learned how to build a question and answer bot using GPT index language. We started by selecting a popular book as our source of knowledge and extracted the relevant information to create an embedding using large language models from OpenAI. We then implemented the Q&A bot, which can generate responses to user queries based on the embedded knowledge.

The Q&A bot showcases the potential of conversational AI and how it can be leveraged to automate customer support processes and provide accurate responses to user queries. The simplicity of the implementation demonstrated in this tutorial allows you to easily adapt and extend the bot for your specific use case.

By following the step-by-step instructions and leveraging the provided code, you can build your own knowledge bot using GPT index and Langchain. Whether you want to automate customer support, provide personalized assistance, or create a virtual assistant, the possibilities are endless.