Create a Custom ChatBOT with ChatGPT: LangChain Tutorial

Table of Contents

1. Introduction
2. Understanding Website Architecture
3. Creating a Knowledge Base
4. Computing Embeddings
5. Storing Embeddings in a Vector Store
6. Information Retrieval with a Chatbot
7. Taking User Queries
8. Generating Natural Language Responses
9. Cost Considerations
10. Conclusion

Introduction

In this article, we will explore the process of creating custom chatbots for websites. Chatbots have become increasingly popular as a way to engage with visitors and provide them with information and assistance. We will discuss the architecture of websites, the creation of a knowledge base, computing embeddings, storing embeddings in a vector store, and the process of information retrieval with a chatbot. Additionally, we will Delve into taking user queries, generating natural language responses, and considering the cost implications of using large language models. By the end of this article, You will have a thorough understanding of how to Create and implement your own custom chatbot for your website.

Understanding Website Architecture

Before we dive into the details of creating a chatbot, it is important to understand the basic architecture of a website. Most websites consist of multiple pages, each with its own unique URL. The structure of a website can be visualized using a sitemap, which lists all the pages and their corresponding addresses. By accessing the sitemap, you can obtain a comprehensive list of all the pages in a website.

Creating a Knowledge Base

The first step in creating a chatbot is to build a knowledge base. A knowledge base is a repository of information that the chatbot will use to provide responses. In the case of a website chatbot, the knowledge base will consist of the content from the website's pages. To create the knowledge base, we need to extract the text from each page and store it in a suitable format for retrieval.

Computing Embeddings

In order to effectively search and retrieve information from the knowledge base, we need to compute embeddings for each document in the knowledge base. Embeddings are numerical representations of text that capture the semantic meaning of the text. These embeddings allow us to perform semantic searches and find similar documents Based on their content. There are various methods and models available for computing embeddings, such as those provided by open AI or other open source models.

Storing Embeddings in a Vector Store

Once we have computed embeddings for each document in the knowledge base, we need to store them in a vector store. A vector store is a data structure that allows efficient storage and retrieval of embeddings. One popular option for a vector store is the Faiss library, which provides efficient similarity search and clustering of dense vectors. Other options, such as Pinecone or Chroma DB, can also be used depending on your specific needs.

Information Retrieval with a Chatbot

With the knowledge base and embeddings stored in a vector store, we can now implement the information retrieval process for our chatbot. When a user interacts with the chatbot and asks a question, the chatbot will compute the embedding for the question and use it to search the knowledge base for similar documents. By comparing the embeddings of the question with the embeddings of the documents in the vector store, the chatbot can retrieve the most Relevant documents as a response.

Taking User Queries

To enable user interaction with the chatbot, we need to provide a mechanism for users to input their queries. This can be done through a chat interface on the website or through other communication channels such as chat widgets or messaging platforms. The chatbot should be designed to understand and interpret the user's queries accurately.

Generating Natural Language Responses

Once the chatbot has retrieved the relevant documents based on the user's query, it needs to generate a natural language response. This is where large language models come into play. A large language model, such as GPT-3 or other open source models, can be used to generate a human-like response based on the Context provided by the retrieved documents. The generated response should be informative, coherent, and engaging for the user.

Cost Considerations

When using large language models and computing embeddings for a chatbot, it is important to consider the cost implications. The pricing for using large language models can vary depending on factors such as the model used, the number of tokens processed, and the level of usage. It is essential to be aware of the pricing structure and plan accordingly to manage costs effectively.

Conclusion

Creating a custom chatbot for your website can enhance user experience and provide valuable information and assistance to visitors. By understanding website architecture, creating a knowledge base, computing embeddings, storing embeddings in a vector store, implementing information retrieval, facilitating user queries, generating natural language responses, and considering cost implications, you can build an effective and engaging chatbot for your website. Incorporating chatbot technology can improve customer engagement, streamline communication, and enhance the overall user experience on your website.

Highlights

• Understanding the architecture of websites and the use of sitemaps
• Creating a knowledge base from the content of web pages
• Computing embeddings to enable semantic searches
• Storing embeddings in a vector store for efficient retrieval
• Implementing information retrieval with a chatbot
• Taking user queries and generating natural language responses
• Considering cost implications when using large language models
• Enhancing user experience and engagement on websites with chatbots

FAQ

Q: What is a knowledge base? A: A knowledge base is a repository of information that a chatbot uses to provide responses. It consists of the content from the website's pages.

Q: How are embeddings computed? A: Embeddings are computed using models such as those provided by open AI. These models transform text into numerical representations that capture the semantic meaning of the text.

Q: How are document embeddings stored? A: Document embeddings are stored in a vector store, such as Faiss, which allows efficient storage and retrieval of embeddings.

Q: What is information retrieval in the context of a chatbot? A: Information retrieval is the process of finding and retrieving relevant documents from a knowledge base based on a user's query.

Q: How are natural language responses generated? A: Natural language responses are generated using large language models, such as GPT-3, which can generate human-like responses based on the context provided by the retrieved documents.

Q: How should cost considerations be managed when using large language models? A: It is important to be aware of the pricing structure of the chosen models and plan accordingly to manage costs effectively. Consider factors such as model usage, token count, and frequency of usage.