In this revolutionary era of AI, we’ve seen the emergence of conversational agents or chatbots. These tools have become crucial for engaging users and enhancing their experience on various digital platforms. Using advanced AI techniques, these chatbots are able to have automated and interactive conversations that closely resemble human interactions. And now, with the introduction of ChatGPT, the ability to answer user queries has reached new heights.
Building chatbots like ChatGPT on custom data can greatly benefit businesses by improving user feedback and experience. That’s why in this article, we’re going to explore how to build a Chatbot solution, similar to ChatGPT, for multiple custom websites using the Retrieval Augmented Generation (RAG) technique.
Let’s dive right into the project and start by understanding the critical components needed to build such an application. Throughout this project, you’ll learn about building large language chat models, the need for RAG, and how to use core components like loaders, chunking, and embeddings to create a Chatbot like ChatGPT. We’ll also explore the importance of in-memory vector databases using Langchain.
But before we proceed, let me give you a brief introduction to Langchain and why we’re using it. Langchain is an open-source framework specifically designed to drive the development of applications powered by large language models (LLMs). It enables the creation of applications that possess critical attributes and context awareness. By connecting LLMs to custom data sources, such as prompt instructions and contextual content, Langchain allows the language model to ground its responses in the provided context, resulting in more nuanced and informed interactions with users. The high-level API provided by Langchain makes it easy to connect language models to other data sources and build complex applications like search engines, advanced recommendation systems, eBook PDF summarization tools, question and answering agents, code assistant chatbots, and many more.
Now, let’s talk about Retrieval Augmented Generation (RAG) and why it matters. While large language models are great at generating responses for various tasks like code generation or writing blog articles, they often struggle when it comes to domain-specific knowledge. They tend to “hallucinate” or provide incorrect answers to domain-specific questions. To overcome this challenge and improve the models’ understanding of domain knowledge, we use an approach called Fine Tuning. However, Fine Tuning requires extensive training time and computation resources, which can be quite expensive. This is where RAG comes in to save the day. Retrieval Augmented Generation ensures that domain-specific data is fed to the language model, allowing it to produce contextually relevant and factual responses without the need for re-training. By utilizing vector databases, RAG also helps scale the application while reducing computation requirements.
Now, let’s take a look at the workflow for our Chat with Multiple Websites project. The figure below illustrates the overall process.
To get started, you’ll need to install Langchain using the pip command. Additionally, you’ll also need to install OpenAI, ChromaDB, and TikToken for specific functionalities. Once you have all the required libraries set up, you’ll need to configure your OpenAI API key.
The next step is to extract and transform the data from multiple websites. For this project, we’ll be using the WebBaseLoader provided by Langchain, which scrapes the content from the specified URLs. Once the data is loaded, we can move on to the chunking process. Chunking involves breaking down the large text into smaller, more manageable segments. In our case, we’ll be using the CharacterTextSplitter provided by Langchain to split the text into chunks.
Now comes the crucial part of converting the chunked data into embeddings. Embeddings are numerical representations of words or tokens that allow us to process and understand textual data in a continuous vector space. Langchain provides the OpenAIEmbeddings module for this purpose.
Once we have the embeddings, we need a way to store and retrieve the data efficiently. This is where vector databases come in. A vector database, such as ChromaDB, allows us to store the embeddings and perform similarity searches to retrieve relevant information.
With the data, embeddings, and vector databases in place, it’s time to define the large language chat model. In our project, we’ll be using ChatOpenAI, specifically the gpt-3.5-turbo-16k model, which can handle multiple data sources.
Finally, we can put it all together and run our queries. The user inputs a prompt, and we use the retrieval functionality of RAG to retrieve the relevant context from the vector database. This context, along with the prompt, is then passed to the chat model to generate the response.
To conclude, we’ve successfully built a Chatbot for multiple websites using Langchain. This Chatbot goes beyond simple responses and aims to provide answers similar to ChatGPT. By utilizing techniques like RAG and leveraging large language chat models, we can enhance the user experience and provide more accurate and relevant information.