Create Your Own ChatGPT Clone | Easy Redis & LangChain Tutorial

Table of Contents

1. Introduction
2. The History of Chat GPT
3. Understanding Language Models and Transformers
4. Introduction to Langchain and Vector Databases
5. Utilizing Langchain as a Vector Database
6. Building an End-to-End Application with Redis and Langchain
7. Architecture for the Chatbot
8. Demo: Building an End-to-End Application with Redis and Langchain
9. Deploying the Application on the Stimulate Cloud
10. Conclusion

Introduction

Welcome to this webinar where I'll be teaching You how to build an end-to-end application with Redis and Langchain. In this webinar, we'll start with a brief history of chat GPT and explore the evolution of language models. We'll then dive into the concept of Langchain as a vector database and discuss how to utilize it effectively. Next, we'll explore the architecture required to build a chatbot application. We'll also cover a live demo where we'll build an end-to-end application using Redis and Langchain. Finally, we'll conclude by discussing the deployment of the application on the Stimulate Cloud. So, let's get started!

The History of Chat GPT

Before we dive into the technical details, let's take a moment to understand the history of chat GPT. Chat GPT, also known as Chatbot GPT, gained immense popularity in just a short period of time. It took only two days for it to reach one million users and a couple of months to reach one hundred million users. This growth can be attributed to the effectiveness of language models known as Transformers.

Understanding Language Models and Transformers

Language models, like chat GPT, are built using a neural network architecture called Transformers. Transformers were initially developed by Google in 2017, with the aim of understanding natural language and Context in order to generate accurate and useful responses. Similar to how humans process information, language models Collect and encode context, allowing them to provide valuable feedback in response to text input.

To train a language model like chat GPT, a large corpus of data is required. For example, Wikipedia is a common source of general knowledge used in training language models. The model is then fine-tuned on specialized datasets to perform specific tasks, such as acting as an instructor, co-pilot, or marketer. Once the model is fine-tuned and tested, it is ready to be deployed and used as a chatbot.

Introduction to Langchain and Vector Databases

Langchain is an orchestration tool that provides context to chat GPT by utilizing a vector database. A vector database stores data as vector embeddings, which are numerical representations of text or other forms of unstructured data. Similarity search is used to retrieve Relevant information from the vector database Based on user input.

The vector embeddings are created by converting the unstructured data into numerical vector representations using embedding models provided by platforms like Hugging Face and OpenAI. These embeddings are then stored in the vector database. When a user poses a question, the system performs a similarity search within the vector database to retrieve the most relevant information.

Utilizing Langchain as a Vector Database

Langchain acts as an intermediary between the user's question and the chat GPT API. When a question is posed, Langchain collects the relevant information from the vector database and provides it as input along with the question to the chat GPT API. This allows the chat GPT model to provide accurate and context-specific responses.

The vector database plays a crucial role in this process. It stores the vector embeddings of the data and performs similarity search to retrieve relevant information based on user queries. The similarity search calculates the Cosine similarity between the vectors, allowing the system to identify the most similar and relevant information.

Building an End-to-End Application with Redis and Langchain

In this section, we'll walk through the process of building an end-to-end application using Redis and Langchain. Redis is a widely used in-memory data structure store that can be used as a persistent vector database. It allows us to store and retrieve vector embeddings efficiently.

To get started, make sure you have the code repository cloned and Docker desktop installed on your computer. The application architecture includes components such as PDF upload, text extraction, chunk creation, embedding creation, vector database integration, and the chatbot chain.

Once the application is built, you can upload a PDF, extract the text, Create chunks, generate embeddings, and store them in the vector database. The application then allows you to ask questions, retrieve relevant information from the vector database, and provide accurate responses using the chat GPT model.

Architecture for the Chatbot

The chatbot architecture consists of multiple components working together to provide an end-to-end application. The main steps involved are:

1. PDF Upload: Users can upload a PDF file that contains the data they want to query.

2. Text Extraction: The application extracts the text from the uploaded PDF file.

3. Chunk Creation: The extracted text is divided into chunks to facilitate efficient processing.

4. Embedding Creation: The text chunks are converted into vector embeddings using embedding models provided by platforms like Hugging Face and OpenAI.

5. Vector Database Integration: Redis is used as a vector database to store the vector embeddings.

6. Query Input: Users can input their queries or questions into the application.

7. Relevant Information Extraction: The application retrieves relevant information from the vector database based on the user's query.

8. Response Generation: The chat GPT model utilizes the relevant information and the user's query to generate accurate and context-specific responses.

Demo: Building an End-to-End Application with Redis and Langchain

In the live demo, we will showcase the process of building an end-to-end application using Redis and Langchain. We will walk through the steps of uploading a PDF, extracting text, creating chunks, generating embeddings, storing them in the vector database, and utilizing the chat GPT model to provide accurate responses.

The demo will provide a hands-on experience of how these technologies work together to build a powerful chatbot application. You will learn how to integrate different components and leverage the capabilities of Redis and Langchain to enhance the functionality of the application.

Deploying the Application on the Stimulate Cloud

Once the application is built and tested, it can be deployed on the Stimulate Cloud for wider usage and scalability. The Stimulate Cloud provides an environment for hosting and managing applications without the need for complex infrastructure setup.

Deploying the application on the Stimulate Cloud allows users to access it from anywhere, making it convenient and easily accessible. The cloud environment ensures reliable performance and scalability, making it suitable for applications with high user demand.

Conclusion

In this webinar, we explored the process of building an end-to-end application with Redis and Langchain. We learned about the history of chat GPT, the role of language models and transformers, and the concept of Langchain as a vector database. We covered the architecture for the chatbot application, walked through a live demo, and discussed the deployment of the application on the Stimulate Cloud.

Building an end-to-end application with Redis and Langchain allows us to harness the power of language models and leverage the capabilities of vector databases. By implementing these technologies, we can create intelligent chatbots that provide accurate and context-specific responses. This opens up a world of possibilities for various applications, including customer support, information retrieval, and knowledge sharing. So, let's embark on this Journey of building intelligent chatbots and revolutionize the way we Interact with technology.